http://2012.igem.org/wiki/index.php?title=Special:Contributions&feed=atom&limit=250&target=Cqian2012.igem.org - User contributions [en]2024-03-28T10:28:40ZFrom 2012.igem.orgMediaWiki 1.16.0http://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-27T03:43:45Z<p>Cqian: </p>
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<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
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CONTENT=<br />
<br />
[[Image:UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png|250px|right|]]<br />
<html><br />
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<p>Flux-variability analysis (FVA) was applied to optimize the bioreactor system of OSCAR for the newly incorporated metabolic pathways<br />
(<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Decarboxylation">Decarboxylation</a>,<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/CatecholDegradation">Decatecholization</a>,<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Desulfurization">Desulfurization</a>, and<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Denitrogenation">Denitrogenation</a>). FVA combines the framework of metabolic pathways with experimental enzymatic data to provide a computational platform for predicting what changes in metabolite levels will result in increased end-product. We developed a model using <a href="http://www.mathworks.com/products/matlab/">MATLAB</a> computer language for predicting what metabolites could be added to your growth media to increase production of hydrocarbons in the <i>E. coli</i> chassis. We also created a graphical user interface called the <i>OSCAR Optimizer</i> to make the the FVA program user-friendly and to allow current and future iGEM teams/scientists to input their own synthetic pathways into the FVA for analysis. Finally, we validated this model in the wet-lab by optimizing the PetroBrick (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K590025">BBa_K590025</a>) system to increase hydrocarbon production, thereby saving time, and resources.</p><br />
<br />
<h3><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the MATLAB package to test our model!</h3><br />
<center><h3>Click <a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf"> here </a> to view our OSCAR Optimizer Manual.</b></center></h3><br />
<br />
<h2>Background</h2><br />
<h3>What is Flux Analysis?</h3><br />
<p>Flux Balance Analysis (FBA) uses linear programming of metabolic networks to convert each metabolite into a mathematical coefficient. These coefficients can be connected to each other by changes associated with each enzymatic step of the pathway. FBA can then apply a mathematical method to examine how metabolites relate to each other in the network and allows the user to make generalized predictions for the growth of the organism, metabolite levels, and product output inside the cell. Flux Variability Analysis (FVA) is an extension of FBA that determines the range of reactions that result in an optimal flux through the metabolic network. Unlike FBA, this allows the user to not only determine the most optimized network for increasing cell growth, but also other parameters like product output.</p><br />
<br />
<p><b>What are the constraints in the FVA model?</b></p><br />
<p>As illustrated in <b>Figure 1</b>, metabolic networks can be encoded as stoichiometric matrices, in which each row represents a specific metabolite and each column represents a biochemical reaction. The entries in each column represent the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed or produced have a negative or positive, respectively.</p><br />
<br />
</html><br />
[[Image:Calgary FluxExample.jpg|700px|thumb|center|Figure 1: Flux Balance Analysis. FBA uses metabolic network (left) and simulates the connections in the network as a linear algebra matrix (right). Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, the state of the system is affected by each metabolite change.]]<br />
<html><br />
<br />
<p><b>Why use FVA as opposed to FBA?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, FBA only returns a single flux distribution that corresponds to maximal bacterial growth under the given growth conditions, even if other optimal parameters may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Thus, FVA can be employed to study the entire range of achievable cellular functions, as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wet-lab.</p><br />
<br><br />
<br />
<h2>Using FVA to optimize OSCAR</h2><br />
<h3>What Are We Trying To Model?</h3><br />
<p>FVA provides a method to modulate the inputs of endogenous metabolic pathways, but could also investigate what chemicals are needed in the growth media to upregulate an introduced pathway in <i>E. coli</i>. In the latter case, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to improve production rate. Development of a tool to model the optimal flux would also benefit numerous iGEM teams and research labs who engineer bacteria with novel pathways. </p><br />
<br />
<h3>How could systems like OSCAR benefit from this model?</h3><br />
<p>Much like chemical reactions which need optimal environmental conditions to achieve maximum production rate, microbes require certain conditions to accomplish cellular tasks at maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized to reduce cost of production to a minimum. Conditions in the microbial bioreactor systems are more crucial than chemical synthesis reactors ⎯ simply due to the increase in simultaneous reactions occurring. Furthermore, the selection and concentration of compounds in the growth media can severely affect the growth rate. If a computational model can predict an optimal set of metabolites for media composition, this will save time, resources, and funds. </p><br />
<br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|right|thumb|300px|Figure 2: Diagram of the relationship between growth rate and production rate, and the computed optimal growth rate.]]<br />
<html><h3>How does the program work?</h3><br />
<br />
<p>Our program is built upon flux variability analysis applied by the functions in the Cobra Toolbox. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the <br />
<a href="http://gcrg.ucsd.edu/Home"><br />
Systems Biology Research Group (Dr. B. Palsson)<br />
</a><br />
at the University of San Diego. Using this as a base, we constructed reactions and metabolites for the hydrocarbon production components of OSCAR. Specifically, new reactions corresponding to the PetroBrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. Running FVA will give an output correlated to a optimized growth rate. Moreover, this FVA algorithm running at optimal biomass flux rates can provide a range of outputs for your product of interest (<b>Figure 2</b>). This allows the user to select an optimum point where compound production is maximized. Finally, the program will analysis the data with an algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br />
<br />
<h2>Algorithm</h2><br />
<h3>Conceptualization</h3><br />
</html><br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|right|300px|Figure 3: Diagram of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]] <br />
<html><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a broader analysis of growth and production rates compared to FBA. Since biomass rate reflects the growth condition, cells must have a positive biomass flux rate to survive and proliferate. This positive value is indicative of a real cellular system since cells prefer to have increases in growth rather than increases in product output; however, our goal is to increase the production flux rate above a zero value. Therefore, among all the possible set of fluxes, the optimal flux is located where growth and production rate are at the optimum point for cell survival and compound output.</p><br />
<br />
<br />
<p> Differences between the <i>maximum</i> and <i>minimum</i> production rates are compared for each reaction in a set of fluxes. Reactions that demonstrated higher flux in the maximum production rate versus the minimum production rate represent metabolites that could be increased to increase overall product output (<b>Figure 3</b>). This analysis generates a list of compounds that should increase product output if increased in concentration in the growth media.</p><br />
<br />
<br />
<br />
<p>Note that not all compounds can be imported into the cell. Hence, only the metabolites that have native transporters in <i>E. coli</i> were considered for wet-lab verification. We used a model based on glucose minimal media; however, this could be applied to any media type.</p><br />
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<h3>Flowcart of the Model</h3><br />
<br />
</html><br />
[[Image:Calgary_FluxModelMethod.jpg|center|thumb|600px|Figure 4: Methodology the program uses to identify metabolite hits which should be supplemented to the media of your organism to increase output.]]<br />
<html><br />
<br />
<p>Disclaimer: Original model built using minimum media with glucose.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the differences in percentage of flux rate for each reaction between production maximum and minimum set.</p><br />
<p>4. Collect all reactions with different percentages between two sets that exceed the user input threshold. (The input threshold determines the size of the difference in flux that the user is interested in. We used a 100% difference in our model.).</p><br />
<p>5. Score each compound in all collected reactions, with the initial score of zero for each compound (Scoring is determined by the difference in the value of a particular compound when the flux sets of the desired compound production are maximized and minimized as determined by FVA). This process is additive if the particular compound is found in more than one reaction (i.e., ATP) and only includes the reactions identified from step 4.</p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to the growth media and the run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model (step 7) to that from the raw model (step 2), if the rate is improved, mark the compound as an effector.</p><br />
<br />
<br><br />
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<h2>Graphical user interface (GUI) development - Creating OSCAR Optimizer</h2><br />
<p>The GUI allows for easier use of our program by everyone in iGEM and beyond. This will enable individuals to use our program who are inexperienced with programming programs such as MATLAB and/or the Cobra Toolbox. We developed the GUI using the GUIDE program designed in MATLAB. For more information please see our manual.</p><br />
<br><br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of the Cobra Toolbox and SBML Toolbox. To run the application, one must have the Cobra Toolbox and SBML Toolbox installed. SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>. Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>. Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
<br><br />
<br />
<h2>Demo</h2><br />
<p>Below is an uploaded tutorial to show anyone how to use the GUI of the OSCAR Optimizer. The GUI interface allows for easy building of different synthetic constructs into the <i>E. coli</i> network but this could based on any model from any organism that is available in SBML format.</p><br />
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<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
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</html><br />
<h4><center>Screen Shots of Our Application</center></h4><br />
[[Image:UCalgary2012_RunTimeBuild.png|350px|left|]]<br />
[[Image:UCalgary2012_DemoOutput.png|350px|right|]]<br />
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[[Image:UCalgary2012_BuildOutput.png|400px|center|]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|400px|center|]]<br />
<html><br />
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<a name="Flux"></a><h2>Wet-lab validation of OSCAR Optimizer</h2><br />
<p>We tested our OSCAR Optimizer FVA Model in the wet-lab by assessing if a suite of identified compounds would increase the output of the Petrobrick. Therefore we ran the model with the <i>AAR</i> and <i>ADC</i> gene components of the Petrobrick system and looked at the predicted metabolites for growth media supplementation (<b>Figure 5</b>).</p><br />
<br />
</html>[[File:Calgary ModelingPetroOutput.jpg|center|thumb|600px|Figure 5: OSCAR Optimizer output for increase hydrocarbon production in OSCAR. This program identified a series of metabolites that are predicted to increase hydrocarbon output, including fructose and AMP with the highest outputs suggesting that their presence should contribute to increased product output the most.]]<html><br />
<br />
<p>Once these compounds were identified, we set-up an assay where we supplemented minimal M9 media and glucose with each of the compounds alone, or in combination. Compounds were added at concentrations of 50 mM except for Ethanol (2.5% v/v), AMP (100mg/L), and L-aspartate (100mg/L). As a positive control, instead of using minimal media a solution of 50:50 LB:Washington Production Media (see <br />
<a href="https://2011.igem.org/Team:Washington/alkanebiosynthesis"><br />
2011 iGEM University of Washington Protocols<br />
</a><br />
) was used to represent normal production. Additionally, we found that the compound formate was predicted to NOT increase hydrocarbons production and this represented a chemical negative control. All compounds were added to <i>E. coli</i> culture containing the PetroBrick at an OD<sub>600</sub> of ~0.05 in the first experiment, and a higher OD<sub>600</sub> of 0.400 for the second experiment and grown for 72 hours at 37<sup>o</sup>C. Cultures were then sonicated after an OD<sub>600</sub> measurement and any produced hydrocarbons were extracted by 1 mL ethyl acetate. This was quantitated based on the peak area of a C15 hydrocarbon product as described in the <br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Decarboxylation">Decarboxylation</a> section (also see <br />
<a href="https://2012.igem.org/Team:Calgary/Notebook/Protocols">Protocols</a> <br />
for relevant procedures). The tabulated results of the quantitated yields are shown in <b>Figure 6</b>. </p><br />
<br />
</html>[[File:Calgary ModellingWetlabDataFinal.png|center|thumb|600px|Figure 6: Model validation, showing the relative abundance of C15 hydrocarbons of <i>E. coli</i> containing the Petrobrick in combination with different compounds as growth media supplements. Data is normalized to the positive control (50:50 LB:Washington Production Media) and the relative production of hydrocarbons is displayed relative to optical density measurements. This data suggests hydrocarbon production can be increased or decreased if the Petrobrick is incubated with specific compounds. Dashed line represents the average output of hydrocarbons in the positive control. PMFA (Pyruvate, Malate, Fumarate, Aspartic Acid) and G+P (Glycine and Pyruvate). ]]<html><br />
<br />
<p> These hydrocarbon production results indicate there is variability in the output of hydrocarbons from the PetroBrick with different compounds (<b>Figure 6</b>). Interestingly, five of the compounds demonstrated production levels higher than that of the minimal media control, suggesting our model could be used for predicting compounds to increase metabolite production. Although, AMP and fructose both decreased output despite having the highest predicted increases in hydrcarbon output, suggesting that our model may produce false positives and there is some error in assessing these predictions. Most excitingly, addition of two compounds, pyruvate and glycine, had increased the relative number of hydrocarbons higher than the complex media of the positive control. As noted by the multiple experiments, there are some differences in the relative output of hydrocarbons in each experiment, however, the trend for each compound is relatively consistent for the different samples. Some of the runs were not able to be repeated in the second assay, which is the reason for some of the lack of data in Figure 6. <b>These results suggest our OSCAR Optimizer model can optimize OSCAR (along with other metabolic systems); however, these predicted candidates need to be tested in the wet-lab to ensure the model is accurate.</b><br />
<br />
<h2> Modelling informing our Killswitch </h2><br />
<br />
<p> Although we had initially rejected the idea of a killswitch based on <b>auxotrophy</b> due to the issue of cost, the results of our model made us think that this might be more feasible than we had initially thought. As glycine alone in minimal media was able to give us higher alkane production than Washington’s production media, supplementing our system with glycine, achieving higher output may actually be feasible. We saw auxotrophy as a method of creating <b>layers</b> in our killswitch system, where we could use it in tandem with the inducible kill systems that we have already been developing. We obtained a glycine knockout stain of <i>E. coli</i> and have begun some initial <a href="https://2012.igem.org/Team:Calgary/Project/HumanPractices/Killswitch/Regulation">characterization data</a>. This was a great example of how our modelling project was actually able to <b>inform</b> other parts of our project. In order to validate that this system was feasible for use we performed three key experiments:<br />
<br />
<ul><br />
<li>Perform a glycine requirement assay to determine the survivability of the knockout strain in our minimal media's used with OSCAR <.</li><br />
<li>Determine if the Petrobrick can be used with a similar activity as in the wild type strain</li><br />
<li>Use the auxotrophic strain in conjunction with our killswitch to show that these can act together.</li> <br />
</ul><br />
<p><a href="https://2012.igem.org/Team:Calgary/Project/Synergy"> Click here</a> to learn more about these assays. </p><br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon the Cobra Toolbox, and the SBML Toolbox. As a consequence, any flaws in the Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (<i>E. Coli</i> iAF1260) which contains constraints that rely on the Stoichiometric Matrix (such as Stoichiometric Coefficients), lower bounds and upper bounds of reactions. These pathways lack genetic and enzymatic regulation, making the connections between reactions in the network much weaker than those in the wet-lab. These missing components may lead to inaccurate results.</p><br />
<br />
<p>At the current stage, our algorithm can only pick metabolites with natural transporters in cell. Therefore, many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. <b>This model does not take into consideration rate limiting steps of reactions, toxic intermediate accumulation, or any form of regulation of the enzymes (e.g., negative feedback). The OSCAR Optimizer should act as a starting point to identify any limitation of available endogenous metabolites in <i>E. coli</i> to increase the starting compound levels of your synthetic circuit for optimal production.</b></P><br />
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</html><br />
[[Image:UCalgary2012_Manual.png|left|200px|link=https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf]]<br />
<html><h2>Documents</h2><br />
<p><a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf">Download</a> the OSCAR Optimizer Manual for more information on how to use the program!</p><br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAR_optimizer_v1.zipFile:UCalgary2012 OSCAR optimizer v1.zip2012-10-27T03:35:53Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 OSCAR optimizer v1.zip&quot;</p>
<hr />
<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-27T03:32:20Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
[[Image:UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png|250px|right|]]<br />
<html><br />
<br />
<p>Flux-variability analysis (FVA) was applied to optimize the bioreactor system of OSCAR for the newly incorporated metabolic pathways<br />
(<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Decarboxylation">Decarboxylation</a>,<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/CatecholDegradation">Decatecholization</a>,<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Desulfurization">Desulfurization</a>, and<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Denitrogenation">Denitrogenation</a>). FVA combines the framework of metabolic pathways with experimental enzymatic data to provide a computational platform for predicting what changes in metabolite levels will result in increased end-product. We developed a model using <a href="http://www.mathworks.com/products/matlab/">MATLAB</a> computer language for predicting what metabolites could be added to your growth media to increase production of hydrocarbons in the <i>E. coli</i> chassis. We also created a graphical user interface called the <i>OSCAR Optimizer</i> to make the the FVA program user-friendly and to allow current and future iGEM teams/scientists to input their own synthetic pathways into the FVA for analysis. Finally, we validated this model in the wet-lab by optimizing the PetroBrick (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K590025">BBa_K590025</a>) system to increase hydrocarbon production, thereby saving time, and resources.</p><br />
<br />
<h3><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the MATLAB package to test our model!</h3><br />
<center><h3>Click <a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf"> here </a> to view our OSCAR Optimizer Manual.</b></center></h3><br />
<br />
<h2>Background</h2><br />
<h3>What is Flux Balance Analysis and Flux Variability Analysis?</h3><br />
<p>Flux Balance Analysis (FBA) uses linear programming of metabolic networks to convert each metabolite into a mathematical coefficient. These coefficients can be connected to each other by changes associated with each enzymatic step of the pathway. FBA can then apply a mathematical method to examine how metabolites relate to each other in the network and allows the user to make generalized predictions for the growth of the organism, metabolite levels, and product output inside the cell. Flux Variability Analysis (FVA) is an extension of FBA that determines the range of reactions that result in an optimal flux through the metabolic network. Unlike FBA, this allows the user to not only determine the most optimized network for increasing cell growth, but also other parameters like product output.</p><br />
<br />
<p><b>What are the constraints in the FVA model?</b></p><br />
<p>As illustrated in <b>Figure 1</b>, metabolic networks can be encoded as stoichiometric matrices, in which each row represents a specific metabolite and each column represents a biochemical reaction. The entries in each column represent the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed or produced have a negative or positive, respectively.</p><br />
<br />
</html><br />
[[Image:Calgary FluxExample.jpg|700px|thumb|center|Figure 1: Flux Balance Analysis. FBA uses metabolic network (left) and simulates the connections in the network as a linear algebra matrix (right). Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, the state of the system is affected by each metabolite change.]]<br />
<html><br />
<br />
<p><b>Why use FVA as opposed to FBA?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, FBA only returns a single flux distribution that corresponds to maximal bacterial growth under the given growth conditions, even if other optimal parameters may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Thus, FVA can be employed to study the entire range of achievable cellular functions, as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wet-lab.</p><br />
<br><br />
<br />
<h2>Using FVA to optimize OSCAR</h2><br />
<h3>What Are We Trying To Model?</h3><br />
<p>FVA provides a method to modulate the inputs of endogenous metabolic pathways, but could also investigate what chemicals are needed in the growth media to upregulate an introduced pathway in <i>E. coli</i>. In the latter case, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to improve production rate. Development of a tool to model the optimal flux would also benefit numerous iGEM teams and research labs who engineer bacteria with novel pathways. </p><br />
<br />
<h3>How could systems like OSCAR benefit from this model?</h3><br />
<p>Much like chemical reactions which need optimal environmental conditions to achieve maximum production rate, microbes require certain conditions to accomplish cellular tasks at maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized to reduce cost of production to a minimum. Conditions in the microbial bioreactor systems are more crucial than chemical synthesis reactors ⎯ simply due to the increase in simultaneous reactions occurring. Furthermore, the selection and concentration of compounds in the growth media can severely affect the growth rate. If a computational model can predict an optimal set of metabolites for media composition, this will save time, resources, and funds. </p><br />
<br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|right|thumb|300px|Figure 2: Diagram of the relationship between growth rate and production rate, and the computed optimal growth rate.]]<br />
<html><h3>How does the program work?</h3><br />
<br />
<p>Our program is built upon flux variability analysis applied by the functions in the Cobra Toolbox. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the <br />
<a href="http://gcrg.ucsd.edu/Home"><br />
Systems Biology Research Group (Dr. B. Palsson)<br />
</a><br />
at the University of San Diego. Using this as a base, we constructed reactions and metabolites for the hydrocarbon production components of OSCAR. Specifically, new reactions corresponding to the PetroBrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. Running FVA will give an output correlated to a optimized growth rate. Moreover, this FVA algorithm running at optimal biomass flux rates can provide a range of outputs for your product of interest (<b>Figure 2</b>). This allows the user to select an optimum point where compound production is maximized. Finally, the program will analysis the data with an algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br />
<br />
<h2>Algorithm</h2><br />
<h3>Conceptualization</h3><br />
</html><br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|right|300px|Figure 3: Diagram of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]] <br />
<html><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a broader analysis of growth and production rates compared to FBA. Since biomass rate reflects the growth condition, cells must have a positive biomass flux rate to survive and proliferate. This positive value is indicative of a real cellular system since cells prefer to have increases in growth rather than increases in product output; however, our goal is to increase the production flux rate above a zero value. Therefore, among all the possible set of fluxes, the optimal flux is located where growth and production rate are at the optimum point for cell survival and compound output.</p><br />
<br />
<br />
<p> Differences between the <i>maximum</i> and <i>minimum</i> production rates are compared for each reaction in a set of fluxes. Reactions that demonstrated higher flux in the maximum production rate versus the minimum production rate represent metabolites that could be increased to increase overall product output (<b>Figure 3</b>). This analysis generates a list of compounds that should increase product output if increased in concentration in the growth media.</p><br />
<br />
<br />
<br />
<p>Note that not all compounds can be imported into the cell. Hence, only the metabolites that have native transporters in <i>E. coli</i> were considered for wet-lab verification. We used a model based on glucose minimal media; however, this could be applied to any media type.</p><br />
<br />
<br />
<h3>Flowcart of the Model</h3><br />
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[[Image:Calgary_FluxModelMethod.jpg|center|thumb|600px|Figure 4: Methodology the program uses to identify metabolite hits which should be supplemented to the media of your organism to increase output.]]<br />
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<p>Disclaimer: Original model built using minimum media with glucose.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the differences in percentage of flux rate for each reaction between production maximum and minimum set.</p><br />
<p>4. Collect all reactions with different percentages between two sets that exceed the user input threshold. (The input threshold determines the size of the difference in flux that the user is interested in. We used a 100% difference in our model.).</p><br />
<p>5. Score each compound in all collected reactions, with the initial score of zero for each compound (Scoring is determined by the difference in the value of a particular compound when the flux sets of the desired compound production are maximized and minimized as determined by FVA). This process is additive if the particular compound is found in more than one reaction (i.e., ATP) and only includes the reactions identified from step 4.</p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to the growth media and the run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model (step 7) to that from the raw model (step 2), if the rate is improved, mark the compound as an effector.</p><br />
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<h2>Graphical user interface (GUI) development - Creating OSCAR Optimizer</h2><br />
<p>The GUI allows for easier use of our program by everyone in iGEM and beyond. This will enable individuals to use our program who are inexperienced with programming programs such as MATLAB and/or the Cobra Toolbox. We developed the GUI using the GUIDE program designed in MATLAB. For more information please see our manual.</p><br />
<br><br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of the Cobra Toolbox and SBML Toolbox. To run the application, one must have the Cobra Toolbox and SBML Toolbox installed. SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>. Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>. Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
<br><br />
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<h2>Demo</h2><br />
<p>Below is an uploaded tutorial to show anyone how to use the GUI of the OSCAR Optimizer. The GUI interface allows for easy building of different synthetic constructs into the <i>E. coli</i> network but this could based on any model from any organism that is available in SBML format.</p><br />
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<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
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<h4><center>Screen Shots of Our Application</center></h4><br />
[[Image:UCalgary2012_RunTimeBuild.png|350px|left|]]<br />
[[Image:UCalgary2012_DemoOutput.png|350px|right|]]<br />
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[[Image:UCalgary2012_BuildOutput.png|400px|center|]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|400px|center|]]<br />
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<a name="Flux"></a><h2>Wet-lab validation of OSCAR Optimizer</h2><br />
<p>We tested our OSCAR Optimizer FVA Model in the wet-lab by assessing if a suite of identified compounds would increase the output of the Petrobrick. Therefore we ran the model with the <i>AAR</i> and <i>ADC</i> gene components of the Petrobrick system and looked at the predicted metabolites for growth media supplementation (<b>Figure 5</b>).</p><br />
<br />
</html>[[File:Calgary ModelingPetroOutput.jpg|center|thumb|600px|Figure 5: OSCAR Optimizer output for increase hydrocarbon production in OSCAR. This program identified a series of metabolites that are predicted to increase hydrocarbon output, including fructose and AMP with the highest outputs suggesting that their presence should contribute to increased product output the most.]]<html><br />
<br />
<p>Once these compounds were identified, we set-up an assay where we supplemented minimal M9 media and glucose with each of the compounds alone, or in combination. Compounds were added at concentrations of 50 mM except for Ethanol (2.5% v/v), AMP (100mg/L), and L-aspartate (100mg/L). As a positive control, instead of using minimal media a solution of 50:50 LB:Washington Production Media (see <br />
<a href="https://2011.igem.org/Team:Washington/alkanebiosynthesis"><br />
2011 iGEM University of Washington Protocols<br />
</a><br />
) was used to represent normal production. Additionally, we found that the compound formate was predicted to NOT increase hydrocarbons production and this represented a chemical negative control. All compounds were added to <i>E. coli</i> culture containing the PetroBrick at an OD<sub>600</sub> of ~0.05 in the first experiment, and a higher OD<sub>600</sub> of 0.400 for the second experiment and grown for 72 hours at 37<sup>o</sup>C. Cultures were then sonicated after an OD<sub>600</sub> measurement and any produced hydrocarbons were extracted by 1 mL ethyl acetate. This was quantitated based on the peak area of a C15 hydrocarbon product as described in the <br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Decarboxylation">Decarboxylation</a> section (also see <br />
<a href="https://2012.igem.org/Team:Calgary/Notebook/Protocols">Protocols</a> <br />
for relevant procedures). The tabulated results of the quantitated yields are shown in <b>Figure 6</b>. </p><br />
<br />
</html>[[File:Calgary ModellingWetlabDataFinal.png|center|thumb|600px|Figure 6: Model validation, showing the relative abundance of C15 hydrocarbons of <i>E. coli</i> containing the Petrobrick in combination with different compounds as growth media supplements. Data is normalized to the positive control (50:50 LB:Washington Production Media) and the relative production of hydrocarbons is displayed relative to optical density measurements. This data suggests hydrocarbon production can be increased or decreased if the Petrobrick is incubated with specific compounds. Dashed line represents the average output of hydrocarbons in the positive control. PMFA (Pyruvate, Malate, Fumarate, Aspartic Acid) and G+P (Glycine and Pyruvate). ]]<html><br />
<br />
<p> These hydrocarbon production results indicate there is variability in the output of hydrocarbons from the PetroBrick with different compounds (<b>Figure 6</b>). Interestingly, five of the compounds demonstrated production levels higher than that of the minimal media control, suggesting our model could be used for predicting compounds to increase metabolite production. Although, AMP and fructose both decreased output despite having the highest predicted increases in hydrcarbon output, suggesting that our model may produce false positives and there is some error in assessing these predictions. Most excitingly, addition of two compounds, pyruvate and glycine, had increased the relative number of hydrocarbons higher than the complex media of the positive control. As noted by the multiple experiments, there are some differences in the relative output of hydrocarbons in each experiment, however, the trend for each compound is relatively consistent for the different samples. Some of the runs were not able to be repeated in the second assay, which is the reason for some of the lack of data in Figure 6. <b>These results suggest our OSCAR Optimizer model can optimize OSCAR (along with other metabolic systems); however, these predicted candidates need to be tested in the wet-lab to ensure the model is accurate.</b><br />
<br />
<h2> Modelling informing our Killswitch </h2><br />
<br />
<p> Although we had initially rejected the idea of a killswitch based on <b>auxotrophy</b> due to the issue of cost, the results of our model made us think that this might be more feasible than we had initially thought. As glycine alone in minimal media was able to give us higher alkane production than Washington’s production media, supplementing our system with glycine, achieving higher output may actually be feasible. We saw auxotrophy as a method of creating <b>layers</b> in our killswitch system, where we could use it in tandem with the inducible kill systems that we have already been developing. We obtained a glycine knockout stain of <i>E. coli</i> and have begun some initial <a href="https://2012.igem.org/Team:Calgary/Project/HumanPractices/Killswitch/Regulation">characterization data</a>. This was a great example of how our modelling project was actually able to <b>inform</b> other parts of our project. In order to validate that this system was feasible for use we performed three key experiments:<br />
<br />
<ul><br />
<li>Perform a glycine requirement assay to determine the survivability of the knockout strain in our minimal media's used with OSCAR <.</li><br />
<li>Determine if the Petrobrick can be used with a similar activity as in the wild type strain</li><br />
<li>Use the auxotrophic strain in conjunction with our killswitch to show that these can act together.</li> <br />
</ul><br />
<p><a href="https://2012.igem.org/Team:Calgary/Project/Synergy"> Click here</a> to learn more about these assays. </p><br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon the Cobra Toolbox, and the SBML Toolbox. As a consequence, any flaws in the Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (<i>E. Coli</i> iAF1260) which contains constraints that rely on the Stoichiometric Matrix (such as Stoichiometric Coefficients), lower bounds and upper bounds of reactions. These pathways lack genetic and enzymatic regulation, making the connections between reactions in the network much weaker than those in the wet-lab. These missing components may lead to inaccurate results.</p><br />
<br />
<p>At the current stage, our algorithm can only pick metabolites with natural transporters in cell. Therefore, many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. <b>This model does not take into consideration rate limiting steps of reactions, toxic intermediate accumulation, or any form of regulation of the enzymes (e.g., negative feedback). The OSCAR Optimizer should act as a starting point to identify any limitation of available endogenous metabolites in <i>E. coli</i> to increase the starting compound levels of your synthetic circuit for optimal production.</b></P><br />
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[[Image:UCalgary2012_Manual.png|left|200px|link=https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf]]<br />
<html><h2>Documents</h2><br />
<p><a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf">Download</a> the OSCAR Optimizer Manual for more information on how to use the program!</p><br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Notebook/FluxAnalysisTeam:Calgary/Notebook/FluxAnalysis2012-10-27T02:54:30Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateNotebookBlue|<br />
TITLE=Flux Analysis Notebook|<br />
CONTENT =<br />
<html><br />
<!--<br />
NOTE: This is a template for entering things for the time being. All dates should be enclosed in <h2> tags and all paragraphs should be enclosed in <p> tags. For bulleted lists, <ul> tags will create the list and <li> tags will surround each list item. If there are any questions, please let me know.<br />
Patrick.<br />
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<h2>Week 1-2 (May 1-11) </h2><br />
<p>Flux Analysis is brought into the project as it can offer predictions of sets of compound that will be used in growth media to improve the output of target compound.</p><br />
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<h2>Week 3 (May 14-18) </h2><br />
<p>This week involved planning of selection proper analysis, models and platform for modelling. The constraint-based reconstruction analysis was chosen to be the core analysis, the published E.coli (iAF1260) and Pseudomonas (iJN746) models were selected to be base chassis and the MatLab was considered to use as modelling platform. In addition, OpenCobra Toolbox that is developed by System Biology Research Team in UCSD would be employed as lower level computation tools.</p><br />
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<h2>Week 4 (May 21-25)</h2><br />
<h3>Concepts</h3><br />
<h4>Flux Balance Analysis (FBA)</h4><br />
<p>Flux balance analysis (FBA) is a mathematical method for analyzing metabolism. It is a direct application of linear programming to biological systems that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization.</p><br />
</html>[[File:UCalgary 2012 FBAEx.png|thumb|600px|center|The results of FBA on a prepared metabolic network of the top six reactions of glycolysis. The predicted flux through each reaction is proportional to the width of the line. Objective function in red, constraints on alpha-D-Glucose and beta-D-Glucose import represented as red bars. Original: Wikipedia]]<html><br />
<h5>The Steady State Assumption</h5><br />
<p>A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero. In chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. </p><br />
<h5>Stoichiometric & Flux Matrix </h5><br />
<p>Stoichiometry is a branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. In a balanced chemical reaction, the relations among quantities of reactants and products typically form a ratio of whole numbers.<br />
<br>Flux matrix, in terms of flux rate, is a rate of turnover of molecules through a reaction pathway. <br />
</p><br />
</html>[[File:UCalgary 2012 FBAexWithSMatrix.png|thumb|600px|center|An example stoichiometric matrix for a network representing the top of glycolysis and that same network after being prepared for FBA.Original:Wikipedia]]<html><br />
<h4> Extended Flux Analysis</h4><br />
<h5>Flux variability analysis</h5><br />
<p>The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built-in to virtually all current analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, produce the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are highly important. </p><br />
<h5>Dynamic FBA</h5><br />
<p>Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict homoeostatic condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.</p><br />
<h4>Metabolic Network Reconstruction</h4><br />
<p>Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including E. coli. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of E. coli have existed for nearly twenty years. The first genome-scale model of E. coli metabolism was released in 2000, and this model continues to be expanded and updated today.<a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></p><br />
<br />
<br />
Metabolic network reconstruction and simulation allows for an in depth insight into comprehending the molecular mechanisms of a particular organism, especially correlating the genome with molecular physiology (Francke, Siezen, and Teusink 2005). A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. </p><br />
<h3>Modelling</h3><br />
<h4>Constraint-based Model</h4><br />
<p>Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. The vector x with length m can then be defined as the concentrations of all the metabolites and the vector v with length n contains the fluxes through each reaction.</p><br />
<h5>Constraint-Based Models of E. coli <a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></h5><br />
<h6>iAF1260</h6><br />
<p>The latest update of the E. coli genome-scale metabolic model is iAF1260, published in 2007. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the EcoCyc database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the E. coli metabolic model, iAF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. iAF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution.</p><br />
<h6>E. coli core model</h6><br />
<p>The core E. coli model is a small-scale model of the central metabolism of E. coli. It is a modified subset of the iAF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.</p><br />
<h3>Tools</h3><br />
<h4><a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCobra Toolbox</a></h4><br />
<p>The Constraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies. It is used as the major program.</p><br />
<h4><a href="http://www.celldesigner.org/">CellDesigner 4.0</a></h4><br />
<p>CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). In this project, it is used to visualize metabolic network.</p><br />
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[[Image:UCalgary2012_EcoliCoreNetwork.png|thumb|300px|Fig1. E. coli core model metabolic network view in CellDesigner. The network contains 95 reactions, it is good to use as a verifier of novel model.]]<br />
[[Image:UCalgary2012_EcoliiAF1260.png|thumb|200px|Fig2. E. coli iAF1260 model metabolic network view in CellDesigner. The network contains more than 2000 reactions and it is not possible to be used as verrifier.]]<br />
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<br />
<h2>Week 5 (May 28 - June 1)</h2><br />
<p>Tests of OpenCobra toolbox basic examples on E.coli core model and Pseudomonas (iJN746) model are passed, which includes following functions:<br />
FBA test (optimizeCbModel),model creation/modification tests, reaction addition/deletion/modification, and gene search (deletion) tests.</p><br />
<p>Generally, The tests outputs are consistant with expected results. FBA test basically generates one pattern of flux rates for each metabolites to contribute the best biomass.<br />
The novel model created is exactly same as the predicted model as well as the modified model. Reactions in models can be easily added and deleted.</p><br />
<p>The gene deletion tests show the same output as the paper, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, demonstrated. </p><br />
<p>In addition, a software, CellDesigner, is employed to verified the novel model built in Cobra Toolbox visually. The CellDesigner generates a visual diagram to show the entire metabolic network.</p><br />
<br />
<br />
<h2>Week 6 (June 4-8) </h2><br />
<p>Another sets of tests of OpenCobra toolbox examples on E.coli core model and Pseudomonas (iJN746) model were exanimated, which includes following functions: fluxVariability, OptKnock, GDLS and optGene.</p><br />
<p>The outputs of tests with fluxVariability, OptKnock and GDLS were biological relevant. The results from OptKnock and GDLS were similar which could be considered as consistent as they were designed to complete similar tasks. However, optGene tests either output results that were understandable or error messages referring to source code. </p><br />
<br />
<h2>Week 7 (June 11-15)</h2><br />
<p>In order to reconstructing a proper novel model, all the symbols in OpenCobra Toolbox such as ‘[c]’, ‘[e]’, ‘[b]’ after every compound as well as the abbreviation like ‘lb’ and ‘ub’ for each reaction had to be well understand. Also, the formula of added reactions must be as precise as possible to ensure the accurate of the results because lack of enzymatic and genetic regulation to those reactions making the formulas become only variable.</p><br />
<p>The novel model built upon E.coli iAF1260 with additional decarboxylation pathway was accomplished. The flux rate of target compound of testPathway test was good; however, this value become extremely low if set the biomass as objective (running flux balance analysis). The results showed, in simulation, if the cell produced the product, its biomass was nearly zero. Vice verse, if the cell grew normally, the production rate was almost zero. </p><br />
<br />
<h2>Week 8 (June 18-22)</h2><br />
<p>Two novel models built upon E.coli iAF1260 with additional desulfurization and denitrification pathway separately were completed. As expected, the results from FBA were similar to decarboxylation pathway. These phenomena indicated that the relationship between cell growth and production was normally negative related. In addition, FBA returned one and only one solution that maximized biomass. Due to this natural of FBA, it restricted freedom of flows in network and many alternative solutions were omitted out. For pathways like above three, the production rate was negatively related to growth rate, the FBA was meaningless since the production rate was always zero or extremely small. Luckily, Flux Variability Analysis (FVA) was able to cover the problems caused by FBA. Therefore, FVA become the major computation tool to simulate the flux rates of cell metabolic activities. Unfortunately, the results from FVA for each pathway were also close to zero.</p><br />
<br />
<h2>Week 9 (June 25-29)</h2><br />
<p>To figure out the outputs from FVA were whether reasonable, testPathway was applied to run as unit test to verify the model with added pathway. The testPahtway was used to test each reaction in each pathway instead of overall pathway. The results showed the pathways were built improperly because upstream reactions had no flux but downstream reactions could have flux rate. The phenomenon reported last week was result in unbalance of metabolic network. In other words, the modified model violated steady state assumption. Therefore terminal reactions were added to three pathways respectively to keep the system remain in steady state and further to solve the problem. The revised models worked well on testPathway and FVA tests.</p><br />
<br />
<h2>Week 10 (July 3-6)</h2><br />
<p>The function FluxAnalysis was employed to generate maximum and minimum flux rate outputs of target compounds for three pathways respectively. The visual graphs of flux rates over entire metabolic networks for each model with three pathways were generated. These graphs were compared and analyzed to find out reactions which flux rates were significantly different between maximum and minimum outputs in each pathway respectively.</p><br />
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[[File:UCalgary2012_CobraToolboxNetwork.png|thumb|800px|center|Fig3. E. coli iAF1260 model Flux Balance Analysis visual output in Cobra Toolbox.]]<br />
<html><br />
<p>Unfortunately, the map was too complicated to compare every single reaction by man source. Also, the conclusions drawn from such comparisons would be not valid since the reactions were highly connected to each other in network rather than distributed. Hence, it is necessary to build an algorithm that could automatically analysis the reactions in network scale.</p><br />
<br />
<h2>Week 11-12 (July 9-20)</h2><br />
<p>To have an algorithm doing such a work, the question had to be answered. How to improve the products flux rates through data from FVA?</p><br />
<p>Two things were noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|thumb|center|350px|Fig4. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|center|350px|Fig5. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>To improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media.</p><br />
<br />
<p>The analysis algorithm was designed. It can automatically analyze reactions and compounds and were able to output metabolites that would improve the production rate.</p><br />
<p>Algorithm:</p><br />
<p>1. Define relationship between growth rate and production rate</p><br />
<p>2. Find out the optimal growth rate that can maximize the production</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold<br />
<p>5. Score each compound in all collected reactions (Initial score is zero for all compounds) <br />
<br> 5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction.<br />
<br> 5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.<br />
<br> 5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed. <br><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate. </p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
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<br />
<h2>Week 13 - 15(July 23-Aug 10)</h2><br />
<p>Algorithm described in past weeks was implemented. The user interface prototype of Analysis tab was created. </p><br />
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[[Image:UCalgary2012_AlgorithmOutput.png|thumb|center|600px|Fig6. Algorithm outputs in Matlab console]]<br />
[[Image:UCalgary2012_BioVsTarget.png|thumb|center|400px|Fig7. Plot of relationship between growth rate and production rate with outlined optimal growth rate]]<br />
[[Image:UCalgary2012_AnalysisTabPrototype.png|thumb|center|600px|Fig8. User interface prototype of Analysis Tab]]<br />
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<h2>Week 16-17 (Aug 13-24)</h2><br />
<p>User interface of Analysis part was completely implemented and full functional. </p><br />
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[[Image:UCalgary2012_Denitri.png|thumb|center|600px|Fig9. Denitrification pathway results showed on user interface]]<br />
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<h2>Week 18-19 (Aug 27-Sep 7)</h2><br />
<p>The user interface prototype of Build tab was created. The interface was completely implemented and fully functional.</p><br />
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[[Image:UCalgary2012_BuildTabPrototype.png|thumb|center|600px|Fig10. User interface prototype of Build tab]]<br />
[[Image:UCalgary2012_BuildTabEg.png|thumb|center|600px|Fig11. Example model built on Build tab]]<br />
[[Image:UCalgary2012_NovelModelExport.png|thumb|center|600px|Fig12. Example model exported to mScript file]]<br />
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<h2>Week 20 (Sept 10-14)</h2><br />
<p>Application went post-development phase and some additional features were added into. <br />
<br>New features:<br />
<br>Searching: Users can search metabolites in loaded model<br />
<br>Updating: Users is able to update reactions they entered in table<br />
<br>Changing export file format: User can read analysis output in spread sheet<br />
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<h2>Week 21-23 (Sept 10-Oct 3)</h2><br />
<p>Model Validated with data from web lab. Assay for decarboxylation pathway was set. E Coli. with petrobrick grew up on nine different growth medias, one positive control (LB+glucose minimum), one negative control (glucose minimum), and eight medias that consisted of glucose minimum and compounds (pyruvate, fumarate, malate, aspartate, fructose, amp, glycine and ethanol) suggested by application outputs. </p><br />
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<h2>Week 24-26 (Oct 15-Oct 26)</h2><br />
<p>The previous wet lab experiments were repeated to get more duplicates. The same conditions were set except different initial cell OD. We added one more media that combines glycine and pyruvate to see the effect of repeated addition. The results showed on <a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysis">Flux Analysis Page</a>.<br />
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</html>[[File:UCalgary2012_GraphicalAbstract.png|thumb|700px|center|thumb|Figure 1. The Calgary team has developed a dual system for the detection of toxic components in tailing ponds and the remediation of these compounds. Tailings ponds are large bodies of water containing waste products produced from the extraction of bitumen in the oil sands. Our biosensor included the identification of a toxin promoter through a transposon screen. An electrochemical detector was implemented with a multiple output system allowing for the detection of multiple compounds simultaneously. This promoter/detector system was then complemented with the production of a biosensor prototype involving both a physical device and a software program for easy data analysis.<br />
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Rather than just sensing toxins in the tailings ponds, a major objective was to detoxify the tailings through the reduction of toxins such as carboxylic acids (mainly naphthenic acids) and catechol, while turning them into usable hydrocarbons. Purification of these hydrocarbons would contribute an added economic and industrial benefit. We aimed to house this system by designing a bioreactor for our bacteria as well as optimize product output through a flux-variability based model. Finally, in order to create higher quality hydrocarbons we explored desulfurization and denitrogenation pathways to upgrade our fuel. To do this in a safe and environmentally sound manner, we built into our design structural containment, as well as genetic control mechanisms through novel inducible ribo-killswitches and an auxotropic marker.]]<html> <br />
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<a name="newparts"></a><br />
<h2>Characterization of new parts submitted to the Registry</h2><br />
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<ul><li><p>(<a class="green" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902000">BBa_K902000</a>) and (<a class="green" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902004">BBa_K902004</a>): two novel hydrolase enzymes were submitted to the registry for the hydrolysis of two different sugar-conjugated electroactive compounds: PNPG and PDPG. Used in conjunction with the existing lacZ part in the registry (<a class="green" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_I732005">BBa_I732005</a>) which hydrolyzes CPRG, this allows for the electrochemical detection of three compounds with a single electrode. A <i>uidA</i> inducible generator (<a class="green" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902002">BBa_K902002</a>) was submitted and characterized electrochemically. This data can be found on our <a class="green" href="https://2012.igem.org/Team:Calgary/Project/FRED/Reporting">Electroreporting</a> page.</li><br />
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<li><p>(<a class="orange" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902008">BBa_K902008</a>),(<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902023">BBa_K902023</a>) and (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902074">BBa_K902074</a>): three novel riboswitches sensitive to magnesium, molybdenum cofactor, and manganese were submitted along with two associated promoters (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902009">BBa_K902009</a> and <a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902073">BBa_K902073</a>) in addition to a rhamnose inducible, glucose repressible (<i>Prha</i>) promoter (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902065">BBa_K902065</a>).</li></p><br />
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<li><p>The magnesium riboswitch (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902008">BBa_K902008</a>) was tested with GFP and a constitutive promoter using this construct, (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902021">BBa_K902021</a>), with its promoter and GFP using this construct (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902017">BBa_K902017</a>) and with its promoter and the <i>S7</i> kill gene using this construct (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902018">BBa_K902018</a>). This data can be found on our killswitch <a class="orange" href="https://2012.igem.org/Team:Calgary/Project/HumanPractices/Killswitch/Regulation">Regulation</a> page. </li></p><br />
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<li><p>The <i>Prha</i> promoter was characterized via fluorescence output using a GFP composite part (<a href="orange" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902066">BBa_K902066</a>). This promoter was additionally characterized with our S7 kill gene as a composite part (<a href="orange" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902084">BBa_K902084</a>). This data can be found on our killswitch <a class="orange" href="https://2012.igem.org/Team:Calgary/Project/HumanPractices/Killswitch/Regulation">Regulation</a> page. </li></p></li></p> <br />
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<li><p>Genes for denitrogenation and desulfurization were biobricked and submitted. The <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Denitrogenation"><i>amdA</i></a>, amidase gene <a class="blue" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902041">(BBa_K902041)</a> was biobricked and shown to be able to remove primary amines from a variety of compounds. A novel oxidoreductase part (<a class="blue" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902058">BBa_K902058</a>) was also submitted and its functionality characterized for use in the desulfurization project. This data can be found on our upgrading <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Desulfurization">Desulfurization</a> page.</p></li></ul><br />
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<h2>Further characterization of parts already present within the registry </h2><br />
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<ul><li><p>The IPTG inducible lacI regulated promoter (<a class="green" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_R0010">BBa_R0010</a>) was tested electrochemically to demonstrate its leakiness when not used in conjunction with strong expression of regulatory elements. This data can be found on our <a class="green" href="https://2012.igem.org/Team:Calgary/Project/FRED/Reporting">Electroreporting</a> page.</li><br />
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<li><p>A β-galactosidase (LacZ) inducible generator construct existing in the registry (<a class="green" href="http://partsregistry.org/Part:BBa_I732901">BBa_I732901</a>) was found to possess a frameshift mutation, affecting its functionality. This part was replaced with a new circuit (<a class="green" href="http://partsregistry.org/Part:BBa_K902090">BBa_K902090</a>), which was characterized for functionality both qualitatively as well as electrochemically. This data can be found on our <a class="green" href="https://2012.igem.org/Team:Calgary/Project/FRED/Reporting">Electroreporting</a> page.<br />
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<li><p>(<a class="blue" href="http://partsregistry.org/Part:BBa_K590025">BBa_K590025</a>), the PetroBrick, submitted by the Washington team in 2011, was characterized for a novel function: the conversion of naphthenic acids and 2-hydroxymuconate- a catechol break-down product from from the xylE gene (<a class="blue" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_J33204">BBa_J33204</a>) into hydrocarbons and potential value added products. This data can be found on both the <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Decarboxylation">Decarboxylation</a> page and the <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/CatecholDegradation">Decatecholization</a> page. We feel that these new and meaningful applications of this part present a distinct improvement on its usefulness for other teams.</li><br />
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<li><p> The output of (<a class="blue" href="http://partsregistry.org/Part:BBa_K590025">BBa_K590025</a>) was also optimized through a program we developed in MATLAB for the optimization of metabolic pathways in synthetic biology metabolic networks. The program allows you to build an artificial synthetic biology network in <i>E. coli</i> and predicts substrates that should be fed to the organism to increase production of the compound. This was characterized and validated in the wetlab with the Petrobrick. This data can be found on our <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysis">Flux Analysis</a> page.</li><br />
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<li><p> An existing <i>xylE</i> gene in the registry (<a class="blue" href= "http://partsregistry.org/Part:BBa_J33204">BBa_J33204</a>) was constructed with a constitutive promoter instead of the glucose-repressible part available within the registry. This allows for increased output in media containing glucose, making it more suitable for a variety of applications such as our own. We validated the functionality of this part which can be found on our <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/CatecholDegradation">Decatecholization</a> page. In addition, we documented a novel application for this part, by using it in conjunction with Washington's PetroBrick (<a class="blue" href="http://partsregistry.org/Part:BBa_K590025">BBa_K590025</a>) to degrade catechol into a further break-down product. </p></li><br />
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<li><p>An <I>E. coli</i> catalase gene from the registry (<a class="blue" href= "http://partsregistry.org/Part:BBa_K137068">BBa_K137068</a>) was also tested in conjunction with a lacI inducible promoter as a new composite part (<a class="blue" href= "http://partsregistry.org/Part:BBa_K902060">BBa_K902060</a>) . This part was characterized in TOP10 <i> E. coli</i> for its ability to allow cells to survive in higher concentrations of hydrogen peroxide. This data can be found on our <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Desulfurization">Desulfurization</a> page. <br />
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<h2>Additional Work and Characterization </h2><br />
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<ul><br />
<li><p>Developed and tested both hardware and software for a biosensor using an electrochemical sensor. The software is available on our wiki as are the results fom the hardware. These are on our <a class="green" href="https://2012.igem.org/Team:Calgary/Project/FRED/Prototype">Device Prototype</a> page</p></li><br />
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<li><p> Characterized one of our constitutively expressed transposon clones to test the lacZ gene electrochemically. In addition, one of our two 'toxin-sensing' transposon hits was characterized electrochemically, demonstrating its ability to respond to and report on NAs at levels detectable by our electrochemical reporting system. This data can be found on our <a class="green" href="https://2012.igem.org/Team:Calgary/Project/FRED/Reporting">Electroreporting</a> and <a class="purple" href="https://2012.igem.org/Team:Calgary/Project/Synergy">Synergy</a> pages respectively. </p></li><br />
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<li><p> Performed an actual test of our biosensor using tailings pond water, showing that we can detect toxins found in tailings. In addition, performed an actual "field test" of our prototype to demonstrate its feasibility and ease of use outside a laboratory setting. This data can be found on our <a class="purple" href="https://2012.igem.org/Team:Calgary/Project/Synergy">Synergy</a> page. </li></p><br />
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<li><p> Submitted novel parts involved in decarboxylation and validated the functionality of an additional enzyme (oleT), capable of converting fatty acids into alkenes by itself. This was done in its host organism. This data can be found in our <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Decarboxylation">Decarboxylation</a> section, however this gene has not yet been submitted due to problems cloning it.</p></li><br />
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<li><p>Designed and prototyped a physical bioreactor for which we obtained both qualitative and quantitative data for its functionality. This is outlined on our <a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Bioreactor">Bioreactor</a> page. In addition, we performed an actual validation assay of our bioreactor, showing that we can use it to grow hydrocarbon producing cells and use our belt skimming device to harvest the hydrocarbons. This data can be found on our <a class="purple" href="https://2012.igem.org/Team:Calgary/Project/Synergy">Synergy</a> page. </p></li><br />
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<li><p>Characterized the biodegradation of carbazole and various sulfur-containing compounds resembling naphthenic acids in the organisms from which we got our genes. This data can be found on our (<a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Desulfurization">Desulfurization</a>) and (<a class="blue" href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Denitrogenation">Denitrogenation</a>)</p></li><br />
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<li><p>Performed initial assays on a glycine knockout strain of <i>E. coli</i>, characterizing its survival in differing concentrations of glycine, its ability to work in conjunction with one of our inducible killswitch constructs (<a href=blue href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902018">BBa_K902018</a>) and finally its ability to work with the Petrobrick, actually substantially increasing our out put of hydrocarbons when grown with glycine as compared to a <i>DH5alpha</i> strain. This data can be found on our <a class="purple" href="https://2012.igem.org/Team:Calgary/Project/Synergy">Synergy</a> page. </li></p><br />
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<li><p>Resubmitted (<a class="orange" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K206009">BBa_K26009</a>) an inconsistent registry composite part that we had to construct from basic parts, resubmitting as the sequence-verified (<a class="orange" href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K902016">BBa_K902016</a>)</p></li><br />
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}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Notebook/FluxAnalysisTeam:Calgary/Notebook/FluxAnalysis2012-10-27T02:44:02Z<p>Cqian: </p>
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<h2>Week 1-2 (May 1-11) </h2><br />
<p>Flux Analysis is brought into the project as it can offer predictions of sets of compound that will be used in growth media to improve the output of target compound.</p><br />
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<h2>Week 3 (May 14-18) </h2><br />
<p>This week involved planning of selection proper analysis, models and platform for modelling. The constraint-based reconstruction analysis was chosen to be the core analysis, the published E.coli (iAF1260) and Pseudomonas (iJN746) models were selected to be base chassis and the MatLab was considered to use as modelling platform. In addition, OpenCobra Toolbox that is developed by System Biology Research Team in UCSD would be employed as lower level computation tools.</p><br />
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<h2>Week 4 (May 21-25)</h2><br />
<h3>Concepts</h3><br />
<h4>Flux Balance Analysis (FBA)</h4><br />
<p>Flux balance analysis (FBA) is a mathematical method for analyzing metabolism. It is a direct application of linear programming to biological systems that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization.</p><br />
</html>[[File:UCalgary 2012 FBAEx.png|thumb|600px|center|The results of FBA on a prepared metabolic network of the top six reactions of glycolysis. The predicted flux through each reaction is proportional to the width of the line. Objective function in red, constraints on alpha-D-Glucose and beta-D-Glucose import represented as red bars. Original: Wikipedia]]<html><br />
<h5>The Steady State Assumption</h5><br />
<p>A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero. In chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. </p><br />
<h5>Stoichiometric & Flux Matrix </h5><br />
<p>Stoichiometry is a branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. In a balanced chemical reaction, the relations among quantities of reactants and products typically form a ratio of whole numbers.<br />
<br>Flux matrix, in terms of flux rate, is a rate of turnover of molecules through a reaction pathway. <br />
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</html>[[File:UCalgary 2012 FBAexWithSMatrix.png|thumb|600px|center|An example stoichiometric matrix for a network representing the top of glycolysis and that same network after being prepared for FBA.Original:Wikipedia]]<html><br />
<h4> Extended Flux Analysis</h4><br />
<h5>Flux variability analysis</h5><br />
<p>The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built-in to virtually all current analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, produce the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are highly important. </p><br />
<h5>Dynamic FBA</h5><br />
<p>Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict homoeostatic condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.</p><br />
<h4>Metabolic Network Reconstruction</h4><br />
<p>Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including E. coli. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of E. coli have existed for nearly twenty years. The first genome-scale model of E. coli metabolism was released in 2000, and this model continues to be expanded and updated today.<a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></p><br />
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Metabolic network reconstruction and simulation allows for an in depth insight into comprehending the molecular mechanisms of a particular organism, especially correlating the genome with molecular physiology (Francke, Siezen, and Teusink 2005). A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. </p><br />
<h3>Modelling</h3><br />
<h4>Constraint-based Model</h4><br />
<p>Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. The vector x with length m can then be defined as the concentrations of all the metabolites and the vector v with length n contains the fluxes through each reaction.</p><br />
<h5>Constraint-Based Models of E. coli <a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></h5><br />
<h6>iAF1260</h6><br />
<p>The latest update of the E. coli genome-scale metabolic model is iAF1260, published in 2007. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the EcoCyc database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the E. coli metabolic model, iAF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. iAF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution.</p><br />
<h6>E. coli core model</h6><br />
<p>The core E. coli model is a small-scale model of the central metabolism of E. coli. It is a modified subset of the iAF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.</p><br />
<h3>Tools</h3><br />
<h4><a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCobra Toolbox</a></h4><br />
<p>The Constraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies. It is used as the major program.</p><br />
<h4><a href="http://www.celldesigner.org/">CellDesigner 4.0</a></h4><br />
<p>CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). In this project, it is used to visualize metabolic network.</p><br />
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[[Image:UCalgary2012_EcoliCoreNetwork.png|thumb|300px|Fig1. E. coli core model metabolic network view in CellDesigner. The network contains 95 reactions, it is good to use as a verifier of novel model.]]<br />
[[Image:UCalgary2012_EcoliiAF1260.png|thumb|200px|Fig2. E. coli iAF1260 model metabolic network view in CellDesigner. The network contains more than 2000 reactions and it is not possible to be used as verrifier.]]<br />
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<h2>Week 5 (May 28 - June 1)</h2><br />
<p>Tests of OpenCobra toolbox basic examples on E.coli core model and Pseudomonas (iJN746) model are passed, which includes following functions:<br />
FBA test (optimizeCbModel),model creation/modification tests, reaction addition/deletion/modification, and gene search (deletion) tests.</p><br />
<p>Generally, The tests outputs are consistant with expected results. FBA test basically generates one pattern of flux rates for each metabolites to contribute the best biomass.<br />
The novel model created is exactly same as the predicted model as well as the modified model. Reactions in models can be easily added and deleted.</p><br />
<p>The gene deletion tests show the same output as the paper, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, demonstrated. </p><br />
<p>In addition, a software, CellDesigner, is employed to verified the novel model built in Cobra Toolbox visually. The CellDesigner generates a visual diagram to show the entire metabolic network.</p><br />
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<br />
<h2>Week 6 (June 4-8) </h2><br />
<p>Another sets of tests of OpenCobra toolbox examples on E.coli core model and Pseudomonas (iJN746) model were exanimated, which includes following functions: fluxVariability, OptKnock, GDLS and optGene.</p><br />
<p>The outputs of tests with fluxVariability, OptKnock and GDLS were biological relevant. The results from OptKnock and GDLS were similar which could be considered as consistent as they were designed to complete similar tasks. However, optGene tests either output results that were understandable or error messages referring to source code. </p><br />
<br />
<h2>Week 7 (June 11-15)</h2><br />
<p>In order to reconstructing a proper novel model, all the symbols in OpenCobra Toolbox such as ‘[c]’, ‘[e]’, ‘[b]’ after every compound as well as the abbreviation like ‘lb’ and ‘ub’ for each reaction had to be well understand. Also, the formula of added reactions must be as precise as possible to ensure the accurate of the results because lack of enzymatic and genetic regulation to those reactions making the formulas become only variable.</p><br />
<p>The novel model built upon E.coli iAF1260 with additional decarboxylation pathway was accomplished. The flux rate of target compound of testPathway test was good; however, this value become extremely low if set the biomass as objective (running flux balance analysis). The results showed, in simulation, if the cell produced the product, its biomass was nearly zero. Vice verse, if the cell grew normally, the production rate was almost zero. </p><br />
<br />
<h2>Week 8 (June 18-22)</h2><br />
<p>Two novel models built upon E.coli iAF1260 with additional desulfurization and denitrification pathway separately were completed. As expected, the results from FBA were similar to decarboxylation pathway. These phenomena indicated that the relationship between cell growth and production was normally negative related. In addition, FBA returned one and only one solution that maximized biomass. Due to this natural of FBA, it restricted freedom of flows in network and many alternative solutions were omitted out. For pathways like above three, the production rate was negatively related to growth rate, the FBA was meaningless since the production rate was always zero or extremely small. Luckily, Flux Variability Analysis (FVA) was able to cover the problems caused by FBA. Therefore, FVA become the major computation tool to simulate the flux rates of cell metabolic activities. Unfortunately, the results from FVA for each pathway were also close to zero.</p><br />
<br />
<h2>Week 9 (June 25-29)</h2><br />
<p>To figure out the outputs from FVA were whether reasonable, testPathway was applied to run as unit test to verify the model with added pathway. The testPahtway was used to test each reaction in each pathway instead of overall pathway. The results showed the pathways were built improperly because upstream reactions had no flux but downstream reactions could have flux rate. The phenomenon reported last week was result in unbalance of metabolic network. In other words, the modified model violated steady state assumption. Therefore terminal reactions were added to three pathways respectively to keep the system remain in steady state and further to solve the problem. The revised models worked well on testPathway and FVA tests.</p><br />
<br />
<h2>Week 10 (July 3-6)</h2><br />
<p>The function FluxAnalysis was employed to generate maximum and minimum flux rate outputs of target compounds for three pathways respectively. The visual graphs of flux rates over entire metabolic networks for each model with three pathways were generated. These graphs were compared and analyzed to find out reactions which flux rates were significantly different between maximum and minimum outputs in each pathway respectively.</p><br />
</html><br />
[[File:UCalgary2012_CobraToolboxNetwork.png|thumb|800px|center|Fig3. E. coli iAF1260 model Flux Balance Analysis visual output in Cobra Toolbox.]]<br />
<html><br />
<p>Unfortunately, the map was too complicated to compare every single reaction by man source. Also, the conclusions drawn from such comparisons would be not valid since the reactions were highly connected to each other in network rather than distributed. Hence, it is necessary to build an algorithm that could automatically analysis the reactions in network scale.</p><br />
<br />
<h2>Week 11-12 (July 9-20)</h2><br />
<p>To have an algorithm doing such a work, the question had to be answered. How to improve the products flux rates through data from FVA?</p><br />
<p>Two things were noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|thumb|center|350px|Fig4. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|center|350px|Fig5. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>To improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media.</p><br />
<br />
<p>The analysis algorithm was designed. It can automatically analyze reactions and compounds and were able to output metabolites that would improve the production rate.</p><br />
<p>Algorithm:</p><br />
<p>1. Define relationship between growth rate and production rate</p><br />
<p>2. Find out the optimal growth rate that can maximize the production</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold<br />
<p>5. Score each compound in all collected reactions (Initial score is zero for all compounds) <br />
<br> 5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction.<br />
<br> 5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.<br />
<br> 5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed. <br><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate. </p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br />
<h2>Week 13 - 15(July 23-Aug 10)</h2><br />
<p>Algorithm described in past weeks was implemented. The user interface prototype of Analysis tab was created. </p><br />
</html><br />
[[Image:UCalgary2012_AlgorithmOutput.png|thumb|center|600px|Fig6. Algorithm outputs in Matlab console]]<br />
[[Image:UCalgary2012_BioVsTarget.png|thumb|center|400px|Fig7. Plot of relationship between growth rate and production rate with outlined optimal growth rate]]<br />
[[Image:UCalgary2012_AnalysisTabPrototype.png|thumb|center|600px|Fig8. User interface prototype of Analysis Tab]]<br />
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<br />
<h2>Week 16-17 (Aug 13-24)</h2><br />
<p>User interface of Analysis part was completely implemented and full functional. </p><br />
</html><br />
[[Image:UCalgary2012_Denitri.png|thumb|center|600px|Fig9. Denitrification pathway results showed on user interface]]<br />
<html><br />
<br />
<h2>Week 18-19 (Aug 27-Sep 7)</h2><br />
<p>The user interface prototype of Build tab was created. The interface was completely implemented and fully functional.</p><br />
</html><br />
[[Image:UCalgary2012_BuildTabPrototype.png|thumb|center|600px|Fig10. User interface prototype of Build tab]]<br />
[[Image:UCalgary2012_BuildTabEg.png|thumb|center|600px|Fig11. Example model built on Build tab]]<br />
[[Image:UCalgary2012_NovelModelExport.png|thumb|center|600px|Fig12. Example model exported to mScript file]]<br />
<html><br />
<br />
<h2>Week 20 (Sept 10-14)</h2><br />
<p>Application went post-development phase and some additional features were added into. <br />
<br>New features:<br />
<br>Searching: Users can search metabolites in loaded model<br />
<br>Updating: Users is able to update reactions they entered in table<br />
<br>Changing export file format: User can read analysis output in spread sheet<br />
<br><br />
</p><br />
<br />
<h2>Week 21-23 (Sept 10-Oct 3)</h2><br />
<p>Model Validated with data from web lab. Assay for decarboxylation pathway was set. E Coli. with petrobrick grew up on nine different growth medias, one positive control (LB+glucose minimum), one negative control (glucose minimum), and seven medias that consisted of glucose minimum and compounds suggested by application outputs. </p><br />
<br />
<h2>Week 24-26 (Oct 15-Oct 26)</h2><br />
<p>The previous wet lab experiments were repeated to get more duplicates. The same conditions were set except different initial cell OD. </p><br />
<br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="center;"></img><br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/ReferencesTeam:Calgary/Project/References2012-10-27T02:41:08Z<p>Cqian: </p>
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<div>{{Team:Calgary/TemplateProjectOrange|<br />
TITLE=References|<br />
CONTENT={{{CONTENT|<br />
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<li>Vaillancourt FH, Bolin JT, Eltis LD. The ins and outs of ring-cleaving dioxygenases. Crit Rev Biochem Mol. 2006; 41:241-267. </li><br><br />
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<li>Venkitasubramanian P, Daniels L, Rosazza JPN. Reduction of Carboxylic Acids by Nocardia Aldehyde Oxidoreductase Requires a Phosphopantetheinylated Enzyme. Journal of Biological Chemistry 2007 Nov 13;282(1):478-485. </li><br><br />
<br />
<li>Vitreschak AG, Rodionov DA, Mironov AA, Gelfand MS. Riboswitches: the oldest mechanism for the regulation of gene expression? Trends Genet 2004 Jan;20(1):44-50.</li></br><br />
<br />
<li>Vogel U, Jensen KF. The RNA chain elongation rate in <i>Escherichia coli</i> depends on the growth rate. J Bacteriol. 1994 May;176(10):2807-13. </li><br><br />
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<li>Waters LS, Sandoval M, and Storz G. The<i> Escherichia coli</i> MntR miniregulon includes genes encoding a small protein and an efflux pump required for manganese homeostasis. J Bacteriol 2011 Nov; 193(21) 5887-97.</li></br><br />
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<li>Wright JK, Overath P. Purification of the lactose:H+ carrier of <i>Escherichia coli</i> and characterization of galactoside binding and transport. Eur J Biochem.1984 Feb 1;138(3):497-508.</li><br><br />
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<li>Xiong AS, Peng RH, Cheng ZM, Li Y, Liu JG, Zhuang J, Gao F, Xu F, Qiao YS, Zhang Z, Chen JM, Yao QH. Concurrent mutations in six amino acids in beta-glucuronidase improve its thermostability. Protein Eng Des Sel. 2007 Jul;20(7):319-25. Epub 2007 Jun 8. </li><br><br />
<br />
<li>Xu P, Yu P, Li FP, Cai XF, Ma CQ. Microbial degradation of sulfur, nitrogen and oxygen heterocycles. Trends in Microbiology 2006; 14(9):398-405.</li><br><br />
<br />
<li>Yoshimura F, Nikaido H. Permeability of Pseudomonas aeruginosa outer membrane to hydrophilic solutes. J Bacteriol. 1982 Nov;152(2):636-42. </li><br><br />
<br />
<li>Young R, Bremer H. Polypeptide-chain-elongation rate in <i>Escherichia coli</i> B/r as a function of growth rate. Biochem J. 1976 Nov 15;160(2):185-94. </li><br><br />
<br />
<li>Zeuthen P, Knudsen KG, Whitehurst DD.Organic nitrogen compounds in gas oil blends, their hydrotreated products and the importance to hydrotreatment. Catalysis Today Feb 2001; 65(2-4):307-314.</li><br><br />
<br />
<li>Zhang X, Wiseman S, Yu H, Liu H, Giesy JP, Hecker M. Assessing the toxicity of naphthenic acids using a microbial genome wide live cell reporter array system. Environ Sci Technol 2011 Mar 1;45(5):1984-1991.</li><br><br />
<br />
<li>Zhang Y, Nelson M, Nietfeldt JW, Burbank DE, Van Etten JL.Characterization of Chlorella virus PBCV-1 CviAII restriction andmodification system. Nucleic Acids Res 1992;20(20):5351-5356.</li><br><br />
<br />
<li>Zhu G, Pang K, Parkin G. New Modes for Coordination of Aromatic Heterocyclic Nitrogen Compounds to Molybdenum: Catalytic Hydrogenation of Quinoline, Isoquinoline, and Quinoxaline by Mo(PMe<sub>3</sub>)<sub>4</sub>H<sub>4</sub>. Journal of the American Chemical Society 2008; 130(5):1564-1565. </li><br><br />
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</ul><br />
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</html>}}}<br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Notebook/FluxAnalysisTeam:Calgary/Notebook/FluxAnalysis2012-10-27T01:52:41Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateNotebookBlue|<br />
TITLE=Flux Analysis Notebook|<br />
CONTENT =<br />
<html><br />
<!--<br />
NOTE: This is a template for entering things for the time being. All dates should be enclosed in <h2> tags and all paragraphs should be enclosed in <p> tags. For bulleted lists, <ul> tags will create the list and <li> tags will surround each list item. If there are any questions, please let me know.<br />
Patrick.<br />
--><br />
<br />
<h2>Week 1-2 (May 1-11) </h2><br />
<p>Brain storm refers to <a href="https://2012.igem.org/wiki/index.php?title=Team:Calgary/Notebook/Hydrocarbon">Hydrocarbon Modelling Part</a>.</p><br />
<p>Flux Analysis is brought into the project as it can offer predictions of sets of compound that will be used in growth media to improve the output of target compound.</p><br />
<br />
<h2>Week 3 (May 14-18) </h2><br />
<p>This week involved planning of selection proper analysis, models and platform for modelling. The constraint-based reconstruction analysis was chosen to be the core analysis, the published E.coli (iAF1260) and Pseudomonas (iJN746) models were selected to be base chassis and the MatLab was considered to use as modelling platform. In addition, OpenCobra Toolbox that is developed by System Biology Research Team in UCSD would be employed as lower level computation tools.</p><br />
<br />
<h2>Week 4 (May 21-25)</h2><br />
<h3>Concepts</h3><br />
<h4>Flux Balance Analysis (FBA)</h4><br />
<p>Flux balance analysis (FBA) is a mathematical method for analyzing metabolism. It is a direct application of linear programming to biological systems that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization.</p><br />
</html>[[File:UCalgary 2012 FBAEx.png|thumb|600px|center|The results of FBA on a prepared metabolic network of the top six reactions of glycolysis. The predicted flux through each reaction is proportional to the width of the line. Objective function in red, constraints on alpha-D-Glucose and beta-D-Glucose import represented as red bars. Original: Wikipedia]]<html><br />
<h5>The Steady State Assumption</h5><br />
<p>A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero. In chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. </p><br />
<h5>Stoichiometric & Flux Matrix </h5><br />
<p>Stoichiometry is a branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. In a balanced chemical reaction, the relations among quantities of reactants and products typically form a ratio of whole numbers.<br />
<br>Flux matrix, in terms of flux rate, is a rate of turnover of molecules through a reaction pathway. <br />
</p><br />
</html>[[File:UCalgary 2012 FBAexWithSMatrix.png|thumb|600px|center|An example stoichiometric matrix for a network representing the top of glycolysis and that same network after being prepared for FBA.Original:Wikipedia]]<html><br />
<h4> Extended Flux Analysis</h4><br />
<h5>Flux variability analysis</h5><br />
<p>The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built-in to virtually all current analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, produce the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are highly important. </p><br />
<h5>Dynamic FBA</h5><br />
<p>Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict homoeostatic condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.</p><br />
<h4>Metabolic Network Reconstruction</h4><br />
<p>Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including E. coli. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of E. coli have existed for nearly twenty years. The first genome-scale model of E. coli metabolism was released in 2000, and this model continues to be expanded and updated today.<a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></p><br />
<br />
<br />
Metabolic network reconstruction and simulation allows for an in depth insight into comprehending the molecular mechanisms of a particular organism, especially correlating the genome with molecular physiology (Francke, Siezen, and Teusink 2005). A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. </p><br />
<h3>Modelling</h3><br />
<h4>Constraint-based Model</h4><br />
<p>Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. The vector x with length m can then be defined as the concentrations of all the metabolites and the vector v with length n contains the fluxes through each reaction.</p><br />
<h5>Constraint-Based Models of E. coli <a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></h5><br />
<h6>iAF1260</h6><br />
<p>The latest update of the E. coli genome-scale metabolic model is iAF1260, published in 2007. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the EcoCyc database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the E. coli metabolic model, iAF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. iAF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution.</p><br />
<h6>E. coli core model</h6><br />
<p>The core E. coli model is a small-scale model of the central metabolism of E. coli. It is a modified subset of the iAF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.</p><br />
<h3>Tools</h3><br />
<h4><a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCobra Toolbox</a></h4><br />
<p>The Constraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies. It is used as the major program.</p><br />
<h4><a href="http://www.celldesigner.org/">CellDesigner 4.0</a></h4><br />
<p>CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). In this project, it is used to visualize metabolic network.</p><br />
<br />
</html><br />
[[Image:UCalgary2012_EcoliCoreNetwork.png|thumb|300px|Fig1. E. coli core model metabolic network view in CellDesigner. The network contains 95 reactions, it is good to use as a verifier of novel model.]]<br />
[[Image:UCalgary2012_EcoliiAF1260.png|thumb|200px|Fig2. E. coli iAF1260 model metabolic network view in CellDesigner. The network contains more than 2000 reactions and it is not possible to be used as verrifier.]]<br />
<html><br />
<br />
<h2>Week 5 (May 28 - June 1)</h2><br />
<p>Tests of OpenCobra toolbox basic examples on E.coli core model and Pseudomonas (iJN746) model are passed, which includes following functions:<br />
FBA test (optimizeCbModel),model creation/modification tests, reaction addition/deletion/modification, and gene search (deletion) tests.</p><br />
<p>Generally, The tests outputs are consistant with expected results. FBA test basically generates one pattern of flux rates for each metabolites to contribute the best biomass.<br />
The novel model created is exactly same as the predicted model as well as the modified model. Reactions in models can be easily added and deleted.</p><br />
<p>The gene deletion tests show the same output as the paper, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, demonstrated. </p><br />
<p>In addition, a software, CellDesigner, is employed to verified the novel model built in Cobra Toolbox visually. The CellDesigner generates a visual diagram to show the entire metabolic network.</p><br />
<br />
<br />
<h2>Week 6 (June 4-8) </h2><br />
<p>Another sets of tests of OpenCobra toolbox examples on E.coli core model and Pseudomonas (iJN746) model were exanimated, which includes following functions: fluxVariability, OptKnock, GDLS and optGene.</p><br />
<p>The outputs of tests with fluxVariability, OptKnock and GDLS were biological relevant. The results from OptKnock and GDLS were similar which could be considered as consistent as they were designed to complete similar tasks. However, optGene tests either output results that were understandable or error messages referring to source code. </p><br />
<br />
<h2>Week 7 (June 11-15)</h2><br />
<p>In order to reconstructing a proper novel model, all the symbols in OpenCobra Toolbox such as ‘[c]’, ‘[e]’, ‘[b]’ after every compound as well as the abbreviation like ‘lb’ and ‘ub’ for each reaction had to be well understand. Also, the formula of added reactions must be as precise as possible to ensure the accurate of the results because lack of enzymatic and genetic regulation to those reactions making the formulas become only variable.</p><br />
<p>The novel model built upon E.coli iAF1260 with additional decarboxylation pathway was accomplished. The flux rate of target compound of testPathway test was good; however, this value become extremely low if set the biomass as objective (running flux balance analysis). The results showed, in simulation, if the cell produced the product, its biomass was nearly zero. Vice verse, if the cell grew normally, the production rate was almost zero. </p><br />
<br />
<h2>Week 8 (June 18-22)</h2><br />
<p>Two novel models built upon E.coli iAF1260 with additional desulfurization and denitrification pathway separately were completed. As expected, the results from FBA were similar to decarboxylation pathway. These phenomena indicated that the relationship between cell growth and production was normally negative related. In addition, FBA returned one and only one solution that maximized biomass. Due to this natural of FBA, it restricted freedom of flows in network and many alternative solutions were omitted out. For pathways like above three, the production rate was negatively related to growth rate, the FBA was meaningless since the production rate was always zero or extremely small. Luckily, Flux Variability Analysis (FVA) was able to cover the problems caused by FBA. Therefore, FVA become the major computation tool to simulate the flux rates of cell metabolic activities. Unfortunately, the results from FVA for each pathway were also close to zero.</p><br />
<br />
<h2>Week 9 (June 25-29)</h2><br />
<p>To figure out the outputs from FVA were whether reasonable, testPathway was applied to run as unit test to verify the model with added pathway. The testPahtway was used to test each reaction in each pathway instead of overall pathway. The results showed the pathways were built improperly because upstream reactions had no flux but downstream reactions could have flux rate. The phenomenon reported last week was result in unbalance of metabolic network. In other words, the modified model violated steady state assumption. Therefore terminal reactions were added to three pathways respectively to keep the system remain in steady state and further to solve the problem. The revised models worked well on testPathway and FVA tests.</p><br />
<br />
<h2>Week 10 (July 3-6)</h2><br />
<p>The function FluxAnalysis was employed to generate maximum and minimum flux rate outputs of target compounds for three pathways respectively. The visual graphs of flux rates over entire metabolic networks for each model with three pathways were generated. These graphs were compared and analyzed to find out reactions which flux rates were significantly different between maximum and minimum outputs in each pathway respectively.</p><br />
</html><br />
[[File:UCalgary2012_CobraToolboxNetwork.png|thumb|800px|center|Fig3. E. coli iAF1260 model Flux Balance Analysis visual output in Cobra Toolbox.]]<br />
<html><br />
<p>Unfortunately, the map was too complicated to compare every single reaction by man source. Also, the conclusions drawn from such comparisons would be not valid since the reactions were highly connected to each other in network rather than distributed. Hence, it is necessary to build an algorithm that could automatically analysis the reactions in network scale.</p><br />
<br />
<h2>Week 11-12 (July 9-20)</h2><br />
<p>To have an algorithm doing such a work, the question had to be answered. How to improve the products flux rates through data from FVA?</p><br />
<p>Two things were noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|thumb|center|350px|Fig4. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|center|350px|Fig5. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>To improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media.</p><br />
<br />
<p>The analysis algorithm was designed. It can automatically analyze reactions and compounds and were able to output metabolites that would improve the production rate.</p><br />
<p>Algorithm:</p><br />
<p>1. Define relationship between growth rate and production rate</p><br />
<p>2. Find out the optimal growth rate that can maximize the production</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold<br />
<p>5. Score each compound in all collected reactions (Initial score is zero for all compounds) <br />
<br> 5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction.<br />
<br> 5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.<br />
<br> 5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed. <br><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate. </p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br />
<h2>Week 13 - 15(July 23-Aug 10)</h2><br />
<p>Algorithm described in past weeks was implemented. The user interface prototype of Analysis tab was created. </p><br />
</html><br />
[[Image:UCalgary2012_AlgorithmOutput.png|thumb|center|600px|Fig6. Algorithm outputs in Matlab console]]<br />
[[Image:UCalgary2012_BioVsTarget.png|thumb|center|400px|Fig7. Plot of relationship between growth rate and production rate with outlined optimal growth rate]]<br />
[[Image:UCalgary2012_AnalysisTabPrototype.png|thumb|center|600px|Fig8. User interface prototype of Analysis Tab]]<br />
<html><br />
<br />
<h2>Week 16-17 (Aug 13-24)</h2><br />
<p>User interface of Analysis part was completely implemented and full functional. </p><br />
</html><br />
[[Image:UCalgary2012_Denitri.png|thumb|center|600px|Fig9. Denitrification pathway results showed on user interface]]<br />
<html><br />
<br />
<h2>Week 18-19 (Aug 27-Sep 7)</h2><br />
<p>The user interface prototype of Build tab was created. The interface was completely implemented and fully functional.</p><br />
</html><br />
[[Image:UCalgary2012_BuildTabPrototype.png|thumb|center|600px|Fig10. User interface prototype of Build tab]]<br />
[[Image:UCalgary2012_BuildTabEg.png|thumb|center|600px|Fig11. Example model built on Build tab]]<br />
[[Image:UCalgary2012_NovelModelExport.png|thumb|center|600px|Fig12. Example model exported to mScript file]]<br />
<html><br />
<br />
<h2>Week 20 (Sept 10-14)</h2><br />
<p>Application went post-development phase and some additional features were added into. <br />
<br>New features:<br />
<br>Searching: Users can search metabolites in loaded model<br />
<br>Updating: Users is able to update reactions they entered in table<br />
<br>Changing export file format: User can read analysis output in spread sheet<br />
<br><br />
</p><br />
<br />
<h2>Week 21-23 (Sept 10-Oct 3)</h2><br />
<p>Model Validated with data from web lab. Assay for decarboxylation pathway was set. E Coli. with petrobrick grew up on nine different growth medias, one positive control (LB+glucose minimum), one negative control (glucose minimum), and seven medias that consisted of glucose minimum and compounds suggested by application outputs. </p><br />
<br />
<h2>Week 24-26 (Oct 15-Oct 26)</h2><br />
<p>The previous wet lab experiments were repeated to get more duplicates. The same conditions were set except different initial cell OD. </p><br />
<br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="center;"></img><br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Notebook/FluxAnalysisTeam:Calgary/Notebook/FluxAnalysis2012-10-27T01:07:35Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateNotebookBlue|<br />
TITLE=Flux Analysis Notebook|<br />
CONTENT =<br />
<html><br />
<!--<br />
NOTE: This is a template for entering things for the time being. All dates should be enclosed in <h2> tags and all paragraphs should be enclosed in <p> tags. For bulleted lists, <ul> tags will create the list and <li> tags will surround each list item. If there are any questions, please let me know.<br />
Patrick.<br />
--><br />
<br />
<h2>Week 1-2 (May 1-11) </h2><br />
<p>Brain storm refers to <a href="https://2012.igem.org/wiki/index.php?title=Team:Calgary/Notebook/Hydrocarbon">Hydrocarbon Modelling Part</a>.</p><br />
<p>Flux Analysis is brought into the project as it can offer predictions of sets of compound that will be used in growth media to improve the output of target compound.</p><br />
<br />
<h2>Week 3 (May 14-18) </h2><br />
<p>This week involved planning of selection proper analysis, models and platform for modelling. The constraint-based reconstruction analysis was chosen to be the core analysis, the published E.coli (iAF1260) and Pseudomonas (iJN746) models were selected to be base chassis and the MatLab was considered to use as modelling platform. In addition, OpenCobra Toolbox that is developed by System Biology Research Team in UCSD would be employed as lower level computation tools.</p><br />
<br />
<h2>Week 4 (May 21-25)</h2><br />
<h3>Concepts</h3><br />
<h4>Flux Balance Analysis (FBA)</h4><br />
<p>Flux balance analysis (FBA) is a mathematical method for analyzing metabolism. It is a direct application of linear programming to biological systems that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization.</p><br />
</html>[[File:UCalgary 2012 FBAEx.png|thumb|600px|center|The results of FBA on a prepared metabolic network of the top six reactions of glycolysis. The predicted flux through each reaction is proportional to the width of the line. Objective function in red, constraints on alpha-D-Glucose and beta-D-Glucose import represented as red bars. Original: Wikipedia]]<html><br />
<h5>The Steady State Assumption</h5><br />
<p>A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero. In chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. </p><br />
<h5>Stoichiometric & Flux Matrix </h5><br />
<p>Stoichiometry is a branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. In a balanced chemical reaction, the relations among quantities of reactants and products typically form a ratio of whole numbers.<br />
<br>Flux matrix, in terms of flux rate, is a rate of turnover of molecules through a reaction pathway. <br />
</p><br />
</html>[[File:UCalgary 2012 FBAexWithSMatrix.png|thumb|600px|center|An example stoichiometric matrix for a network representing the top of glycolysis and that same network after being prepared for FBA.Original:Wikipedia]]<html><br />
<h4> Extended Flux Analysis</h4><br />
<h5>Flux variability analysis</h5><br />
<p>The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built-in to virtually all current analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, produce the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are highly important. </p><br />
<h5>Dynamic FBA</h5><br />
<p>Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict homoeostatic condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.</p><br />
<h4>Metabolic Network Reconstruction</h4><br />
<p>Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including E. coli. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of E. coli have existed for nearly twenty years. The first genome-scale model of E. coli metabolism was released in 2000, and this model continues to be expanded and updated today.<a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></p><br />
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Metabolic network reconstruction and simulation allows for an in depth insight into comprehending the molecular mechanisms of a particular organism, especially correlating the genome with molecular physiology (Francke, Siezen, and Teusink 2005). A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. </p><br />
<h3>Modelling</h3><br />
<h4>Constraint-based Model</h4><br />
<p>Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. The vector x with length m can then be defined as the concentrations of all the metabolites and the vector v with length n contains the fluxes through each reaction.</p><br />
<h5>Constraint-Based Models of E. coli <a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></h5><br />
<h6>iAF1260</h6><br />
<p>The latest update of the E. coli genome-scale metabolic model is iAF1260, published in 2007. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the EcoCyc database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the E. coli metabolic model, iAF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. iAF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution.</p><br />
<h6>E. coli core model</h6><br />
<p>The core E. coli model is a small-scale model of the central metabolism of E. coli. It is a modified subset of the iAF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.</p><br />
<h3>Tools</h3><br />
<h4><a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCobra Toolbox</a></h4><br />
<p>The Constraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies. It is used as the major program.</p><br />
<h4><a href="http://www.celldesigner.org/">CellDesigner 4.0</a></h4><br />
<p>CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). In this project, it is used to visualize metabolic network.</p><br />
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[[Image:UCalgary2012_EcoliCoreNetwork.png|thumb|300px|Fig1. E. coli core model metabolic network view in CellDesigner. The network contains 95 reactions, it is good to use as a verifier of novel model.]]<br />
[[Image:UCalgary2012_EcoliiAF1260.png|thumb|200px|Fig2. E. coli iAF1260 model metabolic network view in CellDesigner. The network contains more than 2000 reactions and it is not possible to be used as verrifier.]]<br />
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<h2>Week 5 (May 28 - June 1)</h2><br />
<p>Tests of OpenCobra toolbox basic examples on E.coli core model and Pseudomonas (iJN746) model are passed, which includes following functions:<br />
FBA test (optimizeCbModel),model creation/modification tests, reaction addition/deletion/modification, and gene search (deletion) tests.</p><br />
<p>Generally, The tests outputs are consistant with expected results. FBA test basically generates one pattern of flux rates for each metabolites to contribute the best biomass.<br />
The novel model created is exactly same as the predicted model as well as the modified model. Reactions in models can be easily added and deleted.</p><br />
<p>The gene deletion tests show the same output as the paper, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, demonstrated. </p><br />
<p>In addition, a software, CellDesigner, is employed to verified the novel model built in Cobra Toolbox visually. The CellDesigner generates a visual diagram to show the entire metabolic network.</p><br />
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<h2>Week 6 (June 4-8) </h2><br />
<p>Another sets of tests of OpenCobra toolbox examples on E.coli core model and Pseudomonas (iJN746) model were exanimated, which includes following functions: fluxVariability, OptKnock, GDLS and optGene.</p><br />
<p>The outputs of tests with fluxVariability, OptKnock and GDLS were biological relevant. The results from OptKnock and GDLS were similar which could be considered as consistent as they were designed to complete similar tasks. However, optGene tests either output results that were understandable or error messages referring to source code. </p><br />
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<h2>Week 7 (June 11-15)</h2><br />
<p>In order to reconstructing a proper novel model, all the symbols in OpenCobra Toolbox such as ‘[c]’, ‘[e]’, ‘[b]’ after every compound as well as the abbreviation like ‘lb’ and ‘ub’ for each reaction had to be well understand. Also, the formula of added reactions must be as precise as possible to ensure the accurate of the results because lack of enzymatic and genetic regulation to those reactions making the formulas become only variable.</p><br />
<p>The novel model built upon E.coli iAF1260 with additional decarboxylation pathway was accomplished. The flux rate of target compound of testPathway test was good; however, this value become extremely low if set the biomass as objective (running flux balance analysis). The results showed, in simulation, if the cell produced the product, its biomass was nearly zero. Vice verse, if the cell grew normally, the production rate was almost zero. </p><br />
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<h2>Week 8 (June 18-22)</h2><br />
<p>Two novel models built upon E.coli iAF1260 with additional desulfurization and denitrification pathway separately were completed. As expected, the results from FBA were similar to decarboxylation pathway. These phenomena indicated that the relationship between cell growth and production was normally negative related. In addition, FBA returned one and only one solution that maximized biomass. Due to this natural of FBA, it restricted freedom of flows in network and many alternative solutions were omitted out. For pathways like above three, the production rate was negatively related to growth rate, the FBA was meaningless since the production rate was always zero or extremely small. Luckily, Flux Variability Analysis (FVA) was able to cover the problems caused by FBA. Therefore, FVA become the major computation tool to simulate the flux rates of cell metabolic activities. Unfortunately, the results from FVA for each pathway were also close to zero.</p><br />
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<h2>Week 9 (June 25-29)</h2><br />
<p>To figure out the outputs from FVA were whether reasonable, testPathway was applied to run as unit test to verify the model with added pathway. The testPahtway was used to test each reaction in each pathway instead of overall pathway. The results showed the pathways were built improperly because upstream reactions had no flux but downstream reactions could have flux rate. The phenomenon reported last week was result in unbalance of metabolic network. In other words, the modified model violated steady state assumption. Therefore terminal reactions were added to three pathways respectively to keep the system remain in steady state and further to solve the problem. The revised models worked well on testPathway and FVA tests.</p><br />
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<h2>Week 10 (July 3-6)</h2><br />
<p>The function FluxAnalysis was employed to generate maximum and minimum flux rate outputs of target compounds for three pathways respectively. The visual graphs of flux rates over entire metabolic networks for each model with three pathways were generated. These graphs were compared and analyzed to find out reactions which flux rates were significantly different between maximum and minimum outputs in each pathway respectively.</p><br />
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[[File:UCalgary2012_CobraToolboxNetwork.png|thumb|800px|center|Fig3. E. coli iAF1260 model Flux Balance Analysis visual output in Cobra Toolbox.]]<br />
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<p>Unfortunately, the map was too complicated to compare every single reaction by man source. Also, the conclusions drawn from such comparisons would be not valid since the reactions were highly connected to each other in network rather than distributed. Hence, it is necessary to build an algorithm that could automatically analysis the reactions in network scale.</p><br />
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<h2>Week 11-12 (July 9-20)</h2><br />
<p>To have an algorithm doing such a work, the question had to be answered. How to improve the products flux rates through data from FVA?</p><br />
<p>Two things were noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
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[[Image:Ucalgary2012_OptimalPoint.png|thumb|center|350px|Fig4. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|center|350px|Fig5. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
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<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>To improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media.</p><br />
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<p>The analysis algorithm was designed. It can automatically analyze reactions and compounds and were able to output metabolites that would improve the production rate.</p><br />
<p>Algorithm:</p><br />
<p>1. Define relationship between growth rate and production rate</p><br />
<p>2. Find out the optimal growth rate that can maximize the production</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold<br />
<p>5. Score each compound in all collected reactions (Initial score is zero for all compounds) <br />
<br> 5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction.<br />
<br> 5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.<br />
<br> 5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed. <br><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate. </p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
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<h2>Week 13 - 15(July 23-Aug 10)</h2><br />
<p>Algorithm described in past weeks was implemented. The user interface prototype of Analysis tab was created. </p><br />
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[[Image:UCalgary2012_AlgorithmOutput.png|thumb|center|600px|Fig6. Algorithm outputs in Matlab console]]<br />
[[Image:UCalgary2012_BioVsTarget.png|thumb|center|400px|Fig7. Plot of relationship between growth rate and production rate with outlined optimal growth rate]]<br />
[[Image:UCalgary2012_AnalysisTabPrototype.png|thumb|center|600px|Fig8. User interface prototype of Analysis Tab]]<br />
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<h2>Week 16-17 (Aug 13-24)</h2><br />
<p>User interface of Analysis part was completely implemented and full functional. </p><br />
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[[Image:UCalgary2012_Denitri.png|thumb|center|600px|Fig9. Denitrification pathway results showed on user interface]]<br />
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<h2>Week 18-19 (Aug 27-Sep 7)</h2><br />
<p>The user interface prototype of Build tab was created. The interface was completely implemented and fully functional.</p><br />
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[[Image:UCalgary2012_BuildTabPrototype.png|thumb|center|600px|Fig10. User interface prototype of Build tab]]<br />
[[Image:UCalgary2012_BuildTabEg.png|thumb|center|600px|Fig11. Example model built on Build tab]]<br />
[[Image:UCalgary2012_NovelModelExport.png|thumb|center|600px|Fig12. Example model exported to mScript file]]<br />
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<h2>Week 20 (Sept 10-14)</h2><br />
<p>Application went post-development phase and some additional features were added into. <br />
<br>New features:<br />
<br>Searching: Users can search metabolites in loaded model<br />
<br>Updating: Users is able to update reactions they entered in table<br />
<br>Changing export file format: User can read analysis output in spread sheet<br />
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<h2>Week 21-22 (Sept 10-22)</h2><br />
<p>Model Validated with data from web lab. Assay for decarboxylation pathway was set. E Coli. with petrobrick grew up on nine different growth medias, one positive control (LB+glucose minimum), one negative control (glucose minimum), and seven medias that consisted of glucose minimum and compounds suggested by application outputs. </p><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="center;"></img><br />
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}}</div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAROptimizerManual.pdfFile:UCalgary2012 OSCAROptimizerManual.pdf2012-10-26T05:57:14Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 OSCAROptimizerManual.pdf&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-04T03:58:43Z<p>Cqian: </p>
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<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
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[[Image:UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png|250px|right|]]<br />
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<p>Flux-variability analysis (FVA) was applied to optimize the bioreactor system of OSCAR for the newly incorporated metabolic pathways<br />
(<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Decarboxylation">Decarboxylation</a>,<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/CatecholDegradation">Decatecholization</a>,<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Desulfurization">Desulfurization</a>, and<br />
<a href="https://2012.igem.org/Team:Calgary/Project/OSCAR/Denitrogenation">Denitrogenation</a>). FVA combines the framework of metabolic pathways with experimental enzymatic data to provide a computational platform for predicting what changes in metabolite levels will result in increased end-product. We developed a model using <a href="http://www.mathworks.com/products/matlab/">MATLAB</a> computer language for predicting what metabolites could be added to your growth media to increase production of hydrocarbons in the <i>E. coli</i> chassis. We also created a graphical user interface to make the the FVA program user-friendly and to allow current and future iGEM teams/scientists to input their own synthetic pathways into the FVA for analysis. Finally, we validated this model in the wet-lab by optimizing the PetroBrick (<a href="http://partsregistry.org/wiki/index.php?title=Part:BBa_K590025">BBa_K590025</a>) system to increase hydrocarbon production, thereby saving time, and resources.</p><br />
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<h3><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the MATLAB package to test our model! </center></b><br />
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<center><b>Click <a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf"> here </a> to view our OSCAR Optimizer Manual.</b></center></h3><br />
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<h2>Background</h2><br />
<h3>Why FVA uses Flux Balance Analysis?</h3><br />
<p>Flux Balance Analysis (FBA) uses linear programming of metabolic networks to convert each metabolite into a mathematical coefficient. These coefficients can be connected to each other by changes associated with each enzymatic step of the pathway. FBA can then apply a mathematical method to examine how metabolites relate to each other in the network and allows us to make generalized predictions about organism growth, product output, and metabolite levels inside the cell. FVA is an extension of FBA that determines the range of reactions that result in an optimal flux through the metabolic network. What this means is that unlike FBA which just determines the most optimized network for increasing cell growth, we can modify the system to make something else, such as product output, the component to be optimized.</p><br />
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[[Image:Calgary FluxExample.jpg|700px|thumb|center|Figure 1: Flux Balance Analysis. FBA uses metabolic network (left) and simulates the connections in the network as a linear algebra matrix (right). Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, this changes the state of the system through change each metabolite variable.]]<br />
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<p><b>What Are The Constraints In The FVA Model?</b></p><br />
<p>As illustrated in Figure 1, metabolic networks can be encoded as stoichiometric matrices, in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient.</p><br />
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<p><b>Why use Flux Variability Analysis as Opposed To Flux Balance Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, FBA only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wetlab.</p><br />
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<h2>Using FVA to optimize OSCAR</h2><br />
<h3>What Are We Trying To Model?</h3><br />
<p>FVA provides a method to modulate the inputs in endogenous metabolic pathways or investigate what chemicals can be added to the growth media to upregulate an introduced pathway in <i>E. coli</i>. Development of a tool to model the optimal flux would also benefit numerous iGEM teams who engineer bacteria with novel pathways. To do this, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
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<h3>How Could Systems Like OSCAR Benefit From the Model?</h3><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized while reducing cost of production to a minimum. In microbiological bioreactor systems the conditions of growth media is much more crucial than in chemical synthesis reactors. Furthermore, the selection of media compounds is one of the most significant conditions for growth media and selecting a mix of compounds is very important for this process. If a model can predict an optimal set of metabolites that need to be added into media, this will save time, resources, and funds. </p><br />
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<h3>How Does The Program Work?</h3><br />
<p>Our program is built upon flux variability analysis applied by the functions in the Cobra Toolbox. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the Palsson Group University of San Diego. Using this as a base, we constructed reactions and metabolites for our hydrocarbon production component of our project. Specifically, new reactions corresponding to the PetroBrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. By running flux variability analysis, the program will give an output correlated to a optimized growth rate. By running the FVA algorithm at optimal biomass flux rates you can get a range of output for your product of interest (as illustrated in Figure 2). This allows you to determine an optimum point where compound production is maximized. Finally, the program will analysis the data with an algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
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[[Image:Ucalgary2012_OptimalPoint.png|center|thumb|340px|Figure 2. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
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<h2>Algorithm</h2><br />
<p><b>Conceptualization</b></p><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a broader analysis of growth and production rates compared to FBA. Since biomass rate reflects the growth condition, cells must have positive values of biomass flux rate in order to survive and proliferate. This positive growth rate is indicative of a real system as cells are optimized to prefer increases in growth rather than increases in product output. On the other hand, our goal is to increase the production flux rate above a zero value. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth and production rate are at the optimum point for cell survival and compound output.</p><br />
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<p>The differences of values for each reaction in a set of fluxes that maximize and minimize production rates can be compared. Reactions that demonstrated higher flux in the maximum production rate versus the minimum production rate represented substrates targets that could be increased in order to increase product output (as identified in Figure 3). Therefore we can create a list of compounds that should increase product output by increasing their concentration in solution.</p><br />
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[[Image:UCalgary2012_MaxAndMin.png|thumb|center|330px|Figure 3. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]] <html><br />
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<p>However not all substrates can be uptaken by the cell. Hence, only the metabolites that had natural transporters in the cell were considered in our finalized list of compounds. We used a model based on glucose minimal media, however, this could be applied to any type of media.</p><br />
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<p><b>Model Steps</b></p><br />
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[[Image:Calgary_FluxModelMethod.jpg|center|thumb|600px|Figure 4. Methodology the program uses to identify metabolite hits which should be supplemented to the media of your organism to increase output.]]<br />
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<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions with different percentages between two sets that exceed the user input threshold. (The input threshold determines the size of the difference in flux that the user is interested in. We used a 100% difference in our model.).</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). (Scoring is determined by the difference in the value of a particular compound's value when the user's production of the compound of interest is maximized and minimized flux sets as determined by the FVA) This process is additive if the particular compound is found in more than one reaction (i.e. ATP is found in many reactions sets) and only includes the reactions identified from step 4.</p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to the growth media and the run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model (step 7) to that from the raw model (step 2), if the rate is improved, mark the compound as an effector.</p><br />
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<h2>Graphical User Interface (GUI) Development</h2><br />
<p>A graphical user interface allows for easier use of our program by everyone in iGEM and beyond. This will open up our program to be used by individuals who do not have experience with programs such as MATLAB or the Cobra Toolbox. We developed the GUI using the GUIDE program designed in MATLAB. For more information please see our manual.</p><br />
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<h2>Demo</h2><br />
<p>Here we have uploaded a video showing some of the screens for using the model as a basic tutorial for teams to see how our program is used. The GUI interface allows for easy building of different synthetic constructs into the <i>E. coli</i> network but this could use any model from any organism that is available in SBML format.</p><br />
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<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
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<h4><center>Screen Shots of Our Application</center></h4><br />
[[Image:UCalgary2012_RunTimeBuild.png|350px|left|]]<br />
[[Image:UCalgary2012_DemoOutput.png|350px|right|]]<br />
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[[Image:UCalgary2012_BuildOutput.png|400px|center|]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|400px|center|]]<br />
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<h2>Wetlab Validation of the Model</h2><br />
<p>In order to assess if our program could be used to identify compounds that would selectively increase the output of a target compound, we thought it would be important to test this in a wetlab system. To do this we used the PetroBrick construct and attempted to increase it's output by supplementing the media with particular components predicted by our model to increase hydrocarbon output. Therefore we ran the model with the <i>AAR</i> and <i>ADC</i> gene components of the Petrobrick system and looked at the predicted metabolites that we should add to solution. This is illustrated in the figure below.</p><br />
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</html>[[File:Calgary ModelingPetroOutput.jpg|center|thumb|600px|Figure 5. Our Metabolite Optimizer was ran to increase the amount of hydrcarbons OSCAR could produce. By running the program as described above we identified a series of metabolites that are predicted to increase hydrocarbon output. Both fructose and AMP gave very high outputs suggesting that their presence should contribute to high product output the most.]]<html><br />
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<p>Once these compounds were identified, we set up an assay where we supplemented minimal M9 media and glucose with each of the compounds alone, or in combinations. Compounds were added at concentrations of 50 mM except for Ethanol (2.5% v/v), AMP (100mg/L), and L-aspartate (100mg/L). As a positive control, instead of using minimal media a solution of 50:50 LB:Washington Production Media (see protocols section for composition) was used to exhibit what normal production looked like. Additionally, we found a compound that was predicted to NOT increase the production of hydrocarbons, formate, and used this as a control. These compounds were allowed to incubate with the PetroBrick starting at an OD<sub>600</sub> of ~0.05 and grown for 72 hours at 37<sup>o</sup>C. Once grown, OD<sub>600</sub> measurements were taken prior to sonication of the samples and extraction of any produced hydrocarbons using 1mL of ethyl acetate. This was quantitated based on the peak area of a C15 hydrocarbon product which is described on our decarboxylation wiki page (also see protocols for relevant procedures dealing with this section). Once quantitated these result yielded the following: </p><br />
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</html>[[File:Calgary FluxValidation.jpg|center|thumb|600px|Figure 6. Model validation, showing the relative abundance of C15 hydrocarbons of the Petrobrick in combination with different compounds as growth media substrates. Data is normalized to the positive control (50:50 LB:Washington Production Media) and the relative production of hydrocarbons is displayed relative to optical density measurements. This data suggests incubate the Petrobrick with specific compounds may greatly increase production or decrease it dramatically. Dashed line represents the average minimal media, glucose control level comparitively to the other samples. ]]<html><br />
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<p> These results suggest that there is some natural variability in the output of hydrocarbons from the PetroBrick with different compounds. It was interesting to observe that five of the compounds demonstrated production levels higher than that of the minimal media control which suggested that our model was correct for predicting these compounds. However AMP and Fructose which both were thought to have shown increases in hydrcarbon output demonstrated large decreases suggesting that while our model may make some correct predictions there is clearly some error in assessing these predictions. What was very exciting however, is that for two of these compounds (pyruvate and glycine) their addition to the growth media increased the relative number of hydrocarbons higher than that of a complex media as in the positive control. <b>This suggests that we can indeed use our model to optimize OSCAR along with other metabolic systems, however, these results should be tested in the wetlab to ensure the model is predicting them accurately.</b> <br />
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<h2>Drawbacks</h2><br />
<p>This application is built upon the Cobra Toolbox, and the SBML Toolbox. As a consequence, any flaws in the Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (<i>E. Coli</i> iAF1260) which contains constraints that rely on the Stoichiometric Matrix (such as Stoichiometric coefficients), lower bounds and upper bounds of reactions. They lack genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in the wetlab. The missing rules could lead to inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. <b>This model does not take into consideration rate limiting steps of reactions, toxic intermediate accumulation, or any form of regulation of the enzymes (an example being negative feedback). The model should be used as a starting point to decrease a bottleneck from the amount of naturally available metabolites in <i>E. coli</i> to increase the amount of starting compound your synthetic circuit can use.</b></P><br />
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<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of the Cobra Toolbox and SBML Toolbox. To run the application, one must have the Cobra Toolbox and SBML Toolbox installed. SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>. Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>. Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
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<h2>Documents</h2><br />
<p><a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf">Download</a> the OSCAR Optimizer Manual for more information on how to use the program!</p><br />
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[[Image:UCalgary2012_Manual.png|center|300px|link=https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf]]<br />
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAR_optimizer_v1.zipFile:UCalgary2012 OSCAR optimizer v1.zip2012-10-04T03:52:03Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 OSCAR optimizer v1.zip&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAROptimizerManual.pdfFile:UCalgary2012 OSCAROptimizerManual.pdf2012-10-04T03:26:06Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 OSCAROptimizerManual.pdf&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_Manual.pngFile:UCalgary2012 Manual.png2012-10-04T03:22:12Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 Manual.png&quot;: Reverted to version as of 23:06, 3 October 2012</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_Manual.pngFile:UCalgary2012 Manual.png2012-10-04T03:21:57Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 Manual.png&quot;: Reverted to version as of 03:13, 4 October 2012</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_Manual.pngFile:UCalgary2012 Manual.png2012-10-04T03:21:29Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 Manual.png&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_Manual.pngFile:UCalgary2012 Manual.png2012-10-04T03:20:40Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 Manual.png&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_Manual.pngFile:UCalgary2012 Manual.png2012-10-04T03:13:07Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 Manual.png&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAR_optimizer_v1.zipFile:UCalgary2012 OSCAR optimizer v1.zip2012-10-04T03:12:30Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 OSCAR optimizer v1.zip&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/File:Calgary_FluxModelMethod.jpgFile:Calgary FluxModelMethod.jpg2012-10-04T02:44:40Z<p>Cqian: uploaded a new version of &quot;File:Calgary FluxModelMethod.jpg&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAR_optimizer_v1.zipFile:UCalgary2012 OSCAR optimizer v1.zip2012-10-04T02:32:56Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 OSCAR optimizer v1.zip&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T23:13:19Z<p>Cqian: </p>
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<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
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<p><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the Package required to run our model! </center></b></p><br />
<p><center><b>Click <a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf"> here </a> to get a copy of our OSCAR Optimizer Manual.</b></center></p><br />
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<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that converts each metabolite in the network into a mathematical coefficient. These coefficients can be related to each other by changes associated with each enzymatic step of the pathway. By applying a mathematical method to examine how metabolites move through the system FBA allows us to make generalized predictions about organism growth, product output, and metabolite levels inside of a cell. This analysis requires the Steady State Assumption, which states that all state variables are constant in spite of ongoing processes that strive to change them. This can be further applied to Flux Variability Analysis (FVA) which extends the process to determines the ranges of fluxes that correspond to an optimal solution determined through FBA. In other words, FVA will determine the range of values that can be achieved by modifying various inputs in the model.</p><br />
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[[Image:Calgary FluxExample.jpg|500px|thumb|right|Figure 1: Flux Balance Analysis. FBA involves taking a metabolic network and simulating the connections in the network as a linear algebra matrix. Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, this changes the state of the system through change each metabolite variable.]]<br />
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<p><b>What Are The Constraints In The Model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices, in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient.</p><br />
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<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wetlab.</p><br />
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<h2>Introduction</h2><br />
<p><b>What Are We Trying To Model?</b></p><br />
<p>Because flux balance provides an easy method to look at how metabolic pathways can be modulated by their inputs. What chemicals can be added into a solution in order to upregulate a synthetic pathway we are introducing into <i> E. coli</i>? If we could develop a tool to make this kind of modelling possible it would benefit numerous iGEM teams. To do this, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
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<p><b>How Could Systems Like OSCAR Benefit From the Model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized while reducing cost of production to a minimum. In microbiological bioreactor systems the conditions of growth media is much more crucial than in chemical synthesis reactors. Furthermore, the selection of media compounds is one of the most significant conditions for growth media and selecting a mix of compounds is very important for this process. If a model can predict an optimal set of metabolites that need to be added into media, this will save time, resources, and funds. </p><br />
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<p><b>How Does The Program Work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the Palsson Group University of San Diego. Using this as a base, we constructed reactions and metabolites for our hydrocarbon production component of our project. Specifically, new reactions corresponding to the Petrobrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis the data with an algorith to generate a set of media compounds that is expected to accelerate production rate. </p><br />
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<h2>Algorithm</h2><br />
<p><b>Conceptualization</b></p><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a better quantification of growth and production rates. Since biomass rate reflects the growth condition, cells must have positive values of biomass flux rate in order to survive and proliferate. This positive growth rate is indicative of a real system as cells are optimized to prefer increases in growth than increases in product output. On the other hand, our goal is to increase the production flux rate above a zero value. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>The algorithm is designed to determine the optimal flux rate of biomass and the value would be set as a new constraint of biomass. Then flux variability analysis would identify the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective (i.e. identify the pathways that are effected to optimize the synthetic pathway of interest). </p><br />
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[[Image:Ucalgary2012_OptimalPoint.png|left|thumb|340px|Figure 2. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|right|thumb|330px|Figure 3. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
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<p>The differences of values for each reaction in a set of fluxes that maximize and minimize production rates became interesting. By comparing these, some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. These mapped to reactants that would directly effect these reactions based on their quantities. Consequently, the question became how to identify metabolites that by increasing their quantities would improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However not all substrates can be uptaken by the cell or therefore absorbed from the growth media. Hence, only the metabolites that had natural transporters in cell were considered in the final output. Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
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<p><b>Model Steps</b></p><br />
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[[Image:Calgary_FluxModelMethod.jpg|center|thumb|600px|Figure 4. Methodology the program uses to identify metabolite hits which should be supplemented to the media of your organism to increase output.]]<br />
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<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
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<h2>Graphical User Interface (GUI) Development</h2><br />
<p>A graphical user interface allows for easier use of our program by everyone in iGEM and beyond. <b>Chenzhe write up how you made the GUI</b></p><br />
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<h2>Demo</h2><br />
<p>Here we have uploaded a video showing some of the screens for using the model as a basic tutorial for teams to see how our program is used. The GUI interface allows for easy building of different synthetic constructs into the <i>E. coli</i> network.</p><br />
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<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
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<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
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<h2>Wetlab Validation of the Model</h2><br />
<p>While developing this program for identifying metabolites to add the growth medias selectively was interesting, we wanted to ensure that this program had relevance in the lab. To do this we used the Petrobrick construct and attempted to increase it's output by supplementing the media with particular components predicted by our model. Therefore we ran the model with the AAR and ADC gene components of the Petrobrick system and looked at the predicted metabolites that we should add to solution. This is illustrated in the figure below.</p><br />
<br />
</html>[[File:Calgary ModelingPetroOutput.jpg|center|thumb|600px|Figure 5. Our Metabolite Optimizer was ran to increase the amount of hydrcarbons OSCAR could produce. By running the program as described above we identified a series of metabolites that are predicted to increase hydrocarbon output. Both fructose and AMP gave very high outputs suggesting that their presence should contribute to high product output the most.]]<html><br />
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<p>Once these compounds were identified, we set up an assay where we supplemented minimal M9 media and glucose with each of the compounds alone, or in combinations. Compounds were added at concentrations of 50 mM except for Ethanol (2.5% v/v), AMP (100mg/L), and L-aspartate (100mg/L). As a positive control, instead of using minimal media a solution of 50:50 LB:Washington Production Media (see protocols section for composition) was used to exhibit what normal production looked like. These compounds were allowed to incubate with a Petrobrick starting at an OD<sub>600</sub> of ~0.05 and grown for 72 hours at 37<sup>o</sup>C. Once grown, OD<sub>600</sub> measurements were taken prior to sonication of the samples and extraction of any produced hydrocarbons using 1mL of ethyl acetate. This was quantitated based on the peak area of a C15 hydrocarbon product which is described on our decarboxylation wiki page (also see protocols for relevant procedures dealing with this section). Once quantitated these result yielded the following: </p><br />
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</html>[[File:Calgary FluxValidation.jpg|center|thumb|600px|Figure 6. Model validation, showing the relative abundance of C15 hydrocarbons of the Petrobrick in combination with different compounds as growth media substrates. Data is normalized to the positive control (50:50 LB:Washington Production Media) and the relative production of hydrocarbons is displayed relative to optical density measurements. This data suggests incubate the Petrobrick with specific compounds may greatly increase production or decrease it dramatically. Dashed line represents the average minimal media, glucose control level comparitively to the other samples. ]]<html><br />
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<p> These results suggest that there is some natural variability in the output of hydrocarbons from the Petrobrick with different compounds. It was interesting to observe that five of the compounds demonstrated production levels higher than that of the minimal media control which our model was testing which suggested that our model was correct for predicting these compounds. However AMP and Fructose which both were thought to have shown increases in hydrcarbon output demonstrated large decreases suggesting that while our model may make some correct predictions there is clearly some error in assessing these predictions. What was very exciting however, is that for two of these compounds (pyruvate and glycine) their addition to the growth media increased the relative number of hydrocarbons higher than that of a complex media as in the positive control. <b>This suggests that we can indeed use our model to optimize OSCAR along with other metabolic systems, however, these results should be tested in the wetlab to ensure the model is predicting them accurately.</b> <br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (<i>E. Coli</i> iAF1260) which contains constraints that rely on the Stoichiometric Matrix (such as Stoichiometric coefficients), lower bounds and upper bounds of reactions. They lack genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. <b>This model does not take into consideration rate limiting steps of reactions, toxic intermediate accumulation, or any form of regulation of the enzymes (an example being negative feedback). The model shoudl be used as a starting point to decrease a bottleneck from the amount of naturally available metabolites in <i>E. coli</i> to increase the amount of starting compound your synthetic circuit can use.</b></P><br />
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<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of Cobra Toolbox and SBML Toolbox. To run the application, one must have Cobra Toolbox and SBML Toolbox installed. SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>. Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>. Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
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<h2>Documents</h2><br />
<p><a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf">Download</a> our Manual if you would like more information on how to use the program!<br />
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[[Image:UCalgary2012_Manual.png|center|thumb|200px|]]<br />
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}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T23:11:34Z<p>Cqian: </p>
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<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<p><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the Package required to run our model! Click <a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf"> here </a> to get a copy of our OSCAR Optimizer Manual.</b></center></p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that converts each metabolite in the network into a mathematical coefficient. These coefficients can be related to each other by changes associated with each enzymatic step of the pathway. By applying a mathematical method to examine how metabolites move through the system FBA allows us to make generalized predictions about organism growth, product output, and metabolite levels inside of a cell. This analysis requires the Steady State Assumption, which states that all state variables are constant in spite of ongoing processes that strive to change them. This can be further applied to Flux Variability Analysis (FVA) which extends the process to determines the ranges of fluxes that correspond to an optimal solution determined through FBA. In other words, FVA will determine the range of values that can be achieved by modifying various inputs in the model.</p><br />
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</html><br />
[[Image:Calgary FluxExample.jpg|500px|thumb|right|Figure 1: Flux Balance Analysis. FBA involves taking a metabolic network and simulating the connections in the network as a linear algebra matrix. Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, this changes the state of the system through change each metabolite variable.]]<br />
<html><br />
<br />
<p><b>What Are The Constraints In The Model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices, in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient.</p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wetlab.</p><br />
<br><br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What Are We Trying To Model?</b></p><br />
<p>Because flux balance provides an easy method to look at how metabolic pathways can be modulated by their inputs. What chemicals can be added into a solution in order to upregulate a synthetic pathway we are introducing into <i> E. coli</i>? If we could develop a tool to make this kind of modelling possible it would benefit numerous iGEM teams. To do this, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>How Could Systems Like OSCAR Benefit From the Model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized while reducing cost of production to a minimum. In microbiological bioreactor systems the conditions of growth media is much more crucial than in chemical synthesis reactors. Furthermore, the selection of media compounds is one of the most significant conditions for growth media and selecting a mix of compounds is very important for this process. If a model can predict an optimal set of metabolites that need to be added into media, this will save time, resources, and funds. </p><br />
<br />
<p><b>How Does The Program Work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the Palsson Group University of San Diego. Using this as a base, we constructed reactions and metabolites for our hydrocarbon production component of our project. Specifically, new reactions corresponding to the Petrobrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis the data with an algorith to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptualization</b></p><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a better quantification of growth and production rates. Since biomass rate reflects the growth condition, cells must have positive values of biomass flux rate in order to survive and proliferate. This positive growth rate is indicative of a real system as cells are optimized to prefer increases in growth than increases in product output. On the other hand, our goal is to increase the production flux rate above a zero value. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>The algorithm is designed to determine the optimal flux rate of biomass and the value would be set as a new constraint of biomass. Then flux variability analysis would identify the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective (i.e. identify the pathways that are effected to optimize the synthetic pathway of interest). </p><br />
<br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|left|thumb|340px|Figure 2. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|right|thumb|330px|Figure 3. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br><br />
<br />
<p>The differences of values for each reaction in a set of fluxes that maximize and minimize production rates became interesting. By comparing these, some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. These mapped to reactants that would directly effect these reactions based on their quantities. Consequently, the question became how to identify metabolites that by increasing their quantities would improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However not all substrates can be uptaken by the cell or therefore absorbed from the growth media. Hence, only the metabolites that had natural transporters in cell were considered in the final output. Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
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<br />
<p><b>Model Steps</b></p><br />
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[[Image:Calgary_FluxModelMethod.jpg|center|thumb|600px|Figure 4. Methodology the program uses to identify metabolite hits which should be supplemented to the media of your organism to increase output.]]<br />
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<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
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<br><br />
<br />
<h2>Graphical User Interface (GUI) Development</h2><br />
<p>A graphical user interface allows for easier use of our program by everyone in iGEM and beyond. <b>Chenzhe write up how you made the GUI</b></p><br />
<br><br />
<h2>Demo</h2><br />
<p>Here we have uploaded a video showing some of the screens for using the model as a basic tutorial for teams to see how our program is used. The GUI interface allows for easy building of different synthetic constructs into the <i>E. coli</i> network.</p><br />
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<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
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</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
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<h2>Wetlab Validation of the Model</h2><br />
<p>While developing this program for identifying metabolites to add the growth medias selectively was interesting, we wanted to ensure that this program had relevance in the lab. To do this we used the Petrobrick construct and attempted to increase it's output by supplementing the media with particular components predicted by our model. Therefore we ran the model with the AAR and ADC gene components of the Petrobrick system and looked at the predicted metabolites that we should add to solution. This is illustrated in the figure below.</p><br />
<br />
</html>[[File:Calgary ModelingPetroOutput.jpg|center|thumb|600px|Figure 5. Our Metabolite Optimizer was ran to increase the amount of hydrcarbons OSCAR could produce. By running the program as described above we identified a series of metabolites that are predicted to increase hydrocarbon output. Both fructose and AMP gave very high outputs suggesting that their presence should contribute to high product output the most.]]<html><br />
<br />
<p>Once these compounds were identified, we set up an assay where we supplemented minimal M9 media and glucose with each of the compounds alone, or in combinations. Compounds were added at concentrations of 50 mM except for Ethanol (2.5% v/v), AMP (100mg/L), and L-aspartate (100mg/L). As a positive control, instead of using minimal media a solution of 50:50 LB:Washington Production Media (see protocols section for composition) was used to exhibit what normal production looked like. These compounds were allowed to incubate with a Petrobrick starting at an OD<sub>600</sub> of ~0.05 and grown for 72 hours at 37<sup>o</sup>C. Once grown, OD<sub>600</sub> measurements were taken prior to sonication of the samples and extraction of any produced hydrocarbons using 1mL of ethyl acetate. This was quantitated based on the peak area of a C15 hydrocarbon product which is described on our decarboxylation wiki page (also see protocols for relevant procedures dealing with this section). Once quantitated these result yielded the following: </p><br />
<br />
</html>[[File:Calgary FluxValidation.jpg|center|thumb|600px|Figure 6. Model validation, showing the relative abundance of C15 hydrocarbons of the Petrobrick in combination with different compounds as growth media substrates. Data is normalized to the positive control (50:50 LB:Washington Production Media) and the relative production of hydrocarbons is displayed relative to optical density measurements. This data suggests incubate the Petrobrick with specific compounds may greatly increase production or decrease it dramatically. Dashed line represents the average minimal media, glucose control level comparitively to the other samples. ]]<html><br />
<br />
<p> These results suggest that there is some natural variability in the output of hydrocarbons from the Petrobrick with different compounds. It was interesting to observe that five of the compounds demonstrated production levels higher than that of the minimal media control which our model was testing which suggested that our model was correct for predicting these compounds. However AMP and Fructose which both were thought to have shown increases in hydrcarbon output demonstrated large decreases suggesting that while our model may make some correct predictions there is clearly some error in assessing these predictions. What was very exciting however, is that for two of these compounds (pyruvate and glycine) their addition to the growth media increased the relative number of hydrocarbons higher than that of a complex media as in the positive control. <b>This suggests that we can indeed use our model to optimize OSCAR along with other metabolic systems, however, these results should be tested in the wetlab to ensure the model is predicting them accurately.</b> <br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (<i>E. Coli</i> iAF1260) which contains constraints that rely on the Stoichiometric Matrix (such as Stoichiometric coefficients), lower bounds and upper bounds of reactions. They lack genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. <b>This model does not take into consideration rate limiting steps of reactions, toxic intermediate accumulation, or any form of regulation of the enzymes (an example being negative feedback). The model shoudl be used as a starting point to decrease a bottleneck from the amount of naturally available metabolites in <i>E. coli</i> to increase the amount of starting compound your synthetic circuit can use.</b></P><br />
<br><br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of Cobra Toolbox and SBML Toolbox. To run the application, one must have Cobra Toolbox and SBML Toolbox installed. SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>. Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>. Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
<br><br />
<br />
<h2>Documents</h2><br />
<p><a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf">Download</a> our Manual if you would like more information on how to use the program!<br />
</html><br />
[[Image:UCalgary2012_Manual.png|center|thumb|200px|]]<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/File:UCalgary2012_Manual.pngFile:UCalgary2012 Manual.png2012-10-03T23:06:18Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 Manual.png&quot;</p>
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<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T19:15:31Z<p>Cqian: </p>
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<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
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<p><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the Package required to run our model!</b></center></p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that converts each metabolite in the network into a mathematical coefficient. These coefficients can be related to each other by changes associated with each enzymatic step of the pathway. By applying a mathematical method to examine how metabolites move through the system FBA allows us to make generalized predictions about organism growth, product output, and metabolite levels inside of a cell. This analysis requires the Steady State Assumption, which states that all state variables are constant in spite of ongoing processes that strive to change them. This can be further applied to Flux Variability Analysis (FVA) which extends the process to determines the ranges of fluxes that correspond to an optimal solution determined through FBA. In other words, FVA will determine the range of values that can be achieved by modifying various inputs in the model.</p><br />
<br />
</html><br />
[[Image:Calgary FluxExample.jpg|300px|right|Figure 1: Flux Balance Analysis. FBA involves taking a metabolic network and simulating the connections in the network as a linear algebra matrix. Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, this changes the state of the system through change each metabolite variable.]]<br />
<html><br />
<br />
<p><b>What Are The Constraints In The Model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices, in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient.</p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wetlab.</p><br />
<br><br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What Are We Trying To Model?</b></p><br />
<p>Because flux balance provides an easy method to look at how metabolic pathways can be modulated by their inputs. What chemicals can be added into a solution in order to upregulate a synthetic pathway we are introducing into <i> E. coli</i>? If we could develop a tool to make this kind of modelling possible it would benefit numerous iGEM teams. To do this, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>How Could Systems Like OSCAR Benefit From the Model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized while reducing cost of production to a minimum. In microbiological bioreactor systems the conditions of growth media is much more crucial than in chemical synthesis reactors. Furthermore, the selection of media compounds is one of the most significant conditions for growth media and selecting a mix of compounds is very important for this process. If a model can predict an optimal set of metabolites that need to be added into media, this will save time, resources, and funds. </p><br />
<br />
<p><b>How Does The Program Work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the Palsson Group University of San Diego. Using this as a base, we constructed reactions and metabolites for our hydrocarbon production component of our project. Specifically, new reactions corresponding to the Petrobrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis the data with an algorith to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptualization</b></p><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a better quantification of growth and production rates. Since biomass rate reflects the growth condition, cells must have positive values of biomass flux rate in order to survive and proliferate. This positive growth rate is indicative of a real system as cells are optimized to prefer increases in growth than increases in product output. On the other hand, our goal is to increase the production flux rate above a zero value. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>The algorithm is designed to determine the optimal flux rate of biomass and the value would be set as a new constraint of biomass. Then flux variability analysis would identify the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective (i.e. identify the pathways that are effected to optimize the synthetic pathway of interest). </p><br />
<br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|left|thumb|340px|Figure 2. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|right|thumb|330px|Figure 3. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br />
<p>The differences of values for each reaction in a set of fluxes that maximize and minimize production rates became interesting. By comparing these, some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. These mapped to reactants that would directly effect these reactions based on their quantities. Consequently, the question became how to identify metabolites that by increasing their quantities would improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However not all substrates can be uptaken by the cell or therefore absorbed from the growth media. Hence, only the metabolites that had natural transporters in cell were considered in the final output. Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Model Steps</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
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<br><br />
<br />
<br />
<h2>Demo</h2><br />
<p>Screen Cast of Application in real time</p><br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<p></p><br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of Cobra Toolbox and SBML Toolbox. To run the application, one must have Cobra Toolbox and SBML Toolbox installed. </p><br />
<p>SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>.</p><br />
<p>Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>.</p><br />
<p> Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
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<br />
<h2>Documents</h2><br />
</html><br />
[[Image:UCalgary2012_Manual.png|400px]]<br />
<html><br />
<p><a href="https://static.igem.org/mediawiki/2012/d/d5/UCalgary2012_OSCAROptimizerManual.pdf">Download</a><p><br />
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}}</div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAROptimizerManual.pdfFile:UCalgary2012 OSCAROptimizerManual.pdf2012-10-03T19:12:12Z<p>Cqian: </p>
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<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_Manual.pngFile:UCalgary2012 Manual.png2012-10-03T19:10:31Z<p>Cqian: </p>
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<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T17:53:39Z<p>Cqian: </p>
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<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<br />
<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<p><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the files required to run our model!</b></center></p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that converts each metabolite in the network into a mathematical coefficient. These coefficients can be related to each other by changes associated with each enzymatic step of the pathway. By applying a mathematical method to examine how metabolites move through the system FBA allows us to make generalized predictions about organism growth, product output, and metabolite levels inside of a cell. This analysis requires the Steady State Assumption, which states that all state variables are constant in spite of ongoing processes that strive to change them. This can be further applied to Flux Variability Analysis (FVA) which extends the process to determines the ranges of fluxes that correspond to an optimal solution determined through FBA. In other words, FVA will determine the range of values that can be achieved by modifying various inputs in the model.</p><br />
<br />
</html><br />
[[Image:Calgary FluxExample.jpg|300px|right|Figure 1: Flux Balance Analysis. FBA involves taking a metabolic network and simulating the connections in the network as a linear algebra matrix. Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, this changes the state of the system through change each metabolite variable.]]<br />
<html><br />
<br />
<p><b>What Are The Constraints In The Model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices, in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient.</p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wetlab.</p><br />
<br><br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What Are We Trying To Model?</b></p><br />
<p>Because flux balance provides an easy method to look at how metabolic pathways can be modulated by their inputs. What chemicals can be added into a solution in order to upregulate a synthetic pathway we are introducing into <i> E. coli</i>? If we could develop a tool to make this kind of modelling possible it would benefit numerous iGEM teams. To do this, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>How Could Systems Like OSCAR Benefit From the Model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized while reducing cost of production to a minimum. In microbiological bioreactor systems the conditions of growth media is much more crucial than in chemical synthesis reactors. Furthermore, the selection of media compounds is one of the most significant conditions for growth media and selecting a mix of compounds is very important for this process. If a model can predict an optimal set of metabolites that need to be added into media, this will save time, resources, and funds. </p><br />
<br />
<p><b>How Does The Program Work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the Palsson Group University of San Diego. Using this as a base, we constructed reactions and metabolites for our hydrocarbon production component of our project. Specifically, new reactions corresponding to the Petrobrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis the data with an algorith to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptualization</b></p><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a better quantification of growth and production rates. Since biomass rate reflects the growth condition, cells must have positive values of biomass flux rate in order to survive and proliferate. This positive growth rate is indicative of a real system as cells are optimized to prefer increases in growth than increases in product output. On the other hand, our goal is to increase the production flux rate above a zero value. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>The algorithm is designed to determine the optimal flux rate of biomass and the value would be set as a new constraint of biomass. Then flux variability analysis would identify the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective (i.e. identify the pathways that are effected to optimize the synthetic pathway of interest). </p><br />
<br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|left|thumb|340px|Figure 2. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|right|thumb|330px|Figure 3. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br />
<p>The differences of values for each reaction in a set of fluxes that maximize and minimize production rates became interesting. By comparing these, some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. These mapped to reactants that would directly effect these reactions based on their quantities. Consequently, the question became how to identify metabolites that by increasing their quantities would improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However not all substrates can be uptaken by the cell or therefore absorbed from the growth media. Hence, only the metabolites that had natural transporters in cell were considered in the final output. Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Model Steps</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<p>Screen Cast of Application in real time</p><br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<p></p><br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of Cobra Toolbox and SBML Toolbox. To run the application, one must have Cobra Toolbox and SBML Toolbox installed. </p><br />
<p>SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>.</p><br />
<p>Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>.</p><br />
<p> Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T16:58:21Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<br />
<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<p><center><b>Click <a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> here </a> to download the files required to run our model!</b></center></p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that converts each metabolite in the network into a mathematical coefficient. These coefficients can be related to each other by changes associated with each enzymatic step of the pathway. By applying a mathematical method to examine how metabolites move through the system FBA allows us to make generalized predictions about organism growth, product output, and metabolite levels inside of a cell. This analysis requires the Steady State Assumption, which states that all state variables are constant in spite of ongoing processes that strive to change them. This can be further applied to Flux Variability Analysis (FVA) which extends the process to determines the ranges of fluxes that correspond to an optimal solution determined through FBA. In other words, FVA will determine the range of values that can be achieved by modifying various inputs in the model.</p><br />
<br />
</html><br />
[[Image:Calgary FluxExample.jpg|300px|right|Figure 1: Flux Balance Analysis. FBA involves taking a metabolic network and simulating the connections in the network as a linear algebra matrix. Each metabolite is listed vertically in the table and each reaction is listed horizontally. Based on the metabolites involved in each reaction, this changes the state of the system through change each metabolite variable.]]<br />
<html><br />
<br />
<p><b>What Are The Constraints In The Model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices, in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient.</p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable of examining these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes. FVA can also examine different ranges of bacterial growth vs. product output which is valuable in assessing validity of models in the wetlab.</p><br />
<br><br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What Are We Trying To Model?</b></p><br />
<p>Because flux balance provides an easy method to look at how metabolic pathways can be modulated by their inputs. What chemicals can be added into a solution in order to upregulate a synthetic pathway we are introducing into <i> E. coli</i>? If we could develop a tool to make this kind of modelling possible it would benefit numerous iGEM teams. To do this, we need to specifically model the flux rate of metabolic pathways responding to different growth media conditions and generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>How Could Systems Like OSCAR Benefit From the Model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. During industrial scale up, the optimal conditions for production needs to be maximized while reducing cost of production to a minimum. In microbiological bioreactor systems the conditions of growth media is much more crucial than in chemical synthesis reactors. Furthermore, the selection of media compounds is one of the most significant conditions for growth media and selecting a mix of compounds is very important for this process. If a model can predict an optimal set of metabolites that need to be added into media, this will save time, resources, and funds. </p><br />
<br />
<p><b>How Does The Program Work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses the published <i>E.coli</i> iAF1260 and <i>E.coli</i> core models provided from the Palsson Group University of San Diego. Using this as a base, we constructed reactions and metabolites for our hydrocarbon production component of our project. Specifically, new reactions corresponding to the Petrobrick as well as the upgrading (desulfurization and denitrogenation) pathways were engineered into the <i>E.coli</i> base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis the data with an algorith to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptualization</b></p><br />
<p>FVA can determine the full range of numerical values for each reaction flux within the network. Additionally, it allows for a better quantification of growth and production rates. Since biomass rate reflects the growth condition, cells must have positive values of biomass flux rate in order to survive and proliferate. This positive growth rate is indicative of a real system as cells are optimized to prefer increases in growth than increases in product output. On the other hand, our goal is to increase the production flux rate above a zero value. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>The algorithm is designed to determine the optimal flux rate of biomass and the value would be set as a new constraint of biomass. Then flux variability analysis would identify the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective (i.e. identify the pathways that are effected to optimize the synthetic pathway of interest). </p><br />
<p>The differences of values for each reaction in a set of fluxes that maximize and minimize production rates became interesting. By comparing these, some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. These mapped to reactants that would directly effect these reactions based on their quantities. Consequently, the question became how to identify metabolites that by increasing their quantities would improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However not all substrates can be uptaken by the cell or therefore absorbed from the growth media. Hence, only the metabolites that had natural transporters in cell were considered in the final output. Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Model Steps</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<p>Screen Cast of Application in real time</p><br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<p></p><br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of Cobra Toolbox and SBML Toolbox. To run the application, one must have Cobra Toolbox and SBML Toolbox installed. </p><br />
<p>SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>.</p><br />
<p>Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>.</p><br />
<p> Application Package and source code<a href="https://static.igem.org/mediawiki/2012/5/50/UCalgary2012_OSCAR_optimizer_v1.zip"> download </a>.</p><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/File:UCalgary2012_OSCAR_optimizer_v1.zipFile:UCalgary2012 OSCAR optimizer v1.zip2012-10-03T16:52:26Z<p>Cqian: </p>
<hr />
<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Notebook/FluxAnalysisTeam:Calgary/Notebook/FluxAnalysis2012-10-03T16:31:31Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateNotebookBlue|<br />
TITLE=Flux Analysis Notebook|<br />
CONTENT =<br />
<html><br />
<!--<br />
NOTE: This is a template for entering things for the time being. All dates should be enclosed in <h2> tags and all paragraphs should be enclosed in <p> tags. For bulleted lists, <ul> tags will create the list and <li> tags will surround each list item. If there are any questions, please let me know.<br />
Patrick.<br />
--><br />
<br />
<h2>Week 1-2 (May 1-11) </h2><br />
<p>Brain storm refers to <a href="https://2012.igem.org/wiki/index.php?title=Team:Calgary/Notebook/Hydrocarbon">Hydrocarbon Modelling Part</a>.</p><br />
<p>Flux Analysis is brought into the project as it can offer predictions of sets of compound that will be used in growth media to improve the output of target compound.</p><br />
<br />
<h2>Week 3 (May 14-18) </h2><br />
<p>This week involved planning of selection proper analysis, models and platform for modelling. The constraint-based reconstruction analysis was chosen to be the core analysis, the published E.coli (iAF1260) and Pseudomonas (iJN746) models were selected to be base chassis and the MatLab was considered to use as modelling platform. In addition, OpenCobra Toolbox that is developed by System Biology Research Team in UCSD would be employed as lower level computation tools.</p><br />
<br />
<h2>Week 4 (May 21-25)</h2><br />
<h3>Concepts</h3><br />
<h4>Flux Balance Analysis (FBA)</h4><br />
<p>Flux balance analysis (FBA) is a mathematical method for analyzing metabolism. It is a direct application of linear programming to biological systems that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization.</p><br />
</html>[[File:UCalgary 2012 FBAEx.png|thumb|600px|center|The results of FBA on a prepared metabolic network of the top six reactions of glycolysis. The predicted flux through each reaction is proportional to the width of the line. Objective function in red, constraints on alpha-D-Glucose and beta-D-Glucose import represented as red bars. Original: Wikipedia]]<html><br />
<h5>The Steady State Assumption</h5><br />
<p>A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero. In chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. </p><br />
<h5>Stoichiometric & Flux Matrix </h5><br />
<p>Stoichiometry is a branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. In a balanced chemical reaction, the relations among quantities of reactants and products typically form a ratio of whole numbers.<br />
<br>Flux matrix, in terms of flux rate, is a rate of turnover of molecules through a reaction pathway. <br />
</p><br />
</html>[[File:UCalgary 2012 FBAexWithSMatrix.png|thumb|600px|center|An example stoichiometric matrix for a network representing the top of glycolysis and that same network after being prepared for FBA.Original:Wikipedia]]<html><br />
<h4> Extended Flux Analysis</h4><br />
<h5>Flux variability analysis</h5><br />
<p>The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built-in to virtually all current analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, produce the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are highly important. </p><br />
<h5>Dynamic FBA</h5><br />
<p>Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict homoeostatic condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.</p><br />
<h4>Metabolic Network Reconstruction</h4><br />
<p>Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including E. coli. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of E. coli have existed for nearly twenty years. The first genome-scale model of E. coli metabolism was released in 2000, and this model continues to be expanded and updated today.<a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></p><br />
<br />
<br />
Metabolic network reconstruction and simulation allows for an in depth insight into comprehending the molecular mechanisms of a particular organism, especially correlating the genome with molecular physiology (Francke, Siezen, and Teusink 2005). A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. </p><br />
<h3>Modelling</h3><br />
<h4>Constraint-based Model</h4><br />
<p>Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. The vector x with length m can then be defined as the concentrations of all the metabolites and the vector v with length n contains the fluxes through each reaction.</p><br />
<h5>Constraint-Based Models of E. coli <a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></h5><br />
<h6>iAF1260</h6><br />
<p>The latest update of the E. coli genome-scale metabolic model is iAF1260, published in 2007. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the EcoCyc database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the E. coli metabolic model, iAF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. iAF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution.</p><br />
<h6>E. coli core model</h6><br />
<p>The core E. coli model is a small-scale model of the central metabolism of E. coli. It is a modified subset of the iAF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.</p><br />
<h3>Tools</h3><br />
<h4><a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCobra Toolbox</a></h4><br />
<p>The Constraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies. It is used as the major program.</p><br />
<h4><a href="http://www.celldesigner.org/">CellDesigner 4.0</a></h4><br />
<p>CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). In this project, it is used to visualize metabolic network.</p><br />
<br />
</html><br />
[[Image:UCalgary2012_EcoliCoreNetwork.png|thumb|300px|Fig1. E. coli core model metabolic network view in CellDesigner. The network contains 95 reactions, it is good to use as a verifier of novel model.]]<br />
[[Image:UCalgary2012_EcoliiAF1260.png|thumb|200px|Fig2. E. coli iAF1260 model metabolic network view in CellDesigner. The network contains more than 2000 reactions and it is not possible to be used as verrifier.]]<br />
<html><br />
<br />
<h2>Week 5 (May 28 - June 1)</h2><br />
<p>Tests of OpenCobra toolbox basic examples on E.coli core model and Pseudomonas (iJN746) model are passed, which includes following functions:<br />
FBA test (optimizeCbModel),model creation/modification tests, reaction addition/deletion/modification, and gene search (deletion) tests.</p><br />
<p>Generally, The tests outputs are consistant with expected results. FBA test basically generates one pattern of flux rates for each metabolites to contribute the best biomass.<br />
The novel model created is exactly same as the predicted model as well as the modified model. Reactions in models can be easily added and deleted.</p><br />
<p>The gene deletion tests show the same output as the paper, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, demonstrated. </p><br />
<p>In addition, a software, CellDesigner, is employed to verified the novel model built in Cobra Toolbox visually. The CellDesigner generates a visual diagram to show the entire metabolic network.</p><br />
<br />
<br />
<h2>Week 6 (June 4-8) </h2><br />
<p>Another sets of tests of OpenCobra toolbox examples on E.coli core model and Pseudomonas (iJN746) model were exanimated, which includes following functions: fluxVariability, OptKnock, GDLS and optGene.</p><br />
<p>The outputs of tests with fluxVariability, OptKnock and GDLS were biological relevant. The results from OptKnock and GDLS were similar which could be considered as consistent as they were designed to complete similar tasks. However, optGene tests either output results that were understandable or error messages referring to source code. </p><br />
<br />
<h2>Week 7 (June 11-15)</h2><br />
<p>In order to reconstructing a proper novel model, all the symbols in OpenCobra Toolbox such as ‘[c]’, ‘[e]’, ‘[b]’ after every compound as well as the abbreviation like ‘lb’ and ‘ub’ for each reaction had to be well understand. Also, the formula of added reactions must be as precise as possible to ensure the accurate of the results because lack of enzymatic and genetic regulation to those reactions making the formulas become only variable.</p><br />
<p>The novel model built upon E.coli iAF1260 with additional decarboxylation pathway was accomplished. The flux rate of target compound of testPathway test was good; however, this value become extremely low if set the biomass as objective (running flux balance analysis). The results showed, in simulation, if the cell produced the product, its biomass was nearly zero. Vice verse, if the cell grew normally, the production rate was almost zero. </p><br />
<br />
<h2>Week 8 (June 18-22)</h2><br />
<p>Two novel models built upon E.coli iAF1260 with additional desulfurization and denitrification pathway separately were completed. As expected, the results from FBA were similar to decarboxylation pathway. These phenomena indicated that the relationship between cell growth and production was normally negative related. In addition, FBA returned one and only one solution that maximized biomass. Due to this natural of FBA, it restricted freedom of flows in network and many alternative solutions were omitted out. For pathways like above three, the production rate was negatively related to growth rate, the FBA was meaningless since the production rate was always zero or extremely small. Luckily, Flux Variability Analysis (FVA) was able to cover the problems caused by FBA. Therefore, FVA become the major computation tool to simulate the flux rates of cell metabolic activities. Unfortunately, the results from FVA for each pathway were also close to zero.</p><br />
<br />
<h2>Week 9 (June 25-29)</h2><br />
<p>To figure out the outputs from FVA were whether reasonable, testPathway was applied to run as unit test to verify the model with added pathway. The testPahtway was used to test each reaction in each pathway instead of overall pathway. The results showed the pathways were built improperly because upstream reactions had no flux but downstream reactions could have flux rate. The phenomenon reported last week was result in unbalance of metabolic network. In other words, the modified model violated steady state assumption. Therefore terminal reactions were added to three pathways respectively to keep the system remain in steady state and further to solve the problem. The revised models worked well on testPathway and FVA tests.</p><br />
<br />
<h2>Week 10 (July 3-6)</h2><br />
<p>The function FluxAnalysis was employed to generate maximum and minimum flux rate outputs of target compounds for three pathways respectively. The visual graphs of flux rates over entire metabolic networks for each model with three pathways were generated. These graphs were compared and analyzed to find out reactions which flux rates were significantly different between maximum and minimum outputs in each pathway respectively.</p><br />
</html><br />
[[File:UCalgary2012_CobraToolboxNetwork.png|thumb|800px|center|Fig3. E. coli iAF1260 model Flux Balance Analysis visual output in Cobra Toolbox.]]<br />
<html><br />
<p>Unfortunately, the map was too complicated to compare every single reaction by man source. Also, the conclusions drawn from such comparisons would be not valid since the reactions were highly connected to each other in network rather than distributed. Hence, it is necessary to build an algorithm that could automatically analysis the reactions in network scale.</p><br />
<br />
<h2>Week 11-12 (July 9-20)</h2><br />
<p>To have an algorithm doing such a work, the question had to be answered. How to improve the products flux rates through data from FVA?</p><br />
<p>Two things were noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|thumb|center|350px|Fig4. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|center|350px|Fig5. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>To improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media.</p><br />
<br />
<p>The analysis algorithm was designed. It can automatically analyze reactions and compounds and were able to output metabolites that would improve the production rate.</p><br />
<p>Algorithm:</p><br />
<p>1. Define relationship between growth rate and production rate</p><br />
<p>2. Find out the optimal growth rate that can maximize the production</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold<br />
<p>5. Score each compound in all collected reactions (Initial score is zero for all compounds) <br />
<br> 5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction.<br />
<br> 5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.<br />
<br> 5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed. <br><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate. </p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br />
<h2>Week 13 - 15(July 23-Aug 10)</h2><br />
<p>Algorithm described in past weeks was implemented. The user interface prototype of Analysis tab was created. </p><br />
</html><br />
[[Image:UCalgary2012_AlgorithmOutput.png|thumb|center|600px|Fig6. Algorithm outputs in Matlab console]]<br />
[[Image:UCalgary2012_BioVsTarget.png|thumb|center|400px|Fig7. Plot of relationship between growth rate and production rate with outlined optimal growth rate]]<br />
[[Image:UCalgary2012_AnalysisTabPrototype.png|thumb|center|600px|Fig8. User interface prototype of Analysis Tab]]<br />
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<br />
<h2>Week 16-17 (Aug 13-24)</h2><br />
<p>User interface of Analysis part was completely implemented and full functional. </p><br />
</html><br />
[[Image:UCalgary2012_Denitri.png|thumb|center|600px|Fig9. Denitrification pathway results showed on user interface]]<br />
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<br />
<h2>Week 18-19 (Aug 27-Sep 7)</h2><br />
<p>The user interface prototype of Build tab was created. The interface was completely implemented and fully functional.</p><br />
</html><br />
[[Image:UCalgary2012_BuildTabPrototype.png|thumb|center|600px|Fig10. User interface prototype of Build tab]]<br />
[[Image:UCalgary2012_BuildTabEg.png|thumb|center|600px|Fig11. Example model built on Build tab]]<br />
[[Image:UCalgary2012_NovelModelExport.png|thumb|center|600px|Fig12. Example model exported to mScript file]]<br />
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<br />
<h2>Week 20 (Sept 10-14)</h2><br />
<p>Application went post-development phase and some additional features were added into. <br />
<br>New features:<br />
<br>Searching: Users can search metabolites in loaded model<br />
<br>Updating: Users is able to update reactions they entered in table<br />
<br>Changing export file format: User can read analysis output in spread sheet<br />
<br><br />
</p><br />
<br />
<h2>Week 21-22 (Sept 10-22)</h2><br />
<p>Model Validated with data from web lab. Assay for decarboxylation pathway was set. E Coli. with petrobrick grew up on nine different growth medias, one positive control (LB+glucose minimum), one negative control (glucose minimum), and seven medias that consisted of glucose minimum and compounds suggested by application outputs. </p><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="center;"></img><br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Notebook/FluxAnalysisTeam:Calgary/Notebook/FluxAnalysis2012-10-03T16:31:02Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateNotebookBlue|<br />
TITLE=Flux Analysis Notebook|<br />
CONTENT =<br />
<html><br />
<!--<br />
NOTE: This is a template for entering things for the time being. All dates should be enclosed in <h2> tags and all paragraphs should be enclosed in <p> tags. For bulleted lists, <ul> tags will create the list and <li> tags will surround each list item. If there are any questions, please let me know.<br />
Patrick.<br />
--><br />
<br />
<h2>Week 1-2 (May 1-11) </h2><br />
<p>Brain storm refers to <a href="https://2012.igem.org/wiki/index.php?title=Team:Calgary/Notebook/Hydrocarbon">Hydrocarbon Modelling Part</a>.</p><br />
<p>Flux Analysis is brought into the project as it can offer predictions of sets of compound that will be used in growth media to improve the output of target compound.</p><br />
<br />
<h2>Week 3 (May 14-18) </h2><br />
<p>This week involved planning of selection proper analysis, models and platform for modelling. The constraint-based reconstruction analysis was chosen to be the core analysis, the published E.coli (iAF1260) and Pseudomonas (iJN746) models were selected to be base chassis and the MatLab was considered to use as modelling platform. In addition, OpenCobra Toolbox that is developed by System Biology Research Team in UCSD would be employed as lower level computation tools.</p><br />
<br />
<h2>Week 4 (May 21-25)</h2><br />
<h3>Concepts</h3><br />
<h4>Flux Balance Analysis (FBA)</h4><br />
<p>Flux balance analysis (FBA) is a mathematical method for analyzing metabolism. It is a direct application of linear programming to biological systems that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization.</p><br />
</html>[[File:UCalgary 2012 FBAEx.png|thumb|600px|center|The results of FBA on a prepared metabolic network of the top six reactions of glycolysis. The predicted flux through each reaction is proportional to the width of the line. Objective function in red, constraints on alpha-D-Glucose and beta-D-Glucose import represented as red bars. Original: Wikipedia]]<html><br />
<h5>The Steady State Assumption</h5><br />
<p>A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero. In chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. </p><br />
<h5>Stoichiometric & Flux Matrix </h5><br />
<p>Stoichiometry is a branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. In a balanced chemical reaction, the relations among quantities of reactants and products typically form a ratio of whole numbers.<br />
<br>Flux matrix, in terms of flux rate, is a rate of turnover of molecules through a reaction pathway. <br />
</p><br />
</html>[[File:UCalgary 2012 FBAexWithSMatrix.png|thumb|600px|center|An example stoichiometric matrix for a network representing the top of glycolysis and that same network after being prepared for FBA.Original:Wikipedia]]<html><br />
<h4> Extended Flux Analysis</h4><br />
<h5>Flux variability analysis</h5><br />
<p>The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built-in to virtually all current analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, produce the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are highly important. </p><br />
<h5>Dynamic FBA</h5><br />
<p>Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict homoeostatic condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.</p><br />
<h4>Metabolic Network Reconstruction</h4><br />
<p>Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including E. coli. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of E. coli have existed for nearly twenty years. The first genome-scale model of E. coli metabolism was released in 2000, and this model continues to be expanded and updated today.<a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></p><br />
<br />
<br />
Metabolic network reconstruction and simulation allows for an in depth insight into comprehending the molecular mechanisms of a particular organism, especially correlating the genome with molecular physiology (Francke, Siezen, and Teusink 2005). A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. </p><br />
<h3>Modelling</h3><br />
<h4>Constraint-based Model</h4><br />
<p>Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. The vector x with length m can then be defined as the concentrations of all the metabolites and the vector v with length n contains the fluxes through each reaction.</p><br />
<h5>Constraint-Based Models of E. coli <a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></h5><br />
<h6>iAF1260</h6><br />
<p>The latest update of the E. coli genome-scale metabolic model is iAF1260, published in 2007. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the EcoCyc database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the E. coli metabolic model, iAF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. iAF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution.</p><br />
<h6>E. coli core model</h6><br />
<p>The core E. coli model is a small-scale model of the central metabolism of E. coli. It is a modified subset of the iAF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.</p><br />
<h3>Tools</h3><br />
<h4><a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCobra Toolbox</a></h4><br />
<p>The Constraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies. It is used as the major program.</p><br />
<h4><a href="http://www.celldesigner.org/">CellDesigner 4.0</a></h4><br />
<p>CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). In this project, it is used to visualize metabolic network.</p><br />
<br />
</html><br />
[[Image:UCalgary2012_EcoliCoreNetwork.png|thumb|300px|Fig1. E. coli core model metabolic network view in CellDesigner. The network contains 95 reactions, it is good to use as a verifier of novel model.]]<br />
[[Image:UCalgary2012_EcoliiAF1260.png|thumb|200px|Fig2. E. coli iAF1260 model metabolic network view in CellDesigner. The network contains more than 2000 reactions and it is not possible to be used as verrifier.]]<br />
<html><br />
<br />
<h2>Week 5 (May 28 - June 1)</h2><br />
<p>Tests of OpenCobra toolbox basic examples on E.coli core model and Pseudomonas (iJN746) model are passed, which includes following functions:<br />
FBA test (optimizeCbModel),model creation/modification tests, reaction addition/deletion/modification, and gene search (deletion) tests.</p><br />
<p>Generally, The tests outputs are consistant with expected results. FBA test basically generates one pattern of flux rates for each metabolites to contribute the best biomass.<br />
The novel model created is exactly same as the predicted model as well as the modified model. Reactions in models can be easily added and deleted.</p><br />
<p>The gene deletion tests show the same output as the paper, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, demonstrated. </p><br />
<p>In addition, a software, CellDesigner, is employed to verified the novel model built in Cobra Toolbox visually. The CellDesigner generates a visual diagram to show the entire metabolic network.</p><br />
<br />
<br />
<h2>Week 6 (June 4-8) </h2><br />
<p>Another sets of tests of OpenCobra toolbox examples on E.coli core model and Pseudomonas (iJN746) model were exanimated, which includes following functions: fluxVariability, OptKnock, GDLS and optGene.</p><br />
<p>The outputs of tests with fluxVariability, OptKnock and GDLS were biological relevant. The results from OptKnock and GDLS were similar which could be considered as consistent as they were designed to complete similar tasks. However, optGene tests either output results that were understandable or error messages referring to source code. </p><br />
<br />
<h2>Week 7 (June 11-15)</h2><br />
<p>In order to reconstructing a proper novel model, all the symbols in OpenCobra Toolbox such as ‘[c]’, ‘[e]’, ‘[b]’ after every compound as well as the abbreviation like ‘lb’ and ‘ub’ for each reaction had to be well understand. Also, the formula of added reactions must be as precise as possible to ensure the accurate of the results because lack of enzymatic and genetic regulation to those reactions making the formulas become only variable.</p><br />
<p>The novel model built upon E.coli iAF1260 with additional decarboxylation pathway was accomplished. The flux rate of target compound of testPathway test was good; however, this value become extremely low if set the biomass as objective (running flux balance analysis). The results showed, in simulation, if the cell produced the product, its biomass was nearly zero. Vice verse, if the cell grew normally, the production rate was almost zero. </p><br />
<br />
<h2>Week 8 (June 18-22)</h2><br />
<p>Two novel models built upon E.coli iAF1260 with additional desulfurization and denitrification pathway separately were completed. As expected, the results from FBA were similar to decarboxylation pathway. These phenomena indicated that the relationship between cell growth and production was normally negative related. In addition, FBA returned one and only one solution that maximized biomass. Due to this natural of FBA, it restricted freedom of flows in network and many alternative solutions were omitted out. For pathways like above three, the production rate was negatively related to growth rate, the FBA was meaningless since the production rate was always zero or extremely small. Luckily, Flux Variability Analysis (FVA) was able to cover the problems caused by FBA. Therefore, FVA become the major computation tool to simulate the flux rates of cell metabolic activities. Unfortunately, the results from FVA for each pathway were also close to zero.</p><br />
<br />
<h2>Week 9 (June 25-29)</h2><br />
<p>To figure out the outputs from FVA were whether reasonable, testPathway was applied to run as unit test to verify the model with added pathway. The testPahtway was used to test each reaction in each pathway instead of overall pathway. The results showed the pathways were built improperly because upstream reactions had no flux but downstream reactions could have flux rate. The phenomenon reported last week was result in unbalance of metabolic network. In other words, the modified model violated steady state assumption. Therefore terminal reactions were added to three pathways respectively to keep the system remain in steady state and further to solve the problem. The revised models worked well on testPathway and FVA tests.</p><br />
<br />
<h2>Week 10 (July 3-6)</h2><br />
<p>The function FluxAnalysis was employed to generate maximum and minimum flux rate outputs of target compounds for three pathways respectively. The visual graphs of flux rates over entire metabolic networks for each model with three pathways were generated. These graphs were compared and analyzed to find out reactions which flux rates were significantly different between maximum and minimum outputs in each pathway respectively.</p><br />
</html><br />
[[File:UCalgary2012_CobraToolboxNetwork.png|thumb|800px|center|Fig3. E. coli iAF1260 model Flux Balance Analysis visual output in Cobra Toolbox.]]<br />
<html><br />
<p>Unfortunately, the map was too complicated to compare every single reaction by man source. Also, the conclusions drawn from such comparisons would be not valid since the reactions were highly connected to each other in network rather than distributed. Hence, it is necessary to build an algorithm that could automatically analysis the reactions in network scale.</p><br />
<br />
<h2>Week 11-12 (July 9-20)</h2><br />
<p>To have an algorithm doing such a work, the question had to be answered. How to improve the products flux rates through data from FVA?</p><br />
<p>Two things were noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
</html><br />
[[Image:Ucalgary2012_OptimalPoint.png|thumb|center|350px|Fig4. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|center|350px|Fig5. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
<html><br />
<br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>To improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media.</p><br />
<br />
<p>The analysis algorithm was designed. It can automatically analyze reactions and compounds and were able to output metabolites that would improve the production rate.</p><br />
<p>Algorithm:</p><br />
<p>1. Define relationship between growth rate and production rate</p><br />
<p>2. Find out the optimal growth rate that can maximize the production</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold<br />
<p>5. Score each compound in all collected reactions (Initial score is zero for all compounds) <br />
<br> 5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction.<br />
<br> 5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.<br />
<br> 5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed. <br><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate. </p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br />
<h2>Week 13 - 15(July 23-Aug 10)</h2><br />
<p>Algorithm described in past weeks was implemented. The user interface prototype of Analysis tab was created. </p><br />
</html><br />
[[Image:UCalgary2012_AlgorithmOutput.png|thumb|center|600px|Fig6. Algorithm outputs in Matlab console]]<br />
[[Image:UCalgary2012_BioVsTarget.png|thumb|center|400px|Fig7. Plot of relationship between growth rate and production rate with outlined optimal growth rate]]<br />
[[Image:UCalgary2012_AnalysisTabPrototype.png|thumb|center|600px|Fig8. User interface prototype of Analysis Tab]]<br />
<html><br />
<br />
<h2>Week 16-17 (Aug 13-24)</h2><br />
<p>User interface of Analysis part was completely implemented and full functional. </p><br />
</html><br />
[[Image:UCalgary2012_Denitri.png|thumb|center|600px|Fig9. Denitrification pathway results showed on user interface]]<br />
<html><br />
<br />
<h2>Week 18-19 (Aug 27-Sep 7)</h2><br />
<p>The user interface prototype of Build tab was created. The interface was completely implemented and fully functional.</p><br />
</html><br />
[[Image:UCalgary2012_BuildTabPrototype.png|thumb|center|600px|Fig10. User interface prototype of Build tab]]<br />
[[Image:UCalgary2012_BuildTabEg.png|thumb|center|600px|Fig11. Example model built on Build tab]]<br />
[[Image:UCalgary2012_NovelModelExport.png|thumb|center|600px|Fig12. Example model exported to mScript file]]<br />
<html><br />
<br />
<h2>Week 20 (Sept 10-14)</h2><br />
<p>Application went post-development phase and some additional features were added into. </p>\<br />
<br>New features:<br />
<br>Searching: Users can search metabolites in loaded model<br />
<br>Updating: Users is able to update reactions they entered in table<br />
<br>Changing export file format: User can read analysis output in spread sheet<br />
<br><br />
<br />
<h2>Week 21-22 (Sept 10-22)</h2><br />
<p>Model Validated with data from web lab. Assay for decarboxylation pathway was set. E Coli. with petrobrick grew up on nine different growth medias, one positive control (LB+glucose minimum), one negative control (glucose minimum), and seven medias that consisted of glucose minimum and compounds suggested by application outputs. </p><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="center;"></img><br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/File:UCalgary2012_FVAFinalVersion.zipFile:UCalgary2012 FVAFinalVersion.zip2012-10-03T09:35:43Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 FVAFinalVersion.zip&quot;</p>
<hr />
<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T09:27:23Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<br />
<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
<br />
<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
<br><br />
<br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<p>Screen Cast of Application in real time</p><br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<p></p><br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of Cobra Toolbox and SBML Toolbox. To run the application, one must have Cobra Toolbox and SBML Toolbox installed. </p><br />
<p>SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>.</p><br />
<p>Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>.</p><br />
<p>Source code<a href="https://static.igem.org/mediawiki/2012/2/2a/UCalgary2012_FVAFinalVersion.zip"> download </a>.</p><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T09:26:28Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<br />
<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
<br />
<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
<br><br />
<br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<p>Screen Cast of Application in real time</p><br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<p></p><br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Code</h2><br />
<p>This application is a Matlab extension that runs on top of Cobra Toolbox and SBML Toolbox. To run the application, one must have Cobra Toolbox and SBML Toolbox installed. </p><br />
<p>SBML Toolbox can download from <a href="http://sbml.org/Software/SBMLToolbox">SBML.org</a> or <a href="http://sourceforge.net/projects/sbml/files/SBMLToolbox/4.1.0/SBMLToolbox-4.1.0.zip/download">here</a>.</p><br />
<p>Cobra Toolbox can download from <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCOBRA</a> or <a href="http://sourceforge.net/projects/opencobra/files/cobra/cobra_2.0.5.zip/download">here</a>.</p><br />
<p>Source code<a href="https://static.igem.org/mediawiki/2012/2/2a/UCalgary2012_FVAFinalVersion.zip"> download </a>.</p><br />
<br<br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T09:10:46Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<br />
<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
<br />
<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
<br><br />
<br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<p>Screen Cast of Application in real time</p><br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<p></p><br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Source Code</h2><br />
<p><a href="https://static.igem.org/mediawiki/2012/2/2a/UCalgary2012_FVAFinalVersion.zip">Download</a></p><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/File:UCalgary2012_FVAFinalVersion.zipFile:UCalgary2012 FVAFinalVersion.zip2012-10-03T09:08:44Z<p>Cqian: uploaded a new version of &quot;File:UCalgary2012 FVAFinalVersion.zip&quot;</p>
<hr />
<div></div>Cqianhttp://2012.igem.org/File:UCalgary2012_FVAFinalVersion.zipFile:UCalgary2012 FVAFinalVersion.zip2012-10-03T09:06:33Z<p>Cqian: </p>
<hr />
<div></div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T09:05:38Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<br />
<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
<br />
<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
<br><br />
<br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<p>Screen Cast of Application in real time</p><br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
</html><br />
<p>Screen shots of application in real time</p><br />
[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
[[Image:UCalgary2012_RunTimeBuild.png|230px]]<br />
[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
<p>Screen shots of files exported by application in real time</p><br />
[[Image:UCalgary2012_BuildOutput.png|300px]]<br />
[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
<html><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<p></p><br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Source Code</h2><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T09:02:26Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<br />
<p>In order to better implement OSCAR as a bioreactor system, it is important that we have a mechanism by which we can optimize his newly developed metabolic network. To achieve this we turned to flux variability analysis as a way of predicting ways of upregulating our target pathways (such as hydrcarbon production). We developed a MATLAB based program for predicting metabolites that can added to your media in order to increase production of compounds in synthetic <i>E. coli</i> chassis'. In addition we have made this program user friendly by designing a graphical user interface, and allowing for other teams to add their own synthetic pathways into the model. We validated this model in the wetlab to demonstrate that it can be used to optimize the Petrobrick system to save time, money, and resources.</p><br />
<br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
<br />
<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
<br><br />
<br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
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<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
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<h2>Demo</h2><br />
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<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
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[[Image:UCalgary2012_RunTimeAnalysis.png|230px]]<br />
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[[Image:UCalgary2012_DemoOutput.png|230px]]<br />
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[[Image:UCalgary2012_AnalysisOutput.png|300px]]<br />
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<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
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<h2>Validation</h2><br />
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<h2>Source Code</h2><br />
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<h2>Documents</h2><br />
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CONTENT =<br />
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<!--<br />
NOTE: This is a template for entering things for the time being. All dates should be enclosed in <h2> tags and all paragraphs should be enclosed in <p> tags. For bulleted lists, <ul> tags will create the list and <li> tags will surround each list item. If there are any questions, please let me know.<br />
Patrick.<br />
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<h2>Week 1-2 (May 1-11) </h2><br />
<p>Brain storm refers to <a href="https://2012.igem.org/wiki/index.php?title=Team:Calgary/Notebook/Hydrocarbon">Hydrocarbon Modelling Part</a>.</p><br />
<p>Flux Analysis is brought into the project as it can offer predictions of sets of compound that will be used in growth media to improve the output of target compound.</p><br />
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<h2>Week 3 (May 14-18) </h2><br />
<p>This week involved planning of selection proper analysis, models and platform for modelling. The constraint-based reconstruction analysis was chosen to be the core analysis, the published E.coli (iAF1260) and Pseudomonas (iJN746) models were selected to be base chassis and the MatLab was considered to use as modelling platform. In addition, OpenCobra Toolbox that is developed by System Biology Research Team in UCSD would be employed as lower level computation tools.</p><br />
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<h2>Week 4 (May 21-25)</h2><br />
<h3>Concepts</h3><br />
<h4>Flux Balance Analysis (FBA)</h4><br />
<p>Flux balance analysis (FBA) is a mathematical method for analyzing metabolism. It is a direct application of linear programming to biological systems that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization.</p><br />
</html>[[File:UCalgary 2012 FBAEx.png|thumb|600px|center|The results of FBA on a prepared metabolic network of the top six reactions of glycolysis. The predicted flux through each reaction is proportional to the width of the line. Objective function in red, constraints on alpha-D-Glucose and beta-D-Glucose import represented as red bars. Original: Wikipedia]]<html><br />
<h5>The Steady State Assumption</h5><br />
<p>A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero. In chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. </p><br />
<h5>Stoichiometric & Flux Matrix </h5><br />
<p>Stoichiometry is a branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. In a balanced chemical reaction, the relations among quantities of reactants and products typically form a ratio of whole numbers.<br />
<br>Flux matrix, in terms of flux rate, is a rate of turnover of molecules through a reaction pathway. <br />
</p><br />
</html>[[File:UCalgary 2012 FBAexWithSMatrix.png|thumb|600px|center|An example stoichiometric matrix for a network representing the top of glycolysis and that same network after being prepared for FBA.Original:Wikipedia]]<html><br />
<h4> Extended Flux Analysis</h4><br />
<h5>Flux variability analysis</h5><br />
<p>The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built-in to virtually all current analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, produce the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are highly important. </p><br />
<h5>Dynamic FBA</h5><br />
<p>Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict homoeostatic condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.</p><br />
<h4>Metabolic Network Reconstruction</h4><br />
<p>Metabolic network reconstructions are biochemically, genetically, and genomically (BiGG) structured knowledge bases that seek to formally represent the known metabolic activities of an organism. Network reconstructions also exist for other types of biological networks, including transcription/translation and signaling networks. Genome-scale metabolic networks have been reconstructed for nearly 40 organisms so far, including E. coli. These reconstructions are useful because they can be converted into constraint-based models, allowing useful predictive calculations like flux balance analysis to be performed. Constraint-based models of E. coli have existed for nearly twenty years. The first genome-scale model of E. coli metabolism was released in 2000, and this model continues to be expanded and updated today.<a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></p><br />
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Metabolic network reconstruction and simulation allows for an in depth insight into comprehending the molecular mechanisms of a particular organism, especially correlating the genome with molecular physiology (Francke, Siezen, and Teusink 2005). A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. </p><br />
<h3>Modelling</h3><br />
<h4>Constraint-based Model</h4><br />
<p>Constraint-based models are a way of mathematically encoding a metabolic network reconstruction. Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites that are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. The vector x with length m can then be defined as the concentrations of all the metabolites and the vector v with length n contains the fluxes through each reaction.</p><br />
<h5>Constraint-Based Models of E. coli <a href="http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions">http://ecoliwiki.net/colipedia/index.php/Metabolic_Network_Reconstructions</a></h5><br />
<h6>iAF1260</h6><br />
<p>The latest update of the E. coli genome-scale metabolic model is iAF1260, published in 2007. The total number of genes increased to 1260, along with increases to 2077 reactions and 1039 unique metabolites. The scope of the network was expanded, explicitly accounting for periplasmic reactions and metabolites. The model was reconciled with the lastest version of the EcoCyc database, and thermodynamic analysis was performed to predict the reversibility of reactions. As the latest version of the E. coli metabolic model, iAF1260 continues to be updated as new discoveries are made, and a new version will be released in 2010. iAF1260 and its predecessors have been used in studies of metabolic engineering, biological discovery, phenotypic behavior, network analysis, and bacterial evolution.</p><br />
<h6>E. coli core model</h6><br />
<p>The core E. coli model is a small-scale model of the central metabolism of E. coli. It is a modified subset of the iAF1260 model, and contains 134 genes, 95 reactions, and 72 metabolites. This model is used for educational purposes, since the results of most constraint-based calculations are easier to interpret on this smaller scale. It is also useful for testing new constraint-based analysis methods.</p><br />
<h3>Tools</h3><br />
<h4><a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">openCobra Toolbox</a></h4><br />
<p>The Constraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism at the genome scale1.The COBRA approach focuses on employing physicochemical constraints to define the set of feasible states for a biological network in a given condition based on current knowledge. These constraints include compartmentalization, mass conservation, molecular crowding, and thermodynamic directionality.More recently, transcriptome data have been used to reduce the size of the set of computed feasible states. Although COBRA methods may not provide a unique solution, they provide a reduced set of solutions that may be used to guide biological hypothesis development. Given its initial success, COBRA has attracted attention from many investigators and has developed rapidly in recent years based on contributions from a growing number of laboratories – COBRA methods have been used in hundreds of studies. It is used as the major program.</p><br />
<h4><a href="http://www.celldesigner.org/">CellDesigner 4.0</a></h4><br />
<p>CellDesigner is a structured diagram editor for drawing gene-regulatory and biochemical networks. Networks are drawn based on the process diagram, with graphical notation system proposed by Kitano, and are stored using the Systems Biology Markup Language (SBML), a standard for representing models of biochemical and gene-regulatory networks. Networks are able to link with simulation and other analysis packages through Systems Biology Workbench (SBW). In this project, it is used to visualize metabolic network.</p><br />
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[[Image:UCalgary2012_EcoliCoreNetwork.png|thumb|300px|Fig1. E. coli core model metabolic network view in CellDesigner. The network contains 95 reactions, it is good to use as a verifier of novel model.]]<br />
[[Image:UCalgary2012_EcoliiAF1260.png|thumb|200px|Fig2. E. coli iAF1260 model metabolic network view in CellDesigner. The network contains more than 2000 reactions and it is not possible to be used as verrifier.]]<br />
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<h2>Week 5 (May 28 - June 1)</h2><br />
<p>Tests of OpenCobra toolbox basic examples on E.coli core model and Pseudomonas (iJN746) model are passed, which includes following functions:<br />
FBA test (optimizeCbModel),model creation/modification tests, reaction addition/deletion/modification, and gene search (deletion) tests.</p><br />
<p>Generally, The tests outputs are consistant with expected results. FBA test basically generates one pattern of flux rates for each metabolites to contribute the best biomass.<br />
The novel model created is exactly same as the predicted model as well as the modified model. Reactions in models can be easily added and deleted.</p><br />
<p>The gene deletion tests show the same output as the paper, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, demonstrated. </p><br />
<p>In addition, a software, CellDesigner, is employed to verified the novel model built in Cobra Toolbox visually. The CellDesigner generates a visual diagram to show the entire metabolic network.</p><br />
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<h2>Week 6 (June 4-8) </h2><br />
<p>Another sets of tests of OpenCobra toolbox examples on E.coli core model and Pseudomonas (iJN746) model were exanimated, which includes following functions: fluxVariability, OptKnock, GDLS and optGene.</p><br />
<p>The outputs of tests with fluxVariability, OptKnock and GDLS were biological relevant. The results from OptKnock and GDLS were similar which could be considered as consistent as they were designed to complete similar tasks. However, optGene tests either output results that were understandable or error messages referring to source code. </p><br />
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<h2>Week 7 (June 11-15)</h2><br />
<p>In order to reconstructing a proper novel model, all the symbols in OpenCobra Toolbox such as ‘[c]’, ‘[e]’, ‘[b]’ after every compound as well as the abbreviation like ‘lb’ and ‘ub’ for each reaction had to be well understand. Also, the formula of added reactions must be as precise as possible to ensure the accurate of the results because lack of enzymatic and genetic regulation to those reactions making the formulas become only variable.</p><br />
<p>The novel model built upon E.coli iAF1260 with additional decarboxylation pathway was accomplished. The flux rate of target compound of testPathway test was good; however, this value become extremely low if set the biomass as objective (running flux balance analysis). The results showed, in simulation, if the cell produced the product, its biomass was nearly zero. Vice verse, if the cell grew normally, the production rate was almost zero. </p><br />
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<h2>Week 8 (June 18-22)</h2><br />
<p>Two novel models built upon E.coli iAF1260 with additional desulfurization and denitrification pathway separately were completed. As expected, the results from FBA were similar to decarboxylation pathway. These phenomena indicated that the relationship between cell growth and production was normally negative related. In addition, FBA returned one and only one solution that maximized biomass. Due to this natural of FBA, it restricted freedom of flows in network and many alternative solutions were omitted out. For pathways like above three, the production rate was negatively related to growth rate, the FBA was meaningless since the production rate was always zero or extremely small. Luckily, Flux Variability Analysis (FVA) was able to cover the problems caused by FBA. Therefore, FVA become the major computation tool to simulate the flux rates of cell metabolic activities. Unfortunately, the results from FVA for each pathway were also close to zero.</p><br />
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<h2>Week 9 (June 25-29)</h2><br />
<p>To figure out the outputs from FVA were whether reasonable, testPathway was applied to run as unit test to verify the model with added pathway. The testPahtway was used to test each reaction in each pathway instead of overall pathway. The results showed the pathways were built improperly because upstream reactions had no flux but downstream reactions could have flux rate. The phenomenon reported last week was result in unbalance of metabolic network. In other words, the modified model violated steady state assumption. Therefore terminal reactions were added to three pathways respectively to keep the system remain in steady state and further to solve the problem. The revised models worked well on testPathway and FVA tests.</p><br />
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<h2>Week 10 (July 3-6)</h2><br />
<p>The function FluxAnalysis was employed to generate maximum and minimum flux rate outputs of target compounds for three pathways respectively. The visual graphs of flux rates over entire metabolic networks for each model with three pathways were generated. These graphs were compared and analyzed to find out reactions which flux rates were significantly different between maximum and minimum outputs in each pathway respectively.</p><br />
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[[File:UCalgary2012_CobraToolboxNetwork.png|thumb|800px|center|Fig3. E. coli iAF1260 model Flux Balance Analysis visual output in Cobra Toolbox.]]<br />
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<p>Unfortunately, the map was too complicated to compare every single reaction by man source. Also, the conclusions drawn from such comparisons would be not valid since the reactions were highly connected to each other in network rather than distributed. Hence, it is necessary to build an algorithm that could automatically analysis the reactions in network scale.</p><br />
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<h2>Week 11-12 (July 9-20)</h2><br />
<p>To have an algorithm doing such a work, the question had to be answered. How to improve the products flux rates through data from FVA?</p><br />
<p>Two things were noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
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[[Image:Ucalgary2012_OptimalPoint.png|thumb|center|350px|Fig4. Illustration the relationship of growth rate and production rate, and the computed optimal growth rate.]]<br />
[[Image:UCalgary2012_MaxAndMin.png|thumb|center|350px|Fig5. Illustration of maximum and minimum production rates computed by flux variability analysis based on optimal growth rate.]]<br />
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<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>To improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media.</p><br />
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<p>The analysis algorithm was designed. It can automatically analyze reactions and compounds and were able to output metabolites that would improve the production rate.</p><br />
<p>Algorithm:</p><br />
<p>1. Define relationship between growth rate and production rate</p><br />
<p>2. Find out the optimal growth rate that can maximize the production</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold<br />
<p>5. Score each compound in all collected reactions (Initial score is zero for all compounds) <br />
<br> 5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction.<br />
<br> 5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.<br />
<br> 5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed. <br><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate. </p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
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<h2>Week 13 - 15(July 23-Aug 10)</h2><br />
<p>Algorithm described in past weeks was implemented. The user interface prototype of Analysis tab was created. </p><br />
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[[Image:UCalgary2012_AlgorithmOutput.png|thumb|center|600px|Fig6. Algorithm outputs in Matlab console]]<br />
[[Image:UCalgary2012_BioVsTarget.png|thumb|center|400px|Fig7. Plot of relationship between growth rate and production rate with outlined optimal growth rate]]<br />
[[Image:UCalgary2012_AnalysisTabPrototype.png|thumb|center|600px|Fig8. User interface prototype of Analysis Tab]]<br />
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<h2>Week 16-17 (Aug 13-24)</h2><br />
<p>User interface of Analysis part was completely implemented and full functional. </p><br />
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[[Image:UCalgary2012_Denitri.png|thumb|center|600px|Fig9. Denitrification pathway results showed on user interface]]<br />
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<h2>Week 18-19 (Aug 27-Sep 7)</h2><br />
<p>The user interface prototype of Build tab was created. The interface was completely implemented and fully functional.</p><br />
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[[Image:UCalgary2012_BuildTabPrototype.png|thumb|center|600px|Fig10. User interface prototype of Build tab]]<br />
[[Image:UCalgary2012_BuildTabEg.png|thumb|center|600px|Fig11. Example model built on Build tab]]<br />
[[Image:UCalgary2012_NovelModelExport.png|thumb|center|600px|Fig12. Example model exported to mScript file]]<br />
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<h2>Week 20 (Sept 10-14)</h2><br />
<p>Application debugging and post-development phase</p><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="center;"></img><br />
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<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
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<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
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<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
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<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<br />
<div align="center"><br />
<iframe width="420" height="315" src="http://www.youtube.com/embed/9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
<br />
<br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Source Code</h2><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T08:07:20Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
<br />
<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
<br><br />
<br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<br />
<div align="center"><br />
<iframe width="600" height="338" align="center" src="http://www.youtube.com/watch?v=9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
<iframe width="600" height="450" align="center" src="http://www.youtube.com/embed/N7phu6NmlQo" frameborder="0" allowfullscreen></iframe><br />
</div><br />
<br />
<br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Source Code</h2><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqianhttp://2012.igem.org/Team:Calgary/Project/OSCAR/FluxAnalysisTeam:Calgary/Project/OSCAR/FluxAnalysis2012-10-03T08:01:33Z<p>Cqian: </p>
<hr />
<div>{{Team:Calgary/TemplateProjectBlue|<br />
TITLE=Flux-Variability Analysis for Optimization|<br />
<br />
CONTENT=<br />
<br />
<html><br />
<img src="https://static.igem.org/mediawiki/2012/6/61/UCalgary2012_OSCAR_Flux_Analysis_Low-Res.png" style="float: right; padding: 10px;"></img><br />
<h2>Background</h2><br />
<p><b>What is Metabolic Flux Analysis?</b></p><br />
<p>Metabolic Flux balance analysis (FBA) is an application of linear programming to metabolic network that uses the stoichiometric coefficients for each reaction in the system as the set of constraints for the optimization. Simply, it is a mathematical method for analyzing metabolism. This analysis requires the Steady State Assumption, which is, in chemistry, a steady state is a situation in which all state variables are constant in spite of ongoing processes that strive to change them. In addition, Flux Variability Analysis (FVA) is an extension of Flux balance analysis. FVA determines the ranges of fluxes that correspond to an optimal solution determined through FBA. Hence, FBA is able to calculate the full range of numerical values for each reaction flux within the network. </p><br />
<br />
<p><b>What are the constraints in model?</b></p><br />
<p>Networks can be encoded as stoichiometric matrices (S), in which each row represents a unique metabolite and each column represents a biochemical reaction. The entries in each column of this matrix are the stoichiometric coefficients of the metabolites in the reaction. Metabolites are consumed have a negative coefficient and metabolites that are produced have a positive coefficient. Since most reactions involve only a few metabolites, S is a sparse matrix. The size of S is m*n for a network with m metabolites and n reactions. </p><br />
<br />
<p><b>Why use Flux Variability Analysis?</b></p><br />
<p>Biological systems often contain redundancies that contribute to their robustness. However, flux balance analysis only returns a single flux distribution that corresponds to maximal growth under given growth conditions regardless alternate optimal solutions may exist. FVA is capable to exanimate these redundancies by calculating the full range of numerical values for each reaction flux in a network. Consequently, FVA can be employed to study the entire range of achievable cellular functions as well as the redundancy in optimal phenotypes.</p><br />
<br><br />
<br />
<br />
<br />
<h2>Introduction</h2><br />
<p><b>What is it modeling?</b></p><br />
<p>It models flux rate of metabolic pathways responding to different growth media conditions. The model is expected to generate an optimal set of metabolites that should be added to growth media in order to improve production rate. </p><br />
<br />
<p><b>Why needs this kind of model?</b></p><br />
<p>Same as chemical reactions need optimal environmental conditions to achieve maximum production rate, microbes also require optimized growth conditions to accomplish their tasks in maximum speed. In industrial scale, the optimal conditions for chemical reactions could increase the production rate in hundreds times which means millions of money. This principle also works even if taking biology pathways over chemical reactions. The difference is that in chemical way, the conditions of enzyme, pH, temperature and pressure are more important; however, in microbiological method, the conditions of growth media is more crucial. Further, the selection of media compounds is one of the most significant conditions for growth media. If a model can predict an optimal set of metabolites that need to be added into media, there will be tons of time and lots of money got saved. </p><br />
<br />
<p><b>How dose the program work?</b></p><br />
<p>This program is built upon constraint-based reconstruction analysis and flux variability analysis. It uses published E.coli (iAF1260) and E.coli core models as base chassis. Upon those models, we reconstruct new models of interests. Specifically, new reactions corresponding to the genes we try to engineer into E.coli from other organisms will be added into base chassis. By running flux variability analysis, program will give different sets of flux rates based on distinct constraints. Finally, the program will analysis those data with designed algorithm to generate a set of media compounds that is expected to accelerate production rate. </p><br />
<br><br />
<br />
<br />
<h2>Algorithm</h2><br />
<p><b>Conceptual</b></p><br />
<p>How to improve the products flux rates through data from FVA? The answer was remained unknown. There were only two things being noticed. One was that FVA could determine full range of numerical values for each reaction flux within the network and its output were able to use for analyze, and the other one was the biomass rate normally had trade-off relation with production rate. Since biomass rate reflects the growth condition, cell must have positive value of biomass flux rate in order to producing. On the other hand, the production flux rate should be higher than zero as well. This implied among all possible set of fluxes, the optimal flux set should locate a place where growth rate multiplies production rate is maximum.</p><br />
<p>Once the optimal flux rate of biomass was obtained, the value would be set as a new constraint of biomass. Then flux variability analysis would find out the full range of numerical values for each reaction flux within the network that was restricted to the new biological objective. </p><br />
<p>The differences of values for each reaction in a set of flux that maximized production rate and a set of flux that minimized production rate became interesting. By comparing two sets of fluxes based on visual maps, the results showed some reactions had higher flux rates in production maximum set than production minimum set, some were higher in production minimum set than production maximum set and some had opposite flux directions as most of biological reactions were reversible. In chemical, adding the amount of reactants would force the reactions equilibrium to move forwards, and adding the amount of products could drive the reactions equilibrium to go backwards. Consequently, the question becomes how to find out metabolites that need additional amount to improve the production rate.</p><br />
<p>One of the possible solutions could be comparing two sets of fluxes, determining differences of each reaction between two sets and changing constraints according to reaction needs. For example, if a metabolite needs more in production maximum set than production minimum set, then add more amount of this metabolite by change constraints to improve the production. However, in reality, cell could only uptake limited kinds of metabolites. Some metabolites were able to be produced by cell but not able to be absorbed from growth media. Hence, only the metabolites that had natural transporters in cell would count.</p><br />
<p>Last but not least, to improve production by adding more metabolites to growth media, the analysis should start from a model that was built upon glucose minimum growth media. </p><br />
<br />
<br />
<p><b>Concrete</b></p><br />
<p>Precondition: The original model is built with glucose minimum media.</p><br />
<p>1. Define relationship between growth rate and production rate.</p><br />
<p>2. Find out the optimal growth rate that can maximize the production.</p><br />
<p>3. Get the difference percentage of flux rate for each reaction between production maximum set and production minimum set.</p><br />
<p>4. Collect all reactions have difference percentage between two sets that exceed threshold.</p><br />
<p>5. Score each compound in all collected reactions (Initial score is zero for each compound). </p><br />
<p><span style="padding-left:20px">5.1 The difference of flux rates of one reaction from production maximum set to production minimum set is added to the score for all reactants of this reaction. </span></p><br />
<p><span style="padding-left:20px">5.2 The difference of flux rates of one reaction from production minimum set to production maximum set is added to the score for all products of this reaction.</span></p><br />
<p><span style="padding-left:20px">5.3 Repeat 3.1 to 3.2 till all collected reactions are analyzed.</span></p><br />
<p>6. Determine whether compounds with positive scores have natural transporters in cell. If so, mark the compound as candidate.</p><br />
<p>7. Add each candidate to growth media, and run FVA under optimal growth rate computed in Step 2. Compare the production rate from novel model to that from raw model, if the rate is improved, mark as effector.</p><br />
<br />
<br><br />
<br />
<br />
<h2>Demo</h2><br />
<div align="center"><br />
<iframe width="600" height="480" align="center" src="http://www.youtube.com/watch?v=9oHJhQs5wQk" frameborder="0" allowfullscreen></iframe><br />
</div><br />
<br><br />
<br />
<br />
<h2>Drawbacks</h2><br />
<p>This application is built upon Cobra Toolbox, and Cobra Toolbox is an application of SBML Toolbox. As consequence, any flaws in Cobra Toolbox and SBML Toolbox will affect this application.</P><br />
<p>In this program, pathways added to base chassis model (E. Coli iAF1260) contain constraints only relied on Stoichiometric Matrix such as Stoichiometric coefficients, lower bounds and upper bounds of reactions. They are lack of genetic and enzymatic regulation, which makes the connections between reactions in the network much weaker than those in real. The missing rules could lead to program outputs inaccuracy results.</P><br />
<p>At current stage, the algorithm can only pick metabolites with natural transporters in cell. Many other intermediate metabolites are ignored. The algorithm has no power to trace the intermediate metabolites back to initial metabolites and take those initial metabolites into account. This lack of power could again make the results weak.</P><br />
<br><br />
<br />
<br />
<h2>Validation</h2><br />
<br><br />
<br />
<br />
<h2>Source Code</h2><br />
<br><br />
<br />
<h2>Documents</h2><br />
<br><br />
<br />
<br />
</html><br />
}}</div>Cqian