# Team:Calgary/Project/OSCAR/FluxAnalysis

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Background

Background

- What is Metabolic Flux Analysis?'''Bold text''' + What is Metabolic Flux Analysis?

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 that is able to calculate the full range of numerical values for each reaction flux within the network.

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 that is able to calculate the full range of numerical values for each reaction flux within the network.

-

What are the constraints in model?

+ What are the constraints in model?

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.

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.

-

Why use Flux Variability Analysis?

+ Why use Flux Variability Analysis?

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.

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.

Introduction

Introduction

-

What is it modeling?

+ What is it modeling?

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.

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.

-

Why needs this kind of model?

+ Why needs this kind of model?

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.

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.

-

How dose the program work?

+ How dose the program work?

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.

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.

## Revision as of 22:41, 28 September 2012

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# Flux-Variability Analysis for Optimization

## Background

What is Metabolic Flux Analysis?

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 that is able to calculate the full range of numerical values for each reaction flux within the network.

What are the constraints in model?

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.

Why use Flux Variability Analysis?

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.

## Introduction

What is it modeling?

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.

Why needs this kind of model?

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.

How dose the program work?

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.