Team:Carnegie Mellon/Modelling

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<li class="toc-h1"><a href="#section1">The Model</a>
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<li><a href="#section1-1">1.1 Project Description</a></li>
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<h1 align="center" /><div class="glow1"><b>Modelling Overview</b></div><br /><br /></h1>
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            <h1> Modelling </h1>
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                     <li><a rel="external" href="https://2012.igem.org/Team:Carnegie_Mellon">CMU iGEM Home Page</a></li>
                     <li><a rel="external" href="https://2012.igem.org/Team:Carnegie_Mellon">CMU iGEM Home Page</a></li>
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                     <li><a rel="external" href="#">Project Description</a></li>
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                     <li><a rel="external" href="https://2012.igem.org/Team:Carnegie_Mellon/Modelling/Documentation">Project Documentation</a></li>
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                     <li><a rel="external" href="#">Model</a></li>
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                     <li><a rel="external" href="https://2012.igem.org/Team:Carnegie_Mellon/Modelling/Walkthrough">Model Walkthrough</a></li>
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                     <h1 id="section1-1"> Project Description </h1>
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                 The model will be composed in Matlab. It incorporates file input/output to retrieve measurement data (or approximations of data) to approximate two important characteristics of gene expression, PoPS and translational efficiency. The inputs to the model will be time dependent fluorescence measurements of mRNA and protein. Four time dependent measurements are needed: mRNA fluorescence during synthesis, mRNA fluorescence during degradation (no mRNA production), protein fluorescence during synthesis, and protein fluorescence during degradation (no protein production). Other constants, such as concentrations of the dyes that are put into the cells, can also be input into the model in order to increase precision of the outputs. However, these can be approximated with previously measured values if for some reason they cannot be determined.  
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                 The model is composed in Matlab. It incorporates file input/output to retrieve measurement data to approximate two important characteristics of gene expression, PoPS and translational efficiency. The inputs to the model are time dependent and dye-dependent fluorescence measurements of mRNA and protein. Four time dependent measurements are needed: mRNA fluorescence during synthesis, mRNA fluorescence during degradation (no mRNA production), protein fluorescence during synthesis, and protein fluorescence during degradation (no protein production). Other constants are approximated as needed.
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The output values, PoPS and translational efficiency, will help characterize promoters that will be used in the experiments. These parameters are notoriously difficult to measure in vivo. In addition to the two main output values, the model also calculates other characteristics of the cell, such as degradation constants for mRNA and protein, and transcriptional efficiency.  
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The output values, PoPS and translational efficiency, will help characterize promoters that will be used in the experiments. These parameters are notoriously difficult to measure consistently in vivo. In addition to the two main output values, the model also calculates other characteristics of the cell, such as degradation constants for mRNA and protein, and transcriptional efficiency.  
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    The Model
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             <p>Some copyright and legal notices here. Maybe use the © symbol a bit.</p>
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    The purpose of the model in the scope of the project is to provide an acceptable estimate of desired parameters within the biological system. These
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    parameters can often not be measured or calculated directly, which highlights the importance of the model. This particular model calculates three specific
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    parameters about the system, transcriptional efficiency, translational efficiency, and polymerase per second. These three characteristics of protein
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    expression have historically been difficult to calculate and even estimate, but new measurement capabilities have allowed for the possible development of a
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    model for these processes.
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    Inputs to the model will constitute measurements taken from the actual biological system. Dyes mixed with solutions of the cells will bind to mRNA and
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    protein complexes to cause the cells to fluoresce over time. These fluorescent measurements will form the basis of the inputs to the model. Using a
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    gradient of concentrations of dye vs. time applied to cells, one can obtain estimates about the amount of bound mRNA and protein.
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    Summary of the Model
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    Using the amount of bound mRNA and protein, the model transforms it into total mRNA and protein over time using Micaelis-Menten kinetics. Using the total
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    mRNA and protein measurements, along with either estimates or measurements of the degradation of mRNA and protein*, one can determine the transcriptional
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    and translational efficiency using general differential equations. Finally, polymerase per second can be determined using translational efficiency.
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Latest revision as of 23:14, 4 September 2012

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Modelling Overview


Project Description

The model is composed in Matlab. It incorporates file input/output to retrieve measurement data to approximate two important characteristics of gene expression, PoPS and translational efficiency. The inputs to the model are time dependent and dye-dependent fluorescence measurements of mRNA and protein. Four time dependent measurements are needed: mRNA fluorescence during synthesis, mRNA fluorescence during degradation (no mRNA production), protein fluorescence during synthesis, and protein fluorescence during degradation (no protein production). Other constants are approximated as needed.

The output values, PoPS and translational efficiency, will help characterize promoters that will be used in the experiments. These parameters are notoriously difficult to measure consistently in vivo. In addition to the two main output values, the model also calculates other characteristics of the cell, such as degradation constants for mRNA and protein, and transcriptional efficiency.

The purpose of the model in the scope of the project is to provide an acceptable estimate of desired parameters within the biological system. These parameters can often not be measured or calculated directly, which highlights the importance of the model. This particular model calculates three specific parameters about the system, transcriptional efficiency, translational efficiency, and polymerase per second. These three characteristics of protein expression have historically been difficult to calculate and even estimate, but new measurement capabilities have allowed for the possible development of a model for these processes.

Inputs to the model will constitute measurements taken from the actual biological system. Dyes mixed with solutions of the cells will bind to mRNA and protein complexes to cause the cells to fluoresce over time. These fluorescent measurements will form the basis of the inputs to the model. Using a gradient of concentrations of dye vs. time applied to cells, one can obtain estimates about the amount of bound mRNA and protein.

Using the amount of bound mRNA and protein, the model transforms it into total mRNA and protein over time using Micaelis-Menten kinetics. Using the total mRNA and protein measurements, along with either estimates or measurements of the degradation of mRNA and protein*, one can determine the transcriptional and translational efficiency using general differential equations. Finally, polymerase per second can be determined using translational efficiency.

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