Team:UANLe Mexico

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*** Acknowledgments ***
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>>>To TU_Delf Team
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Thank you so much to TU_Delft iGEM2010 team for providing us with so many examples of html code and how to apply them.
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If you like some of this code please check it out in their wiki: https://2010.igem.org/Team:TU_Delft#page=Modeling/wiki-tips-tricks
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<META NAME="keywords" CONTENT="UANL,iGEM,Universidad,Autonoma,Nuevo,Leon,Biphasic,switch,Lambda,Genetic,Regulation,Quorum,Sensing,pRM,OL,Green Light,Red Light">
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<META NAME="description" CONTENT="iGEM-UANL is the representative team from Universidad Autonoma de Nuevo León, at Monterrey, México. This team is composed of ten students who spent their summer in the lab, having fun with transformations, constructions and plasmidic DNA extractions. This">
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<META NAME="abstract" CONTENT="Information processing through living things remains a challenge to science. Genetic logic-gates and">
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            Modelling
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    Project description
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        During the last years, the iGEM competition has represented a force that promotes the development of ideas and projects by undergraduate students, in this way iGEM can be considered as a unique event because it has stimulated thousands of students go from just imagining projects, to actually making them. All of this has generated an extensive amount of synthetic biology projects, each one of them focused on the resolution of specific problems, some of them so remarkable as they are environmental conflicts and others, less socially noticeable like circuits for genetic regulation.
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Nevertheless, there is an interesting situation regarding the time passed since the start of the iGEM competition and thus the fomentation of synthetic biology, and is the lack of wet-lab projects that actually reach the general community through the implementation of tangible solutions. So, it is time to do something about it, it is time to solve the problems all these projects were made to, by creating an industry based on synthetic biology.
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Our project aims this: the establishment of an industry based on what has been developed until now in this field of synthetic biology by taking the lab solutions to the real problems and making them work. Also, this idea has emerged from the application of one of the principles commonly found in this competition and in synthetic biology, the use of standard genetic parts and their utilization on solving different situations.
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Since nowadays we can find a great variety of projects, there is an extensive opportunity area inside synthetic biology, one of those is the use of biosensors. This topic has been of particular interest to our team because it includes a wide quantity of projects. All these projects mean many possible solutions for different problems. However, development of these solutions outside the iGEM competition has not been observed, thus making evident the necessity for the industry presented.
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Inside the opportunity area that can be found in this topic of biosensors, there are highlighted the advantages of using this technologies over the use of classical and actual methodologies. Among them, there is the viability, simplicity and the promotion of the use of green technology.
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In this way, it can be stated that a business plan for the development of a company based on biosensors, could be a good example for the importance and feasibility of making industry based on synthetic biology projects, not leaving them in just possible solutions, but implementing them for the continuos improvement of this society that we live in.
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          <a name="Overview"></a>Overview
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     <p>We decided to follow a deterministic approach for the mathematical representation of our genetic circuits. The simulations were made using MATLAB’s Simulink, according to the following general expressions for the genetic inhibition and activation elements present in the ODE systems. These expressions were adapted from Mendes, P, <i>et al</i>., (2003).</p>
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<p>The effect of an inhibitor was modeled as follows:</p>
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<p>where α is the maximum transcription rate of a gene, K is the dissociation constant of the inhibitor, I represents its concentration and n is its Hill coefficient.</p>
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<p>In the other hand, the effect of an activator was expressed as follows:</p>
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<p>where α is also the maximum transcription rate of a gene, K is the dissociation constant of the activator, A represents its concentration and n is its Hill coefficient.</p>
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<p>The maximum transcription rate is used instead of the basal transcription rate under the assumption that inducible promoters should not present significant basal activity and that repressible promoters are active at this maximum rate in the absence of an inhibitor. The maximum transcription rate was calculated according to the Team Beijing iGEM 2009 (<a href="https://2008.igem.org/Team:KULeuven/Model/Inverter">https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters</a>).</p>
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<p>The general form of the differential equations used to model the change of the mRNA concentration of a gene is as follows:</p>
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<p>This general form represents an hypothetical gene, whose transcription is affected by an inhibitor I and an activator A. Different Hill Coefficients (n1 and n2) are used for the inhibitor and activator. The equation includes also the degradation rate, µ, for which we used the same numerical value for all the genes included in our circuits, cell division rate (1/30 min) plus substance degradation rate (1/4.4 min), as suggested by the Team Beijing iGEM 2009.</p>
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<p>For the protein concentration differential equations we took into consideration the maximum translation rate, α<sub>T </sub>, (also calculated as suggested also by the Team Beijing iGEM), the degradation rate, µ<sub>T</sub> , and the rate of postranslational modifications, specifically, phosphorylations of some transcription factors.</p>
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<p>Because all the genes in our circuits have been modified with a LVA tag, unless it is otherwise stated, all the protein degradation rates were considered to be equal to the cell division rate (1/30 min) plus the substance degradation rate (1/40 min, as suggested by Team Leuven iGEM 2008, <a href="https://2008.igem.org/Team:KULeuven/Model/Inverter">https://2008.igem.org/Team:KULeuven/Model/Inverter</a>)</p>
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<p>The general form for the differential equation of protein concentration change the following:</p>
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          <a name="References"></a>References
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<ol>
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  <li>Mendes, P, et al., (2003), Artificial gene networks for objective comparison of analysis algorithms, BIOINFORMATICS, Vol. 19 Suppl. 2, pages ii122–ii129</li>
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  <li>Team Beijing iGEM 2009 (<a href="https://2008.igem.org/Team:KULeuven/Model/Inverter">https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters</a>).</li>
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  <li>Team Leuven iGEM 2008,<a href="https://2008.igem.org/Team:KULeuven/Model/Inverter">https://2008.igem.org/Team:KULeuven/Model/Inverter</a>)</li>
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    <div class="lateral-button"><a href="https://2011.igem.org/Team:UANL_Mty-Mexico/Modelling/Overview#Overview">Overview</a></div>
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Revision as of 17:54, 25 July 2012

Team:UANL Mty-Mexico/Templates/Banner-mathmodel Team:UANL Mty-Mexico/Templates/Menu-template Team: UANL_Mty-Mexico

Modelling
Overview

We decided to follow a deterministic approach for the mathematical representation of our genetic circuits. The simulations were made using MATLAB’s Simulink, according to the following general expressions for the genetic inhibition and activation elements present in the ODE systems. These expressions were adapted from Mendes, P, et al., (2003).

The effect of an inhibitor was modeled as follows:

where α is the maximum transcription rate of a gene, K is the dissociation constant of the inhibitor, I represents its concentration and n is its Hill coefficient.

In the other hand, the effect of an activator was expressed as follows:

where α is also the maximum transcription rate of a gene, K is the dissociation constant of the activator, A represents its concentration and n is its Hill coefficient.

The maximum transcription rate is used instead of the basal transcription rate under the assumption that inducible promoters should not present significant basal activity and that repressible promoters are active at this maximum rate in the absence of an inhibitor. The maximum transcription rate was calculated according to the Team Beijing iGEM 2009 (https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters).

The general form of the differential equations used to model the change of the mRNA concentration of a gene is as follows:

This general form represents an hypothetical gene, whose transcription is affected by an inhibitor I and an activator A. Different Hill Coefficients (n1 and n2) are used for the inhibitor and activator. The equation includes also the degradation rate, µ, for which we used the same numerical value for all the genes included in our circuits, cell division rate (1/30 min) plus substance degradation rate (1/4.4 min), as suggested by the Team Beijing iGEM 2009.

For the protein concentration differential equations we took into consideration the maximum translation rate, αT , (also calculated as suggested also by the Team Beijing iGEM), the degradation rate, µT , and the rate of postranslational modifications, specifically, phosphorylations of some transcription factors.

Because all the genes in our circuits have been modified with a LVA tag, unless it is otherwise stated, all the protein degradation rates were considered to be equal to the cell division rate (1/30 min) plus the substance degradation rate (1/40 min, as suggested by Team Leuven iGEM 2008, https://2008.igem.org/Team:KULeuven/Model/Inverter)

The general form for the differential equation of protein concentration change the following:

References
  1. Mendes, P, et al., (2003), Artificial gene networks for objective comparison of analysis algorithms, BIOINFORMATICS, Vol. 19 Suppl. 2, pages ii122–ii129
  2. Team Beijing iGEM 2009 (https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters).
  3. Team Leuven iGEM 2008,https://2008.igem.org/Team:KULeuven/Model/Inverter)

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