Team:BGU Israel/ModelingOverView.html

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<span class="title">Modeling Overview</span>
<span class="title">Modeling Overview</span>
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                   <p>Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Vestibulum tortor quam, feugiat vitae, ultricies eget, tempor sit amet, ante. Donec eu libero sit amet quam egestas semper. Aenean ultricies mi vitae est. Mauris placerat eleifend leo.<span class="pullquote-right">THIS IS A PULL QUOTE RIGHT, BGU_Israel AMET</span> Quisque sit amet est et sapien ullamcorper pharetra.  Ut felis. Praesent dapibus, neque id cursus faucibus, Vestibulum erat wisi, condimentum sed, commodo vitae, ornare sit amet, wisi. Aenean fermentum, elit eget tincidunt condimentum, eros ipsum rutrum orci, sagittis tempus lacus enim ac dui. Donec non enim in turpis pulvinar facilisis. Ut felis. Praesent dapibus, neque id cursus faucibus, tortor neque egestas augue, eu vulputate magna eros eu erat. Aliquam erat volutpat. Nam dui mi, tincidunt quis, accumsan porttitor, facilisis luctus, metus</p>
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                   <p>When one comes to design a deterministic model for a synthetic biology mechanism, what would be the parameters for such a model? </br>
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                Well, the commonly used building blocks are promoters, plasmids, repressing/inducing proteins, sensor proteins, functional proteins (for example the toxin in the P.A.S.E 1 system), and much more, making the possibilities endless…</br>
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                The variation of quantitative behavior amongst each block is enormous- what is the copy number of the plasmid and does it remain constant? What are the transcription and translation rates for each gene? What are the degradation rates for the different components? What is the promoter&rsquo;s strength and what is its basal expression? And on top of all, even if one can obtain a good estimation of the quantitative data, will it remain the same when integrated in a novel system with new interconnections and dependencies?</br>
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              It has been shown that gene expression can vary in response to changing the reporting method alone by up to 44% in a simple expression circuit [1].</br></br>
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              Due to all of this uncertainty, it would be exaggerated to claim that a theoretical deterministic model, not backed up with specific experimental data, and comprised of so many unknowns will give a reliable prediction, this is why we decided to go for a stochastic Birth-death model predicting how the bacteria population will behave influenced by the two most crucial components we identified for our system:</br>
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              <ol class="bulletlist>
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              <li class="bulletlist>The leakage of the protein which is &rdquo;keeping the cell alive&rdquo;.</li>
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              <li class="bulletlist>The strength this protein has on the survival probability of the organism.</li>
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              </ol>
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              Such a model will be adequate to describe a biological system which is stochastic by nature, and its parameters could later be fitted. </br></br>
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              </hr>
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              <b>References</b></br>
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              <p>
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              [1] Lorenzo Pasotti, Nicolò Politi, Susanna Zucca, Maria Gabriella Cusella De Angelis, Paolo Magni
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PLoS One. 2012; 7(7): e39407. Published online 2012 July 20. <a href="https://static.igem.org/mediawiki/2013/e/ec/BGU_pone.0039407%28for_1%29.pdf" target="_blank">View Source</a>.
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Revision as of 14:44, 28 September 2013

BGU_Israel

Modeling Overview

When one comes to design a deterministic model for a synthetic biology mechanism, what would be the parameters for such a model?
Well, the commonly used building blocks are promoters, plasmids, repressing/inducing proteins, sensor proteins, functional proteins (for example the toxin in the P.A.S.E 1 system), and much more, making the possibilities endless…
The variation of quantitative behavior amongst each block is enormous- what is the copy number of the plasmid and does it remain constant? What are the transcription and translation rates for each gene? What are the degradation rates for the different components? What is the promoter’s strength and what is its basal expression? And on top of all, even if one can obtain a good estimation of the quantitative data, will it remain the same when integrated in a novel system with new interconnections and dependencies?
It has been shown that gene expression can vary in response to changing the reporting method alone by up to 44% in a simple expression circuit [1].

Due to all of this uncertainty, it would be exaggerated to claim that a theoretical deterministic model, not backed up with specific experimental data, and comprised of so many unknowns will give a reliable prediction, this is why we decided to go for a stochastic Birth-death model predicting how the bacteria population will behave influenced by the two most crucial components we identified for our system:

    The leakage of the protein which is ”keeping the cell alive”.
  1. View Source.

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