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<h1> Modeling</h1>
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
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      <ul><li><a href="https://2018.igem.org/Team:Nanjing-China/Model">Modeling</a></li></ul></div>
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      <ul><li><a href="#intro">Introduction</a></li>
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      <li><a href="#method">Method</a></li>
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      <li><a href="#r">Refinement:</a></li>
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      <li><a href="#document">Document</a></li>
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      <li><a href="#reference">Reference</a></li>
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      </ul>
 
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<h3> Gold Medal Criterion #3</h3>
 
<p>
 
Convince the judges that your project's design and/or implementation is based on insight you have gained from modeling. This could be either a new model you develop or the implementation of a model from a previous team. You must thoroughly document your model's contribution to your project on your team's wiki, including assumptions, relevant data, model results, and a clear explanation of your model that anyone can understand.
 
<br><br>
 
The model should impact your project design in a meaningful way. Modeling may include, but is not limited to, deterministic, exploratory, molecular dynamic, and stochastic models. Teams may also explore the physical modeling of a single component within a system or utilize mathematical modeling for predicting function of a more complex device.
 
</p>
 
 
<p>
 
Please see the <a href="https://2018.igem.org/Judging/Medals"> 2018
 
Medals Page</a> for more information.
 
</p>
 
 
</div>
 
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<h3>Best Model Special Prize</h3>
 
 
<p>
 
To compete for the <a href="https://2018.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2018.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.
 
<br><br>
 
You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
 
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<div id="for_judge" align="center"><div class="i"><ul><a href="https://2018.igem.org/Team:Nanjing-China/For_Judges"><strong>For_judges</strong></a></ul></div></div>
 
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        <ul>
<h3> Inspiration </h3>
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  <li><a href="https://2018.igem.org/Team:Nanjing-China">N<font size="-1"><sub>2</sub></font> CHASER</a>
<p>
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    <ul>
Here are a few examples from previous teams:
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        <li><a href="https://2018.igem.org/Team:Nanjing-China/Team">Team</a></li>
</p>
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            <li><a href="https://2018.igem.org/Team:Nanjing-China/Members">Members</a></li>
<ul>
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                <li><a href="https://2018.igem.org/Team:Nanjing-China/Attributions">Attributions</a></li>
<li><a href="https://2016.igem.org/Team:Manchester/Model">2016 Manchester</a></li>
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            <li><a href="https://2018.igem.org/Team:Nanjing-China/For_Judges">For_Judges</a></li>
<li><a href="https://2016.igem.org/Team:TU_Delft/Model">2016 TU Delft</li>
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</ul>
<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">2014 ETH Zurich</a></li>
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    </li>
<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">2014 Waterloo</a></li>
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        <li><a href="https://2018.igem.org/Team:Nanjing-China/Background">PROJECT</a>
</ul>
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    <ul>
 +
            <li><a href="https://2018.igem.org/Team:Nanjing-China/Background">Background</a></li>
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        <li><a href="https://2018.igem.org/Team:Nanjing-China/Design">Design</a></li>
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                <li><a href="https://2018.igem.org/Team:Nanjing-China/Results">Results</a></li>
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                <li><a href="https://2018.igem.org/Team:Nanjing-China/Demonstrate"><font size="-0.1">Demonstrate</font></a></li>
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                <li><a href="https://2018.igem.org/Team:Nanjing-China/Hardware">Hardware</a></li> 
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                <li><a href="https://2018.igem.org/Team:Nanjing-China/InterLab">InterLab</a></li>
 +
</ul>
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        </li>
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    <li><a href="https://2018.igem.org/Team:Nanjing-China/Parts">PARTS</a>
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        <ul>
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        <li><a href="https://2018.igem.org/Team:Nanjing-China/Basic_Part">Basic_Part</a></li>
 +
        <li><a href="https://2018.igem.org/Team:Nanjing-China/Composite_Part">Composite</a></li>
 +
            <li><a href="https://2018.igem.org/Team:Nanjing-China/Improve">Improve</a></li>
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</ul>
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            </li>
 +
    <li><a href="https://2018.igem.org/Team:Nanjing-China/Model">MODELING</a></a>
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      </li>
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    <li><a href="https://2018.igem.org/Team:Nanjing-China/Human_Practices">PRACTICES</a>
 +
    <ul>
 +
        <li><a href="https://2018.igem.org/Team:Nanjing-China/Human_Practices"><font size="-1">Human_Practices</font></a></li>
 +
                <li><a href="https://2018.igem.org/Team:Nanjing-China/Safety">Safety</a></li>
 +
                <li><a href="https://2018.igem.org/Team:Nanjing-China/Collaborations"><font size="-0.1">Collaboration</font></a></li>
 +
</ul>
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    </li>
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    <li><a href="https://2018.igem.org/Team:Nanjing-China/Notebook">NOTEBOOK</a></li>
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      </ul>
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  </div>
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    <div class="header"><img src="https://static.igem.org/mediawiki/2018/2/20/T--Nanjing-China--title-MODEL.png" width="100%" width="100%" onload="MM_effectAppearFade(this, 1000, 0, 100, false);MM_effectBlind('HOME', 1000, '0%', '100%', true)"  >
 
</div>
 
</div>
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    <div class="contain" >
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    <div class="word" id="intro">
 +
    <p>This year our team created a mathematical model to optimize the arrangement of the <em>nif</em> gene  cluster. This model helped we refined our design and provided some new  perspectives of our nitrogen-fixation system attranscriptional level.</p>
 +
    <p>We developed this model  with two goals in mind:<br />
 +
      1. We want to  achieve the putative best stoichiometric proportion of each nif gene, which is  <em>nifB: nifH: nifD: nifK: nifE: nifN: nifX: nifV</em>=1: 3: 4: 4: 1: 1: 1: 1.<br />
 +
      2. We want our system as simple as possible, that means minimizing numbers of promoters and each <em>nif</em> gene.</p>
 +
    <p>We made the following assumptions:<br />
 +
      1. There are two kinds of promoters, both of which can successfully launch the expression of every nitrogen fixation gene involved in our system. <br />
 +
      2. One promoter is stronger(called H) while the other is relatively weak(called L). Under promoter H, each gene&rsquo;s transcription level is double that of under promoter L.<br />
 +
      3. The order of genes has little influence on their transcription level.</p>
 +
    <p>We conducted Real-time Quantitative PCR to detect the transcription level of nif gene cluster and the experimental data we received became an important reference for our modeling.</p>
 +
<div class="word-note">
 +
    <table border="1" cellspacing="0" cellpadding="0">
 +
      <tr>
 +
        <td valign="top">
 +
          <p>gene</p></td>
 +
        <td valign="top">Average value of Cq</td>
 +
        <td valign="top">Relative expression level</td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top">16S DNA</td>
 +
        <td width="191" valign="top">6.33</td>
 +
        <td width="191" valign="top">&nbsp;</td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifB</em></td>
 +
        <td width="191" valign="top">19.97</td>
 +
        <td width="191" valign="top">7.80E<sup>-05</sup></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifH</em></td>
 +
        <td width="191" valign="top">17.37</td>
 +
        <td width="191" valign="top">4.74E<sup>-04</sup></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifD</em></td>
 +
        <td width="191" valign="top">18.34</td>
 +
        <td width="191" valign="top">2.42E<sup>-04</sup></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifK</em></td>
 +
        <td width="191" valign="top">20.77</td>
 +
        <td width="191" valign="top">4.48E<sup>-05</sup></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifE</em></td>
 +
        <td width="191" valign="top">22.20</td>
 +
        <td width="191" valign="top">1.66E<sup>-05</sup></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifN<em></td>
 +
        <td width="191" valign="top">22.24</td>
 +
        <td width="191" valign="top">1.62E<sup>-05</sup></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifX</em></td>
 +
        <td width="191" valign="top">22.92</td>
 +
        <td width="191" valign="top">1.01E<sup>-05</sup></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><em>nifV</em></td>
 +
        <td width="191" valign="top">21.25</td>
 +
        <td width="191" valign="top">3.22E<sup>-05</sup></td>
 +
      </tr>
 +
    </table>
 +
    <p><font size="-1">Table1  The result of qPCR </font></p>
 +
    </div>
 +
    </div>
 +
    <div class="word" id="method">
 +
    <h3> Method:</h3>
 +
      <p>To start with, we put all genes into two groups. One group is under the strong promoter while the other is under the  weak one. We constructed two arrays, weak[i] and expected[i].</p>
 +
    <div class="word-note">
 +
      <table border="1" cellspacing="0" cellpadding="0" width="90%">
 +
        <tr>
 +
          <td width="40%" valign="top">Parameters(i=1,2,3,4,5,6,7,8)</td>
 +
          <td width="60%" valign="top">Meanings</td>
 +
        </tr>
 +
        <tr>
 +
          <td  valign="top">weak[i]</td>
 +
          <td valign="top">the relative expression level of each <Em>nif</Em> gene under the weak promoter</td>
 +
        </tr>
 +
        <tr>
 +
          <td  valign="top">weak[i]*</td>
 +
          <td valign="top">the relative expression level of each <Em>nif</Em> gene under the weak promoter after normalization</td>
 +
        </tr>
 +
        <tr>
 +
          <td valign="top">expected[i]</td>
 +
          <td valign="top">the ideal stoichiometric proportion</td>
 +
        </tr>
 +
        <tr>
 +
          <td valign="top">expected[i]*</td>
 +
          <td valign="top">the ideal stoichiometric proportion after normalization</td>
 +
        </tr>
 +
        <tr>
 +
          <td  valign="top">strong[i]</td>
 +
          <td  valign="top">the relative expression level of each <Em>nif</Em> gene under the strong promoter after normalization</td>
 +
        </tr>
 +
        <tr>
 +
          <td  valign="top">e<sub>i</sub></td>
 +
          <td valign="top">the ideal stoichiometric proportion of  the i<sup>th</sup> gene after all preprocessings </td>
 +
        </tr>
 +
        <tr>
 +
          <td valign="top">a<sub>i</sub></td>
 +
          <td valign="top">the relative expression level of the i<sup>th</sup> gene under the weak promoter after all preprocessings</td>
 +
        </tr>
 +
        <tr>
 +
          <td valign="top">m<sub>i</sub></td>
 +
          <td valign="top">the number of the i<Sup>th</Sup> gene under the strong promoter</td>
 +
        </tr>
 +
        <tr>
 +
          <td valign="top">n<sub>i</sub></td>
 +
          <td valign="top">the number of the i<Sup>th</Sup> gene under the weak promoter</td>
 +
        </tr>
 +
      </table>
 +
<p align="center"><font size="-1">Table 2 The table of parameters in our model</font></p>
 +
    </div>
 +
    <p>Then we did some  necessary preprocessings. Firstly, we found the smallest data in weak[i] and  called it &ldquo;min&rdquo;. We normalized all the other data accordingly by doing:</p>
 +
<div align="center" style="width:100%;"><img src="https://static.igem.org/mediawiki/2018/4/46/T--Nanjing-China--model-1-1.jpg" height="55px" /></div>
 +
<div align="center" style="width:100%;"><img src="https://static.igem.org/mediawiki/2018/e/e4/T--Nanjing-China--model-1-2.jpg" height="55px" /></div>
 +
<p>We constructed  strong[i]:</p>
 +
<div align="center"><em>strong[i]=2×weak[i]*</em>   </div>                                                <br />
 +
<p>Secondly, to guarantee the existence of a solution, we adjusted expected[i]* by examining whether it is no less than the corresponding weak[i]*, if not, we did:</p>
 +
<div align="center"><em>expected[i]*=weak[i]* </em>   </div>                                              <br />
 +
      <p>
 +
After that, we  began the organization. In order to minimize the total numbers of genes, we  arranged the strong promoter group first, and considered the weak group later.  Because each gene can be considered separately, here we only describe the  organization of the i<sup>th</sup> gene as an example.<br />
 +
For the i<sup>th</sup>  gene, we tried adding one copy of it under the strong promoter. If </p>
 +
<div align="center"><em>|e<sub>i</sub>-2×a<sub>i</sub>|&lt;e<sub>i</sub>,     </em></div>
 +
<p>we actually added  it. Until we have added (m<sub>i</sub>+1) i<sup>th</sup> genes, and got</p>
 +
<div align="center"><em>|e<sub>i</sub>-2(m<sub>i</sub>+1)×a<sub>i</sub>|&gt;=|e<sub>i</sub>-2mi<sub>i</sub>×a<sub>i</sub>| </em> </div>
 +
<p>Then we stopped  adding it and recorded that we have added m<sub>i</sub> i<sup>th</sup> genes  under the strong promoter.</p>
 +
<p>For the weak  promoter group, we applied a similar method. For the i<sup>th</sup> gene, we  tried adding one copy of it under the weak promoter. If</p>
 +
<div align="center"> <em>|e<sub>i</sub>-2×m<sub>i</sub>×a<sub>i</sub>-a<sub>i</sub>|&lt;|e<sub>i</sub>-2×m<sub>i</sub>×a<sub>i</sub>|, </em>    </div>
 +
<p>we actually added  it. Until we have added (n<sub>i</sub>+1) i<sup>th</sup> genes, and got </p>
 +
<div align="center"><em>|e<sub>i</sub>-2×m<sub>i</sub>×a<sub>i</sub>-(n<sub>i</sub>+1)×a<sub>i</sub>|&gt;=|e<sub>i</sub>-2×mi<sub>i</sub>×a<sub>i</sub>-n<sub>i</sub>×a<sub>i</sub>|</em>  </div>
 +
<p>Then we stopped  adding it and recorded that we have added n<sub>i</sub> i<sup>th</sup> genes  under the weak promoter.</p>
 +
<p>In that way, we  were able to determine numbers of the i<sup>th</sup> gene under the two  promoters with which the deviation was the smallest.</p>
 +
<div class="word-note" align="center">
 +
      <img src="https://static.igem.org/mediawiki/2018/8/8a/T--Nanjing-China--model-1.png"  width="100%"/>
 +
      <p><font size="-1">Fig 1. A flow diagram describing the idea of our modeling process</font></p>
 +
      </div>
 +
      <p>According to this flow diagram, we programmed with Python and got the following results:</p>
 +
      <div class="word-note" align="center">
 +
      <img src="https://static.igem.org/mediawiki/2018/e/ed/T--Nanjing-China--model-2.png"  width="100%"/>
 +
    <p><font size="-1">Fig 2. The best arrangement of <em>nif</em> genes according to our calculation</font></p>
 +
      </div>
 +
      <p>With this arrangement, the proportion of <em>nifB: nifH: nifD: nifK: nifE: nifN: nifX: nifV</em>= 15.44: 46.93: 71.88: 62.10: 16.44: 16.04: 16.0: 15.94, which is close enough to the ideal proportion among all the solutions.</p>
 +
    </div>
 +
    <div class="word" id="r">
 +
   
 +
      <h2>Refinement of  our model:</h2>
 +
        <p>We modified the  putative best expression level of <em>nifB:nifH:nifD:nifK:nifE:nifN:nifX:nifV</em> to 5:3:4:4:1:1:1:1.  We believed in this way, we could better simulate the expression of nitrogenase  in our engineered <em>E.coli</em> strains. We made this change because of three  reasons.</p>
 +
      <p>Firstly, <em>nifB</em> is  indispensable for nitrogenase assembly no matter in diazotrophs or engineered <em>E. coli</em> strains. Apart from the minimal nitrogen fixation gene cluster, the genomic DNA  of wide type <em>Paenibacillus  polymyxa </em>includes analogues of <em>nifM</em>, <em>nifU</em>, <em>nifS</em> and other genes which exist in other nitrogen-fixing microorganisms and  are essential for the correct folding of nitrogenase iron protein. However, the <em>E. coli </em>genome doesn&rsquo;t have such analogues. Nevertheless, it has been reported that the excessive expression of <em>nifB</em> can compensate for the absence  of <em>nifU</em> and <em>nifS</em>. That is, if <em>nifB</em> is overexpressed in <em>E. coli</em>, these auxiliaries are not necessary. Therefore, the expression level  of <em>nifB</em> should be the highest 5.</p>
 +
      <p>Secondly, compared with  nitrogen-fixing microorganisms, <em>E. coli</em> also lacks some genes that provide electron  transfer function, such as <em>nifF</em> and <em>nifJ</em>. So the intracellular reductive power of <em>E. coli</em> is insufficient to accomplish nitrogen fixation. Thus it is necessary to overexpress <em>nifH</em>(nitrogenase reductase) and the value  is set to 3 instead of 5 because our semiconductor, the CdS part, can provide additional electrons.</p>
 +
      <p>Thirdly, we set the expression  level of <em>nifD</em> and <em>nifK</em> to be 4 because molybdenum iron protein is an ɑ2β2 allotetramer and is the core of  nitrogenase.</p>
 +
      <p>Based on the new ideal stoichiometric proportion, we adjusted the code and received a more accurate result.</p>
 +
      <div class="word-note" align="center">
 +
      <img src="https://static.igem.org/mediawiki/2018/5/50/T--Nanjing-China--model-3.png"  width="100%"/>
 +
    <p><font size="-1">Fig 3 The best arrangement of nif genes version 2.0.</font></p>
 +
      </div>
 +
      <p>The achieved stoichiometric proportion of <em>nifB: nifH: nifD: nifK: nifE: nifN: nifX: nifV</em>=77.23: 46.93: 71.88: 62.10: 16.44: 16.04: 16.0: 15.94, which  is close enough to the ideal 5:3:4:4:1:1:1:1.</p>
 +
        <p>This model provided a potential strategy for the improvement of biological activity of nitrogenase expressed in our engineered <em>E. coli</em> strain and offered a great help to our further experiments.</p>
 +
    </div>
 +
    <div class="word" id="document">
 +
    Here is the code we taped and used.
 +
    <object width="100%" height="600px" data="https://static.igem.org/mediawiki/2018/7/7d/T--Nanjing-China--model-code.pdf" type="application/pdf"> 
 +
      <param name="src" value="https://static.igem.org/mediawiki/2018/7/7d/T--Nanjing-China--model-code.pdf"> 
 +
</object>
 +
    TXT download:<a href="https://static.igem.org/mediawiki/2018/f/fe/T--Nanjing-China--model.txt">https://static.igem.org/mediawiki/2018/f/fe/T--Nanjing-China--model.txt</a>
 +
    <div id="code" align="left">
 +
    <p >The number we typed in:</p>
 +
    <ol><li>findSequence([7.8,47.4,24.2,4.48,1.66,1.62,1.01,3.22],[1,3,4,4,1,1,1,1],['nifB','nifH','nifD','nifK','nifE','nifN','nifX','nifV'])</li>
 +
    <li>findSequence([7.8,47.4,24.2,4.48,1.66,1.62,1.01,3.22],[5,3,4,4,1,1,1,1],['nifB','nifH','nifD','nifK','nifE','nifN','nifX','nifV'])</li>
 +
    </ol>
 +
    </div>
 +
    </div>
 +
    <div class="word" id="reference" align="left">
 +
      <h2>References</h2>
 +
      <ol>
 +
        <li>Wang, X.,  et al., <em>Using  synthetic biology to distinguish and overcome regulatory and functional  barriers related to nitrogen fixation. </em>PLoS One,2013. <strong>8</strong>(7):p.e68677.</li>
 +
        <li>Yang, J.,  et al., <em>Modular  electron-transport chains from eukaryotic organelles function to support  nitrogenase activity.</em> Proc Natl Acad Sci U S A, 2017. <strong>114</strong>(12):p.E2460-E2465.</li>
 +
        <li>Yang, J.,  et al., <em>Polyprotein  strategy for stoichiometric assembly of nitrogen fixation components for  synthetic biology. </em>Proc Natl  Acad Sci U S A, 2018. <strong>115</strong>(36):p.E8509-E8517.</li>
 +
        <li>Yang, J.G.,  et al., <em>Reconstruction  and minimal gene requirements for the alternative iron-only nitrogenase in  Escherichia coli. </em>Proceedings  of the National Academy of Sciences of the United States of America, 2014. <strong>111</strong>(35):p.E3718-E3725.</li>
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      </ol>
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Latest revision as of 03:29, 18 October 2018

Nanjing-China2018

This year our team created a mathematical model to optimize the arrangement of the nif gene cluster. This model helped we refined our design and provided some new perspectives of our nitrogen-fixation system attranscriptional level.

We developed this model with two goals in mind:
1. We want to achieve the putative best stoichiometric proportion of each nif gene, which is nifB: nifH: nifD: nifK: nifE: nifN: nifX: nifV=1: 3: 4: 4: 1: 1: 1: 1.
2. We want our system as simple as possible, that means minimizing numbers of promoters and each nif gene.

We made the following assumptions:
1. There are two kinds of promoters, both of which can successfully launch the expression of every nitrogen fixation gene involved in our system.
2. One promoter is stronger(called H) while the other is relatively weak(called L). Under promoter H, each gene’s transcription level is double that of under promoter L.
3. The order of genes has little influence on their transcription level.

We conducted Real-time Quantitative PCR to detect the transcription level of nif gene cluster and the experimental data we received became an important reference for our modeling.

gene

Average value of Cq Relative expression level
16S DNA 6.33  
nifB 19.97 7.80E-05
nifH 17.37 4.74E-04
nifD 18.34 2.42E-04
nifK 20.77 4.48E-05
nifE 22.20 1.66E-05
nifN 22.24 1.62E-05
nifX 22.92 1.01E-05
nifV 21.25 3.22E-05

Table1 The result of qPCR

Method:

To start with, we put all genes into two groups. One group is under the strong promoter while the other is under the weak one. We constructed two arrays, weak[i] and expected[i].

Parameters(i=1,2,3,4,5,6,7,8) Meanings
weak[i] the relative expression level of each nif gene under the weak promoter
weak[i]* the relative expression level of each nif gene under the weak promoter after normalization
expected[i] the ideal stoichiometric proportion
expected[i]* the ideal stoichiometric proportion after normalization
strong[i] the relative expression level of each nif gene under the strong promoter after normalization
ei the ideal stoichiometric proportion of the ith gene after all preprocessings
ai the relative expression level of the ith gene under the weak promoter after all preprocessings
mi the number of the ith gene under the strong promoter
ni the number of the ith gene under the weak promoter

Table 2 The table of parameters in our model

Then we did some necessary preprocessings. Firstly, we found the smallest data in weak[i] and called it “min”. We normalized all the other data accordingly by doing:

We constructed strong[i]:

strong[i]=2×weak[i]*   
                                              

Secondly, to guarantee the existence of a solution, we adjusted expected[i]* by examining whether it is no less than the corresponding weak[i]*, if not, we did:

expected[i]*=weak[i]*    
                                             
     

After that, we began the organization. In order to minimize the total numbers of genes, we arranged the strong promoter group first, and considered the weak group later. Because each gene can be considered separately, here we only describe the organization of the ith gene as an example.
For the ith gene, we tried adding one copy of it under the strong promoter. If

|ei-2×ai|<ei,    

we actually added it. Until we have added (mi+1) ith genes, and got

|ei-2(mi+1)×ai|>=|ei-2mii×ai|  

Then we stopped adding it and recorded that we have added mi ith genes under the strong promoter.

For the weak promoter group, we applied a similar method. For the ith gene, we tried adding one copy of it under the weak promoter. If

|ei-2×mi×ai-ai|<|ei-2×mi×ai|,     

we actually added it. Until we have added (ni+1) ith genes, and got

|ei-2×mi×ai-(ni+1)×ai|>=|ei-2×mii×ai-ni×ai|  

Then we stopped adding it and recorded that we have added ni ith genes under the weak promoter.

In that way, we were able to determine numbers of the ith gene under the two promoters with which the deviation was the smallest.

Fig 1. A flow diagram describing the idea of our modeling process

According to this flow diagram, we programmed with Python and got the following results:

Fig 2. The best arrangement of nif genes according to our calculation

With this arrangement, the proportion of nifB: nifH: nifD: nifK: nifE: nifN: nifX: nifV= 15.44: 46.93: 71.88: 62.10: 16.44: 16.04: 16.0: 15.94, which is close enough to the ideal proportion among all the solutions.

Refinement of our model:

We modified the putative best expression level of nifB:nifH:nifD:nifK:nifE:nifN:nifX:nifV to 5:3:4:4:1:1:1:1. We believed in this way, we could better simulate the expression of nitrogenase in our engineered E.coli strains. We made this change because of three reasons.

Firstly, nifB is indispensable for nitrogenase assembly no matter in diazotrophs or engineered E. coli strains. Apart from the minimal nitrogen fixation gene cluster, the genomic DNA of wide type Paenibacillus polymyxa includes analogues of nifM, nifU, nifS and other genes which exist in other nitrogen-fixing microorganisms and are essential for the correct folding of nitrogenase iron protein. However, the E. coli genome doesn’t have such analogues. Nevertheless, it has been reported that the excessive expression of nifB can compensate for the absence of nifU and nifS. That is, if nifB is overexpressed in E. coli, these auxiliaries are not necessary. Therefore, the expression level of nifB should be the highest 5.

Secondly, compared with nitrogen-fixing microorganisms, E. coli also lacks some genes that provide electron transfer function, such as nifF and nifJ. So the intracellular reductive power of E. coli is insufficient to accomplish nitrogen fixation. Thus it is necessary to overexpress nifH(nitrogenase reductase) and the value is set to 3 instead of 5 because our semiconductor, the CdS part, can provide additional electrons.

Thirdly, we set the expression level of nifD and nifK to be 4 because molybdenum iron protein is an ɑ2β2 allotetramer and is the core of nitrogenase.

Based on the new ideal stoichiometric proportion, we adjusted the code and received a more accurate result.

Fig 3 The best arrangement of nif genes version 2.0.

The achieved stoichiometric proportion of nifB: nifH: nifD: nifK: nifE: nifN: nifX: nifV=77.23: 46.93: 71.88: 62.10: 16.44: 16.04: 16.0: 15.94, which is close enough to the ideal 5:3:4:4:1:1:1:1.

This model provided a potential strategy for the improvement of biological activity of nitrogenase expressed in our engineered E. coli strain and offered a great help to our further experiments.

Here is the code we taped and used. TXT download:https://static.igem.org/mediawiki/2018/f/fe/T--Nanjing-China--model.txt

The number we typed in:

  1. findSequence([7.8,47.4,24.2,4.48,1.66,1.62,1.01,3.22],[1,3,4,4,1,1,1,1],['nifB','nifH','nifD','nifK','nifE','nifN','nifX','nifV'])
  2. findSequence([7.8,47.4,24.2,4.48,1.66,1.62,1.01,3.22],[5,3,4,4,1,1,1,1],['nifB','nifH','nifD','nifK','nifE','nifN','nifX','nifV'])

References

  1. Wang, X., et al., Using synthetic biology to distinguish and overcome regulatory and functional barriers related to nitrogen fixation. PLoS One,2013. 8(7):p.e68677.
  2. Yang, J., et al., Modular electron-transport chains from eukaryotic organelles function to support nitrogenase activity. Proc Natl Acad Sci U S A, 2017. 114(12):p.E2460-E2465.
  3. Yang, J., et al., Polyprotein strategy for stoichiometric assembly of nitrogen fixation components for synthetic biology. Proc Natl Acad Sci U S A, 2018. 115(36):p.E8509-E8517.
  4. Yang, J.G., et al., Reconstruction and minimal gene requirements for the alternative iron-only nitrogenase in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America, 2014. 111(35):p.E3718-E3725.