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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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    <div class="word"  id="over">
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      <h2>Overview</h2>
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      <p> Biosafety is the precaution of large-scale loss of biological integrity in terms of ecology and human’s health.<sup>[1]</sup>These precaution mechanisms include conducting regular reviews of the biosafety in laboratory settings,following strict guidelines,employing an ongoing risk management assessment and so on. Downplaying or failing to fulfill such protocols can lead to augmented risk of exposure to biohazards or pathogens.</p>
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    </div>
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    <div class="word"  id="R&P">
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      <h2>Risk assessment& Precautions:</h2>
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      <p>Our project is to develop a sound whole-cell  photocatalytic nitrogen fixation system, using engineered <em>E.coli</em> BL21 or JM109.<br />
+
        We believe that risks are primarily embodied in three  aspects and we have figured out appropriate solutions to all of them. Firstly,  some chemical and molecular biology reagents we use in experiments might be  harmful and noxious. In order to reduce the adverse effect of these reagents on  the environment and our health, we put the hazardous reagents in proper  storage, and carry out special procedures when dealing with them. For example,  cadmium ion, a toxic substance, is involved for CdS precipitation. We rigorously  recycle the sewage containing Cd ion every time handling it. <br />
+
        Another safety concern comes from the inflammable and  explosive acetylene we use to test the activity of nitrogenase. In order to  avoid gas leakage, the gas cylinders which contains acetylene are operated correctly  and inspected regularly by special security staffs.<br />
+
        Thirdly, although both <em>E.coli</em> BL21 and JM109 are in Risk Group 1 and can cause no  disease to healthy adults, the genetically modified(GM) organisms can  potentially pose threats to the welfare of people and the environment if  released to real world. So we have very strict rules to prevent this from  happening. The waste produced in the lab are periodically collected, sterilized  and categorized by our team members and then recycled by professional chemical waste  recycling companies. These standard procedures guarantee that the GM organisms  would never escape from our labs.<br />
+
      We have inspectors to ensure our operation  correctness and all team  members have received systematical biosafety training from relevant courses in  advance. We all possess the skills in waste disposal, accident prevention,  emergency measures(such as  how to tackle fire, electric leakage and negligent wounds) and so on. During our experiments, we  stringently observed the biosafety guidelines issued both by our university and  WHO. </p>
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<div class="word">
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    <div class="contain" >
     <p><em>Biosafety and the environment:An introduction to the Cartagena Protocol on Biosafety. GE.03-01836/E. United Nations Environment Programme. p. 8.</em></p>
+
    <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>
+
    </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>
 +
      </ol>
 +
    </div>
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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.