Difference between revisions of "Team:Nanjing-China/InterLab"

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<div id="HOME">
 
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    <div class="sub">
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<div class="sub">
       <ul><li><a href="https://2018.igem.org/Team:Nanjing-China/InterLab">InterLab</a></ul></li></div>
+
       <ul><li><a href="https://2018.igem.org/Team:Nanjing-China/Model">Model</a></li></ul></div>
       <ul>
+
       <ul><li><a href="#intro">Introduction</a></li>
    <li><a href="#summary">Summary</a></li>
+
      <li><a href="#method">Method</a></li>
    <li><a href="#problems">Problems</a></li>
+
      <li><a href="#document">Document</a></li></ul>
            <li><a href="#details">Details</a></li></ul>
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    <div class="word" id="intro">
      <div style="position:absolute; top:-90px; z-index:3; left:-10px;">
+
    <p>This year our team created a mathematical  model to optimize the arrangement of the nif gene cluster. This model helped we  optimized our design and provided some new perspectives of our  nitrogen-fixation system in transcriptional level.<br />
     <img src="https://static.igem.org/mediawiki/2018/3/35/T--Nanjing-China--PROJECT-i.png" width="45%" /></div>
+
We developed this model with two goals in  mind:<br />
 +
1.We want to achieve the 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.<br />
 +
2.We want our system as simple as possible, that means  minimizing the number of promoters and copy number of each nif gene.<br />
 +
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 transcriptional level.<br />
 +
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-1">
 +
     <table border="1" cellspacing="0" cellpadding="0">
 +
      <tr>
 +
        <td width="191" valign="top">
 +
          <p>&nbsp;</p>
 +
          <p>gene</p></td>
 +
        <td width="181" valign="top"><p>Average value of Cq</p></td>
 +
        <td width="181" valign="top"><p>Relative expression level</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>16S DNA</p></td>
 +
        <td width="181" valign="top"><p>6.33</p></td>
 +
        <td width="181" valign="top"><p>&nbsp;</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifB</p></td>
 +
        <td width="181" valign="top"><p>19.97</p></td>
 +
        <td width="181" valign="top"><p>7.80E-05</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifH</p></td>
 +
        <td width="181" valign="top"><p>17.37</p></td>
 +
        <td width="181" valign="top"><p>4.74E-04</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifD</p></td>
 +
        <td width="181" valign="top"><p>18.34</p></td>
 +
        <td width="181" valign="top"><p>2.42E-04</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifK</p></td>
 +
        <td width="181" valign="top"><p>20.77</p></td>
 +
        <td width="181" valign="top"><p>4.48E-05</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifE</p></td>
 +
        <td width="181" valign="top"><p>22.20</p></td>
 +
        <td width="181" valign="top"><p>1.66E-05</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifN</p></td>
 +
        <td width="181" valign="top"><p>22.24</p></td>
 +
        <td width="181" valign="top"><p>1.62E-05</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifX</p></td>
 +
        <td width="181" valign="top"><p>22.92</p></td>
 +
        <td width="181" valign="top"><p>1.01E-05</p></td>
 +
      </tr>
 +
      <tr>
 +
        <td width="191" valign="top"><p>nifV</p></td>
 +
        <td width="181" valign="top"><p>21.25</p></td>
 +
        <td width="181" valign="top"><p>3.22E-05</p></td>
 +
      </tr>
 +
    </table>
 +
    <p><font size="-1">Table1  The result of qPCR </font></p>
 
     </div>
 
     </div>
    <div class="word" id="summary">
 
    <h2>Summary</h2>
 
      <p>Our team  Nanjing-China used <em>E. coli</em> K-12 DH5-alpha to conduct the InterLab Study. The instrument used during our measurements is Tecan Infinite M1000  Pro plate reader which could read both fluorescence and absorbance from the  top of the plate. It has variable temperature settings and pathlength correction, which can  be disabled. By using this instrument, we have  accomplished assignments according to the PLATE READER AND CFU PROTOCOL, and  our data has also been accepted.</p>
 
      </div>
 
      <div class="word" id="problems">
 
    <h2>Problems: </h2>
 
      <p>Although we successfully  completed the InterLab study ultimately, during the process we also faced some  problems which quite puzzled us. <br />
 
      At first, we didn&rsquo;t  understand the purpose of InterLab and were quite confused how to start this  project. But fortunately we solved these problems with the assistance from  Vinoo Selvarajah, the Director of the Registry and iGEM HQ Representative for  the 2018 competition and began this project.<br />
 
      Yet, due to the  ignorance of instrumental usage, we were upset by the problem of properly using  the plate reader. And our data is not correct because  of the improper manipulation. With the help of our secondary PI Peiqing Sun and  our advisor Kunlun Li, we finally obtained the correct data and the data were  accepted successfully.<br />
 
      During the process  of accomplishing the InterLab project, we experienced success and failure, we  have also learned plenty of things, such as the manipulation of some  instruments, scientific methods and so on.</p>
 
 
     </div>
 
     </div>
     <div class="word" id="details">
+
     <div class="word" id="method">
      <h2>Details</h2>
+
    <h3> Method:</h3>
        <h3><u>Calibration Protocols</u></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 introduced some parameters shown in table2. </p>
        <p>We used black plates with transparent bottom for the calibration measurements, which had flat-bottomed wells.</p>
+
    <div class="word-1">
  <h3><u>Calibration 1: OD600 Reference point - LUDOX  Protocol</u></h3>
+
    <table border="1" cellspacing="0" cellpadding="0">
      <div class="word-note" align="center">
+
      <tr>
        <table border="1" cellspacing="0" cellpadding="0" width="0">
+
        <td width="200" valign="top">
          <tr>
+
           <p>Parameters/data</p> </td>
            <td width="109" valign="bottom"><p>&nbsp;</p></td>
+
        <td width="200" valign="top"><p>Meanings</p></td>
            <td width="90" valign="bottom"><p align="center">LUDOX CL-X </p></td>
+
      </tr>
            <td width="90" valign="bottom"><p align="center">H2O </p></td>
+
      <tr>
           </tr>
+
        <td width="277" valign="top"><p>weak[ ]</p></td>
          <tr>
+
        <td width="277" valign="top"><p>the expression level of each nif gene    under the weak promoter</p></td>
            <td width="109" valign="bottom"><p align="left">Replicate 1 </p></td>
+
      </tr>
            <td width="90"><p align="center">0.0506 </p></td>
+
      <tr>
            <td width="90"><p align="center">0.0372 </p></td>
+
        <td width="277" valign="top"><p>strong[ ]</p></td>
          </tr>
+
        <td width="277" valign="top"><p>the expression level of each nif gene    under the strong promoter</p></td>
          <tr>
+
      </tr>
            <td width="109" valign="bottom"><p align="left">Replicate 2 </p></td>
+
      <tr>
            <td width="90"><p align="center">0.050799999 </p></td>
+
        <td width="277" valign="top"><p>expected[ ]</p></td>
            <td width="90"><p align="center">0.0353 </p></td>
+
        <td width="277" valign="top"><p>the ideal stoichiometric proportion</p></td>
          </tr>
+
      </tr>
          <tr>
+
      <tr>
            <td width="109" valign="bottom"><p align="left">Replicate 3 </p></td>
+
        <td width="277" valign="top"><p>d</p></td>
            <td width="90"><p align="center">0.0517 </p></td>
+
        <td width="277" valign="top"><p>deviation between the expected expression    level and the actual expression level</p></td>
            <td width="90"><p align="center">0.033100002 </p></td>
+
      </tr>
          </tr>
+
    </table>
          <tr>
+
    <p align="center"><font size="-1">Table  2</font></p>
            <td width="109" valign="bottom"><p align="left">Replicate 4 </p></td>
+
    </div>
            <td width="90"><p align="center">0.050500002 </p></td>
+
    <p> Then we did some necessary preprocessing.  Firstly, we presumed the smallest element in each array was 1 and normalized  all the other data accordingly. In addition, to ensure there is at least one solution, we adjusted expected[] to make each element greater than or equal to  the smallest expression level of the corresponding gene.<br />
            <td width="90"><p align="center">0.034600001 </p></td>
+
       After that, we began the organization. In order to minimize the total number of genes, we arranged the strong promoter  group first, and considered the weak group later. For each gene, we constantly  added one copy of it to the strong promoter group, calculated the current deviation  after each addition and compared the current deviation with the last one. If  the deviation was decreasing ,we added one more copy and repeated the operation  until the last deviation was smaller than the current one. In that way, we were  able to determine the number of each gene with which the deviations were the  smallest and completed the arrangement of the strong group. Similarly, we  arranged the weak group and finally received the result.</p>
          </tr>
+
       <div class="word-1" align="center">
          <tr>
+
       <img src="https://static.igem.org/mediawiki/2018/8/8a/T--Nanjing-China--model-1.png" width="100%"/>
            <td width="109" valign="bottom"><p align="left">Arith. Mean </p></td>
+
    <p><font size="-1">Fig 1. A flow diagram describing the idea of our modeling process</font><br />
            <td width="90" valign="bottom"><p align="center">0.051 </p></td>
+
            <td width="90" valign="bottom"><p align="center">0.035 </p></td>
+
          </tr>
+
          <tr>
+
            <td width="109" valign="bottom"><p align="left">Corrected Abs600 </p></td>
+
            <td width="90" valign="bottom"><p align="center">0.016 </p></td>
+
            <td width="90"><p>&nbsp;</p></td>
+
          </tr>
+
          <tr>
+
            <td width="109" valign="bottom"><p align="left">Reference OD600 </p></td>
+
            <td width="90" valign="bottom"><p align="center">0.074 </p></td>
+
            <td width="90"><p>&nbsp;</p></td>
+
          </tr>
+
          <tr>
+
            <td width="109" valign="bottom"><p align="left">OD600/Abs600 </p></td>
+
            <td width="90" valign="bottom"><p align="center">4.669 </p></td>
+
            <td width="90"></td>
+
          </tr>
+
        </table>
+
      <p><font size="-1">Figure 1. OD600 Reference point</font></p>
+
            </div>
+
        <p>We used LUDOX CL-X as a single point reference to obtain a ratiometric conversion factor to transform our absorbance data into a standard OD600&nbsp;measurement.&nbsp;</p>
+
       <h3><u>Calibration 2: Particle Standard Curve - Microsphere Protocol</u></h3>
+
       <div class="word-note">
+
       <img src="https://static.igem.org/mediawiki/2018/d/dd/T--Nanjing-China--InterLab-1.jpg" width="70%" />
+
      <p><font size="-1">Figure 2. Particle Standard Curve</font></p>
+
 
       </div>
 
       </div>
       <div class="word-note">
+
      <p>According to this flow diagram, we programmed with Python and got the following results:</p>
       <img src="https://static.igem.org/mediawiki/2018/c/c0/T--Nanjing-China--InterLab-2.jpg" width="70%" />
+
       <div class="word-1" align="center">
      <p><font size="-1">Figure 3. Particle Standard Curve (log scale)</font></p>
+
       <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 nif genes according to our calculation</font><br />
 
       </div>
 
       </div>
       <p align="left">We prepared a dilution series of monodisperse silica  microspheres and measure the Abs600 in our plate reader. The size  and optical characteristics of these microspheres were similar to cells, and  there was a known amount of particles per volume. This measurement would allow  us to construct a standard curve of particle concentration which could be used  to convert Abs600 measurements to an estimated number of cells.</p>
+
       <p>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 most close to the ideal proportion among all the solutions.<br/>
        <h3><u>Calibration 3: Fluorescence standard curve - Fluorescein Protocol</u></h3>
+
       This model provided a potential strategy for the improvement of the activity of the nitrogenase expressed in our engineered E.coli strain.</p>
              <div class="word-note">
+
      <img src="https://static.igem.org/mediawiki/2018/4/4e/T--Nanjing-China--InterLab-3.jpg" width="70%" />
+
      <p><font size="-1">Figure 4. Fluorescein Standard Curve</font></p>
+
      </div>
+
      <div class="word-note">
+
      <img src="https://static.igem.org/mediawiki/2018/d/d9/T--Nanjing-China--InterLab-4.jpg" width="70%" />
+
      <p><font size="-1">Figure 5. Fluorescein Standard Curve (log scale)</font></p>
+
       </div>
+
      <p align="left">We prepared a dilution series of fluorescein in four  replicates and measure the fluorescence in a 96 well plate in our plate reader.  By measuring these in our plate reader, we generated a standard curve of  fluorescence for fluorescein concentration. We would be able to use this to  convert our cell based readings to an equivalent fluorescein concentration.</p>
+
<h3><u>Cell measurement protocol</u></h3>
+
<p>After completing all three of the calibration  measurements, we started performing the cell measurements. We use <em>E. coli</em> K-12 DH5-alpha strain, the same plates and volumes that we used in our  calibration protocol, as well as the same settings (e.g., filters or excitation  and emission wavelengths) that we used in our calibration measurements for the  sake of consistence.</p>
+
 
     </div>
 
     </div>
 +
    <div class="word" id="document">
 +
    Here is the codes 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 downlaod:<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>
 
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Revision as of 10:32, 15 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 optimized our design and provided some new perspectives of our nitrogen-fixation system in transcriptional level.
We developed this model with two goals in mind:
1.We want to achieve the 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 the number of promoters and copy number of 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 transcriptional 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 introduced some parameters shown in table2.

Parameters/data

Meanings

weak[ ]

the expression level of each nif gene under the weak promoter

strong[ ]

the expression level of each nif gene under the strong promoter

expected[ ]

the ideal stoichiometric proportion

d

deviation between the expected expression level and the actual expression level

Table 2

Then we did some necessary preprocessing. Firstly, we presumed the smallest element in each array was 1 and normalized all the other data accordingly. In addition, to ensure there is at least one solution, we adjusted expected[] to make each element greater than or equal to the smallest expression level of the corresponding gene.
After that, we began the organization. In order to minimize the total number of genes, we arranged the strong promoter group first, and considered the weak group later. For each gene, we constantly added one copy of it to the strong promoter group, calculated the current deviation after each addition and compared the current deviation with the last one. If the deviation was decreasing ,we added one more copy and repeated the operation until the last deviation was smaller than the current one. In that way, we were able to determine the number of each gene with which the deviations were the smallest and completed the arrangement of the strong group. Similarly, we arranged the weak group and finally received the result.

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 most close to the ideal proportion among all the solutions.
This model provided a potential strategy for the improvement of the activity of the nitrogenase expressed in our engineered E.coli strain.

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