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

 
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       <ul><li><a href="https://2018.igem.org/Team:Nanjing-China/InterLab">InterLab</a></ul></li></div>
       <ul><li><a href="#intro">Introduction</a></li>
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     <div class="contain">
    <div class="word" id="intro">
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    <div class="word">
    <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 />
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      <div style="position:absolute; top:-90px; z-index:3; left:-10px;">
We developed this model with two goals in  mind:<br />
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     <img src="https://static.igem.org/mediawiki/2018/3/35/T--Nanjing-China--PROJECT-i.png" width="45%" /></div>
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 />
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2.We want our system as simple as possible, that means  minimizing the number of promoters and copy number of each nif gene.<br />
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We made the following assumptions:<br />
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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 />
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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 />
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3.The order of genes has little influence  on their transcriptional level.<br />
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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>
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    <div class="word-1">
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     <table border="1" cellspacing="0" cellpadding="0">
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      <tr>
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        <td width="191" valign="top">
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          <p>&nbsp;</p>
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          <p>gene</p></td>
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        <td width="181" valign="top"><p>Average value of Cq</p></td>
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        <td width="181" valign="top"><p>Relative expression level</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>16S DNA</p></td>
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        <td width="181" valign="top"><p>6.33</p></td>
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        <td width="181" valign="top"><p>&nbsp;</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifB</p></td>
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        <td width="181" valign="top"><p>19.97</p></td>
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        <td width="181" valign="top"><p>7.80E-05</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifH</p></td>
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        <td width="181" valign="top"><p>17.37</p></td>
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        <td width="181" valign="top"><p>4.74E-04</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifD</p></td>
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        <td width="181" valign="top"><p>18.34</p></td>
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        <td width="181" valign="top"><p>2.42E-04</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifK</p></td>
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        <td width="181" valign="top"><p>20.77</p></td>
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        <td width="181" valign="top"><p>4.48E-05</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifE</p></td>
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        <td width="181" valign="top"><p>22.20</p></td>
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        <td width="181" valign="top"><p>1.66E-05</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifN</p></td>
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        <td width="181" valign="top"><p>22.24</p></td>
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        <td width="181" valign="top"><p>1.62E-05</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifX</p></td>
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        <td width="181" valign="top"><p>22.92</p></td>
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        <td width="181" valign="top"><p>1.01E-05</p></td>
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      </tr>
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      <tr>
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        <td width="191" valign="top"><p>nifV</p></td>
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        <td width="181" valign="top"><p>21.25</p></td>
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        <td width="181" valign="top"><p>3.22E-05</p></td>
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      </tr>
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    </table>
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    <p><font size="-1">Table1  The result of qPCR </font></p>
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     </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. </p>
 +
      <p>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.</p>
 +
      <p>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.</p>
 +
      <p>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="method">
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     <div class="word" id="details">
    <h3> Method:</h3>
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      <h2>Details</h2>
      <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>
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        <h3><u>Calibration Protocols</u></h3>
    <div class="word-1">
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        <p>We used black plates with transparent bottom for the calibration measurements, which had flat-bottomed wells.</p>
    <table border="1" cellspacing="0" cellpadding="0">
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  <h3><u>Calibration 1: OD600 Reference point - LUDOX  Protocol</u></h3>
      <tr>
+
      <div class="word-note" align="center">
        <td width="200" valign="top">
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        <table border="1" cellspacing="0" cellpadding="0" width="0">
           <p>Parameters/data</p> </td>
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          <tr>
        <td width="200" valign="top"><p>Meanings</p></td>
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            <td width="109" valign="bottom"><p>&nbsp;</p></td>
      </tr>
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            <td width="90" valign="bottom"><p align="center">LUDOX CL-X </p></td>
      <tr>
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            <td width="90" valign="bottom"><p align="center">H<sub>2</sub>O </p></td>
        <td width="277" valign="top"><p>weak[ ]</p></td>
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           </tr>
        <td width="277" valign="top"><p>the expression level of each nif gene    under the weak promoter</p></td>
+
          <tr>
      </tr>
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            <td width="109" valign="bottom"><p align="left">Replicate 1 </p></td>
      <tr>
+
            <td width="90"><p align="center">0.0506 </p></td>
        <td width="277" valign="top"><p>strong[ ]</p></td>
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            <td width="90"><p align="center">0.0372 </p></td>
        <td width="277" valign="top"><p>the expression level of each nif gene    under the strong promoter</p></td>
+
          </tr>
      </tr>
+
          <tr>
      <tr>
+
            <td width="109" valign="bottom"><p align="left">Replicate 2 </p></td>
        <td width="277" valign="top"><p>expected[ ]</p></td>
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            <td width="90"><p align="center">0.050799999 </p></td>
        <td width="277" valign="top"><p>the ideal stoichiometric proportion</p></td>
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            <td width="90"><p align="center">0.0353 </p></td>
      </tr>
+
          </tr>
      <tr>
+
          <tr>
        <td width="277" valign="top"><p>d</p></td>
+
            <td width="109" valign="bottom"><p align="left">Replicate 3 </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">0.0517 </p></td>
      </tr>
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            <td width="90"><p align="center">0.033100002 </p></td>
    </table>
+
          </tr>
    <p align="center"><font size="-1">Table  2</font></p>
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          <tr>
    </div>
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            <td width="109" valign="bottom"><p align="left">Replicate 4 </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">0.050500002 </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>
+
            <td width="90"><p align="center">0.034600001 </p></td>
       <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%"/>
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          <tr>
    <p><font size="-1">Fig 1. A flow diagram describing the idea of our modeling process</font><br />
+
            <td width="109" valign="bottom"><p align="left">Arith. Mean </p></td>
 +
            <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>
      <p>According to this flow diagram, we programmed with Python and got the following results:</p>
+
       <div class="word-note">
       <div class="word-1" align="center">
+
       <img src="https://static.igem.org/mediawiki/2018/c/c0/T--Nanjing-China--InterLab-2.jpg" width="70%" />
       <img src="https://static.igem.org/mediawiki/2018/e/ed/T--Nanjing-China--model-2.png" width="100%"/>
+
      <p><font size="-1">Figure 3. Particle Standard Curve (log scale)</font></p>
    <p><font size="-1">Fig 2. The best arrangement of nif genes according to our calculation</font><br />
+
 
       </div>
 
       </div>
       <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/>
+
       <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>
       This model provided a potential strategy for the improvement of the activity of the nitrogenase expressed in our engineered E.coli strain.</p>
+
        <h3><u>Calibration 3: Fluorescence standard curve - Fluorescein Protocol</u></h3>
 +
              <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.
 
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Latest revision as of 02:34, 18 October 2018

Nanjing-China2018

Summary

Our team Nanjing-China used E. coli 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.

Problems:

Although we successfully completed the InterLab study ultimately, during the process we also faced some problems which quite puzzled us.

At first, we didn’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.

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.

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.

Details

Calibration Protocols

We used black plates with transparent bottom for the calibration measurements, which had flat-bottomed wells.

Calibration 1: OD600 Reference point - LUDOX Protocol

 

LUDOX CL-X

H2O

Replicate 1

0.0506

0.0372

Replicate 2

0.050799999

0.0353

Replicate 3

0.0517

0.033100002

Replicate 4

0.050500002

0.034600001

Arith. Mean

0.051

0.035

Corrected Abs600

0.016

 

Reference OD600

0.074

 

OD600/Abs600

4.669

Figure 1. OD600 Reference point

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 measurement. 

Calibration 2: Particle Standard Curve - Microsphere Protocol

Figure 2. Particle Standard Curve

Figure 3. Particle Standard Curve (log scale)

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.

Calibration 3: Fluorescence standard curve - Fluorescein Protocol

Figure 4. Fluorescein Standard Curve

Figure 5. Fluorescein Standard Curve (log scale)

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.

Cell measurement protocol

After completing all three of the calibration measurements, we started performing the cell measurements. We use E. coli 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.