Difference between revisions of "Template:SYSU-Software/statics/html/Projects/Validation.html"

Line 114: Line 114:
  
 
     <div class="header2"> Wet-lab Validation</div>
 
     <div class="header2"> Wet-lab Validation</div>
 +
    <div class="content">
 +
        We verified the robustness of CO-RAD through wet lab. With our software, we solved the difficulty encountered
 +
        in routine molecular biology experiments. Specially, we use CO-RAD to help design the an experiments. Details
 +
        are listed belo
 +
    </div>
 +
    <ol>
 +
        <li>
 +
            We designed our protocol and circuit in CO-RAD. Our design is supposed to express GFP when IPTG exist.
 +
            <div class="oneLine">
 +
                <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/f/fb/T--SYSU-Software--wet-valid-1.png" style="margin: 20px;">
 +
                <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/3/3e/T--SYSU-Software--wet-valid-2.png" style="margin: 20px;">
 +
            </div>
 +
            <div class="oneLine">
  
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/f/fb/T--SYSU-Software--wet-valid-1.png">
+
                <div class="caption" style="margin: 20px;">Figure 1: Circuit design in CO-RAD. </div>
−
    <div class="caption">Figure 1: This is our circuit design in CO-RAD
+
                <div class="caption" style="margin: 20px;">Figure 2: Protocol in CO-RAD.</div>
 +
            </div>
 +
        </li>
 +
 
 +
        <li>
 +
            Problems occurred when we assemblied our circuit. We tried to extend 170bp from our template by 5 times of
 +
            PCR. The sequence includes GFP, terminator, RBS . We can see that we did not find our target band (900bp)
 +
            in our result.
 +
 
 +
            <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/1/10/T--SYSU-Software--wet-3.png">
 +
            <div class="caption">Figure 3: Our gel electrophoresis result. There is plenty of mismatching product and
 +
                no target bands in our result.
 +
        </li>
 +
        <li>
 +
            We need help, so we shared our design to other users on CO-RAD, and our adviser, Professor Shendong,
 +
            suggested to add spacer DNA in our circuit. Following his suggestion, we added spacer DNA in our circuit
 +
            and revised our protocol.
 +
 
 +
            <div class="oneLine">
 +
 
 +
                <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/a/a9/T--SYSU-Software--wet-4.png" style="margin:20px;">
 +
                <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/db/T--SYSU-Software--wet-5.png" style="margin:20px;">
 +
            </div>
 +
            <div class="caption">Figure 4: Circuit and protocol design from our adviser.
 +
            </div>
 +
        </li>
 +
 
 +
        <li>
 +
            We optimized our protocol after discussion.
 +
            <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/9/9c/T--SYSU-Software--wet-6.png">
 +
            <div class="caption">Figure 5: Historical status of our design. </div>
 +
        </li>
 +
 
 +
        <li>
 +
            We found that high template concentration and high annealing temperature are the keys of success in our
 +
            experiment. More matching sequence in primer is also essential.
 +
            <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/c/c3/T--SYSU-Software--wet-7.png">
 +
            <div class="caption">Figure 6: Our target bands and plenty of mismatching product.</div>
 +
        </li>
 +
 
 +
        <li>
 +
            Again, we received another suggestion from Professor Shendong. With his help, we redesigned our protocol
 +
            and extended our sequence successfully. DNA Sequencing result accorded with our prospect. Unfortunately,
 +
            due to the lack of time, we didn't construct our vector and transform it into E.coli to express GFP.
 +
 
 +
            <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/a/a5/T--SYSU-Software--wet-8.png">
 +
            <div class="caption">Figure 7: Our final result. Our target bands were clearer than before.</div>
 +
        </li>
 +
    </ol>
 +
 
 +
    <div class="content">
 +
        Due to the lack of time, we chose to cooperate with XJTU-China, using their experimental data to validate our
 +
        simulation module and evolutionary algorithm in CO-RAD.
 +
        <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/d3/T--SYSU-Software--wet-9.png">
 
     </div>
 
     </div>
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/3/3e/T--SYSU-Software--wet-valid-2.png">
+
 
−
     <div class="caption">Figure 2: This is our protocol design in CO-RAD</div>
+
     <div class="content">
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/1/10/T--SYSU-Software--wet-3.png">
+
        In this genetic circuit above, PsiR is a predicted LacI family transcription factor with high affinity for
−
    <div class="caption">Figure 3: This is our gel electrophoresis result. There is plenty of mismatching product and
+
        D-Psicose. This implies that PsiR is potentially capable of binding a consensus sequence in the promoter region
−
         no target bands in our result
+
        and preventing transcription of the regulated promoters in the absence of D-Psicose. With addition of
 +
         D-psicose, EGFP can be successfully produced.
 
     </div>
 
     </div>
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/a/a9/T--SYSU-Software--wet-4.png">
+
 
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/db/T--SYSU-Software--wet-5.png">
+
     <div class="content">
−
     <div class="caption">Figure 4: Circuit and protocol design from our adviser</div>
+
        With our visual designer and the embedded mathematical ODE systems model, we constructed XJTU-China’s circuit
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/9/9c/T--SYSU-Software--wet-6.png">
+
        and dynamically analyzed the amounts of materials in it. We pay attention to the expression of EGFP, since its
−
    <div class="caption">Figure 5: This is our version control. The differences between each version are clear</div>
+
        amount can be evaluated in unit of optical density. Related formulas and parameters are shown below:
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/c/c3/T--SYSU-Software--wet-7.png">
+
        $$\frac{d[PsiR]}{dt} = \frac{k_{PsiR}[gene]_{PsiR}}{1+[Psicose]^n1}-d_{PsiR}[PsiR]$$
−
    <div class="caption">Figure 6: We can see there are our target bands and plenty of mismatching product</div>
+
        $$\frac{d[EGFP]}{dt} = \frac{k_{EGFP}[gene]_{EGFP}}{1+[PsiR]^n2}-d_{PsiR}[EGFP]$$
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/a/a5/T--SYSU-Software--wet-8.png">
+
     </div>
−
    <div class="caption">Figure 7: This is our final result. We can see our target bands are clearer than before</div>
+
 
−
    <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/d3/T--SYSU-Software--wet-9.png">
+
−
     <div class="caption"></div>
+
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/b/bf/T--SYSU-Software--wet-10.png">
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/b/bf/T--SYSU-Software--wet-10.png">
−
     <div class="caption"></div>
+
     <div class="caption">
 +
        figure9. parameter in ODE system
 +
    </div>
 +
 
 +
    <div class="content">
 +
        simulation result:
 +
    </div>
 +
 
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/d7/T--SYSU-Software--wet-11.png">
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/d7/T--SYSU-Software--wet-11.png">
−
     <div class="caption"></div>
+
     <div class="caption"> figure10: the simulation result of our software
 +
        Experiment & model result.
 +
    </div>
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/7/7c/T--SYSU-Software--wet-12.png">
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/7/7c/T--SYSU-Software--wet-12.png">
−
     <div class="caption"></div>
+
     <div class="caption"> ​​
 +
        figure11. experiment and model result
 +
    </div>
 +
    <div class="content">
 +
        Compared with experimental data of XJTU-China, our software simulation showed good
 +
        performance, which proved that our simulation works.
 +
    </div>
 +
 
 +
    <div class="content">
 +
        In terms of evolutionary algorithm, we input the related parameters including target production amounts,
 +
        initial material amount, degradation rate and repression rate, and use the optimization module to optimize
 +
        kinetic parameter. We find optimization result is similar with experimental result, as well as optimized K with
 +
        experimental K. Here are the results:
 +
    </div>
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/d1/T--SYSU-Software--wet-13.png">
 
     <img class="wet-valid" src="https://static.igem.org/mediawiki/2018/d/d1/T--SYSU-Software--wet-13.png">
−
     <div class="caption"></div>
+
     <div class="caption"> figure12. optimized K and corresponding simulation result
 +
    </div>
 +
    <div class="content">
 +
        Results prove the robustness of our ODE systems models and evolutionary algorithm.
 +
    </div>
 
     <div class="next-page-identify" data-value="8"></div> <!-- 这个是跳页标记 -->
 
     <div class="next-page-identify" data-value="8"></div> <!-- 这个是跳页标记 -->
 
</div>
 
</div>

Revision as of 18:26, 17 October 2018

Project
Validation
Installability
To set up your own server, please follow the installation guide below:
Dependency
       To run CO-RAD, you should get prepared on your server as follow:
  1. Make sure your server has over 8GB memory available, while 16GB is recommended. This is crucial for our search system.
  2. Python>= 3.6, with main following packages installed:
    • Django >= 2.0
    • pySBOL == 2.3.0.
    • tensorflow == 1.11.0
                   Numpy, xlrd, pandas, sklearn Details for these packages are listed below.
  3. Supportive data. Click <a href="https://github.com/xwy27/Designer#">here</a> to download, and save it at some path.
Installation
  • Repo Clone. Clone the repository from github. With git installed, you can simply type git clone <a href="https://github.com/igemsoftware2018/SYSU-Software-2018">https://github.com/igemsoftware2018/SYSU-Software-2018</a> in your terminal. Or you can visit our repository and download the source file, unzip it and copy it to your custom directory.
  • Installation. The main installation process is packed into scripts. Run setup.sh (setup.bat for Windows) for installation. It may take several minutes to load ocean of data to the database. After finishing, simply run runserver.sh(runserver.bat on windows) to let CO-RAD get started to run! 
       Mac OS Mojave

       Enviroment

       <img src="T--SYSU-Software--macos_config.png" />
MacBook Pro(13-inch, 2018, Four Thunderbolt 3 Ports) with maxOS Mojave (Version 10.14), 
           2.3GHz Intel Core i5 and 16GB 2133 MHz LPDDR3.



       Result

       <img src="T--SYSU-Software--macos_run.png" />

           Successfully installed.

       Windows 10 Enterprise Edition

       Enviroment

       <img src="T--SYSU-Software--win_config.png" />
Windows 10 Enterprise edition with 8GB RAM and Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz, 64-bit operating system
       Result

       <img src="T--SYSU-Software--win_run.png" />
Successfully installed.
       Linux Arch Distribution

       Environment

       <img src="T--SYSU-Software--arch_config.png" />
Linux Arch Distribution with 4 GB RAM and Intel(R) Core(TM)2 Duo CPU T660 @ 2.20GHz. 
       Result

       <img src="T--SYSU-Software--arch_run.png" />
Successfully installed.
Wet-lab Validation
       We verified the robustness of CO-RAD through wet lab. With our software, we solved the difficulty encountered
       in routine molecular biology experiments. Specially, we use CO-RAD to help design the an experiments. Details
       are listed belo
  1. We designed our protocol and circuit in CO-RAD. Our design is supposed to express GFP when IPTG exist. 
                   <img class="wet-valid" src="T--SYSU-Software--wet-valid-1.png" style="margin: 20px;">
               <img class="wet-valid" src="T--SYSU-Software--wet-valid-2.png" style="margin: 20px;">
    Figure 1: Circuit design in CO-RAD.
    Figure 2: Protocol in CO-RAD.
  2. Problems occurred when we assemblied our circuit. We tried to extend 170bp from our template by 5 times of PCR. The sequence includes GFP, terminator, RBS . We can see that we did not find our target band (900bp) in our result. <img class="wet-valid" src="T--SYSU-Software--wet-3.png">
    Figure 3: Our gel electrophoresis result. There is plenty of mismatching product and 
                   no target bands in our result.
       </li>
  3. We need help, so we shared our design to other users on CO-RAD, and our adviser, Professor Shendong, suggested to add spacer DNA in our circuit. Following his suggestion, we added spacer DNA in our circuit and revised our protocol. 
                   <img class="wet-valid" src="T--SYSU-Software--wet-4.png" style="margin:20px;">
               <img class="wet-valid" src="T--SYSU-Software--wet-5.png" style="margin:20px;">
    Figure 4: Circuit and protocol design from our adviser.
  4. We optimized our protocol after discussion. <img class="wet-valid" src="T--SYSU-Software--wet-6.png">
    Figure 5: Historical status of our design.
  5. We found that high template concentration and high annealing temperature are the keys of success in our experiment. More matching sequence in primer is also essential. <img class="wet-valid" src="T--SYSU-Software--wet-7.png">
    Figure 6: Our target bands and plenty of mismatching product.
  6. Again, we received another suggestion from Professor Shendong. With his help, we redesigned our protocol and extended our sequence successfully. DNA Sequencing result accorded with our prospect. Unfortunately, due to the lack of time, we didn't construct our vector and transform it into E.coli to express GFP. <img class="wet-valid" src="T--SYSU-Software--wet-8.png">
    Figure 7: Our final result. Our target bands were clearer than before.
    7.    </ol>

           Due to the lack of time, we chose to cooperate with XJTU-China, using their experimental data to validate our
       simulation module and evolutionary algorithm in CO-RAD.
       <img class="wet-valid" src="T--SYSU-Software--wet-9.png">

           In this genetic circuit above, PsiR is a predicted LacI family transcription factor with high affinity for
       D-Psicose. This implies that PsiR is potentially capable of binding a consensus sequence in the promoter region
       and preventing transcription of the regulated promoters in the absence of D-Psicose. With addition of
       D-psicose, EGFP can be successfully produced.

           With our visual designer and the embedded mathematical ODE systems model, we constructed XJTU-China’s circuit
       and dynamically analyzed the amounts of materials in it. We pay attention to the expression of EGFP, since its
       amount can be evaluated in unit of optical density. Related formulas and parameters are shown below:
       $$\frac{d[PsiR]}{dt} = \frac{k_{PsiR}[gene]_{PsiR}}{1+[Psicose]^n1}-d_{PsiR}[PsiR]$$
       $$\frac{d[EGFP]}{dt} = \frac{k_{EGFP}[gene]_{EGFP}}{1+[PsiR]^n2}-d_{PsiR}[EGFP]$$

       <img class="wet-valid" src="T--SYSU-Software--wet-10.png">

           figure9. parameter in ODE system

           simulation result:

       <img class="wet-valid" src="T--SYSU-Software--wet-11.png">
    figure10: the simulation result of our software 
           Experiment & model result.

       <img class="wet-valid" src="T--SYSU-Software--wet-12.png">
    ​​ 
           figure11. experiment and model result

           Compared with experimental data of XJTU-China, our software simulation showed good
       performance, which proved that our simulation works.

           In terms of evolutionary algorithm, we input the related parameters including target production amounts,
       initial material amount, degradation rate and repression rate, and use the optimization module to optimize
       kinetic parameter. We find optimization result is similar with experimental result, as well as optimized K with
       experimental K. Here are the results:

       <img class="wet-valid" src="T--SYSU-Software--wet-13.png">
    figure12. optimized K and corresponding simulation result 
           Results prove the robustness of our ODE systems models and evolutionary algorithm.