Template:SYSU-Software/statics/html/Projects/Validation.html

   <script type="text/x-mathjax-config">
       MathJax.Hub.Config({
       tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}
       });
   </script>
   <script src="https://2018.igem.org/common/MathJax-2.5-latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> </script>

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:

Make sure your server has over 8GB memory available, while 16GB is recommended. This is crucial for our search system.

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.

(Not required). To activate the search engine based on neutral network, please make sure you have enough available memory (>8GB). Then click <a href="http://sysu-corad.com:8080/wordVectors.zip">here</a> to download the supportive data, unzip and save it at [ProjectRootDirectory]\igem2018\search\nn_search, where [ProjectDirectory] is the path of this project.

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="" />

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="" />

           Successfully installed.

       Windows 10 Enterprise Edition

       Enviroment

       <img src="" />

Windows 10 Enterprise edition with 8GB RAM and Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz, 64-bit operating system

       Result

       <img src="" />

Successfully installed.

       Linux Arch Distribution

       Environment

       <img src="" />

Linux Arch Distribution with 4 GB RAM and Intel(R) Core(TM)2 Duo CPU T660 @ 2.20GHz.

       Result

       <img src="" />

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

We designed our protocol and circuit in CO-RAD. Our design is supposed to express GFP when IPTG exist.
```
               <img class="wet-valid" src="" style="margin: 20px;">
               <img class="wet-valid" src="" style="margin: 20px;">
```
Figure 1: Circuit design in CO-RAD.

Figure 2: Protocol in CO-RAD.
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="">
Figure 3: Our gel electrophoresis result. There is plenty of mismatching product and
```
               no target bands in our result.
       </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.
```
               <img class="wet-valid" src="" style="margin:20px;">
               <img class="wet-valid" src="" style="margin:20px;">
```
Figure 4: Circuit and protocol design from our adviser.
We optimized our protocol after discussion. <img class="wet-valid" src="">
Figure 5: Historical status of our design.
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="">
Figure 6: Our target bands and plenty of mismatching product.
Again, we received another suggestion from Professor Shendong. With his help, we redesigned our protocol and extended our sequence successfully. 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="">
Figure 7: Our final result. Our target bands were clearer than before.

   </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="">

       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="">

       Figure 8. parameter in ODE system

       simulation result:

   <img class="wet-valid" src="">

Figure 9: the simulation result of our software

       Experiment & model result.

   <img class="wet-valid" src="">

       Figure 10. 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="">

Figure 11. optimized K and corresponding simulation result

       Results prove the robustness of our ODE systems models and evolutionary algorithm.