Difference between revisions of "Team:SCU-China/judging/model"

 
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                 <ul>
 
                 <ul>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/overview">Overview</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/overview">Overview</a></li>
−
                    <li><a href="https://2018.igem.org/Team:SCU-China/project/interlab">Interlab</a></li>
 
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/regulation">Precise Regulation</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/regulation">Precise Regulation</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/circuits">Logic Circuits</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/circuits">Logic Circuits</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/indigo">CRISProgrammer of Indigo</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/indigo">CRISProgrammer of Indigo</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/parts">Parts</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/project/parts">Parts</a></li>
 +
                    <li><a href="https://2018.igem.org/Team:SCU-China/project/interlab">Interlab</a></li>
 
                 </ul>
 
                 </ul>
 
             </li>
 
             </li>
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             <li><a>Human practice</a>
 
             <li><a>Human practice</a>
 
                 <ul>
 
                 <ul>
−
                    <li><a href="https://2018.igem.org/Team:SCU-China/practice/hp">Integrated Human Practice</a></li>
 
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/practice/collaboration">Collaboration</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/practice/collaboration">Collaboration</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/practice/meet">Meet Up</a></li>
 
                     <li><a href="https://2018.igem.org/Team:SCU-China/practice/meet">Meet Up</a></li>
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     </div>
 
     </div>
 
     <div class="head">
 
     <div class="head">
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         <img src="https://static.igem.org/mediawiki/2018/6/6c/T--SCU-China--bg.jpg" style="width:100%;">
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         <img src="https://static.igem.org/mediawiki/2018/5/52/T--SCU-China--headback.jpg" style="width:100%;">
 
         <div class="headline sides">
 
         <div class="headline sides">
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            <div>Project Overview</div>
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−
            <div class="smallhead">Synthetic biology is now developing into its zenith time, and the concepts of BioBricks and standardized assembly methods have proven to be efficient, universally compatible and highly extensible. This year, Team SCU_China plans to bring the standardization in synthetic biology to a completely new level. </div>
+
         
  
 
         </div>
 
         </div>
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             <div class="sidebar" id="sidebar">
 
             <div class="sidebar" id="sidebar">
 
                 <ul>
 
                 <ul>
−
                     <li><a href="#part1">Overview</a></li>
+
                     <li><a href="#part1">· Overview</a></li>
−
                     <li><a href="#part2">Mismatch</a></li>
+
                     <li><a href="#part2">· Mismatch</a></li>
−
                     <li><a href="#part3">Orthogonality</a></li>
+
                     <li><a href="#part3">· Orthogonality</a></li>
 
                    
 
                    
 
                 </ul>
 
                 </ul>
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             <div class="maincontent">
 
             <div class="maincontent">
 
                 <div class="phrase" id="part1">
 
                 <div class="phrase" id="part1">
 +
</br></br>
 
                     <div class="subtitle">Overview</div>
 
                     <div class="subtitle">Overview</div>
 +
</br></br><hr style="height:1px;border:none;border-top:1px solid #555555;" /></br>
 
                     <div class="text">
 
                     <div class="text">
 
                     Huge scale of our experimental design based firmly on modelling results. Our Model can be divided into two parts, each has significant function in guiding the direction of our whole design, and the future work. Inspired by the modelling results, our team can optimize the experiment without wasting our time. Besides, the results help us to make the system stable and feasible.
 
                     Huge scale of our experimental design based firmly on modelling results. Our Model can be divided into two parts, each has significant function in guiding the direction of our whole design, and the future work. Inspired by the modelling results, our team can optimize the experiment without wasting our time. Besides, the results help us to make the system stable and feasible.
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                 <div class="phrase" id="part2">
 
                 <div class="phrase" id="part2">
 +
</br></br>
 
                     <div class="subtitle">Mismatch</div>
 
                     <div class="subtitle">Mismatch</div>
−
                     <div class="text"> Why we did this model?  </div>
+
</br></br><hr style="height:1px;border:none;border-top:1px solid #555555;" /></br>
 +
                     <div style="font-weight: bold;" class="text"> Why we did this model?  </div>
 
                     <div class="text">
 
                     <div class="text">
 
                     1) The gradient regulation just based on some experiments focusing on the efficiency of sgRNA. No essay shows particular results and specialized experiment about the idea. It’s quite risky for us to have a try, no matter how fascinating the idea is. As a result, we should confirm its feasibility at first.
 
                     1) The gradient regulation just based on some experiments focusing on the efficiency of sgRNA. No essay shows particular results and specialized experiment about the idea. It’s quite risky for us to have a try, no matter how fascinating the idea is. As a result, we should confirm its feasibility at first.
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                     3) The construction of mismatch sequence library is a huge work. With only two months to finish our project, it does not look like pragmatic to verify the idea by experiment.
 
                     3) The construction of mismatch sequence library is a huge work. With only two months to finish our project, it does not look like pragmatic to verify the idea by experiment.
 
                     </div>
 
                     </div>
−
                     <div class="showimg"><img style="width:640px;" src="https://static.igem.org/mediawiki/2018/7/7c/T--SCU-China--dyna1.gif" /><br />Figure 2. dCas9-mediated repression in E. coli. <br />The sgRNA directs dCas9 to promoter or open reading frame regions to prevent RNA polymerase binding or elongation, respectively</div>
+
 
 +
                    <div style="font-weight: bold;" class="text"> How was our model constructed? </div>
 +
                    <div class="text"> 
 +
                      Here in our model, the effects of the following factors are considered on the off-target mutation. 1. The position of base mismatch: studies show that the closer the mismatch is to the PAM site, the easier it is to cause off-target effect; 2. The number of base mismatches; 3. The continuous base mismatches; 4. Thermodynamics structure relationship of mismatches: different base mismatches often require different energies. For example, stability enhancement for rG : dT mismatch is the highest of all mismatches;
 +
                    <br />
 +
                    With the help of the experimental data published by Evan, and the design of our own sgRNA (design through the general workflow, Figure 1), we encode all the base sequences according to the mismatch type and the energy intensity between the mismatched bases, on the basis of which the depth neural network with multi-layer perceptron is established and trained.
 +
                    </div>
 +
 
 +
 
 +
 
 +
                     <div class="showimg"><img style="width:480px;" src="https://static.igem.org/mediawiki/2018/6/60/T--SCU-China--judgemodel1.jpg" /></div>
 +
 
 +
 
 +
                  <div style="font-weight: bold;" class="text"> What did the modeling tell us?</div>
 +
                  <div class="text">
 +
                  When the mutations happen on seed region, the repression level is persistently low. While the mismatches happen on off-seed region, once the amounts of that live up to 3, the repression level slumps immediately. Unfortunately, it seems the mismatch strategy cannot work perfectly like our team wanted based on the modeling results. Thus, we turn to other alternatives to achieve the goal.</div>
 +
                   
 
                 </div>
 
                 </div>
  
 
                 <div class="phrase" id="part3">
 
                 <div class="phrase" id="part3">
 +
</br></br>
 
                     <div class="subtitle">Orthogonality</div>
 
                     <div class="subtitle">Orthogonality</div>
 +
</br></br><hr style="height:1px;border:none;border-top:1px solid #555555;" /></br>
 +
                    <div style="font-weight: bold;" class="text"> Why we did this model?  </div>
 
                     <div class="text">
 
                     <div class="text">
−
                     Inspired by the modularization and “Do not re-invent the wheel” philosophy in computer programming, this year SCU_China iGEM team came up with the idea of using the dCas9 protein to manipulate the expression of proteins and implement complex logic in a single E. coli cell to achieve the similar “call-and-return” controlling paradigm.
+
                     1) In both basic logic circuits and more complex logic circuits, there are definitely more than one kind of sgRNA function in a cell simultaneously. To reduce the crosstalk to the maximum extent, we should insure the specificity of sgRNA.
 
                     <br />
 
                     <br />
−
                    Moreover, we want to construct versatile “library strains” that contains many, and maybe redundant coding sequences of commonly used proteins (like enzymes for industrial production etc.) which are dormant by default; such sequences could be on a large plasmid transformed in advance or integrated into the genome. When we need to use the function of particular proteins whose coding sequences are already exist in the cell (to “call” them), we just simply transform a much smaller (compared to the CDS) “Minimid” containing expression cassettes of sgDNAs targeting the desired proteins into the library strain, and then the dCas9-sgRNA complex would initiate/inhibit the transcription and then expression of the proteins of interest.
+
                  2) For our versatile promotor, the orthogonality is an all-important factor to make sure the stable and accuracy of regulation. We must filter the genome and plasmid backbone sequence, getting rid of futile targeting.
−
                    <br />
+
                 
−
                    This way, we could not only simplify the construction of plasmids (for it is small and could even be directly synthesized), but also provide a rapid way of constructing different strains for production and a possible method to avoid genetic pollution. What’s more, utilizing dCas9 system also enables us to implement complex logics in one cell. Our prospect is that using the system, finally we could really literally “program” the cell, i.e. using computer language to code the logic, and then use a “genetic compiler” to convert the logic to nucleotide sequences (for “Minimid”) containing a series of well-designed sgDNAs.
+
 
                     </div>
 
                     </div>
 +
 +
                  <div style="font-weight: bold;" class="text"> How was our model constructed?</div>
 +
                  <div class="text"> We used a specific algorithm to generate a set of orthogonal sgRNAs.</div>
 +
 +
                  <div style="font-weight: bold;" class="text">What did the modeling tell us?</div>
 +
                  <div class="text">The output of the algorithm is a set of orthogonal sgRNAs. By using the sgRNAs, we construct our versatile promoters – spacer sequence containing promoters. And the sgRNAs can be used in targeting correspondent DNA orthogonally.</div>
 +
             
 +
             
 
                 </div>
 
                 </div>
  
 
                 <div class="phrase" id="part4">
 
                 <div class="phrase" id="part4">
−
                     <div class="subtitle">Indigo synthesis</div>
+
 
 +
                     <div class="subtitle"></div>
 +
 
 
                    
 
                    
 
                 </div>
 
                 </div>

Latest revision as of 00:50, 18 October 2018

Team:SCU-China - 2018



Overview



Huge scale of our experimental design based firmly on modelling results. Our Model can be divided into two parts, each has significant function in guiding the direction of our whole design, and the future work. Inspired by the modelling results, our team can optimize the experiment without wasting our time. Besides, the results help us to make the system stable and feasible.


Mismatch



Why we did this model?
1) The gradient regulation just based on some experiments focusing on the efficiency of sgRNA. No essay shows particular results and specialized experiment about the idea. It’s quite risky for us to have a try, no matter how fascinating the idea is. As a result, we should confirm its feasibility at first.
2) So many factors will influence the binding between the sgRNA-dCas9 complex with targeted DNA region. The hydrogen bound between bases, the specificity of sgRNA, the three-dimension structure of sgRNA scaffold. Before we start our experiment, we should consider all likely elements which have an effect on the results as thorough as possible. In order to save time, and generalize all the condition, the model stands out to be a perfect method.
3) The construction of mismatch sequence library is a huge work. With only two months to finish our project, it does not look like pragmatic to verify the idea by experiment.
How was our model constructed?
Here in our model, the effects of the following factors are considered on the off-target mutation. 1. The position of base mismatch: studies show that the closer the mismatch is to the PAM site, the easier it is to cause off-target effect; 2. The number of base mismatches; 3. The continuous base mismatches; 4. Thermodynamics structure relationship of mismatches: different base mismatches often require different energies. For example, stability enhancement for rG : dT mismatch is the highest of all mismatches;
With the help of the experimental data published by Evan, and the design of our own sgRNA (design through the general workflow, Figure 1), we encode all the base sequences according to the mismatch type and the energy intensity between the mismatched bases, on the basis of which the depth neural network with multi-layer perceptron is established and trained.
What did the modeling tell us?
When the mutations happen on seed region, the repression level is persistently low. While the mismatches happen on off-seed region, once the amounts of that live up to 3, the repression level slumps immediately. Unfortunately, it seems the mismatch strategy cannot work perfectly like our team wanted based on the modeling results. Thus, we turn to other alternatives to achieve the goal.


Orthogonality



Why we did this model?
1) In both basic logic circuits and more complex logic circuits, there are definitely more than one kind of sgRNA function in a cell simultaneously. To reduce the crosstalk to the maximum extent, we should insure the specificity of sgRNA.
2) For our versatile promotor, the orthogonality is an all-important factor to make sure the stable and accuracy of regulation. We must filter the genome and plasmid backbone sequence, getting rid of futile targeting.
How was our model constructed?
We used a specific algorithm to generate a set of orthogonal sgRNAs.
What did the modeling tell us?
The output of the algorithm is a set of orthogonal sgRNAs. By using the sgRNAs, we construct our versatile promoters – spacer sequence containing promoters. And the sgRNAs can be used in targeting correspondent DNA orthogonally.
SCU_China 2018 iGEM Team