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>
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                    <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>
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                    <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>
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             <div class="sidebar" id="sidebar">
 
             <div class="sidebar" id="sidebar">
 
                 <ul>
 
                 <ul>
−
                     <li><a href="#part1">Call-and-return in Computer Programming</a></li>
+
                     <li><a href="#part1">· Overview</a></li>
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                     <li><a href="#part2">CRISPR-dCas9</a></li>
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                     <li><a href="#part2">· Mismatch</a></li>
−
                     <li><a href="#part3">Call-and-return in E. coli using CRISPR-dCas9</a></li>
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                     <li><a href="#part3">· Orthogonality</a></li>
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                    <li><a href="#part4">Indigo synthesis</a></li>
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                 </ul>
 
                 </ul>
 
             </div>
 
             </div>
 
             <div class="maincontent">
 
             <div class="maincontent">
 
                 <div class="phrase" id="part1">
 
                 <div class="phrase" id="part1">
−
                     <div class="subtitle">Call-and-return in Computer Programming</div>
+
</br></br>
−
                     <div class="text">In modern computer programming, a complete program is decomposed into different modules with relatively
+
                     <div class="subtitle">Overview</div>
−
                     independent scopes, and within each module the operations are further grouped into different functions
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</br></br><hr style="height:1px;border:none;border-top:1px solid #555555;" /></br>
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                    or subroutines. During the execution of a program, different functions in different modules are
+
                     <div class="text">
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                    “called” (or used) with some parameters given; after the “callee” (the function that has been
+
                     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.
−
                    called) completed its operations and/or calculations, the results are returned to its “caller”
+
−
                    (the function that calls). Such hierarchical calls for and returns from functions enables the computer
+
−
                    engineers not only to divide and conquer large and complex programs, but also to allow the common and
+
−
                    already written modules and functions, such as the functions for adding, subtracting, file reading/writing etc.,
+
−
                    to be reused directly instead of coding for them again. The principle is intuitively called “Do not re-invent
+
−
                    the wheel”. There is already a plethora of handy “libraries”, which are the collections of commonly
+
−
                    used functions, could be directly used and thus facilitates fast program development.（Figure 1）
+
 
                     </div>
 
                     </div>
−
                    <div class="showimg"><img style="width:640px" src="https://static.igem.org/mediawiki/2018/d/d1/T--SCU-China--proj.png"/><br />Figure 1. This figure depicts how the functions are called and return during the execution of a program. Functions can be called hierarchically, and one function could be called more than once (not shown).</div>
+
 
 +
 
 +
 
 +
 
 +
                 
 
                 </div>
 
                 </div>
  
 
                 <div class="phrase" id="part2">
 
                 <div class="phrase" id="part2">
−
                     <div class="subtitle">CRISPR-dCas9</div>
+
</br></br>
 +
                     <div class="subtitle">Mismatch</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">
−
                     CRISPR technique, known as the bacterial immune system against invading viruses, has become an indispensable tool in biological research. Mainly, there are three different types of the CRISPR systems, including type I, II, III. Types I and III loci contain multiple Cas proteins forming complexes with crRNA to recognize and destroy target nucleic acids. In contrast, type II usually has reduced number of Cas proteins. And the CRISPR Cas9 system belonging to type II, after being optimized and artificially designed, is being widely used in gene editing, epigenetic regulation, chromatin engineering, and imaging. CRISPR Cas9 technique consists of sgRNAs, Cas9 nucleases, and perhaps of repair templates. The sgRNA has a scaffold motif and a 20-nt guide RNA motif which binds to Cas9 and guides this nuclease to the spacer sequence in target DNA respectively. Protospacer-adjacent motifs (PAMs) is an essential element in the non-target DNA strand for target recognition and cleavage. The location of PAM varies from different types of CRISPR Cas systems. And the composition of PAM can also vary widely according to the Cas9 deriving from what bacteria species. The CRISPR Cas9 (type II) used in our project is from Streptococcus pyogenes of which the PAM is located in the 3’ end of the protospacer and its sequence is usually NGG. Wild Cas9 nucleases are activated once binding to both sgRNA and PAM and produce double strand break (DSB) of target DNA.
+
                     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.
 
                     <br />
 
                     <br />
−
                     Besides, the mutation of Cas9 nuclease enables it to have diverse function. One of these mutated Cas9 that has been widely used in gene regulation is ‘dead’ Cas9 (dCas9). Cas9 nucleases carry out strand-specific cleavage by using the conserved HNH and RuvC nuclease domains. Both of the DNA cleaving catalytic residues (D10A in the RuvC domain and H840A in the HNH domain) mutation abolishes dCas9 cleavage activity but does not impair DNA binding activity. When dCas9 binds to genes regardless of promoter and transcriptional regions, it becomes the barrier preventing RNA polymerase from transcription initiation and elongation (Figure 2). Taking advantage of this characteristic, CRISPR dCas9 technique is used to regulate transcriptional rates thereby gene expression rates. And in some complex systems, CRISPR dCas9 technique is used as switches to construct orthogonal systems to fulfill the spatiotemporal regulation of a series of genes.
+
                     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.
 +
                    <br />
 +
                    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">
−
                     <div class="subtitle">Call-and-return in E. coli using CRISPR-dCas9</div>
+
</br></br>
 +
                     <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="text">
+
                     <div class="subtitle"></div>
−
                    To show the power and practicality of the design above, our team aims to apply it to a sustainable dyeing strategy. Indigo, as a current widely used dye, its chemical production, transportation and using employs many toxic and not environment-friendly chemicals. However, the enzymes that catalyze the relevant reactions are found and the biological solution seems promising. SCU_China iGEM team designed a strategy that not only circumvents the use of toxic reagents for indigo chemical synthesis but also removes the need for a reducing agent for dye solubilization. Our project will provide a fundamental contribution to the construction of engineering bacteria and clean manufacturing.
+
 
−
                    </div>
+
                 
 
                 </div>
 
                 </div>
 
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             </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