Difference between revisions of "Team:JiangnanU China/Model"

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     <title>JiangnanU_China</title>
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<p style="margin: 10%">
 
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    ## Model
 
  
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    This page is used by the judges to evaluate your team for the [medal
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<body data-spy="scroll" data-target="#myScrollspy" data-offset="20">
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    criterion](https://2019.igem.org/Judging/Medals) or [ award listed below](https://2019.igem.org/Judging/Awards).
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     Delete this box in order to be evaluated for this medal criterion and/or award. See more information at [
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<div class="container nowbg">
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    Instructions for Pages for awards](https://2019.igem.org/Judging/Pages_for_Awards).
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     <div class="row nowbg">
 +
        <div class="col-xs-12 col-sm-3 col-lg-3">
 +
            <nav id="myScrollspy" class="hidden-xs hidden-sm">
 +
                <ul class="nav nav-pills nav-stacked" data-spy="affix">
 +
                    <li><a href="#introduction">Introduction</a></li>
 +
                    <li><a href="#assumption">Assumption</a></li>
 +
                    <li><a href="#gra_ewm">GRA_EWM Model</a></li>
 +
                    <li><a href="#bp_ann">BP-ANN Model</a></li>
 +
                </ul>
 +
            </nav>
 +
        </div>
 +
        <div class="col-lg-8 col-md-8 col-sm-12 col-xs-12 nowbg" id="head">
 +
            <!--总览-->
 +
            <div class="split"></div>
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            <div class="split_small"></div>
  
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    Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick
 
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    Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and
 
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    modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many
 
−
    times in a computer before moving to the lab. This award is for teams who build a model of their system and use it
 
−
    to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.
 
  
−
</p>
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            <!--Lead IN-->
 +
            <div class="spcmkr" id="introduction"></div>
 +
            <div class="fb_72"><b>Lead In</b></div>
 +
            <!--            二级标题Introduction-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Introduction</b></div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                1. The stakeholders informing us that benefits matter most in application, we realized that it’s
 +
                essential to make our anti-phage strains grow as robust as original strain under the current
 +
                fermentation conditions. However, it’ s difficult to tell which strain grows most suitable or which
 +
                device has the least impact on cell growth from the growth curve graph of all those strains (Figure 1).
 +
                Therefore, we established a mathematical model to evaluate the growth properties of strains containing
 +
                different parts for the most suitable device in future application.
 +
            </div>
  
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/f/f9/T--JiangnanU_China--model_1.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <div class="fm_22">
 +
                2. The phage-induced promoters are vital in our genetic circuit, which would response the phage
 +
                stimulation and start transcription of <i>antP</i> and P-1 (antimicrobial peptide) against the phage
 +
                infection
 +
                (Figure 2). And the phage-induced promoters we used were selected from <i>E. coli</i>, so there might be
 +
                some
 +
                potential problems such as leakage and inclusion body due to the inappropriate promoter strength. Thus
 +
                we developed a quantitative design method for phage-induced promoters based on strength prediction using
 +
                artificial neural network, which allows us to choose or design promoters with desired strength without
 +
                extra experiments.
 +
            </div>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/e/e0/T--JiangnanU_China--model_2.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
            <!--            二级标题Assumption-->
 +
            <div class="fb_48" id="assumption">
 +
                Assumption
 +
            </div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                (1) The promoter strengths in the training set are the same using various vectors.
 +
                (2) The impact of parts in cell growth are relatively the same under different situation.
 +
            </div>
 +
 +
 +
            <!--GRA_EWM Model-->
 +
            <div class="spcmkr" id="gra_ewm"></div>
 +
            <div class="fb_72"><b>GRA_EWM Model</b></div>
 +
            <!--            二级标题Symbol Description-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Symbol Description</b></div>
 +
            <br/>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/1/14/T--JiangnanU_China--model_3.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <!--            二级标题Weights Calculation of The Growth Curve-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Weights Calculation of The Growth Curve</b></div>
 +
            <br/>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/2/26/T--JiangnanU_China--model_4.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/d/da/T--JiangnanU_China--model_5.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <!--            二级标题Analysis of The Weight Distribution-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Analysis of The Weight Distribution</b></div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                As showed in Figure 3, the <i>OD600</i> measured after 4 hour to 10 hour were more useful. However, the
 +
                weight
 +
                given by the experts might consider plateau phase as an important period for industry fermentation.
 +
            </div>
 +
 +
            <!--            二级标题Grey Relational Analysis for Picking The Most Suitable Strain-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Grey Relational Analysis for Picking The Most Suitable Strain</b></div>
 +
            <br/>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/c/c5/T--JiangnanU_China--model_6.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/1/1b/T--JiangnanU_China--model_7.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <!--            二级标题Conclusion-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Conclusion</b></div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                Figure 4 demonstrates that the impact of the distinguishing coefficient on the result of GRA is very
 +
                significant. In particular, for all tested distinguishing coefficients, part <i>gntR</i> always ranks
 +
                first,
 +
                which means it is the most suitable part for future application.
 +
            </div>
 +
 +
            <!--BP-ANN Model-->
 +
            <div class="spcmkr" id=""></div>
 +
            <div class="fb_72"><b>BP-ANN Model</b></div>
 +
            <div class="split_small"></div>
 +
            <div class="fm_22">
 +
                Accurate and controllable regulatory elements like promoters are indispensable tools to quantitatively
 +
                regulate gene expression for rational pathway engineering, which means a promoter with proper strength
 +
                might be an easy solution to leakage and inclusion body. In order to select or design promoters with
 +
                desired strength without experiments, we developed a quantitative strength prediction method using
 +
                artificial neural network (ANN) with Neural Network Toolbox under Matlab environment.
 +
            </div>
 +
            <!--二级标题Construction of Early Phage-induced Promoter Strength Library-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Construction of Early Phage-induced Promoter Strength Library</b></div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                The endogenous promoters in <i>E.coli</i> BL21(DE3) we selected to respond to phage infection might
 +
                start
 +
                transcription without phage stimulation, so we selected 19 promoters with distributed strength from T4
 +
                phage mentioned in previous study as the phage-induced promoter library. (attachment: T4PE.docx)
 +
            </div>
 +
 +
            <!--二级标题Computational Platform Construction-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Construction of Early Phage-induced Promoter Strength Library</b></div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                Matlab2019b (Mathworks Inc., <a>https://www.mathworks.com/</a>) ran on a personal laptop with Windows 10
 +
                64-bit(Microsoft Inc., http://www.microsoft.com/) operation system. Neural Network Toolbox within Matlab
 +
                served as the basic tool for artificial neural network (ANN) model construction, data fitting and
 +
                prediction. All programs used in this work were designed and run upon Neural Network Toolbox and Matlab
 +
                environment.
 +
            </div>
 +
 +
            <!--二级标题Construction And Training of ANN Predicting Models-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Construction And Training of ANN Predicting Models</b></div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                The initial BP-ANN model was built by Neural Network Toolbox. The model contains four layers, including
 +
                an input layer, an output layer and two hidden layer. Neuron numbers of two hidden layer and a output
 +
                layer were 26, 5, 1 respectively. The initial weight for all neuron connections were randomly assigned
 +
                by Matlab functions. And other parameters were set as followed:
 +
            </div>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/a/ae/T--JiangnanU_China--model_8.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <div class="fm_22">
 +
                net = newff(minmax(p),[26,5,1],{'tansig', 'tansig', 'purelin'},'trainlm');
 +
                net.trainParam.epochs = 15000;
 +
                net.trainParam.mc = 0.98;
 +
                net.trainParam.goal = 1e-6;
 +
                net.trainParam.lr = 0.01;
 +
                And the original sequence data were translated to digital data and served as the input matrix according
 +
                to the following rules via python program (attachment: seqDigtal.py):
 +
            </div>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/3/35/T--JiangnanU_China--model_9.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <div class="fm_22">
 +
                Among all generated models, NET_26_5 (attachment: NET_26_5.mat) shows the highest correlation
 +
                coefficient values of 0.93336 for test set prediction, and its correlation coefficient values for
 +
                fitting the training data set reaches up to 0.99104. (Figure 3)
 +
            </div>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/6/65/T--JiangnanU_China--model_10.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <div class="fm_22">
 +
                And all the promoter prediction strengths were showed in Figure 4. (attachment:
 +
                pStrengthVsStrength.xlsx)
 +
            </div>
 +
 +
            <div class="split_small"></div>
 +
            <img src="https://static.igem.org/mediawiki/2019/3/32/T--JiangnanU_China--model_11.png" width="100%"
 +
                height="auto" alt="">
 +
            <div class="split_small"></div>
 +
 +
            <!--二级标题Conclusion-->
 +
            <div class="split_small"></div>
 +
            <div class="fb_48"><b>Conclusion</b></div>
 +
            <br/>
 +
            <div class="fm_22">
 +
                We could predict the designed promoter strength based on this BP-ANN model, which could save a lot time
 +
                for us to test millions of designed promoters.
 +
            </div>
 +
 +
            <!--            书签-->
 +
            <div class="split_small"></div>
 +
            <a href="#head"><img src="https://static.igem.org/mediawiki/2019/2/24/T--JiangnanU_China--host_back.png"
 +
                                alt="back" style="width: 6%;height:auto;margin-left: 46%"></a>
 +
        </div>
 +
    </div>
 +
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Revision as of 03:14, 19 October 2019

JiangNan