Difference between revisions of "Team:Shanghai HS United/Model"

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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and 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. </p>
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<p>Please note you can compete for both the Gold Medal criterion #3 and the Best Model prize with this page. </p>
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<h3> Gold Medal Criterion #3</h3>
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<p>
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        <img src="https://static.igem.org/mediawiki/2021/9/97/T--Shanghai_HS_United--bg_1.png" alt="">
Use modeling to gain insight into how your project works or should be implemented. Explain your model's assumptions, data, parameters, and results in a way that anyone could understand.
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        <h1>Modeling</h1>
<br><br>
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Please see the <a href="https://2021.igem.org/Judging/Medals">2021 Medals Page</a> for more information.
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    <!--内容-->
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        <section class="article p-t-72 p-b-54">
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            <section class="en-indent-all">
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                <p>In order to evaluate the flavonoids degradation ability of FLR enzyme, we conducted the enzyme activity
 +
                    testsby using 4 kinds of flavonoids samples: apigenin, chrysin, luteolin, diosmetin, repectively.</p>
  
<div class="column two_thirds_size">
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                <p>Following is the data we collected during the tests:</p>
<h3>Best Model Special Prize</h3>
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<p>Models and computer simulations provide a great way to describe the functioning and operation of BioBrick Parts and Devices. Synthetic biology is an engineering discipline and part of engineering is simulation and modeling to determine system behavior before building your design. 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 before or in conjunction with experiments in the wetlab.
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                <div class="img-container m-t-12 m-b-18">
</p><p>
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                    <span class="figure">Table 1. data of enzyme activity tests</span>
To compete for the <a href="https://2021.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2021.igem.org/Judging/Judging_Form">judging form</a>.
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                    <img src="https://static.igem.org/mediawiki/2021/b/b1/T--Shanghai_HS_United--img_model_1.jpg" alt="" style="width: 75%">
</p>
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                </div>
  
</div>
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                <p>We designed to build models to forecast the degradation rate of our FLR enzyme by time for different
 +
                    flavonoids samples. Therefore, we chose the appropriate equation to fit these points by Matlab.</p>
  
 +
                <p>Since these curves seem all decline trends and there are not many points yet, we chose to adopt the
 +
                    following negative exponential function to prevent the negative value of the concentration:</p>
  
<div class="column third_size">
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                <img src="https://static.igem.org/mediawiki/2021/0/07/T--Shanghai_HS_United--img_model_12.jpg" alt="" style="width: 230px;">
<div class="highlight decoration_A_full">
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<h3> Inspiration </h3>
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<p>You can look at what other teams did to get some inspiration! <br />
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Here are a few examples:</p>
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<ul>
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<li><a href="https://2018.igem.org/Team:GreatBay_China/Model">2018 GreatBay China</a></li>
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<li><a href="https://2018.igem.org/Team:Leiden/Model">2018 Leiden</a></li>
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<li><a href="https://2019.igem.org/Team:IISER_Kolkata/Model">2019 IISER Kolkata</a></li>
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<li><a href="https://2019.igem.org/Team:Exeter/Model">2019 Exeter</li>
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<li><a href="https://2019.igem.org/Team:Mingdao/Model">2019 Mingdao</a></li>
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<li><a href="https://2020.igem.org/Team:Harvard/Model">2020 Harvard</a></li>
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<li><a href="https://2020.igem.org/Team:Leiden/Model">2020 Leiden</a></li>
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</ul>
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</div>
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</div>
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                <p>And its analytic expression will be:</p>
 +
 +
                <img src="https://static.igem.org/mediawiki/2021/3/3a/T--Shanghai_HS_United--img_model_11.jpg" alt="" style="width: 230px;">
 +
 +
                <p>According to the modeling results for each samples showing below, the fitting degrees are the same,
 +
                    1.</p>
 +
 +
                <div class="half" style="text-align: center">
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                    <div class="left">
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                        <img src="https://static.igem.org/mediawiki/2021/e/ec/T--Shanghai_HS_United--img_model_2.jpg" alt="" style="width: 75%">
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                        <span class="figure">a</span>
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                    </div>
 +
                    <div class="right">
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                        <img src="https://static.igem.org/mediawiki/2021/9/95/T--Shanghai_HS_United--img_model_3.jpg" alt="" style="width: 75%">
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                        <span class="figure">b</span>
 +
                    </div>
 +
                </div>
 +
 +
                <div class="half" style="text-align: center">
 +
                    <div class="left">
 +
                        <img src="https://static.igem.org/mediawiki/2021/c/c5/T--Shanghai_HS_United--img_model_4.jpg" alt="" style="width: 75%">
 +
                        <span class="figure">c</span>
 +
                    </div>
 +
                    <div class="right">
 +
                        <img src="https://static.igem.org/mediawiki/2021/a/ae/T--Shanghai_HS_United--img_model_5.jpg" alt="" style="width: 75%">
 +
                        <span class="figure">d</span>
 +
                    </div>
 +
                </div>
 +
 +
                <span class="figure">Figure 1. Modeling results of apigenin(a), chrysin(b), luteolin(c), diosmetin(d)</span>
 +
 +
                <div class="img-container">
 +
                    <img src="https://static.igem.org/mediawiki/2021/0/0e/T--Shanghai_HS_United--img_model_6.jpg" alt="" style="width: 75%">
 +
                    <img class="m-t-12" src="https://static.igem.org/mediawiki/2021/5/51/T--Shanghai_HS_United--img_model_7.jpg" alt="" style="width: 75%">
 +
                    <img class="m-t-12" src="https://static.igem.org/mediawiki/2021/0/09/T--Shanghai_HS_United--img_model_8.jpg" alt="" style="width: 75%">
 +
                    <img class="m-t-12" src="https://static.igem.org/mediawiki/2021/2/27/T--Shanghai_HS_United--img_model_9.jpg" alt="" style="width: 75%">
 +
                    <span class="figure">Figure 2. The fitting curves of apigenin(a), chrysin(b), luteolin(c), diosmetin(d)</span>
 +
                </div>
 +
 +
                <p>As seen above (figure 2), these 4 curves present very similar trends and these functions could be served
 +
                    as a reference to forecast the duration of how our FLR enzyme achieves its efficiency if we could settle
 +
                    the recommending degradation ratio of the flavonoids. For example, for chrysin, it might take 1 hour
 +
                    around for the FLR enzyme to degrade the half. </p>
 +
 +
                <p>Besides, we also took the derivative of these curves to analyze their degradation rate by hours.</p>
 +
 +
                <div class="img-container m-t-18">
 +
                    <img src="https://static.igem.org/mediawiki/2021/b/b1/T--Shanghai_HS_United--img_model_10.jpg" alt="" style="width: 75%">
 +
                    <span class="figure">Figure 3. The curves of the degradation rate against hours for apigenin, chrysin, luteolin, diosmetin</span>
 +
                </div>
 +
 +
                <p>In figure 3, it indicates that the degradation rate for diosmetin is much faster than the other three
 +
                    during the first 2 hours but it immediately goes to flatten out. It might be related to the
 +
                    concentration of the substrate (diosmetin) which means that the FLR enzyme would be very suitable for the
 +
                    degradation of high concentration flavonoids (diosmetin).</p>
 +
            </section>
 +
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            <span>Email: xiaowenxuan0526@gmail.com; caroline.niujl@gmail.com</span>
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            <span>Wechat Official Account: FlavoneLabRouse</span>
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Latest revision as of 13:49, 11 October 2021

In order to evaluate the flavonoids degradation ability of FLR enzyme, we conducted the enzyme activity testsby using 4 kinds of flavonoids samples: apigenin, chrysin, luteolin, diosmetin, repectively.

Following is the data we collected during the tests:

Table 1. data of enzyme activity tests

We designed to build models to forecast the degradation rate of our FLR enzyme by time for different flavonoids samples. Therefore, we chose the appropriate equation to fit these points by Matlab.

Since these curves seem all decline trends and there are not many points yet, we chose to adopt the following negative exponential function to prevent the negative value of the concentration:

And its analytic expression will be:

According to the modeling results for each samples showing below, the fitting degrees are the same, 1.

a
b
c
d
Figure 1. Modeling results of apigenin(a), chrysin(b), luteolin(c), diosmetin(d)
Figure 2. The fitting curves of apigenin(a), chrysin(b), luteolin(c), diosmetin(d)

As seen above (figure 2), these 4 curves present very similar trends and these functions could be served as a reference to forecast the duration of how our FLR enzyme achieves its efficiency if we could settle the recommending degradation ratio of the flavonoids. For example, for chrysin, it might take 1 hour around for the FLR enzyme to degrade the half.

Besides, we also took the derivative of these curves to analyze their degradation rate by hours.

Figure 3. The curves of the degradation rate against hours for apigenin, chrysin, luteolin, diosmetin

In figure 3, it indicates that the degradation rate for diosmetin is much faster than the other three during the first 2 hours but it immediately goes to flatten out. It might be related to the concentration of the substrate (diosmetin) which means that the FLR enzyme would be very suitable for the degradation of high concentration flavonoids (diosmetin).