Difference between revisions of "Team:Mingdao/Part Collection"

 
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<body>
 
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      <img class="top-picture" src="https://static.igem.org/mediawiki/2018/9/90/T--Mingdao--Phil13-2.png" >
 
     <div class="bg-container" style="max-height:none;">
 
     <div class="bg-container" style="max-height:none;">
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       <div class="my-main-container">
 
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         <div class="main-content">
 
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           <div class="text-area">
 
           <div class="text-area">
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            <h1 id = "d-introduction">Modeling</h1>
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<br />
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        <h2 id="d-intro"> </h2>
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            <h2>Introduction</h2>
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<br />
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<p style="text-indent:2em">
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<p>In our project, we want to calculate the bacteria concentration in the testers.
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Mosquito Toll-Amp signaling plays an important roll in response to and regulating blood infectious pathogens.
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<p>
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<p>
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However, our devices can only detect GFP intensity, so we can only utilize GFP intensity to calculate bacteria concentration.
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<p>
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<p>
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What’s more, with the view to making sure our system works successfully, we need to make sure that testers can detect GFP in our devices. Since the GFP in mosquitoes take some time to be synthesized, we can detect the green fluorescence only few hours after the mosquitoes take in the tester’s blood. To prevent from the misleading of our devices and system, we should calculate the very beginning time that the testers can detect the green fluorescence in the devices.
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</p>
 
</p>
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<p>
 
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<p>
 
 
<br />  
 
<br />  
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<h3>Guiding Questions</h3>
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<p style="text-indent:2em">
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<p>
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To genetically engineer mosquitoes as blood testing tools, we’ve collected DNA materials as BioBrick parts for future assembly.
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<p>
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<p>1. How many bacteria can be tested in our model ? (Model 1)
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<p>
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2. How long do our devices take to send out signal ? (Model 2)</p>
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<p>
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<p>
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<br />
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<h3>Focus on Our Model</h3>
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<p>
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<p>
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                <p>Since our devices can only detect the GFP intensity, we can only utilize GFP intensity to calculate E.coli concentration. After obtaining E.coli concentration, we will utilize it to calculate the very beginning time that testers can detect GFP. Finally, the two parameters will be demonstrated on our devices for the testers to take as reference.  
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</p>
 
</p>
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<p>
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<br />  
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<p>
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<p style="text-indent:2em">
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<img class="center" src="https://static.igem.org/mediawiki/2018/f/fa/T--Mingdao--Modeling001.jpg" alt=" " style="width:70%" >
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First, we designed a universal pathogen blood testing kit consisting of BioBrick parts of GAM1 promoter, GFP and polyA, as well as Ac5 promoter as a positive expression control.
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            <img class="center" src="https://static.igem.org/mediawiki/2017/a/a8/T--CSMU_NCHU_Taiwan--safety-line.png" alt="" style="width:100%">
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<br />
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            <h2 id = "d-model1">Model 1: Calculating E.coli Concentration by GFP Intensity</h2>
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<br />
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<h3>Method</h3>
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<p>
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<p>
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<p>To find the mathematical relationship between GFP and E.coli concentration, we measure the GFP growing curve with different MOI value every two hours. Then, perform a series of calculation and finally arrive at the mathematical relationship between GFP and E.coli concentration.</p>
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<p>
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<br />
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<h3>Obtaining the Mathematical Relationship</h3>
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<p>
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<p>Table 1.0 shows the corresponding Fluorescence intensity with different MOI value. However, the units of E.coli we want should be transformed into another form.</p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/f/fb/T--Mingdao--Modeling019.jpg"alt=" " style="width:35%" >
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<img class="center" src="https://static.igem.org/mediawiki/2018/1/12/T--Mingdao--Modeling017.jpg"alt=" " style="width:35%" >
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<br />
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<h3>Conversion of MOI to E.coli density</h3>
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<p>
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<p>
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<p>The equation of E.coli density is shown below:
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/1/10/T--Mingdao--Modeling02.jpg"alt=" " style="width:70%" >
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<p>
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Since the MOI value refers to the ratio of E.coli cells to mosquito cells, we can use the density of mosquito cells to calculate the E.coli density. Plus, the mosquito cells are seeded at the density of 1.8×〖10〗^5 cells/well, and the volume of each well is 100μL.
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<p>
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Thus, the equation become
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/a/ac/T--Mingdao--Modeling03.jpg"alt=" " style="width:70%" >
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<p>
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<p>Then, we will turn the MOI in Table 1.0 into E.coli density to form Table 1.1. Next, we will use Table 1.1 to keep figuring out the mathematical relationship between E.coli concentration and GFP intensity.</p>
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<br />
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<h3>Forming the mathematical expression</h3>
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<p>
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<p>
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<p>Before we keep working on our calculation, it’s worth noticed that the GFP intensity has already existed while there is no E.coli . Thus, the intensity of GFP with no E.coli should be eliminated as discussing the relationship between E.coli concentration and GFP intensity, which means [GFP] should minus the [〖GFP〗_0] with no E.coli, and add it back in our final result.
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<p>
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With that in mind, we form the Table 1.2
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/3/36/T--Mingdao--Modeling019r8.jpg"alt=" " style="width:35%">
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<p>
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Now we can begin with our data analyzing.
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<p>
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</p>
 
</p>
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<br />
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<br />  
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<h3>Data Analyzing</h3>
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<p style="text-indent:2em">
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<p>
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Second, we designed a HIV viral blood testing kit consisting of BioBrick parts of Ac5 promoter, CD4 extracellular domain, Drosophila transmembrane and intracellular domains, and poly A, as well as Ac5-GFP-polyA as a positive expression control.
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<p>
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<P>Figure 2.0 shows the graphic expression between the [E.coli] and GFP intensity, the Exponential Function is shown below:<p>
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</p>
 
</p>
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<p>
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<br /><br />
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/f/f0/T--Mingdao--phil14-2.png" alt="" style="width: 100%; margin-bottom: 20px;">
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<img class="center" src="https://static.igem.org/mediawiki/2018/7/71/T--Mingdao--Modeling07.jpg"alt=" " style="width:70%" >
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<br /><br />
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/3/38/T--Mingdao--phil14-3.png" alt="" style="width: 80%; margin-bottom: 20px;">
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<p>
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<p>Next, we will bring in that [〖GFP〗_0 ]=813 to the Exponential Function and obtain the final graphic expression and function.</p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/0/0a/T--Mingdao--Modeling08.jpg" alt=" " style="width:70%">
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<p>
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<p> Combining the constants, we arrive at</p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/7/7e/T--Mingdao--Modeling10.jpg" alt=" " style="width:70%">
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<br />
 
<br />
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<h2><strong>Conclusion</strong></h2>
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<br /><br />
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<br />
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          <h5 id="d-kit1">UNIVERSAL PATHOGEN BLOOD TESTING KIT I</h5>
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<h3>Application</h3>
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<img class="center" src="https://static.igem.org/mediawiki/2018/2/2f/T--Mingdao--phil14-4.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<p>
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<br /><br />
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/a/a0/T--Mingdao--phil13M1.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<p>With the formula, we can now build a calculator to calculate the [E.coli] by GFP Intensity, and apply the formula to our devices to from a well-designed prototype. The devices will calculate the [E.coli] automatically based on the GFP intensity they detect. As a result, the testers will be able to know the [E.coli] in their blood through our devices.</p>
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<br /><br />
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<br />
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<img class="center" src="https://static.igem.org/mediawiki/2018/c/c7/T--Mingdao--phil14-9.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<h3>Limitation</h3>
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<br /><br />
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/b/bd/T--Mingdao--phil14-10.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<p>
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<br /><br />
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<p>However, there are some limitations to our Model 1. Not knowing when all the E.coli cells bind to the GAM 1 promoter, we can’t make sure when we can utilize the formula to conduct calculation.
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<img class="center" src="https://static.igem.org/mediawiki/2018/f/f9/T--Mingdao--phil14-11.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<p>
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<br /><br />
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Consequently, we will conduct Model 2 to figure out the limitation.</p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/6/60/T--Mingdao--phil14-12.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<p>
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<br /><br />
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          <h5 id="d-kit2">HUMAN HIV VIRUS BLOOD TESTING KIT II</h5>
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<img class="center" src="https://static.igem.org/mediawiki/2018/6/6c/T--Mingdao--phil14-13.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<br /><br />
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<img class="center" src="https://static.igem.org/mediawiki/2018/d/db/T--Mingdao--phil14-14.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<br /><br />
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      <img class="center" src="https://static.igem.org/mediawiki/2017/a/a8/T--CSMU_NCHU_Taiwan--safety-line.png" style="width:100%">
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<img class="center" src="https://static.igem.org/mediawiki/2018/e/e5/T--Mingdao--phil14-15.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<br /><br />
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<img class="center" src="https://static.igem.org/mediawiki/2018/2/29/T--Mingdao--phil14-16.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<br /><br />
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<br />
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<img class="center" src="https://static.igem.org/mediawiki/2018/7/70/T--Mingdao--phil14-17.png" alt="" style="width: 70%; margin-bottom: 20px;">
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<h2 id="d-model2">Model 2: Number of E.coli Cells Binding to The GAM 1 Promoter Increase With Time</h2>
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<br /><br />
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<br />
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<h3>Method</h3>
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<p>
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<p>
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<p>To know when all the E.coli cells bind to GAM 1 promoter, we measure the GFP growing curves with different MOI value every two hours, and differentiate the growing curves to find the time that the instant GFP transcription rate reaches the maximum, which is the time all the E.coli cells bind to GAM 1 promoter.
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<p>
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After the differentiation, we will be able to obtain the graphic expression between the binding time and the [E.coli]. By analyzing the graphic expression, the very beginning time that the testers can detect GFP can be calculated.
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<p>
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In addition, the limitation mentioned in Model 1 can also be quantified via the graphic expression we obtained in Model 2.
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<p>
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</p>
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<br />
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<h3>Standardization of GFP Growing Curve</h3>
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<p>
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<p>
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<p>Since we need to differentiate the GFP growing curve, the standardization is essential. Thus, we will digitize the GFP growing curve so that we can conduct the differentiation successfully. </p>
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<br />
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<h3>Raw Data</h3>
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<p>
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<p>
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<p>
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We perform two experiments respectively with different purposes.
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<p>
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The first one is to made for the blank so there is no DNA inside the well and no inducement of GAM 1 promoter, since the cells itself contribute to some absorbance, too.
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<p>
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The second one consists of E.coli with induced GAM 1 promoter. Both of the experiments are measured by our plate reader every two hours.
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<p>
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The Table 2.0 and Table 2.1 are shown below</p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/b/b2/T--Mingdao--Modeling11.jpg" alt=" " style="width:100%">
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/3/37/T--Mingdao--Modeling12.jpg" alt=" " style="width:100%">
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<br />
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<h3>Absorbance of green fluorescence protein</h3>
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<p>
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<p>
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<p>The actual absorbance of GFP is the absorbance of the E.coli cells with induced GAM 1 promoter minus the absorbance of E.coli cells without DNA and no inducement of GAM 1 promoter. After the calculation, the result are shown as Table 2.2</p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/2/22/T--Mingdao--Modeling13.jpg" alt=" " style="width:100%">
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<br />
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<h3>Standardization</h3>
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<p>
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<p>
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<p>Then, we will illustrate the growing curves and find the formula of each growing curve so that we can differentiate them. It’s noticed that we don’t adopt the GFP growing curve without E.coli, since its GAM 1 promoter isn’t induced.
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<p>
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After analyzing, we find that cubic equation perfectly fits to our experiment data. Those cubic equations are shown as Figure 3.0 to Figure 3.4</p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/c/cb/T--Mingdao--Modeling002.jpg" alt=" " style="width:70%">
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/4/47/T--Mingdao--Modeling003.jpg" alt=" " style="width:70%">
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/4/41/T--Mingdao--Modeling004.jpg" alt=" " style="width:70%">
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/9/9c/T--Mingdao--Modeling005.jpg" alt=" " style="width:70%">
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/6/6b/T--Mingdao--Modeling006.jpg" alt=" " style="width:70%">
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<p>
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<p>
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<p>Also, the mathematical expressions of these cubic equations are shown as Table 3.0 and the graphic expressions are shown as Figure 4.0</p>
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<p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/a/a8/T--Mingdao--Modeling007.jpg" alt=" " style="width:70%">
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/4/46/T--Mingdao--Modeling008.jpg" alt=" " style="width:70%">
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<br />
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<h3><strong>Number of E.coli Cells Binding to GAM 1 Promoter</strong></h3>
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<br />
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<h3>Derivative of the green fluorescence growing curve<h3>
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<p>
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<p>
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<p>When all the E.coli cells bind to GAM 1 promoter, the instant GFP transcription rate will reach the maximum value. As a consequence, what we need to do is to differentiate the GFP growing curves at different MOI value, and find the maximum of the differentiated formulas as well as the corresponding time.
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<p>
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With that in mind, we conduct the derivative of the mathematical expressions in Table 3.0 and form Table 3.1
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</p>
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<p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/9/99/T--Mingdao--Modeling009.jpg" alt=" " style="width:70%">
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<br />
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<h3>Obtaining The Maximum and The Corresponding Time</h3>
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<p>
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<p>
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<p>To calculate the maximum of the derivative of the green fluorescence growing curve, we need to conduct the second derivative and find the maximum and corresponding time. The result is shown as Table 3.2</p>
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<p>
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<p>
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<img class="center" src="https://2018.igem.org/File:T--Mingdao--Modeling010.jpg"alt=" " style="width:70%">
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<p>
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<p>
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<p>Then, we will turn the MOI value into E.coli density to form Table 3.3
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Also, the graphic expression of the relationship between time and E.coli density is shown as Figure 5.0</p>
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<p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/7/74/T--Mingdao--Modeling011.jpg" alt=" " style="width:70%">
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/d/d7/T--Mingdao--Modeling013.jpg" alt=" " style="width:70%">
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<p>
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<p>
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<p>We also arrive at the equation between time and E.coli concentration</p>
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<p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2018/d/d2/T--Mingdao--Modeling014.jpg" alt=" " style="width:70%">
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<br />
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<h3><strong>Conclusion</strong></h3>
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<br />
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<h3>Application</h3>
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<p>
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<p>
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<p>With the formula, we can utilize the [E.coli] calculated in Model 1 to calculate how long the testers should wait to detect green fluorescence in our devices and demonstrate it on our devices to inform the testers. Then, the formula will also be applied to our calculator as well.</p>
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<p>
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<p>
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<img class="center" src="https://static.igem.org/mediawiki/2017/a/a8/T--CSMU_NCHU_Taiwan--safety-line.png" alt="" style="width:100%">
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                                        <div style = "border-style:solid; text-align:center; padding:20px" class="col-sm-12">
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                                    <div class="row">
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                                    <h2 style = "padding:0">CALCULATOR</h2>
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                                        <div class="col-sm-6">
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                                        <h3 style="padding:0"> E.coli Concentration Calculator</h3>
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                                        <script>
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                                            function round(value, decimals) {
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                                                return Number(Math.round(value+'e'+decimals)+'e-'+decimals);
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                                            }
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                                            calculateee = function(){
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                                                var XInputLOCSP = document.getElementById("inputLOCS").value;
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                                                var XCrystDamP = 54.163*(2.7182^(0.0024*(XInputLOCSP)));
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+
−
                                                XInputLOCSPid.innerHTML = round(XInputLOCSP,1);
+
−
                                                XCrysDamPid.innerHTML = round(XCrystDamP,4);
+
−
                                                XGSRPid.innerHTML = round(XGSRP,2);
+
−
                                                XNPConcPid.innerHTML = round(XNPConcP,2);
+
−
                                                XEyedropPid.innerHTML = round(XEyedropP,2);
+
−
                                                XResultPid.innerHTML = round(XResultP,2);
+
−
 
+
−
 
+
−
                                            }
+
−
                                        </script>
+
−
 
+
−
 
+
−
                                        <span style="width:70%">Type in the value:</span><input id = "inputLOCS" type="text" style="width:30%">
+
−
 
+
−
                                        <br><span style="font-size:13px">We guarentee that by applying this prevention eyedrop daily, your LOCS score will remain below your threshold for 50 years.</span>
+
−
                                        <br><button onclick = "calculateee()">Calculate!</button><br>
+
−
                                        <br>
+
−
                                        <span>Prevention Results </span>
+
−
 
+
−
                                        <table class="table table-hover fixed" style="font:16px"> 
+
−
                                            <col width="150px" />
+
−
                                            <col width="150px" />
+
−
                                            <col width="100px" />
+
−
                                            <thead>
+
−
                                                <tr>
+
−
                                                    <th>Variable</th>
+
−
                                                    <th>Value</th>
+
−
                                                    <th>Source</th>
+
−
                                                </tr>
+
−
                                            </thead>
+
−
                                            <tbody align="right">
+
−
                                                <tr>
+
−
                                                    <td>The Value You Imput</td>
+
−
                                                    <td> <span id="XInputLOCSPid">&nbsp;&nbsp;&nbsp;</span></td>
+
−
 
+
−
                                                    <td></td>
+
−
                                                </tr>
+
−
                                                <tr>
+
−
                                                    <td>The Value You Get</td>
+
−
                                                    <td> <span id="XCrysDamPid">&nbsp;&nbsp;&nbsp; </span>&nbsp;Density</td>
+
−
 
+
−
                                                    <td>Model 1</td>
+
−
                                                </tr>
+
−
                                               
+
−
                                            </tbody>
+
−
                                        </table>
+
−
 
+
−
                                    </div>
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
                                        <div class="col-sm-6">
+
−
                                        <h3 style="padding:0">Responding Time Calculator</h3>
+
−
                                        <script>
+
−
                                            function round(value, decimals) {
+
−
                                                return Number(Math.round(value+'e'+decimals)+'e-'+decimals);
+
−
                                            }
+
−
                                            calculateeeT = function(){
+
−
                                                var XInputLOCST = document.getElementById("inputLOCSTget").value;
+
−
                                                var XCrysDamT = 0.0002*(XInputLOCST)+6.0864;
+
−
                                                var XAbsorbanceT = XCrysDamT/9.276;
+
−
                                                var XCH25HT = XAbsorbanceT/0.228;
+
−
 
+
−
                                                var XEyedropT = XCH25HT/14.04/0.001;
+
−
                                                var XResultT = XEyedropT*50/1000;
+
−
                                                var XNumofEyedropT = Math.ceil(XResultT/0.75);
+
−
 
+
−
                                                XInputLOCSTid.innerHTML = round(XInputLOCST,1);
+
−
                                                XCrysDamTid.innerHTML = round(XCrysDamT,4);
+
−
                                                XAbsorbanceTid.innerHTML = round(XAbsorbanceT,3)
+
−
                                                XCH25HTid.innerHTML = round(XCH25HT,2);
+
−
 
+
−
                                                XEyedropTid.innerHTML = round(XEyedropT,2);
+
−
                                                XResultTid.innerHTML = round(XResultT,2);
+
−
                                                XNumofEyeDropTid.innerHTML = XNumofEyedropT;
+
−
 
+
−
 
+
−
                                            }
+
−
                                        </script>
+
−
 
+
−
 
+
−
                                        <span style="width:70%">Enter value:</span><input id = "inputLOCSTget" type="text" style="width:30%">
+
−
 
+
−
                                        <br><span style="font-size:13px">By applying the following treatment, leaving an hour before each dose of eyedrops, we guarentee that it will lower your LOCS score to essentially 0.</span>
+
−
                                        <br><button onclick = "calculateeeT()">Calculate!</button><br>
+
−
                                        <br>
+
−
                                        <span>Treatment Results </span>
+
−
 
+
−
                                        <table class="table table-hover fixed" style="font:16px"> 
+
−
                                            <col width="150px" />
+
−
                                            <col width="150px" />
+
−
                                            <col width="100px" />
+
−
                                            <thead>
+
−
                                                <tr>
+
−
                                                    <th>Variable</th>
+
−
                                                    <th>Value</th>
+
−
                                                    <th>Source</th>
+
−
                                                </tr>
+
−
                                            </thead>
+
−
                                            <tbody align="right">
+
−
                                                <tr>
+
−
                                                    <td>The value you imput</td>
+
−
                                                    <td> <span id="XInputLOCSTid">&nbsp;&nbsp;&nbsp;</span></td>
+
−
 
+
−
                                                    <td></td>
+
−
                                                </tr>
+
−
                                                <tr>
+
−
                                                    <td>The value you get</td>
+
−
                                                    <td> <span id="XCrysDamTid">&nbsp;&nbsp;&nbsp; </span>&nbsp; Hr </td>
+
−
 
+
−
                                                    <td>Model 2</td>
+
−
                                                </tr>
+
−
                                                <tr>
+
−
                                                   
+
−
                                            </tbody>
+
−
                                        </table>
+
−
 
+
−
                                    </div>
+
−
                                  </div>
+
−
                            </div>
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
 
+
−
<img class="center" src="https://static.igem.org/mediawiki/2017/a/a8/T--CSMU_NCHU_Taiwan--safety-line.png" alt="" style="width:100%">
+
−
<br />
+
−
<h2 id="d-conclusion">Conclusion</h2>
+
−
<br />
+
−
<p>Our model not only help to build the formulaic system which applied to our devices, but also make us better understand our project. Because our devices can only detect the GFP intensity, our model is required to build a well-designed devices and system.
+
−
In our model 1, we obtain the formula which allows us to calculate [E.coli] from GFP intensity. While in model 2, we obtain the formula which allows us to calculate how long the testers should wait to get the result of the test based on the [E.coli] calculated in mode 1. For [E.coli] and the time interval, they will be demonstrated on our devices to show them to the testers.
+
−
To sum up, Our model act as a bridge between our devices and the testers, and quantifies the significant parameters in our project, which allow the masses to simply get the result of the test without complex calculations.</p>
+
−
 
+
−
 
+
−
           
+
−
 
+
−
 
+
  
 
           </div>
 
           </div>
Line 576: Line 235:
 
       </div>
 
       </div>
 
     </div>
 
     </div>
 +
 
     <div class="path-btns" style="left:0;">
 
     <div class="path-btns" style="left:0;">
 
       <div class="path">
 
       <div class="path">
Line 583: Line 243:
 
           </svg>
 
           </svg>
 
         </div>
 
         </div>
−
         <div id="d-introduction-btn" class="path-dot"></div>
+
         <div id="intro-btn" class="path-dot"></div>
 
         <div class="pathWord path-word-sm">
 
         <div class="pathWord path-word-sm">
 
           <p>Introduction</p>
 
           <p>Introduction</p>
 
         </div>
 
         </div>
 
       </div>
 
       </div>
 +
 
       <div class="path">
 
       <div class="path">
 
         <div class="pathSvg">
 
         <div class="pathSvg">
Line 594: Line 255:
 
           </svg>
 
           </svg>
 
         </div>
 
         </div>
−
         <div id="d-model1-btn" class="path-dot" style="top: 100px"></div>
+
         <div id="kit1-btn" class="path-dot" style="top: 100px"></div>
 
         <div class="pathWord path-word-sm">
 
         <div class="pathWord path-word-sm">
−
           <p>Model 1</p>
+
           <p>Kit 1</p>
−
        </div>
+
−
      </div>
+
−
      <div class="path">
+
−
        <div class="pathSvg">
+
−
          <svg width="80" height = "100">
+
−
            <rect x ="36" y="20" width="6" height="80" style="fill:#385e66"/>
+
−
          </svg>
+
−
        </div>
+
−
        <div id="d-model2-btn" class="path-dot" style="top: 200px"></div>
+
−
        <div class="pathWord path-word-sm">
+
−
          <p>Model 2</p>
+
 
         </div>
 
         </div>
 
       </div>
 
       </div>
 +
 
       <div class="path">
 
       <div class="path">
 
         <div class="pathSvg">
 
         <div class="pathSvg">
Line 616: Line 267:
 
           </svg>
 
           </svg>
 
         </div>
 
         </div>
−
         <div id="d-conclusion-btn" class="path-dot" style="top: 300px"></div>
+
         <div id="kit2-btn" class="path-dot" style="top: 200px"></div>
 
         <div class="pathWord path-word-sm">
 
         <div class="pathWord path-word-sm">
−
           <p>Conclusion</p>
+
           <p>Kit 2</p>
 
         </div>
 
         </div>
 
       </div>
 
       </div>
 +
 
     </div>
 
     </div>
 +
 
     <div class="top">
 
     <div class="top">
−
       <img class="center" src="https://static.igem.org/mediawiki/2017/5/52/T--CSMU_NCHU_Taiwan--top.png" alt="">
+
       <img class="center" src="https://static.igem.org/mediawiki/2018/5/58/T--Mingdao--go_to_top.jpg" alt="">
 
     </div>
 
     </div>
−
  </body>
+
 
 +
</body>
 
   <script type="text/javascript">
 
   <script type="text/javascript">
−
     $("#d-introduction-btn").click(function() {
+
     $("#intro-btn").click(function() {
 
       $('html, body').animate({
 
       $('html, body').animate({
−
           scrollTop: $("#d-introduction").offset().top
+
           scrollTop: $("#d-intro").offset().top
−
       }, 500);
+
       }, 400);
 
     });
 
     });
−
     $("#d-model1-btn").click(function() {
+
     $("#kit1-btn").click(function() {
 
       $('html, body').animate({
 
       $('html, body').animate({
−
           scrollTop: $("#d-model1").offset().top
+
           scrollTop: $("#d-kit1").offset().top
−
       }, 500);
+
       }, 400);
 
     });
 
     });
−
     $("#d-model2-btn").click(function() {
+
     $("#kit2-btn").click(function() {
 
       $('html, body').animate({
 
       $('html, body').animate({
−
           scrollTop: $("#d-model2").offset().top
+
           scrollTop: $("#d-kit2").offset().top
−
       }, 500);
+
       }, 400);
−
    });
+
−
    $("#d-conclusion-btn").click(function() {
+
−
      $('html, body').animate({
+
−
          scrollTop: $("#d-conclusion").offset().top
+
−
      }, 500);
+
 
     });
 
     });
  
 
     $(document).ready(function(){
 
     $(document).ready(function(){
 
       $('.top').on('click', function(){
 
       $('.top').on('click', function(){
−
         $('html, body').animate({scrollTop: '0px'}, 500);
+
         $('html, body').animate({scrollTop: '0px'}, 400);
 
       });
 
       });
−
         $("#d-introduction-btn").css('background-color', '#385e66');
+
         $("#intro-btn").css('background-color', '#385e66');
 
         var scroll_pos = 0;
 
         var scroll_pos = 0;
 
         $(document).scroll(function() {
 
         $(document).scroll(function() {
 
             scroll_pos = $(this).scrollTop();
 
             scroll_pos = $(this).scrollTop();
  
−
             d_introduction_pos = $("#d-introduction").offset().top -100
+
             d_intro_pos = $("#d-intro").offset().top -100
−
             d_model1 = $("#d-model1").offset().top -100
+
             d_k1_pos = $("#d-kit1").offset().top -100
−
             d_model2= $("#d-model2").offset().top -100
+
             d_k2_pos= $("#d-kit2").offset().top -100
−
            d_conclusion_pos = $("#d-conclusion").offset().top -100
+
  
 
             // introduction
 
             // introduction
−
             if(scroll_pos < d_model1_pos) {
+
             if(scroll_pos < d_k1_pos) {
 
                 $(".path-dot").css('background-color', '#fff')
 
                 $(".path-dot").css('background-color', '#fff')
−
                 $("#d-introduction-btn").css('background-color', '#385e66');
+
                 $("#intro-btn").css('background-color', '#385e66');}
 
             // model1  
 
             // model1  
−
            } else if(scroll_pos < d_model2_pos){
+
            else if(scroll_pos < d_k2_pos){
−
               if(scroll_pos >= d_model1_pos){
+
               if(scroll_pos >= d_k1_pos){
−
                $(".path-dot").css('background-color', '#fff')
+
−
                $("#d-model1-btn").css('background-color', '#385e66');}
+
−
 
+
−
            // model2
+
−
            } else if(scroll_pos < d_conclusion_pos){
+
−
              if(scroll_pos >= d_model2_pos){
+
 
                 $(".path-dot").css('background-color', '#fff')
 
                 $(".path-dot").css('background-color', '#fff')
−
                 $("#d-model2-btn").css('background-color', '#385e66');}
+
                 $("#kit1-btn").css('background-color', '#385e66');}
 
             }
 
             }
 
              
 
              
 
             // conclusion
 
             // conclusion
−
             else if( scroll_pos >= d_conclusion_pos) {
+
             else if( scroll_pos >= d_k2_pos) {
 
                 $(".path-dot").css('background-color', '#fff')
 
                 $(".path-dot").css('background-color', '#fff')
−
                 $("#d-conclusion-btn").css('background-color', '#385e66');
+
                 $("#kit2-btn").css('background-color', '#385e66');
 
             }
 
             }
 
         });
 
         });

Latest revision as of 03:34, 18 October 2018

HOME
Description

Mosquito Toll-Amp signaling plays an important roll in response to and regulating blood infectious pathogens.


To genetically engineer mosquitoes as blood testing tools, we’ve collected DNA materials as BioBrick parts for future assembly.


First, we designed a universal pathogen blood testing kit consisting of BioBrick parts of GAM1 promoter, GFP and polyA, as well as Ac5 promoter as a positive expression control.


Second, we designed a HIV viral blood testing kit consisting of BioBrick parts of Ac5 promoter, CD4 extracellular domain, Drosophila transmembrane and intracellular domains, and poly A, as well as Ac5-GFP-polyA as a positive expression control.








UNIVERSAL PATHOGEN BLOOD TESTING KIT I












HUMAN HIV VIRUS BLOOD TESTING KIT II










Introduction

Kit 1

Kit 2