Difference between revisions of "Team:TCFSH Taiwan/Model"

 
(25 intermediate revisions by the same user not shown)
Line 215: Line 215:
 
     margin: auto;
 
     margin: auto;
 
     z-index:999;
 
     z-index:999;
 +
    margin-top: 40px;
 
     }
 
     }
 
}
 
}
Line 354: Line 355:
 
     padding-bottom:10px;
 
     padding-bottom:10px;
 
     font-weight:400 !important;
 
     font-weight:400 !important;
 +
    color: #e6e600;
 
}
 
}
  
Line 427: Line 429:
 
    
 
    
 
           <div class="node" id="node1">
 
           <div class="node" id="node1">
           <div class="topic"><p class="text_color"><a href="#title1" style="text-decoration:none;color:#F3F7F7;">Model Introduction</p></div>
+
           <div class="topic"><p class="text_color">Model Introduction</p></div>
 
             <div class="active-circle">
 
             <div class="active-circle">
 
               <div class="splash"></div>
 
               <div class="splash"></div>
Line 440: Line 442:
 
    
 
    
 
           <div class="node" id="node2">
 
           <div class="node" id="node2">
           <div class="topic"><p class="text_color"><a href="#title2" style="text-decoration:none;color:#F3F7F7;">What are we modeling?</a></p></div>
+
           <div class="topic"><p class="text_color">What are we modeling?</p></div>
 
             <div class="active-circle">
 
             <div class="active-circle">
 
               <div class="splash"></div>
 
               <div class="splash"></div>
Line 453: Line 455:
  
 
           <div class="node" id="node2">
 
           <div class="node" id="node2">
           <div class="topic"><p class="text_color"><a href="#title2" style="text-decoration:none;color:#F3F7F7;">Model</a></p></div>
+
           <div class="topic"><p class="text_color">Model</p></div>
 
             <div class="active-circle">
 
             <div class="active-circle">
 
               <div class="splash"></div>
 
               <div class="splash"></div>
Line 469: Line 471:
 
<!--圖片-->
 
<!--圖片-->
 
   <div class="img-container">
 
   <div class="img-container">
         <img src="https://static.igem.org/mediawiki/2016/0/01/NCTU_MODELING_BIG_PICTURE.png" class="main-img" width="100%">
+
         <img src="https://static.igem.org/mediawiki/2017/1/12/Model_tcfsh.jpeg" class="main-img" width="100%">
 
   </div>
 
   </div>
 
   
 
   
Line 479: Line 481:
 
     <div id="modelingContainer">
 
     <div id="modelingContainer">
 
       <p class="title">Model Introduction</p>
 
       <p class="title">Model Introduction</p>
       <p class="content">In our opinion, modelling has always played an important role in every subject, even beyond science. In our project, it comes up with real data, and thus make biological theories easier to be realized and observed. Carl Gauss said that “Mathematics is the queen of the science.” A proposition of mathematics is reliable and indisputable, whereas other science theories have always been in a risk of being overthrown. The reason why modelling has good reputation and a certain status is that it theorems the scientific phenomenon, and makes them more trustworthy. By conducting modelling, we can have a reasonable embryonic form to estimate a possible solution of a difficult problem. However, the reaction series or the operation mechanism of an unknown equation needs to be reasonably presumed, and this is the most difficult part in the whole process. After the right theories come out, we can amend our hypothetical surmise, and remake another model. In the modelling process we’ve done, the main technique we used is DE (differential equation). We use derivative to describe the difference of any variables within a very short time. But we’ve met some very complicated equations when solving the problem, so we use the program MATLAB to help calculate the results.</p>
+
       <p class="content">Modeling has always played an important role in every field of science. In our project, modeling comes up with real data, and thus makes biological theories easier to be realized and observed. Carl Gauss said, “Mathematics is the queen of the science.” A proposition of mathematics is reliable and indisputable, whereas other science theories have always been at risk of being overthrown. The reason why modeling has a good reputation and a certain status is that it theorems scientific phenomena, and makes them more trustworthy. By conducting modeling, we can have a reasonable embryonic form to formulate a possible solution to a difficult problem. However, the reaction series or the operation mechanism of an unknown equation needs to be reasonably presumed, and this is the most difficult part in the whole process. Inappropriate assumption can lead to erroneous results. Once the right theories are established, we can amend our hypothetical surmise, and build another model. In the modeling process we’ve done, the main technique we used is DE (differential equation). We use derivative to describe the difference of any variables that vary within a very short time. But we’ve encountered some very complicated equations when trying to solve the problem, so we use the program MATLAB to help calculate the results.</p>
 
</div>
 
</div>
  
Line 486: Line 488:
 
       <p class="title">What are we modeling?</p>
 
       <p class="title">What are we modeling?</p>
 
       <p class="content">
 
       <p class="content">
         <br> - The growth of E. coli</br>
+
         <br> - The growth of <span style="font-style:italic;">E. coli</span></br>
 
         <br> - The Expression of Different Color</br>
 
         <br> - The Expression of Different Color</br>
         <br> - The Concentration Function f:(substance,time)→concentration
+
         <br> - The Concentration Function f:(substance,time)→concentration</br>
 
         <br> - Math Is Long, Life Is Short: Math in Our Life</br></p>
 
         <br> - Math Is Long, Life Is Short: Math in Our Life</br></p>
 
</div>
 
</div>
Line 497: Line 499:
 
       <p class="title">Model</p>
 
       <p class="title">Model</p>
 
     <div class="modelingPart">
 
     <div class="modelingPart">
         <h2 class="content-1" id="titleA" style="color:#33FFCC">I. The growth of <span style="font-style:italic;">E. coli</span></h2>
+
         <h2 class="content-1" id="titleA" style="color:#33FFCC">I. The growth of <span style="font-style:italic;">E. coli</span> (Click to see more)</h2>
 
         <div class="modelingPartContent" id="partA">
 
         <div class="modelingPartContent" id="partA">
  
             <p class="content">At first, we assume that E. coli proliferate and die at the same ratio over time, and the value difference is the birth rate (<span style="font-style:italic;">μ<sub>g</sub></span>). So, we do derivative with this assumption.</p>
+
             <p class="content">At first, we assume that <span style="font-style:italic;">E. coli</span> proliferate and die at the same ratio over time, and the value difference is the birth rate (<span style="font-style:italic;">μ<sub>g</sub></span>). So, we do derivative with this assumption.</p>
  
 
             <img src="https://static.igem.org/mediawiki/2017/7/7e/Model_1.jpeg" class="bigphoto" width="70%">
 
             <img src="https://static.igem.org/mediawiki/2017/7/7e/Model_1.jpeg" class="bigphoto" width="70%">
Line 530: Line 532:
 
         <!--2.-->
 
         <!--2.-->
 
     <div class="modelingPart">
 
     <div class="modelingPart">
         <h2 class="content-1" id="titleB" style="color:#44FCCE">II. Method</h2>
+
         <h2 class="content-1" id="titleB" style="color:#44FCCE">II. The Expression of Different Color (Click to see more)</h2>
 
         <div class="modelingPartContent" id="partB">
 
         <div class="modelingPartContent" id="partB">
           <p class="content">The method of toxin selection can be separated into three part: crawler, filter, and selection.</p>
+
           <p class="content-1">Assumption</p>
<ul style="list-style-image:none;list-style-type:decimal;">
+
          <p class="content">1. In order to write the equations down simply, we assume that all the chemical reaction rates are proportional to the concentration of each reagent (e.g. for the reaction: A+B+C→D+E,the forward rate <span style="font-style:italic;">r<sub>+</sub>=k<sub>+</sub>[A][B][C]).</p></span>
           <li class="list">Toxin Collection—we planned to collect information of toxin peptides to establish our own database for Pantide from protein databases and some research results like taxon and toxicity from published papers. </li>
+
          <p class="content">2.For every substances produced by biobricks, we assume that their production rate =<span style="font-style:italic;">φ[mRNA]</span>, <br>[mRNA]= the concentration of the promoted biobrick</br><br>φ= the result of multiplication of rate constant, coefficient of correction (since a biobrick is different from a reagtant), a dimension <span style="font-style:italic;">T<sup>-1</sup></span></br></p>
           <li class="list">Toxin Filtering—based on background knowledge of toxin peptides, we set up some conditions to filter out those unsuitable to use as Pantide.</li>
+
 
          <li class="list">Toxin Processing—we used online protein analytic tools to classify the remained peptides into groups by their similarity. Finally, we select out three distinct peptides from different groups to proof concept of Pantide.</li>
+
           <p class="content-1">Equations & Solutions</p>
</ul>
+
          <img src="https://static.igem.org/mediawiki/2017/8/8c/Equations.png" class="bigphoto" width="70%">
 +
          <p class="content">According to the picture, we can write down 3 equations as follows:</p>
 +
          <img src="https://static.igem.org/mediawiki/2017/c/c4/Modeling5.jpeg" class="bigphoto" width="70%">
 +
          <p class="content">P.S. φ= the result of multiplication of rate constant, coefficient of correction (since a promoter is different from a reagtant), a dimension <span style="font-style:italic;">𝑇<sup>−1</sup></span></p>
 +
 
 +
          <p class="content">By solving these 3 equations, the solution expressed by <span style="font-style:italic;">φ、k and [P<sup>a</sup>]</span> are as follows:
 +
           <img src="https://static.igem.org/mediawiki/2017/f/f0/Modeling6.jpeg" class="bigphoto" width="70%">
 +
 
 +
          <p class="content">When the concentration of each activated promoter reaches to each of their steady state, then we can simplify the equations as follows:</p>
 +
          <img src="https://static.igem.org/mediawiki/2017/5/57/Modeling7.jpeg" class="bigphoto" width="70%">
 +
 
 +
          <p class="content">Besides, since <span style="font-style:italic;">lim<sub>t→∞</sub>⁡(1-1/e<sup>kt</sup>) = 1</span>, satisfying the definition of the horizontal asymptotes. And d(1-1/e<sup>kt</sup>)/dt=ke<sup>-kt</sup>>0 (t∈[0,∞)), so it is a strictly increasing function.
 +
            <br>So, this is a strictly increasing and convergent function with an upper bound 1.</br>
 +
          <br>Then the result is that the extremum of the concentration is:</br></p>
 +
 
 +
          <img src="https://static.igem.org/mediawiki/2017/e/e8/Modeling8.jpeg" class="bigphoto" width="70%">
 +
 
 +
          <p class="content-1">Degradation Rate Constant Calculation</p>
 +
          <p class="content">As for the other variable written in the solutions, the degradation rate constant, can also be solved with differential equations. Since the degradation rate is an “order one” reaction, the equation can be written as follow:</p>
 +
          <p class="content"><span style="font-style:italic;">dM/dt= -k<sub>d</sub>M</span></p>
 +
          <p class="content">Then, after solving the equation and substituting the boundary conditions<br><span style="font-style:italic;">(t = 0⇒M = M<sub>0</sub>)</span>, the the solution is:</br></p>
 +
          <img src="https://static.igem.org/mediawiki/2017/2/24/Modeling9.jpeg" class="bigphoto" width="70%">
 +
 
 +
          <p class="content">According to the project 2008 iGEM KULeuven and 2014 iGEM Edinburgh had done, both GFP-LVA and RFP-LVA degrades to half of the amount within 50 to 60 minutes, so we assume that cjblue is the same. The RFP and BFP reference are as follow (the latter degrades to half of the amount about 50 minutes while the former does about 3 hours). So we can get</p>
 +
          <img src="https://static.igem.org/mediawiki/2017/e/ef/Modeling10.jpeg" class="bigphoto" width="70%">
 +
 
 +
          <p class="content">From these degradation rate constants and the relation between concentration and time, the “[cjblue],[RFP],[BFP]-t Diagram” is as follow:</p>
 +
 
 +
          <img src="https://static.igem.org/mediawiki/2017/e/e5/Modeling11.png" class="bigphoto" width="70%">
 +
 
 +
          <p class="content">According to this simulation diagram, we can know that cjblue and BFP increase faster—coming to 90% of maximum only takes about 3 hours. As for RFP, it takes about 5 hours to reach 70% of maximum, which is also acceptable.</p>
 +
          <p class="content">Through mathematical modeling, when observing the sicker changing to a specific color, we can calculate the ratio of each kind of chromoprotein by quantifying it.</p>
 +
          <img src="" class="bigphoto" width="70%">
 +
 
 +
 
 
         </div>       
 
         </div>       
 
     </div>
 
     </div>
Line 545: Line 581:
 
     <div class="modelingPart">
 
     <div class="modelingPart">
 
        
 
        
         <h2 class="content-1" id="titleC" style="color:#5BFCD4">III. Step 1: Crawler</h2>
+
         <h2 class="content-1" id="titleC" style="color:#5BFCD4">III. The Concentration Function (Click to see more)</h2>
 
         <div class="modelingPartContent" id="partC">
 
         <div class="modelingPartContent" id="partC">
             <p class="content">In the beginning, we searched on UniProtKB/Swiss-Prot. It is a freely accessible database of protein sequence and functional information that is the manually annotated and reviewed section. (<a href="http://www.uniprot.org" style="color:#44E287;">http://www.uniprot.org/</a>) By searching the keyword “insecticidal NOT crystal” we wanted to find all the proteins that have insecticidal activity excluding those crystal proteins of Bacillus thuringiensis, and we got 216 proteins as results.</p>
+
             <p class="content-1">Equations</p>
             <p class="content">Using the result, we established our Pantide database by crawling 11 entries of the protein information from UniProt. The entries are as follows.</p>
+
            <img src="https://static.igem.org/mediawiki/2017/a/a0/Modelpic.jpeg" class="bigphoto" width="70%">
 +
            <p class="content">Accroding to their feedback mechanism, we can write down the simultaneous equations as follows.</p>
 +
            <img src="https://static.igem.org/mediawiki/2017/f/fc/Modeling12.jpeg" class="bigphoto" width="70%">
 +
             <p class="content">Since the designations are too complex to be written, we change these deignations to simple ones. Meanwhile, we’ll explain all the individual meanings of every designations. (See the following tables)</p>
  
<ul style="list-style-image:none;list-style-type:disc;">
 
            <li class="list">The name of the protein</li>
 
            <li class="list">The description of protein function</li>
 
            <li class="list">The organisms/source of the protein sequence</li>
 
            <li class="list">The length of amino acids</li>
 
            <li class="list">The number of disulfides bonds</li>
 
            <li class="list">Propeptide & signal peptide—If the proteins have an N-terminal signal peptide and propeptide, a part of protein will be cleaved during maturation or activation.</li>
 
            <li class="list">Uniprot entry & Arachnoserver id—the accession number of protein in UniProtKB and ArachnoServer*.</li>
 
</ul>
 
            <p class="content-2">*ArachnoServer is a manually curated database for protein toxins derived from spider venom.(<a href="http://www.arachnoserver.org/" style="color:#44E287;">http://www.arachnoserver.org/</a>).</p>
 
            <p class="content">We also crawled other seven entries of protein toxicity recorded by Arachnoserver—molecular target, taxon, ED50, LD50, PD50, qualitative information, protein sequence from Arachnoserver. The term, Molecular target, is the effect site of toxin peptides, such as voltage-gated ion channels, GABA receptors and so on. Taxon, ED50, LD50, PD50, and the qualitative information are the toxicity against taxon that had been tested by experiments. The protein sequence from two databases is entirely the same.</p>
 
            <p class="content">We utilized BeautifulSoup 4.4.0, sqlite3 and gevent modules in Python 3.5 to develop our crawler. Moreover, we have submitted the code to GitHub.<br>(Link:<a href="https://github.com/chengchingwen/iGEM/blob/master/crawler.py" style="color:#44E287;">https://github.com/chengchingwen/iGEM/blob/master/crawler.py</a>)</p>
 
  
 +
            <p class="content-1">Designation Description Table</p>
 +
            <p class="content">Concentration</p>
 +
            <img src="https://static.igem.org/mediawiki/2017/0/08/Modeling13.jpeg" class="bigphoto" width="70%">
 +
 +
            <p class="content">Constant</p>
 +
            <img src="https://static.igem.org/mediawiki/2017/0/00/Formula.jpeg" class="bigphoto" width="70%">
 +
 +
            <p class="content">Here comes the script.</p>
 +
            <img src="https://static.igem.org/mediawiki/2017/e/ea/Ript1.png" class="bigphoto" width="70%">
 +
            <p class="content">          </p>
 +
            <img src="https://static.igem.org/mediawiki/2017/8/88/Script2.png" class="bigphoto" width="70%">
 +
 +
 +
 
         </div>
 
         </div>
 
     </div>
 
     </div>
Line 569: Line 610:
 
     <!--4.-->
 
     <!--4.-->
 
     <div class="modelingPart" >
 
     <div class="modelingPart" >
         <h2 class="content-1" id="titleD"  style="color:#6EFFDB">IV. Step 2: Filter</h2>
+
         <h2 class="content-1" id="titleD"  style="color:#6EFFDB">IV. Math is Long, Life is Short: Math in Our Life (Click to see more)</h2>
 
         <div class="modelingPartContent" id="partD">
 
         <div class="modelingPartContent" id="partD">
               <p class="content">After crawling the data, we used DB Browser for SQLite software to browse and used SQL to process our Pantide database. We tried to build a filter to find out peptides suitable to use as Pantide.</p>
+
               <p class="content">Since it requires complicate and large quantity of computing, you might think mathematics as an unreasonable tool. All it can do is endlessly derivation, and not being able to utilize in the real world. But in fact, mathematics are around us everywhere, while we have not notice them. The following math stuffs will be approachable, including offering formulas, for companies to decide whether they want to use our project; calculate the number of samples, so you can know how much surveys you need to do; offering possible data, give some reference for the team after, etc.</p>
               <p class="content">According to the previous articles, we knew that around 90% of spider venom toxin peptides contain ICK structure which is the most important domain that reacts with the voltage-gated ion channels of insects and some other receptors specifically. <sup>[2]</sup></p>
+
               <p class="content-1">The minimum Number of Cargo Packed in a Box</p>
               <p class="content">Therefore, to find these spider venom toxin peptides from Pantide database, we could start from searching for ICK structure, whose mass is among 1-10 kDa containing at least three disulfide bonds. <sup>[2]</sup> So we set a filter with three conditions.</p>
+
              <img src="https://static.igem.org/mediawiki/2017/2/24/Modeling14.jpeg" class="bigphoto" width="70%">
 +
               <p class="content">After having a meeting with Professor Cheng-Ming Chang, we learned that the companies would only like to spend less than 2‰ of the price of the item to guarantee the quality of those item. According to this matter of fact, we can list the following equation:</p>
 +
              <img src="https://static.igem.org/mediawiki/2017/b/b9/Modeling15.jpeg" class="bigphoto" width="70%">
  
 
+
              <p class="content-1">Sample Size Estimation</p>
<ul style="list-style-image:none;list-style-type:decimal;">
+
              <p class="content">Assume that the data of people‘s habits and opinions roughly obey the form of normal distribution. Then, according to the 68-95-99.7 rule, we can know that at 95% confident level, if we allow a deviation (<span style="font-style:italic;">E</span>), the number of samples we should grab is…</p>
          <li class="list">The organism we choose must be spiders or tarantulas.</li>
+
              <img src="https://static.igem.org/mediawiki/2017/f/fe/Modeling16.jpeg" class="bigphoto" width="70%">
          <li class="list">The length of the a.a. sequences are between 27 and 271 base pairs (1 kDa of protein has averagely nine amino acids, encoded by 27 base pairs)</li>
+
          <li class="list">The number of disulfide bonds is greater or equal to 3.
+
          After filtering with the three conditions, 113 peptides remained. Next, we set another filter to find out insecticidal peptides.</li>
+
          <li class="list">Molecular target contains “invertebrate,” but we also remain peptides without data.
+
          <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The reason why we keep the peptides without data was that they have the probability to be effective. In this stage, we got 63 candidates.</li>
+
          <p class="content-2">For efficacy experiment of Pantide, we choose our testee-Spodoptera litura as target insect. While there are 14 kinds of distinct Taxon in our database, including 4 Lepidoptera genus. Thus, we also set the other filter to find out peptides against Lepidoptera:</p>
+
          <li class="list">Taxon contains at least one of <i>Spodoptera litura</i>, <i>Heliothis virescens</i>, <i>Manduca sexta</i> and <i>Spodoptera exigua</i>, but we also remain peptides without data
+
          <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;On the other hand, because we designed to produce Pantide by <i>E.coli</i>, that is difficult to express proteins containing disulfide bonds. We had chosen <i>E.coli</i> Rosetta-gami strain for enhanced disulfide bond formation, but to express a protein with more than four disulfide bonds is still a heavy load. So we finally filtered out those peptides containing too much disulfide bonds.</li>
+
          <li class="list">The number of disulfide bonds is less than or equal to four.
+
          <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The result was that we got 46 peptides which have the possibility to use as Pantide in proof concept experiment, and all of them is targeted to insects’ voltage-gated ion channels (excluding NULL).</li>
+
</ul>
+
  
 
         </div>     
 
         </div>     
 
     </div>
 
     </div>
   
 
    <div class="modelingPart">
 
        <h2 class="content-1" id="titleE" style="color:#80FFDF">V. Step 3: Selection</h2>
 
        <div class="modelingPartContent" id="partE">
 
            <p class="content">In this step, we tried to find three peptides that have different molecular target or mechanism from filtering result to do the test experiment. The method we used was to classify the remained peptides into groups by their structure similarity.</p>
 
            <p class="content">We used online analytic tools on NCBI to process those peptides.</p>
 
            <p class="content">We started with using Protein BLAST (Basic Local Alignment Search Tool) to search from the whole protein database for the similar query protein sequences related to all the 46 peptides and put those related peptides into groups.</p>
 
            <p class="content">The next was using COBALT (Constraint-based Multiple Alignment Tool) to align the sequence between groups to find out whether or not the two groups have the similar structure while they were not got together on the last step because of side chains and other factors. At last, we separated 46 peptides into four groups, containing 27, 12, 3, 2 peptides, and two alone.</p>
 
            <p class="content">Then we chose the three larger groups and used Conserved Domains Search, and found out that they belonged to the three conserved protein domain family. There are Omega-toxin Superfamily (cl05707), Toxin_28 Superfamily (cl06928) and Toxin_20 Superfamily (cl06915). The strings in brackets are unique ID of superfamilies in the conserved protein domain family database. Finally, we selected the representative peptides from each superfamily and got these three peptides, ω-hexatoxin-Hv1a, μ-segestritoxin-Sf1a and Orally active insecticidal peptide (OAIP).</p>
 
    </div>
 
</div>
 
  
 +
    <div>
 +
      <p class="title">Conclusion</p>
 +
      <p class="content">
 +
        <br> - Through combining modeling and <a href="https://2017.igem.org/Team:TCFSH_Taiwan/Demonstrate" style="color: orange">device</a>, we are able to design a better application.</br>
 +
        <br> - Through mathematical modelling, we can estimate how much LB filled in the sticker is adequate.</br>
 +
        <br> - Cjblue and BFP comes to 90% of maximum only takes about 3 hours. As for RFP, it takes about 5 hours to reach 70% of maximum, which is also acceptable.</br>
 +
        <br> - When observing the sicker changing to a specific color, we can calculate the ratio of each kind of chromoprotein/fluorescent by quantifying it, even know what time the sticker is activated; when it is exposed to UV or sunlight!</br>
 +
        <br> - If you aren’t sure whether the sticker for your product is cost-effective or not, mathematical modelling will be your best solution!</br></p>
  
    <div class="modelingPart">
 
        <h2 class="content-1" id="titleF" style="color:#88FCDF">VI. Future</h2>
 
        <div class="modelingPartContent" id="partF">
 
          <p class="content">To promote the applicability of Pantide, we still need to extend our database. The next step is to integrate with other toxin peptide databases, such as scorpions or cone snails, collect more peptides’ information from research results, and even combine with bioinformatics to build a new scoring system, and search for new potential peptides.</p>
 
        </div>
 
    </div>
 
 
 
    <div class="modelingPart">
 
        <h2 class="content-1" id="titleG" style="color:#9CFFE6">Reference</h2>
 
        <div class="modelingPartContent" id="partG">
 
          <p class="reference-content">[1] King, G.F.; Gentz, M.C.; Escoubas, P.; Nicholson, G.M. A rational nomenclature for naming peptide toxins from spiders and other venomous animals. Toxicon 2008, 52, 264–276.</p>
 
          <p class="reference-content">[2] Monique J. Windley, Volker Herzig, Sławomir A. Dziemborowicz, Margaret C. Hardy, Glenn F. King and Graham M. Nicholson (2012). Spider-Venom Peptides as Bioinsecticides. Toxins, 4, 191-227.</p>
 
        </div>
 
    </div>
 
  
 
</div>
 
</div>
Line 626: Line 640:
 
</section>
 
</section>
 
</h1>
 
</h1>
<!------------JS---------->
+
<!--JS-->
 
<script>
 
<script>
 
$(function(){
 
$(function(){

Latest revision as of 03:53, 2 November 2017

Model Introduction

Modeling has always played an important role in every field of science. In our project, modeling comes up with real data, and thus makes biological theories easier to be realized and observed. Carl Gauss said, “Mathematics is the queen of the science.” A proposition of mathematics is reliable and indisputable, whereas other science theories have always been at risk of being overthrown. The reason why modeling has a good reputation and a certain status is that it theorems scientific phenomena, and makes them more trustworthy. By conducting modeling, we can have a reasonable embryonic form to formulate a possible solution to a difficult problem. However, the reaction series or the operation mechanism of an unknown equation needs to be reasonably presumed, and this is the most difficult part in the whole process. Inappropriate assumption can lead to erroneous results. Once the right theories are established, we can amend our hypothetical surmise, and build another model. In the modeling process we’ve done, the main technique we used is DE (differential equation). We use derivative to describe the difference of any variables that vary within a very short time. But we’ve encountered some very complicated equations when trying to solve the problem, so we use the program MATLAB to help calculate the results.

What are we modeling?


- The growth of E. coli

- The Expression of Different Color

- The Concentration Function f:(substance,time)→concentration

- Math Is Long, Life Is Short: Math in Our Life

Model

I. The growth of E. coli (Click to see more)

At first, we assume that E. coli proliferate and die at the same ratio over time, and the value difference is the birth rate (μg). So, we do derivative with this assumption.

Substituting the boundary condition, t = 0, N = N0, we then have ∴ eC2-C1=N0 Thus, the equation that expresses the relation between bacteria and time is:

N = N0∙eμgt

What’s more, it is useless to say that E. coli consumes their “food”, LB, all the time. Thus, if E. coli consumes their food steadily, the LB consuming rate will be proportional to N, then we can write down the equation:

By substituting the boundary condition, we then have
𝐶= − 𝑛𝐿𝐵0/𝑘𝑐𝑜𝑛−𝑁0/𝜇𝑔

So the relation between nLB and t is:

II. The Expression of Different Color (Click to see more)

Assumption

1. In order to write the equations down simply, we assume that all the chemical reaction rates are proportional to the concentration of each reagent (e.g. for the reaction: A+B+C→D+E,the forward rate r+=k+[A][B][C]).

2.For every substances produced by biobricks, we assume that their production rate =φ[mRNA],
[mRNA]= the concentration of the promoted biobrick

φ= the result of multiplication of rate constant, coefficient of correction (since a biobrick is different from a reagtant), a dimension T-1

Equations & Solutions

According to the picture, we can write down 3 equations as follows:

P.S. φ= the result of multiplication of rate constant, coefficient of correction (since a promoter is different from a reagtant), a dimension 𝑇−1

By solving these 3 equations, the solution expressed by φ、k and [Pa] are as follows:

When the concentration of each activated promoter reaches to each of their steady state, then we can simplify the equations as follows:

Besides, since limt→∞⁡(1-1/ekt) = 1, satisfying the definition of the horizontal asymptotes. And d(1-1/ekt)/dt=ke-kt>0 (t∈[0,∞)), so it is a strictly increasing function.
So, this is a strictly increasing and convergent function with an upper bound 1.

Then the result is that the extremum of the concentration is:

Degradation Rate Constant Calculation

As for the other variable written in the solutions, the degradation rate constant, can also be solved with differential equations. Since the degradation rate is an “order one” reaction, the equation can be written as follow:

dM/dt= -kdM

Then, after solving the equation and substituting the boundary conditions
(t = 0⇒M = M0), the the solution is:

According to the project 2008 iGEM KULeuven and 2014 iGEM Edinburgh had done, both GFP-LVA and RFP-LVA degrades to half of the amount within 50 to 60 minutes, so we assume that cjblue is the same. The RFP and BFP reference are as follow (the latter degrades to half of the amount about 50 minutes while the former does about 3 hours). So we can get

From these degradation rate constants and the relation between concentration and time, the “[cjblue],[RFP],[BFP]-t Diagram” is as follow:

According to this simulation diagram, we can know that cjblue and BFP increase faster—coming to 90% of maximum only takes about 3 hours. As for RFP, it takes about 5 hours to reach 70% of maximum, which is also acceptable.

Through mathematical modeling, when observing the sicker changing to a specific color, we can calculate the ratio of each kind of chromoprotein by quantifying it.

III. The Concentration Function (Click to see more)

Equations

Accroding to their feedback mechanism, we can write down the simultaneous equations as follows.

Since the designations are too complex to be written, we change these deignations to simple ones. Meanwhile, we’ll explain all the individual meanings of every designations. (See the following tables)

Designation Description Table

Concentration

Constant

Here comes the script.

IV. Math is Long, Life is Short: Math in Our Life (Click to see more)

Since it requires complicate and large quantity of computing, you might think mathematics as an unreasonable tool. All it can do is endlessly derivation, and not being able to utilize in the real world. But in fact, mathematics are around us everywhere, while we have not notice them. The following math stuffs will be approachable, including offering formulas, for companies to decide whether they want to use our project; calculate the number of samples, so you can know how much surveys you need to do; offering possible data, give some reference for the team after, etc.

The minimum Number of Cargo Packed in a Box

After having a meeting with Professor Cheng-Ming Chang, we learned that the companies would only like to spend less than 2‰ of the price of the item to guarantee the quality of those item. According to this matter of fact, we can list the following equation:

Sample Size Estimation

Assume that the data of people‘s habits and opinions roughly obey the form of normal distribution. Then, according to the 68-95-99.7 rule, we can know that at 95% confident level, if we allow a deviation (E), the number of samples we should grab is…

Conclusion


- Through combining modeling and device, we are able to design a better application.

- Through mathematical modelling, we can estimate how much LB filled in the sticker is adequate.

- Cjblue and BFP comes to 90% of maximum only takes about 3 hours. As for RFP, it takes about 5 hours to reach 70% of maximum, which is also acceptable.

- When observing the sicker changing to a specific color, we can calculate the ratio of each kind of chromoprotein/fluorescent by quantifying it, even know what time the sticker is activated; when it is exposed to UV or sunlight!

- If you aren’t sure whether the sticker for your product is cost-effective or not, mathematical modelling will be your best solution!