Difference between revisions of "Team:XJTU-China/Drylab-Models"

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           <ul class="nav nav-pills nav-stacked" id="side-nav" data-spy="affix">
 
           <ul class="nav nav-pills nav-stacked" id="side-nav" data-spy="affix">
 
             <!--li class="nav-item"><a href="#section1">Overview</a></li>
 
             <!--li class="nav-item"><a href="#section1">Overview</a></li>
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             <li class="nav-item"><a href="#section21">Laboratory Open Day</a></li>
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             <li class="nav-item"><a href="#section22">Teaching in Mawu Primary School</a></li>
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             <li class="nav-item"><a href="#section2">Psicose Synthesis Kinetic Model</a></li>
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             <li class="nav-item"><a href="#section3">Questionaire on Sweeteners</a></li>
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             <li class="nav-item"><a href="#section3">Production Simulink Model</a></li>
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             <li class="nav-item"><a href="#section4">Our WeChat Official Account</a></li>
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             <li class="nav-item"><a href="#section4">Microfluidics Model</a></li>
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             <li class="nav-item"><a href="#section5">Energy saving and Emission Reduction Creative Competition</a></li>
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             <li class="nav-item"><a href="#section5">Market Prediction</a></li>
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            <li class="nav-item"><a href="#section61">Brochure about Scientific Diet</a></li>
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            <li class="nav-item"><a href="#section62">Visiting the Jinqiu nursing Home</a></li>
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             <li class="nav-item"><a href="#Model-A">Psicose Synthesis Kinetic Model</a></li>
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            <li class="nav-item"><a href="#section7">Summary</a></li-->
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             <li class="nav-item"><a href="#Model-B">Production Simulink Model</a></li>
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             <li class="nav-item"><a href="#Model-A">Model A</a></li>
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             <li class="nav-item"><a href="#Model-C">Microfluidics Model</a></li>
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             <li class="nav-item"><a href="#Model-B">Model B</a></li>
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             <li class="nav-item"><a href="#Model-D">Market Prediction</a></li>
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             <li class="nav-item"><a href="#Model-C">Model C</a></li>
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             <li class="nav-item"><a href="#Model-D">Model D</a></li>
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           </ul>
 
           </ul>
 
         </nav>
 
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           <div class="container-fluid" id="inner-container">
 
           <div class="container-fluid" id="inner-container">
 
             <h1 class="font-weight-bold text-center">Modelling</h1>
 
             <h1 class="font-weight-bold text-center">Modelling</h1>
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            <p>In our design, we aim to manufacture psicose, which has a lot of advantages over other sugar or sweetener as is mentioned in the research of the psicose. The advantages of psicose include low energy, benefit to diabetes and hyperlipidemia, making psicose become more and more popular in people. People can get a psicose sweetener or food rich in psicose, in which psicose is synthesized by biological ways, because using chemical ways is not a good choice to synthesize clear and edible psicose in a food grade for the containing of unhealthy and poisonous by-product in chemical synthesis methods.</p>
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<p>Nevertheless, biological methods in producing psicose are inefficient due to the low enzyme efficiency of D-psicose 3-epimerase. The efficiency of D-psicose 3-epimerase is different due to the various environment system and the different concentration of substrate (sounds amazing due to the efficiency of enzyme irrelevant in most occasions). As a result, the temperature, PH value and the concentration are considered in our model to describe the efficiency of D-pisocose 3-epimerase, which is significant in our manufacture.</p>
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<p>In order to predict the concentration of different substance in E.coli, we set the kinetic model according to the reaction rate theory and enzymatic reaction kinetics as our first model in our project. And the production and conversion rate model is included to simulate the directed evolution model and the natural evolution model, and then we can get the time we need in our directed evolution method of DTE. The third model we set up is the microfluidics model to predict and simulate the situation in the microfluidics chip, which is our hardware for the gradient concentration of the psicose and antibiotic to let us have a high throughput experiment. The fourth</p>
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<p>In order to predict the concentration of different substance in E.coli, we set the kinetic model according to the reaction rate theory and enzymatic reaction kinetics as our first model in our project. And the production and conversion rate model is included to simulate the directed evolution model and the natural evolution model, and then we can get the time we need in our directed evolution method of DTE. The third model we set up is the microfluidics model to predict and simulate the situation in the microfluidics chip, which is our hardware for the gradient concentration of the psicose and antibiotic to let us have a high throughput experiment. The fourth model we set is the market model to predict the future market and the coefficient between different age groups and the tendency to adopt psicose.</p>
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<p>model we set is the market model to predict the future market and the coefficient between different age groups and the tendency to adopt psicose.</p>
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<p>Although the market size is estimated large enough from the market research we have done, modelling is needed to describe exactly how much is the efficiency of D-psicose 3-epimerase. we analysis the efficiency of D-psicose 3-epimerase is affected by the temperature, PH value and the concentration of substrate. In this way, we can predict the catalytic efficiency of D-psicose 3-epimerase by simulating the catalytic process.</p>
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<p>The third model we would like to model is the market model for the application of psicose. From the research, the conclusion that the psicose is very much needed among public and patients is analyzed. By the statistics we get, the potential market and the value curve of psicose appear from our mind by running the market model.</p>
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<ol>
 
<ol>
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<li>Psicose Synthesis Kinetic Model(finished )</li>
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<li>Psicose Synthesis Kinetic Model</li>
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<li>Production Simulink Model(finished )</li>
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<li>Production Simulink Model</li>
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<li>Market Model(finished )</li>
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<li>Market Model</li>
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<li>Microfluidics Model(finished )</li>
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<li>Microfluidics Model</li>
  
 
</ol>
 
</ol>
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<h2>Psicose synthesis kinetic model</h2>
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</div>
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<div class="page-header" id="section2">
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<h2>Psicose synthesis kinetic model</h2></div>
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<p>In our design, the DTE process is one of the most significant part in manufacturing psicose. The main process of psicose manufacture is catalyzed by D-psicose 3-epimerase. The models of device A, B, C and D are as follows.</p>
 
<p>In our design, the DTE process is one of the most significant part in manufacturing psicose. The main process of psicose manufacture is catalyzed by D-psicose 3-epimerase. The models of device A, B, C and D are as follows.</p>
 
<img src=""/>
 
<img src=""/>
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<p>Where $[EGFP]$ is the concentration of $EGFP$ and $[ABR]$ is the concentration of antibiotic resistance protein expression.</p>
 
<p>Where $[EGFP]$ is the concentration of $EGFP$ and $[ABR]$ is the concentration of antibiotic resistance protein expression.</p>
 
<img src="">
 
<img src="">
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<p>Device C is the same with device B. The only difference is the hairpin between gene of $EGFP$ and gene of $RFP$. Similarly, we can get the function of hairpin and its coefficient.</p>
 
<p>Device C is the same with device B. The only difference is the hairpin between gene of $EGFP$ and gene of $RFP$. Similarly, we can get the function of hairpin and its coefficient.</p>
 
<p>The first few equations are the same as in device B:</p>
 
<p>The first few equations are the same as in device B:</p>
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<p>Where $[RFP]$ is the concentration of red fluorescence protein, $k$ is the coefficient of hairpin.</p>
 
<p>Where $[RFP]$ is the concentration of red fluorescence protein, $k$ is the coefficient of hairpin.</p>
 
<img src="">
 
<img src="">
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<p>In device D, $IPTG$ gets in cells and bind with $pLacR$, which is a repressor for gene $DTE$. After $IPTG$ binding with $pLacR$, $DTE$ starts to express, and as an enzyme, to catalysis $fructose$ to turn into $psicose$. As more and more $psicose$ are produced, more and more the repressor of gene $EGFP$, $pLacR$ are inactivated, so expression of $EGPF$ increase. At the same time, expression of $ABR$ also increase since the gene of $ABR$ and the gene of $EGFP$ are connected in series by a hairpin.</p>
 
<p>In device D, $IPTG$ gets in cells and bind with $pLacR$, which is a repressor for gene $DTE$. After $IPTG$ binding with $pLacR$, $DTE$ starts to express, and as an enzyme, to catalysis $fructose$ to turn into $psicose$. As more and more $psicose$ are produced, more and more the repressor of gene $EGFP$, $pLacR$ are inactivated, so expression of $EGPF$ increase. At the same time, expression of $ABR$ also increase since the gene of $ABR$ and the gene of $EGFP$ are connected in series by a hairpin.</p>
 
<p>For device D, $psicose$ and $fuctose$ get in cells by diffusion:</p>
 
<p>For device D, $psicose$ and $fuctose$ get in cells by diffusion:</p>
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<p>$$\frac{\text{d}[PsiI]}{\text{d}t}=\frac{-\gamma_{F}([PsiI]-[PsiO])}{V_{cell}}+\frac{k_2[DTE][FI]}{K_M+[FI]}-\delta_{PsiI}[PsiI]$$</p>
 
<p>$$\frac{\text{d}[PsiI]}{\text{d}t}=\frac{-\gamma_{F}([PsiI]-[PsiO])}{V_{cell}}+\frac{k_2[DTE][FI]}{K_M+[FI]}-\delta_{PsiI}[PsiI]$$</p>
 
<p>$$\frac{\text{d}[IPTGI]}{\text{d}t}=\frac{-\gamma_{IPTG}([IPTGI]-[IPTGO])}{V_{cell}}+m_{IPTG,pLacR}[IPTGI][pLacR]-\delta_{IPTG}[IPTGI]$$</p>
 
<p>$$\frac{\text{d}[IPTGI]}{\text{d}t}=\frac{-\gamma_{IPTG}([IPTGI]-[IPTGO])}{V_{cell}}+m_{IPTG,pLacR}[IPTGI][pLacR]-\delta_{IPTG}[IPTGI]$$</p>
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<p><br>Where $k_2$ is reaction rate coefficient of transition state product’s decomposition reaction, $K_M$ is the Michaelis contant.</p>
 
<p><br>Where $k_2$ is reaction rate coefficient of transition state product’s decomposition reaction, $K_M$ is the Michaelis contant.</p>
 
<p><br>The rest of the equations are the same with which in device A and device C:</p>
 
<p><br>The rest of the equations are the same with which in device A and device C:</p>
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<p>$$\frac{\text{d}[EGFP]}{\text{d}t}=H\frac{\beta_{EGFP}K^n}{K^n+[pPsiR]^n}-\delta_{EGFP}[EGFP]$$</p>
 
<p>$$\frac{\text{d}[EGFP]}{\text{d}t}=H\frac{\beta_{EGFP}K^n}{K^n+[pPsiR]^n}-\delta_{EGFP}[EGFP]$$</p>
 
<p>$$\frac{\text{d}[ABR]}{\text{d}t}=k\frac{\text{d}[EGFP]}{\text{d}t}$$</p>
 
<p>$$\frac{\text{d}[ABR]}{\text{d}t}=k\frac{\text{d}[EGFP]}{\text{d}t}$$</p>
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</div>
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            <div class="page-header" id="section3">
 
<h2>Production and Conversion Rate Simulink Model</h2>
 
<h2>Production and Conversion Rate Simulink Model</h2>
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</div>
 
<p>To understand how we get psicose in a deep view, replication, transcription and translation are involved to describe the synthesis of psicose and thus come to our production on a large scale. As we all know, the enzyme will degrade due to the mutation of coding sequence during DNA replication and the transcription error. Although the rate mutation and transcription error are one in 100 million and one in 10 thousand, the yield of psicose may not decrease because even errors happen in the process of replication and transcription, the type and order of amino acid may not reverse as well as the function of our enzyme which lead to the same catalytic rate of the original one.</p>
 
<p>To understand how we get psicose in a deep view, replication, transcription and translation are involved to describe the synthesis of psicose and thus come to our production on a large scale. As we all know, the enzyme will degrade due to the mutation of coding sequence during DNA replication and the transcription error. Although the rate mutation and transcription error are one in 100 million and one in 10 thousand, the yield of psicose may not decrease because even errors happen in the process of replication and transcription, the type and order of amino acid may not reverse as well as the function of our enzyme which lead to the same catalytic rate of the original one.</p>
 
<p>However, the butterfly effect tells us that we cannot ignore the small change in our system, even it is little enough for us to ignore. Considering the mutation rate and transcription error, the production of psicose is closely related to the cycling times, error rate and original production. The production of psicose is a function with variables of cycling times, error rate and original production.</p>
 
<p>However, the butterfly effect tells us that we cannot ignore the small change in our system, even it is little enough for us to ignore. Considering the mutation rate and transcription error, the production of psicose is closely related to the cycling times, error rate and original production. The production of psicose is a function with variables of cycling times, error rate and original production.</p>
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<p>Meanwhile, the concentration of substrate is changing during the process of fermentation in our psicose-production system. We assume the concentration of substrate changes little and can be processed as a constant in order to simplify our process and to find out the discipline of production change, as well as simulating our system in directed evolution method. The concentration of substrate is as follows</p>
 
<p>Meanwhile, the concentration of substrate is changing during the process of fermentation in our psicose-production system. We assume the concentration of substrate changes little and can be processed as a constant in order to simplify our process and to find out the discipline of production change, as well as simulating our system in directed evolution method. The concentration of substrate is as follows</p>
 
<p>$$C_{sub}\approx C_{sub}(t_0)=constant$$</p>
 
<p>$$C_{sub}\approx C_{sub}(t_0)=constant$$</p>
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<p>In this way, we can get the original production of psicose from the very beginning. The original production of psicose can be calculated as a constant as below</p>
 
<p>In this way, we can get the original production of psicose from the very beginning. The original production of psicose can be calculated as a constant as below</p>
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<p>$$oriPrd=\lim_{t_0\rightarrow 0}\int_0^{t_0}\alpha N(t)C_{sub}(t)dt$$</p>
 
<p>$$oriPrd=\lim_{t_0\rightarrow 0}\int_0^{t_0}\alpha N(t)C_{sub}(t)dt$$</p>
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<p>Finally, based on the probability distribution function, the variation of production is defined and we can compare the psicose production in natural system and directed evolution system.</p>
 
<p>Finally, based on the probability distribution function, the variation of production is defined and we can compare the psicose production in natural system and directed evolution system.</p>
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<h2>Microfludics Model</h2>
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</div>
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<div class="page-header" id="section3">
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<h2>Microfludics Model</h2></div>
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<p>In our Lab tour, we find that it is extremely complicated to use pipette to prepare and transfer solutions, especially gradient concentration solutions. High throughput methods to get the gradient concentration of solutions are well needed. In this case, we made a hardware by using the principle of microfluidics and then we simulate it whether it can give us different concentration by using the microfluidics device before the microfluidics chip is finally made. To get the downstream concentration of high concentration and low concentration, particle collision is used to demonstrate the downstream concentration.</p>
 
<p>In our Lab tour, we find that it is extremely complicated to use pipette to prepare and transfer solutions, especially gradient concentration solutions. High throughput methods to get the gradient concentration of solutions are well needed. In this case, we made a hardware by using the principle of microfluidics and then we simulate it whether it can give us different concentration by using the microfluidics device before the microfluidics chip is finally made. To get the downstream concentration of high concentration and low concentration, particle collision is used to demonstrate the downstream concentration.</p>
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<img src="">
 
<img src="">
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<p>When the fluid flows into the vertical channel of the microfluidics chip, the number of the solute is equal and both are half of the original quantity. In this case, the concentration of the both sides or both directions are the same with the half original concentration.</p>
 
<p>When the fluid flows into the vertical channel of the microfluidics chip, the number of the solute is equal and both are half of the original quantity. In this case, the concentration of the both sides or both directions are the same with the half original concentration.</p>
 
<p>$$m_3=m_4=\fac{1}{2}m_1\qquad m_5=m_6=\frac{1}{2}m_2$$</p>
 
<p>$$m_3=m_4=\fac{1}{2}m_1\qquad m_5=m_6=\frac{1}{2}m_2$$</p>
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<p>Taking $m,n,c$ we have mentioned, we can get the concentration distribution as follows: </p>
 
<p>Taking $m,n,c$ we have mentioned, we can get the concentration distribution as follows: </p>
 
<img src="">
 
<img src="">
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</div>
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<div class="page-header" id="section4">
 
<h2>Potential Market Model</h2>
 
<h2>Potential Market Model</h2>
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</div>
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<p>In our market model, we’d like to</p>
 
<p>In our market model, we’d like to</p>
 
<h2>Results and Discussion </h2>
 
<h2>Results and Discussion </h2>
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<img src="">
 
<img src="">
 
<img src="">
 
<img src="">
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<img src="">
 
<img src="">
 
<img src="">
 
<img src="">
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<h2>Strengths and Prospect</h2>
 
<h2>Strengths and Prospect</h2>
 
<h2>Reference</h2>
 
<h2>Reference</h2>

Revision as of 21:50, 15 October 2018