Difference between revisions of "Team:NJU-China/Model"

 
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     <link href="https://2016.igem.org/Team:NJU-China/mdlib/mdiconcss?action=raw&amp;ctype=text/css" rel="stylesheet">
 
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     <link href="https://2016.igem.org/Team:NJU-China/mdlib/customedcss?action=raw&amp;ctype=text/css" rel="stylesheet">
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         <ul id="nav-mobile" class="side-nav fixed">
 
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             <li class="logo">
 
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                 <img class="background responsive-img" src="https://static.igem.org/mediawiki/2016/c/c8/NJU_China_2016_iGEM_logo.png" alt="NJU-China LOGO"> </li>
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                 <a href="https://2016.igem.org/Team:NJU-China"><img class="background responsive-img" src="https://static.igem.org/mediawiki/2016/c/c8/NJU_China_2016_iGEM_logo.png" alt="NJU-China LOGO"></a>
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            </li>
 
             <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Background" class="waves-effect waves-teal">Background</a></li>
 
             <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Background" class="waves-effect waves-teal">Background</a></li>
 
             <li class="bold no-padding">
 
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                 <ul class="collapsible collapsible-accordion">
 
                 <ul class="collapsible collapsible-accordion">
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                     <li class="bold"> <a class="collapsible-header waves-effect waves-teal">Project</a>
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                     <li class="bold"> <a class="collapsible-header waves-effect waves-teal">Project Design</a>
 
                         <div class="collapsible-body">
 
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                             <ul>
 
                             <ul>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Project#Design">Design</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Project_Design">RNAi Module</a></li>
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                                <li><a href="https://2016.igem.org/Team:NJU-China/Project#Results">Results</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Project_Design#Targeting_Module">Targeting Module</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Project#Conclusion">Conclusion</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Project_Design#Assembly">Assembly</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Project#Future_Work">Future Work</a></li>
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                             </ul>
 
                             </ul>
 
                         </div>
 
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                 </ul>
 
                 </ul>
 
             </li>
 
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            <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Modeling" class="waves-effect waves-teal">Modeling</a></li>
 
             <li class="bold no-padding">
 
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                     <li class="bold"> <a class="collapsible-header waves-effect waves-teal">Model</a>
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                     <li class="bold"> <a class="collapsible-header waves-effect waves-teal">Results</a>
 
                         <div class="collapsible-body">
 
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                             <ul>
 
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Model#Delivery_Model">Delivery Model</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Results">Parts</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Model#RNAi_Model">RNAi Model</a></li>
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                                <li><a href="https://2016.igem.org/Team:NJU-China/Results#Validations">Validations</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Model#Signaling_Model">Signaling Model</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Results#Safety">Safety</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Results#Conclusions">Conclusions</a></li>
 
                             </ul>
 
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             <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Human_Practices" class="waves-effect waves-teal">Human Practices</a></li>
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            <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Safety" class="waves-effect waves-teal">Safety</a></li>
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            <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Human_Practice" class="waves-effect waves-teal">Human Practice</a></li>
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             <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Team" class="waves-effect waves-teal">Team</a></li>
 
             <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Team" class="waves-effect waves-teal">Team</a></li>
 
             <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Attributions" class="waves-effect waves-teal">Attributions</a></li>
 
             <li class="bold"><a href="https://2016.igem.org/Team:NJU-China/Attributions" class="waves-effect waves-teal">Attributions</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Notebook#Methods">Methods</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Notebook/Calendar">Calendar</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Notebook#Protocol">Protocol</a></li>
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                                 <li><a href="https://2016.igem.org/Team:NJU-China/Notebook/Protocol">Protocol</a></li>
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                                <li><a href="https://2016.igem.org/Team:NJU-China/Notebook#Notebook">Notebook</a></li>
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                             </ul>
 
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                 <li><a class="btn-floating blue button-collapse hide-on-large-only tooltipped" data-position="left" data-delay="50" data-tooltip="Show Navigation" data-activates="nav-mobile"><i class="material-icons">menu</i></a></li>
 
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                <p>Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg</p>
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                <p>Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz. Abcdefg hijklmnop qrs tuv wx yz.</p>
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                    <p>We would like to see whether our strategy could apply in treatment of other types of cancer. We create a model to test whether our strategy outperforms traditional surgery in terms of preventing metastasis of lymphoma. This work is collaborated with SCUT-China. </p>
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            </div>
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                    <p>We made following assumptions to simplify our model: (a) the process of tumorigenesis is irreversible, (b) lymphonode has no effect in metastasis after removal, and (c) tumor must grow to certain sizes and proliferate before metastasis.</p>
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                    <p>We use following variables in our model:</p>
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                        <span class="variable">I</span> : Number of Malignant lymphonode
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                    <ul class="tabs z-depth-1">
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                        <br>
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                         <li class="tab col s3"><a class="active" href="#Delivery_Model">Delivery Model</a></li>
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                         <span class="variable">R</span> : Number of Removed lymphonode
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                         <li class="tab col s3"><a href="#RNAi_Model">RNAi Model</a></li>
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                        <br>
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                         <li class="tab col s3"><a href="#Signaling_Model">Signaling Model</a></li>
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                        <span class="variable">S</span> : Number of Normal lymphonode
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                        <br>
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                         <span class="variable">I<sub>0</sub></span> : Initial Number of malignant lymphonode
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                    <p>We compare following strategies with dynamic epidemic model：</p>
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                            <div class="collapsible-header active"><i class="material-icons">filter_drama</i>First</div>
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                    <p>1. Increasing the resistance of normal lymphonode to tumorigenesis</p>
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                     <p>2. Removing malignant lymphonode</p>
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                                <p>Lorem ipsum dolor sit amet.</p>
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                    <p>3. Inhibit the proliferation of tumor cells</p>
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                            </div>
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                    <p>We now know that we can achieve 2 by surgery, and strategy 3 is the potential application field of our research.</p>
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                        </li>
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                    <p>We use following probability transition model：</p>
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                    <div align="middle"><img src="https://static.igem.org/mediawiki/2016/c/cb/NJU_China_2016_iGEM_Modeling_1.png" class="responsive-img" ></div>
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                            <div class="collapsible-header active"><i class="material-icons">place</i>Second</div>
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                    <p><span class="variable">α</span> is the growth rate of tumor cells in terms of size. <span class="variable">β</span> measures the growth rate of tumor cells in terms of number. P measures the survival rate of tumor cells during metastasis.</p>
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                    <p>Thus, we have：</p>
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                    <div align="middle"><img src="https://static.igem.org/mediawiki/2016/9/93/NJU_China_2016_iGEM_Modeling_2.png" class="responsive-img" ></div>
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                    <p><span class="variable">λ</span> represents strategy 1, which is roughly proportional to the resistance of normal cells to malignant cells。</p>
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                        </li>
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                    <p>We also create the following switch for simulation：</p>
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                    <div align="middle"><img src="https://static.igem.org/mediawiki/2016/f/fe/NJU_China_2016_iGEM_Modeling_3.png" class="responsive-img" ></div>
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                            <div class="collapsible-header active"><i class="material-icons">whatshot</i>Third</div>
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                    <p>The biological meaning of the switch is that there exists a period of time when metastasis hasn’t been noticed (t < 0.2T). Then a surgery was performed to remove the malignant lymphonode corresponding to strategy . We then performed numerical simulation as below.</p>
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                            <div class="collapsible-body">
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                    <p>We first simulate the control status with parameter below:</p>
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                     <div class="modelVariable" align="middle">
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                            </div>
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                        <span class="variable">I</span><sub>0</sub>=10, <span class="variable">α</span>=1, <span class="variable">β</span>=1, <span class="variable">γ</span>=0.05, <span class="variable">λ</span>=0.3, <span class="variable">p</span>=0.95, <span class="variable">T</span>=100
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                    <div align="middle"><img src="https://static.igem.org/mediawiki/2016/c/c4/NJU_China_2016_iGEM_Modeling_4.jpg" class="responsive-img" ></div>
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                    <p>Combing strategy 2 and 3，we have：</p>
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                    <div class="modelVariable" align="middle">
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                        <span class="variable">I</span><sub>0</sub>=10, <span class="variable">α</span>=1.5, <span class="variable">β</span>=1.5, <span class="variable">γ</span>=0.1, <span class="variable">λ</span>=0.3, <span class="variable">p</span>=0.95, <span class="variable">T</span>=100
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                            <div class="collapsible-header active"><i class="material-icons">filter_drama</i>First</div>
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                    <div align="middle"><img src="https://static.igem.org/mediawiki/2016/3/3a/NJU_China_2016_iGEM_Modeling_5.jpg" class="responsive-img" ></div>
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                    <p>The result shows that by combing strategy 2 and 3 we successfully delay the metastasis time of tumor cells. We next investigate the effect of <span class="variable">p</span>, <span class="variable">λ</span> on metastasis:</p>
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                    <p>We create a gradient of <span class="variable">p</span>=0.95, 0.94, ..., 0.8 and performed the simulation:</p>
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                    <div align="middle"><img src="https://static.igem.org/mediawiki/2016/2/23/NJU_China_2016_iGEM_Modeling_6.jpg" class="responsive-img" ></div>
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                    <p>The perform the same analysis for <span class="variable">λ</span>:</p>
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                    <div class="modelVariable" align="middle"><span class="variable">λ</span>=0.3, 0.31, ..., 0.49, 0.5</div>
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                            <div class="collapsible-header active"><i class="material-icons">place</i>Second</div>
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                    <div align="middle"><img src="https://static.igem.org/mediawiki/2016/8/81/NJU_China_2016_iGEM_Modeling_7.jpg" class="responsive-img" ></div>
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                    <p>The results show that increasing the resistance of normal lymphocyte (increasing <span class="variable">λ</span>) and control the survival rate of tumor cells (decreasing p) help to inhibit the metastasis. This will help design our future work.</p>
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Latest revision as of 02:34, 20 October 2016

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We would like to see whether our strategy could apply in treatment of other types of cancer. We create a model to test whether our strategy outperforms traditional surgery in terms of preventing metastasis of lymphoma. This work is collaborated with SCUT-China.

We made following assumptions to simplify our model: (a) the process of tumorigenesis is irreversible, (b) lymphonode has no effect in metastasis after removal, and (c) tumor must grow to certain sizes and proliferate before metastasis.

We use following variables in our model:

I : Number of Malignant lymphonode
R : Number of Removed lymphonode
S : Number of Normal lymphonode
I0 : Initial Number of malignant lymphonode

We compare following strategies with dynamic epidemic model：

1. Increasing the resistance of normal lymphonode to tumorigenesis

2. Removing malignant lymphonode

3. Inhibit the proliferation of tumor cells

We now know that we can achieve 2 by surgery, and strategy 3 is the potential application field of our research.

We use following probability transition model：

α is the growth rate of tumor cells in terms of size. β measures the growth rate of tumor cells in terms of number. P measures the survival rate of tumor cells during metastasis.

Thus, we have：

λ represents strategy 1, which is roughly proportional to the resistance of normal cells to malignant cells。

We also create the following switch for simulation：

The biological meaning of the switch is that there exists a period of time when metastasis hasn’t been noticed (t < 0.2T). Then a surgery was performed to remove the malignant lymphonode corresponding to strategy . We then performed numerical simulation as below.

We first simulate the control status with parameter below:

I0=10, α=1, β=1, γ=0.05, λ=0.3, p=0.95, T=100

Combing strategy 2 and 3，we have：

I0=10, α=1.5, β=1.5, γ=0.1, λ=0.3, p=0.95, T=100

The result shows that by combing strategy 2 and 3 we successfully delay the metastasis time of tumor cells. We next investigate the effect of p, λ on metastasis:

We create a gradient of p=0.95, 0.94, ..., 0.8 and performed the simulation:

The perform the same analysis for λ:

λ=0.3, 0.31, ..., 0.49, 0.5

The results show that increasing the resistance of normal lymphocyte (increasing λ) and control the survival rate of tumor cells (decreasing p) help to inhibit the metastasis. This will help design our future work.