Difference between revisions of "Team:NJU-China/Modeling"
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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> | <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> | ||
<p>We use following variables in our model:</p> | <p>We use following variables in our model:</p> | ||
| − | <div class="modelVariable"> | + | <div class="modelVariable" align="middle"> |
<span class="variable">I</span>: Number of Malignant lymphonode<br> | <span class="variable">I</span>: Number of Malignant lymphonode<br> | ||
<span class="variable">R</span>: Number of Removed lymphonode<br> | <span class="variable">R</span>: Number of Removed lymphonode<br> | ||
<span class="variable">S</span>: Number of Normal lymphonode<br> | <span class="variable">S</span>: Number of Normal lymphonode<br> | ||
| − | < | + | <span class="variable">I<sub>0</sub></span>: Initial Number of malignant lymphonode<br> |
</div> | </div> | ||
<p>We compare following strategies with dynamic epidemic model：</p> | <p>We compare following strategies with dynamic epidemic model：</p> | ||
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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> | <p>We now know that we can achieve 2 by surgery, and strategy 3 is the potential application field of our research.</p> | ||
<p>We use following probability transition model：</p> | <p>We use following probability transition model：</p> | ||
| − | <img src="" class="responsive-img"> | + | <img src="https://static.igem.org/mediawiki/2016/c/cb/NJU_China_2016_iGEM_Modeling_1.png" class="responsive-img"> |
<p>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.</p> | <p>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.</p> | ||
<p>Thus, we have：</p> | <p>Thus, we have：</p> | ||
| − | <img src="" class="responsive-img"> | + | <img src="https://static.igem.org/mediawiki/2016/9/93/NJU_China_2016_iGEM_Modeling_2.png" class="responsive-img"> |
<p>λ represents strategy 1, which is roughly proportional to the resistance of normal cells to malignant cells。</p> | <p>λ represents strategy 1, which is roughly proportional to the resistance of normal cells to malignant cells。</p> | ||
<p>We also create the following switch for simulation：</p> | <p>We also create the following switch for simulation：</p> | ||
| − | <img src="" class="responsive-img"> | + | <img src="https://static.igem.org/mediawiki/2016/f/fe/NJU_China_2016_iGEM_Modeling_3.png" class="responsive-img"> |
<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> | <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> | ||
<p>We first simulate the control status with parameter below:</p> | <p>We first simulate the control status with parameter below:</p> | ||
<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 | <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 | ||
| − | <img src="" class="responsive-img"> | + | <img src="https://static.igem.org/mediawiki/2016/c/c4/NJU_China_2016_iGEM_Modeling_4.jpg" class="responsive-img"> |
| − | Combing strategy 2 and 3，we have： | + | <p>Combing strategy 2 and 3，we have：</p> |
<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 | <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 | ||
| − | <img src="" class="responsive-img"> | + | <img src="https://static.igem.org/mediawiki/2016/3/3a/NJU_China_2016_iGEM_Modeling_5.jpg" class="responsive-img"> |
<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> | <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> | ||
<p>We create a gradient of <span class="variable">p</span>=0.95, 0.94, ..., 0.8 and performed the simulation:</p> | <p>We create a gradient of <span class="variable">p</span>=0.95, 0.94, ..., 0.8 and performed the simulation:</p> | ||
| − | <img src="" class="responsive-img"> | + | <img src="https://static.igem.org/mediawiki/2016/2/23/NJU_China_2016_iGEM_Modeling_6.jpg" class="responsive-img"> |
<p>The perform the same analysis for <span class="variable">λ</span>:</p> | <p>The perform the same analysis for <span class="variable">λ</span>:</p> | ||
<div class="modelVariable"><span class="variable">λ</span>=0.3, 0.31, ..., 0.49, 0.5</div> | <div class="modelVariable"><span class="variable">λ</span>=0.3, 0.31, ..., 0.49, 0.5</div> | ||
| − | <img src="" class="responsive-img"> | + | <img src="https://static.igem.org/mediawiki/2016/8/81/NJU_China_2016_iGEM_Modeling_7.jpg" class="responsive-img"> |
<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> | <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> | ||
</div> | </div> | ||
Revision as of 14:58, 19 October 2016
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:
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:
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.
