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<h4><br>In order to verify whether our de-ubiquitination system works well, we added cytokines of a certain concentration to the Jurkat cell lines, and then Jurkat cells were induced to Th17 cells. Later, we tranferred our de-ubiquitination system into Th17 cells to observe the working status of the system. (See <a href="https://2017.igem.org/Team:CPU_CHINA/experiment">experimental design</a>)</h4> | <h4><br>In order to verify whether our de-ubiquitination system works well, we added cytokines of a certain concentration to the Jurkat cell lines, and then Jurkat cells were induced to Th17 cells. Later, we tranferred our de-ubiquitination system into Th17 cells to observe the working status of the system. (See <a href="https://2017.igem.org/Team:CPU_CHINA/experiment">experimental design</a>)</h4> | ||
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<h4 align=middle>Figure1. the different subsets of naive T cells induced by different cytokines</h4> | <h4 align=middle>Figure1. the different subsets of naive T cells induced by different cytokines</h4> | ||
<h4><br>Based on previous work, we know that IL-6 and TGF-β of certain concentrations can induce naïve T cells to differentiate into Th-17 cells. And this protocol has existed. But why is this appropriate concentration able to induce its successful differentiation? To solve this problem, we created a <a href="https://2017.igem.org/Team:CPU_CHINA/rdmodel">model of Th17/iTreg differentiation. READ MORE</a></h4> | <h4><br>Based on previous work, we know that IL-6 and TGF-β of certain concentrations can induce naïve T cells to differentiate into Th-17 cells. And this protocol has existed. But why is this appropriate concentration able to induce its successful differentiation? To solve this problem, we created a <a href="https://2017.igem.org/Team:CPU_CHINA/rdmodel">model of Th17/iTreg differentiation. READ MORE</a></h4> | ||
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<h4 align=middle>Figure2. Induction of differentiation from naïve CD4+ T cells to Th17 and iTreg</h4> | <h4 align=middle>Figure2. Induction of differentiation from naïve CD4+ T cells to Th17 and iTreg</h4> | ||
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<h2 id="tip2" style="cursor: pointer;">MODEL TO SOLVE</h2> | <h2 id="tip2" style="cursor: pointer;">MODEL TO SOLVE</h2> | ||
<h4><br>In addition to serving the experiments, it is equally important to consider how to characterize the system from a quantitative perspective and think about the possible future problems that need to be addressed, which, as the core of the modeling, we called “model to solve”.</h4> | <h4><br>In addition to serving the experiments, it is equally important to consider how to characterize the system from a quantitative perspective and think about the possible future problems that need to be addressed, which, as the core of the modeling, we called “model to solve”.</h4> | ||
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<h4 align=middle>Figure3. Rheumatoid arthritis(RA)related signaling pathway(from KEGG)</h4> | <h4 align=middle>Figure3. Rheumatoid arthritis(RA)related signaling pathway(from KEGG)</h4> | ||
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<h4 align=middle>Figure4. The structure of our SynNotch-CAR-Treg system</h4> | <h4 align=middle>Figure4. The structure of our SynNotch-CAR-Treg system</h4> |
Revision as of 15:45, 30 October 2017
Introduction
What can modeling do for our project? After careful consideration, our team thinks that what modeling can do does not merely simulate and verify our system though it is important. We hope that with the agency of our modeling, we can find some solutions to the problems we have met or will meet during the process of our project. Thus, we establish “3S” as goals of modeling:
Model to serve, Model to solve, Model for security
Modeling, with its strictness and precision, can be an excellent tool for us to understand the disease and the biological process, and plays an important role in our project. To satisfy our experimental requirements, we created a model of Th17/iTreg differentiation. This model provides a new framework that can be used to analyze the dynamic characteristics of Th17/iTreg differentiation network. In addition, we created a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines by establishing ordinary differential equations (ODE). This model can be used to investigate the involvement of cytokines in the disease process. We finally explore the feasibility of our project by coupling the model with our project. Finally, aimed at security, we explored the relationships between our system and RA models.
What did our model achieve?
We achieved 3 main aims in our modelling work
We presented a novel mathematical model of TH17-iTreg differentiation that reveals how the control system generates phenotypic diversity and how its final state can be regulated by various signals.
We created a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrateed that mathematical modeling can be used to investigate the involvement of cytokines in the disease process.
We combined the two-variable model with our project to find the method of solving the kinetic parameters of the system, and the relationship between parameters and system security.
All of our models are available on our Github page.
Reciprocal Differentiation Model | Cytokine-mediated inflammation in RA | Exploration |
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A mathematical model for the reciprocal differentiation of T helper 17 cells and induced regulatory T cells.
READ MORE |
A two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines in rheumatoid arthritis. READ MORE |
A mathematical representation of our SynNotch CAR-Tregs system and exploration of the previous two models
READ MORE |