Team:CPU CHINA/Model


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 of models
A mathematical model for the reciprocal differentiation of T helper 17 cells and induced regulatory T cells.
A two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines in rheumatoid arthritis.
A mathematical representation of our SynNotch CAR-Tregs system and exploration of the previous two models

MODEL TO SERVE(Lab Integration)

Wet lab is an indispensable component of iGEM, on which we need spend most time compared with other aspects of our projects. Experience is of great importance when problems occur. But for those of us who have just entered the lab, experience is exactly what we lack most. At that time, some backup (from dry lab) turns out to be important. So we combine modeling with experiment and let modeling serves for experiments, which is called “model to serve”.

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 experimental design)

Figure1. the different subsets of naive T cells induced by different cytokines

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 model of Th17/iTreg differentiation. READ MORE

Figure2. Induction of differentiation from naïve CD4+ T cells to Th17 and iTreg

A single primary differentiation signal, TGF-β, can give rise to multiple cell types with distinct functions, while other polarizing differentiation signals, such as IL-6 as ATRA, skew the system to particular type(s) of cells. If we regard TGF-β as tossing dice for the naive cells, those polarizing signals may load the dice, although they may not toss the dice themselves.


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”.

Figure3. Rheumatoid arthritis(RA)related signaling pathway(from KEGG)

The interaction between cytokines plays a decisive role in the development and progression of RA (Figure 2). And Inflammation is associated with imbalance in cytokines in the environment. To solve this problem, we establish Syn-Notch-CAR-Treg system which shows its ability to attenuate RA symptoms with the help of CAR and Syn-Notch. In the light of the macroscopic results, this system achieves its functions by changing the balance of cytokines in the environment: pro-inflammatory cytokines can simultaneously induce the production of anti-inflammatory cytokines and pro-inflammatory cytokines, while anti-inflammatory cytokines lead to a decrease in pro-inflammatory cytokines (Figure 3). Based on that, we have created a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines. We have also obtained some instructive results by studying the dynamic characteristics of the model (stability, oscillation, etc.). READ MORE

Figure4.  The structure of our SynNotch-CAR-Treg system


Safety has always been the eternal theme. When we design a biological treatment system (Figure 4) that may be injected into body, regardless of whether it is traditional genetic engineering and synthetic biology, safety is always the first factor that we need to consider on the top priority.

Our system mainly consists of two parts. One is Syn-Notch part: the existence of IL-17A stimulates the deubiquitination of FOXP3, thereby promoting the secretion of anti-inflammatory cytokines. The other one is CAR part: CAR can activate T cells (by strengthening cytokine secretion) and kill B cells in the presence of CD20 (on the surface of mature B cells), thereby reducing the proportion of pro-inflammatory cytokines in inflammatory environment from these two aspects.

We have combined the model with our project. Considering the behavior of the two coupling systems, we explored the relationships between parameter variations and system security by analyzing the model parameters. READ MORE