Overview
There are several periodically expressed proteins in humans, such as the GnRH of concern in our team. Development of GnRH expression in vitro can be achieved by a biological circuit known as "Synthetic Oscillator"(hereinafter called SO). On the one hand, due to the complexity of the SO subsystem, it is necessary to construct models to assist the experiments. On the other hand, building a model can help users to better understand the biochemical mechanism and quantitative data of SO.
Under the background and principle of understanding the SO subsystem, we can build the dynamic ordinary differential equation model and visual model of the gene circuit by using biochemistry and biophysics, so as to verify the experimental data, simulate the biochemical mechanism and predict the experimental results. Therefore, according to different experimental requirements, we constructed three models of Activator-Inhibitor Models, Multiple Inhibitors Models and Probabilistic models (including biochemical model and ODE model).
Problem Analysis
1 | What is Synthetic Oscillator？How do we construct genetic circuits to implement Synthetic Oscillator? (Link to Gene circuit models)
2 | How to characterize the gene circuit by using mathematical terms? (Link to ODE models)
Based on ODE and biochemical models, we developed APPs to present our model functionality.:
(1)APP 1: Model band APP. It can intuitively reflect the relationship between the specific values of various parameters and waveforms in Multiple Inhibitors Models.
(2)APP 2: Trend visualization APP. It is able to analyze the effects of different plasmids on a protein over a certain time range, including period and peak.
(3)APP 3: Visual Analytics APP. It presents our model results and biological processes in the form of animations.
Click here to jump to the APP interface. (Link to Model app)
3 | How do we analyze the waves generated by the model?(Link to Waveform Analysis)
4 | How do we fit and determine parameters? (Link to Parametric design)
Research paper
We summarized all our work results into a paper, which contains our modeling ideas, mathematical equations, parameter design, waveform analysis and other results.
If you want a quick look at all of our work, Here is a PDF of our paper for quick viewing (The file may load slowly because it has a large memory, please wait patiently).
Acknowledgement
Here we would like to thank the members of our CSU-iGEM 2023 modeling team, Zihang Zheng, Jubin Wu, Zeyang Liu and Xin Liao for their hard work in the team.
We would like to thank Ye Liang for his patient guidance. And thank you very much for the mutual help of each group in the iGEM team, and thank you very much for the efforts of Yaru Wang, Xiaoying Liu, Sijia Wang, Shuaiqi Zhang, Zheng Yang and Yaohui Han for their participation in the construction and discussion of the model.
