Team:Fudan/Model
|
|
|
For SwordS to generate an antigen density-dependent, triple HCC therapeutic response pattern, design of the gene transcription network (GTN) plays a central role in our project. Traditional method to analyze a GTN is using the Hill Equation to describe the relation between each pair of transcription factor and its receptor. However, we found it incomprehensible to apply the Hill Equation while studying specific biochemical reactions in cell like TF-Receptor binding process.
The Hill Equation concerns all about concentrations of substances in a system, and is itself founded on the theory of chemical equilibrium. However, biochemical reactions in a single cell cannot directly influence the reactions in another cell. Meanwhile, the receptors we are interested in usually exit in a small amount in a single cell. Specially, we are often concerned about a single gene site in the nucleus. Thus, there will be neither chemical equilibrium nor the concept of concentration. Thus, the Hill Equation appears to be fundamentally flawed in such situations. Furthermore, the Hill Equation is incapable of studying the behaviours of a half-bound complex, which we believe will be rather important in future researches. Since its parameters are substantially statistical, it cannot provide enough accuracy and flexibility either, especially in field like disease treatment where extreme fine tuning is critical.
...To create a more rigorous method to analyze gene transcription networks, and more basically, biochemical reactions in cell, we take a probabilistic perspective towards the issue...
|
In the Theoretical Basis part, we build up a Probabilistic Model by
introducing some key concepts from Probability Theory and the
theory of Markov Chain. We apply our model to the analysis of
multi-binding process in cell, and further construct a concise method
of simulation. Our model reveals the relationship between the
inherent randomness and the phenotypical properties of biochemical systems. As a result, we provide a more fundamental
explanation for the traditional Hill Equation while achieving
higher accuracy and flexibility.
|
|
In the Network Modelling part, we apply the Probabilistic Model
built up previously to analyze our gene transcription network. We
demonstrate how to extend our model from a single reaction to a
network and how to make stochastic simulation based on it. You would
see how life creates order from chaos and gain mush more insights into
the vast and numerous biochemical reactions in cell from our model.
|
◆ Want to better understand our model, or want to analyze your own gene transcription network in a convenient and elegant way, see our [Software]!
