Stochastic Modeling of Chemical Reactions in Biology

Inherent fluctuations may play an important role in biological or biophysical systems when the system involves some species with low copy numbers. In this talk, I will present my recent work on stochastic modeling of chemical reaction networks. In the first part of the talk, I will show two examples of enzyme kinetics and glucose metabolism, where we use a continuous-time Markov chain model to describe the temporal evolution of the system dynamics with different time scales.  We apply a multiscale approximation method to reduce the models with some key features.

In the second part of the talk, I will show another example of glucose metabolism where we see different-sized enzyme complexes. We hypothesize that the size of enzyme complexes is related to their functional roles and model how glucose flux can be regulated under different scenarios using differential equations. We will also see a microscopic model using the Langevin dynamics describing the movement and interactions of enzyme complexes.