Speaker
Description
In this talk, I will introduce stochastic models for biochemical reaction networks based on continuous-time Markov chains. These models can be formulated using Kurtz's random time change representation and simulated exactly using Monte Carlo algorithms, such as Gillespie's stochastic simulation algorithm. Through several biological examples, we will explore how stochasticity gives rise to important phenomena, including stochastic extinction and random switching between distinct states.
I will then extend these ideas to spatial biochemical reaction networks. We will discuss stochastic modeling and simulation approaches that incorporate spatial heterogeneity, including compartment-based models and spatial Gillespie algorithms. Finally, I will introduce hybrid stochastic methods that couple different modeling regimes to efficiently simulate multiscale biological systems.
