Recent and past results on stochastically-modelled biochemical reaction networks

When a biological system is modelled using a mathematical process, the next step is often to estimate the system parameters. Although computational and statistical techniques have been developed to estimate parameters for complex systems, this can be a difficult task. As a result, researchers have focused on revealing parameter-independent dynamical properties of a system. In this talk, we will discuss the study of qualitative behaviors of stochastic biochemical systems using reaction networks, which are graphical configurations of biochemical systems. The goal of this talk is to (1) introduce the basic modelling aspects of stochastically-modelled reaction networks and (2) discuss important results in this literature, including the random time representation, relationships between stochastic and deterministic models, and derivation of stability via network structures.