Speaker
Description
Stochastic chemical reaction networks (CRNs) provide a fundamental framework for modeling stochastic dynamics in systems biology, population dynamics, and chemistry. A stationary distribution of stochastic CRN describes its long-term behavior. An analytic formula for a stationary distribution can be obtained for only in limited cases, linear or finite-state systems.
Interestingly, the analytic form of stationary distribution can be obtained when the underlying network satisfies topological conditions, specifically weak reversibility and deficiency zero, and kinetic conditions such as mass-action or kinetics satisfying factorizability. Moreover, other studies resolved certain violations of topological conditions with the network translations. In this talk, I will introduce a “dummy species extension” framework to overcome violations of kinetic conditions. By adding an additional species, originally non-factorizable networks are transformed into factorizable systems within this framework, enabling the analytic derivation of their stationary distribution. I will demonstrate its applications with several examples from biological network systems.
