Derivation of stationary distributions of stochastic chemical reaction networks via network translation

Hyukpyo Hong (KAIST)

13-May-2021, 15:00-15:30 (5 years ago)

Abstract: Long-term behaviors of biochemical reaction networks are described by steady states in deterministic models and stationary distributions in stochastic models. Unlike deterministic steady states, stationary distributions capturing inherent fluctuations of reactions are extremely difficult to derive analytically due to the curse of dimensionality. In this talk, we introduce a new method to derive stationary distributions from deterministic steady states by transforming reaction networks to have a special dynamic property based on chemical reaction network theory. Specifically, we merge nodes and edges to make a steady state complex balanced, i.e., the in- and out-flows of each node are equal, and then we derive a stationary distribution from the complex balanced steady state. Furthermore, we provide a user-friendly computational package, called CASTANET, that transforms BRNs and then analytically derives their stationary distributions.

algebraic geometrydynamical systemsprobability

Audience: researchers in the topic

( video )


Seminar on the Mathematics of Reaction Networks

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This seminar series focuses on progress in mathematical theory for the study of reaction networks, mainly in biology and chemistry. The scope is broad and accommodates works arising from dynamical systems, stochastics, algebra, topology and beyond.

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The organizers.

Organizers: Daniele Cappelletti*, Stefan Müller*, Tung Nguyen*, Polly Yu*
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