[논문]Derivation of stationary distributions of biochemical reaction networks via structure transformation

Hong, Hyukpyo; Kim, Jinsu; Ali Al-Radhawi, M.; Sontag, Eduardo D.; Kim, Jae Kyoung

doi:10.1038/s42003-021-02117-x

[해외논문] Derivation of stationary distributions of biochemical reaction networks via structure transformation 원문보기

Communications biology, v.4 no.1, 2021년, pp.620 -

Hong, Hyukpyo (Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea) , Kim, Jinsu (Department of Mathematics, University of California, Irvine, CA USA) , Ali Al-Radhawi, M. (Department of Electrical and Computer Engineering, Northeastern University, Boston, MA USA) , Sontag, Eduardo D. (Department of Electrical and Computer Engineering, Northeastern University, Boston, MA USA) , Kim, Jae Kyoung (Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea)

Abstract ▼ AI-Helper

Long-term behaviors of biochemical reaction networks (BRNs) 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. Here, we develop a method to derive analytic stationary distributions from deterministic steady states by transforming BRNs to have a special dynamic property, called complex balancing. Specifically, we merge nodes and edges of BRNs to match in- and out-flows of each node. This allows us to derive the stationary distributions of a large class of BRNs, including autophosphorylation networks of EGFR, PAK1, and Aurora B kinase and a genetic toggle switch. This reveals the unique properties of their stochastic dynamics such as robustness, sensitivity, and multi-modality. Importantly, we provide a user-friendly computational package, CASTANET, that automatically derives symbolic expressions of the stationary distributions of BRNs to understand their long-term stochasticity.Hong, Kim and colleagues develop a method for analytically deriving the stationary distributions of stochastic biochemical reaction networks using network structure transformation. They provide this method in a user-friendly computational package, CASTANET, available open-source.

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