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SUMMARY:Hyukpyo Hong (KAIST)
DTSTART:20210513T150000Z
DTEND:20210513T153000Z
DTSTAMP:20260421T123629Z
UID:MoRN/21
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MoRN/21/">De
 rivation of stationary distributions of stochastic chemical reaction netwo
 rks via network translation</a>\nby Hyukpyo Hong (KAIST) as part of Semina
 r on the Mathematics of Reaction Networks\n\n\nAbstract\nLong-term behavio
 rs of biochemical reaction networks are described by steady states in dete
 rministic 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 meth
 od 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 t
 he complex balanced steady state. Furthermore\, we provide a user-friendly
  computational package\, called CASTANET\, that transforms BRNs and then a
 nalytically derives their stationary distributions.\n
LOCATION:https://researchseminars.org/talk/MoRN/21/
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