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SUMMARY:Hye-Won Kang (University of Maryland Baltimore)
DTSTART:20260409T153000Z
DTEND:20260409T160000Z
DTSTAMP:20260421T123618Z
UID:MoRN/144
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MoRN/144/">M
 ultiscale Approximation and Parameter Estimation in Stochastic Models of t
 he Glycolytic Pathway</a>\nby Hye-Won Kang (University of Maryland Baltimo
 re) as part of Seminar on the Mathematics of Reaction Networks\n\n\nAbstra
 ct\nIn this talk\, I will introduce a glycolytic pathway that includes mul
 tiple enzyme-catalyzed reactions.  We assume that some enzymes are present
  in low copy numbers and thus adopt a continuous-time Markov chain framewo
 rk to capture stochastic effects. To further reduce network complexity\, w
 e apply a multiscale approximation method and derive a reduced ODE model t
 hat describes the system's behavior on a slow timescale.\n\nThe reduced mo
 del involves two key species and contains fewer parameters—expressed as 
 functions of those in the full model--which facilitates more tractable par
 ameter estimation. Assuming that only the reduced species are observable\,
  we generate synthetic data from the full model and use it to estimate the
  parameters in the reduced model. This approach demonstrates how time-seri
 es data from a subset of species can enable effective estimation of compos
 ite parameters in a reduced system.\n\nThis is joint work with Arnab Gangu
 ly.\n
LOCATION:https://researchseminars.org/talk/MoRN/144/
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