BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Wasiur Khuda Bukhsh (University of Nottingham\, UK) DTSTART:20241107T160000Z DTEND:20241107T173000Z DTSTAMP:20260421T124628Z UID:MoRN/106 DESCRIPTION:Title: E nzyme kinetic reactions as interacting particle systems: Stochastic averag ing and parameter inference\nby Wasiur Khuda Bukhsh (University of Not tingham\, UK) as part of Seminar on the Mathematics of Reaction Networks\n \n\nAbstract\nIn this talk\, I will consider a stochastic model of multist age Michaelis--Menten (MM) type enzyme kinetic reactions describing the co nversion of substrate molecules to a product through several intermediate species. The high-dimensional\, multiscale nature of these reaction networ ks presents significant computational challenges\, especially in statistic al estimation of reaction rates. This difficulty is amplified when direct data on system states are unavailable\, and one only has access to a rando m sample of product formation times. To address this\, we proceed in two s tages. First\, under certain technical assumptions akin to those made in t he Quasi-steady-state approximation (QSSA) literature\, we prove a stochas tic averaging principle that yields a lower-dimensional model. Next\, for statistical inference of the parameters of the original MM reaction networ k\, we develop a mathematical framework involving an interacting particle system (IPS) and prove a propagation of chaos result that allows us to wri te a product-form likelihood function. The novelty of the IPS-based infere nce method is that it does not require information about the state of the system and works with only a random sample of product formation times. We provide numerical examples to illustrate the efficacy of the theoretical r esults. Preprint: https://arxiv.org/abs/2409.06565\n LOCATION:https://researchseminars.org/talk/MoRN/106/ END:VEVENT END:VCALENDAR