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.

Likelihood Functions for Individual-Level Chemical Reaction Models

Grzegorz A. Rempala (The Ohio State University)            ( view )

Thu Feb 13, 11:30-12:00 (3 weeks ago)

Abstract: When analyzing chemical reaction systems, it is often useful to consider the fate of individual molecules. For such systems, one can construct an individual-level likelihood function—a statistical tool that evaluates how well a specific parametric reaction model fits empirical data. These likelihood functions are typically applied in the context of time series data representing realizations of reaction network trajectories. In this talk, I will introduce the concept, discuss its essential applications, and explore useful approximations, particularly in the setting of mass-transfer models. A key example will be the stochastic SIR network model, though similar constructions can be applied to enzyme kinetics and other reaction systems.