Multiscaling methods and parameter inference in stochastic biochemical networks

11 Jul 2018, 10:30
New Law School/--026 (University of Sydney)

New Law School/--026

University of Sydney



Multiscaling methods and parameter inference in stochastic biochemical networks

  • Grzegorz Rempała (The Ohio State University)


A reaction network is a chemical system involving multiple reactions and chemical species. Mathematical models of such networks are important tools of modern biosciences with applications to rage of biological problems from molecular experiments to population level studies. The simplest stochastic models of such networks treat the biological system as a continuous time Markov chain on the number of molecules of each species with reactions as possible transitions of the chain. In many cases of biological interest some of the chemical species in the network are present in much greater abundance than others and reaction rate constants can vary over several orders of magnitude. This minisymposium will discuss approaches to approximation of such models that take the multiscale nature of the systems into account. In particular, such approximations often allow for inference on specific components of the network on the scale of interest and for incorporation of species transition constraints. While some of the talks will focus on new ideas for MCMC-based statistical inference for multiscale systems others will consider newly found connections between multiscale limits for constrained reaction networks and the rapidly growing research area of stochastic models for social networks and communicable diseases.

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