L.J. WeGrzegorz Rempala
Department of Mathematics
University of Louisville, Kentucky
Estimating Reaction Contents in Stochastic
Intracellular Networks
One of the key issues of interest in analyzing stochastic kinetic models of
reaction networks involving RNA and DNA molecules (like e.g. gene
transcription) is how to infer the values of the reaction constants. Under
mass action kinetics assumption this is relatively straightforward when the
system trajectories are fully observed; however, this is rarely the case in
practice. The talk shall summarize some recent developments in the area of
Bayesian inference for reaction constants using MCMC methodology in
"data-poor" settings. In particular, it shall attempt to indicate the
benefits, as well as the challenges of this approach with some examples of
inferences for well-known biochemical networks models, like gene
transcription and auto-regulation.
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