Title: Biochemical dynamic networks with trackable species
Abstract: When considering fate of a specific molecule of interest in a large stochastic chemical system, it is sometimes convenient to provide an approximate reduced model that aggregates the dynamics of all the remaining molecules. For a wide class of stochastic chemical systems, I will provide an explicit formulae for an error of using such a reduced system when tracing a selected single molecule. I will also give some examples of application of such approximations in various contexts both for macro (e.g., COVID epidemic) and micro (e.g., gene transcription ) biological models. This is joint work with Daniele Cappelletti from Politecnico di Torino.
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