Machine learning-assisted analysis of stochastic biochemical reaction networks

Biochemical reaction networks represent complex cellular regulatory mechanisms. These networks are typically analyzed using discrete stochastic simulation models. The models typically involve numerous reactions involving a large number of chemical species, governed by highly uncertain parameters. Likelihood-free parameter inference Given existing data pertaining to a biochemical reaction network, one is often interested in inferring the […]