Inferring parameters of gene regulatory networks via particle filtering

Gene regulatory networks are highly complex dynamical systems of biomolecular components - genes, mRNA, proteins. These components interact with each other and through those interactions determine gene expression levels, i.e., determine the rate of gene transcription to mRNA and, consequently, the rate of mRNA translation to proteins. In this paper, a particle filter with MCMC move step is employed for the estimation of parameters in a gene regulatory network modeled by a chemical Langevin equation. Simulations demonstrate that the proposed technique outperforms previously considered methods.