Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks

Inferring quantities of interest from fluorescence microscopy time-lapse measurements of cells is a key step in parameterizing models of biomolecular reaction networks, and also in comparing different models. In this article, we propose a method which performs inference in continuous-time Markov chain models and thus takes into account the discrete nature of molecule counts. It targets the important situation of inference from many measured cells. Our method, a complement to a recently proposed approach, is based on particle Markov chain Monte Carlo and can be argued to have improved scaling behavior as the number of measured cells increases. We numerically demonstrate the performance of our algorithm on simulated data