Flow cytometry based state aggregation of a stochastic model of protein expression

In this article, we introduce the new approach fluorescence grid based aggregation (FGBA) to justify a dynamical model of protein expression using experimental fluorescence histograms. First, we describe the dynamics of the gene-protein system by a chemical master equation (CME), while the protein production rates are unknown. Second, we aggregate the states of the CME into unknown group sizes. Then, we show that these unknown values can be replaced by the data from the experimental fluorescence histograms. Consequently, final probability distributions correspond to the experimental fluorescence histograms.