Flow Cytometry Based State Aggregation of a Stochastic Model of Protein Expression

TL;DR

This paper introduces FGBA, a novel method that uses fluorescence histograms to aggregate states in a stochastic gene-protein expression model, enabling better alignment with experimental data.

Contribution

The paper presents a new fluorescence grid based aggregation approach that links a chemical master equation model with experimental fluorescence data.

Findings

Successfully aggregates CME states using fluorescence histograms

Produces probability distributions consistent with experimental data

Provides a new framework for model-data integration in protein expression

Abstract

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. In this approach, first, we describe the dynamics of the gene-protein system by a chemical master equation (CME), while the protein production rates are unknown. Then, we aggregate the states of the CME into unknown group sizes. 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.