title: Finite state projection based bounds to compare chemical master equation models using single-cell data.
abstract: Emerging techniques now allow for precise quantification of distributions of biological molecules in single cells. These rapidly advancing experimental methods have created a need for more rigorous and efficient modeling tools. Here, we derive new bounds on the likelihood that observations of single-cell, single-molecule responses come from a discrete stochastic model, posed in the form of the chemical master equation. These strict upper and lower bounds are based on a finite state projection approach, and they converge monotonically to the exact likelihood value. These bounds allow one to discriminate rigorously between models and with a minimum level of computational effort. In practice, these bounds can be incorporated into stochastic model identification and parameter inference routines, which improve the accuracy and efficiency of endeavors to analyze and predict single-cell behavior. We demonstrate the applicability of our approach using simulated data for three example models as well as for experimental measurements of a time-varying stochastic transcriptional response in yeast.
authors.affiliation: School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado 80523, USA.
authors.forename: Zachary
authors.initials: Z
authors.lastname: Fox
authors.affiliation: Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
authors.forename: Gregor
authors.initials: G
authors.lastname: Neuert
authors.affiliation: School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado 80523, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Munsky
mesh_terms: D055592:Biophysical Phenomena; D002477:Cells / Q000737:chemistry*; D008956:Models, Chemical; D059010:Single-Cell Analysis; D013269:Stochastic Processes