Finite state projection based bounds to compare chemical master equation models using single-cell data

The Journal of Chemical Physics
Zachary FoxBrian Munsky

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.

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Citations

Apr 7, 2018·Physical Biology·Lisa WeberBrian Munsky
Oct 23, 2017·The Journal of Chemical Physics·Ankit GuptaMustafa Khammash
Aug 24, 2018·The Journal of Chemical Physics·Garrett R Dowdy, Paul I Barton
Jul 1, 2018·Proceedings of the National Academy of Sciences of the United States of America·Brian MunskyGregor Neuert
Jul 22, 2019·The Journal of Chemical Physics·Juan KuntzMauricio Barahona
Apr 22, 2017·Physical Biology·Rob Johnson, Brian Munsky
Feb 17, 2018·The Journal of Chemical Physics·Alessandro Ceccato, Diego Frezzato

Related Concepts

Stochastic Processes
Biophysical Phenomena
Single-Cell Analysis
Exertion
Transcription, Genetic
Yeasts
Chemicals
Analysis
Small Molecule

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