ArticleThe Journal of chemical physics2016
Finite state projection based bounds to compare chemical master equation models using single-cell data.
Article in The Journal of chemical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Methods in quantitative biology-from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.Physical biology · 2025Review
- Sequential design of single-cell experiments to identify discrete stochastic models for gene expression.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2024Article
- Spectral neural approximations for models of transcriptional dynamics.Biophysical journal · 2024Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.Nature communications · 2022Article
- Solving the chemical master equation for monomolecular reaction systems and beyond: a Doi-Peliti path integral view.Journal of mathematical biology · 2021Article
- BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO.International journal for uncertainty quantification · 2020Article
- Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments.Complexity · 2020Article
- Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models.The journal of physical chemistry. B · 2019Article
- The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments.PLoS computational biology · 2019Article
- Distribution shapes govern the discovery of predictive models for gene regulation.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Short linear motifs in intrinsically disordered regions modulate HOG signaling capacity.BMC systems biology · 2018Article
- Identification of gene regulation models from single-cell data.Physical biology · 2018Article
- The finite state projection approach to analyze dynamics of heterogeneous populations.Physical biology · 2017Article
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3 authors.
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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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