Journal article
Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models
The journal of physical chemistry. B, v 123(10), pp 2217-2234
14 Mar 2019
PMID: 30777763
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an adaptive delayed acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare both algorithms to an adaptive Metropolis algorithm with full FSP-based likelihood evaluations on three example models and simulated data to show that the new ADAMH variants achieve substantial speedup in comparison to the full FSP approach. By reducing the computational costs of parameter estimation, we expect the ADAMH approach to enable efficient data-driven estimation for more complex gene regulation models.
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Details
- Title
- Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models
- Creators
- Huy D. Vo - Colorado State UniversityZachary Fox - Colorado State UniversityAnia Baetica - University of California, San FranciscoBrian Munsky - Colorado State University
- Publication Details
- The journal of physical chemistry. B, v 123(10), pp 2217-2234
- Publisher
- Amer Chemical Soc
- Number of pages
- 18
- Grant note
- DGE-1450032 / National Science Foundation; National Science Foundation (NSF) R25GM105608; R3SGM124747 / National Institute of General Medical Sciences of the National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS) R35GM124747; R25GM105608 / NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000461537200004
- Scopus ID
- 2-s2.0-85062818642
- Other Identifier
- 991021889908604721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biophysics
- Chemistry, Physical