ArticlePLoS computational biology2016
Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
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- Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level.PLoS computational biology · 2022Article
- Using average transcription level to understand the regulation of stochastic gene activation.Royal Society open science · 2022Article
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- Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments.Patterns (New York, N.Y.) · 2021Article
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- Generalizing RNA velocity to transient cell states through dynamical modeling.Nature biotechnology · 2020Article
- TopoFilter: a MATLAB package for mechanistic model identification in systems biology.BMC bioinformatics · 2020Article
- Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect.PLoS biology · 2019Article
- The Linear Noise Approximation for Spatially Dependent Biochemical Networks.Bulletin of mathematical biology · 2019Article
- Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.Journal of the Royal Society, Interface · 2019Article
- Filtering and inference for stochastic oscillators with distributed delays.Bioinformatics (Oxford, England) · 2019Article
- Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models.The journal of physical chemistry. B · 2019Article
- Intrinsic and extrinsic noise of gene expression in lineage trees.Scientific reports · 2019Article
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6 authors.
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Abstract
Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.