ArticleBMC systems biology2016
Generalized method of moments for estimating parameters of stochastic reaction networks.
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- An Entropy-Based Approach to Model Selection with Application to Single-Cell Time-Stamped Snapshot Data.Entropy (Basel, Switzerland) · 2025Article
- BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells.NPJ systems biology and applications · 2023Article
- Examining the Relationships between the Incidence of Infectious Diseases and Mood Disorders: An Analysis of Data from the Global Burden of Disease Studies, 1990-2019.Diseases (Basel, Switzerland) · 2023Article
- McSNAC: A software to approximate first-order signaling networks from mass cytometry data.Quantitative biology (Beijing, China) · 2023Article
- Inference and uncertainty quantification of stochastic gene expression via synthetic models.Journal of the Royal Society, Interface · 2022Article
- Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.Journal of the Royal Society, Interface · 2019Article
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
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2 authors.
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Abstract
backgroundDiscrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.
resultsWe propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.
conclusionsThe generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.
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