ArticlePLoS computational biology2014
Uncoupled analysis of stochastic reaction networks in fluctuating environments.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A tool for modeling gene regulatory networks (GRN_modeler) and its applications to synthetic biology.Molecular systems biology · 2025Article
- Advanced methods for gene network identification and noise decomposition from single-cell data.Nature communications · 2024Article
- Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.Nature communications · 2022Article
- Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty.ACS synthetic biology · 2021Article
- Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.PLoS computational biology · 2021Article
- Frequency spectrum of chemical fluctuation: A probe of reaction mechanism and dynamics.PLoS computational biology · 2019Article
- Stochastic shielding and edge importance for Markov chains with timescale separation.PLoS computational biology · 2018Article
- Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization.Journal of the Royal Society, Interface · 2017Article
- Reconstructing dynamic molecular states from single-cell time series.Journal of the Royal Society, Interface · 2016Article
- Stochastic Simulation of Biomolecular Networks in Dynamic Environments.PLoS computational biology · 2016Article
- Molecular circuits for dynamic noise filtering.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.PLoS computational biology · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dynamics of stochastic reaction networks within cells are inevitably modulated by factors considered extrinsic to the network such as, for instance, the fluctuations in ribosome copy numbers for a gene regulatory network. While several recent studies demonstrate the importance of accounting for such extrinsic components, the resulting models are typically hard to analyze. In this work we develop a general mathematical framework that allows to uncouple the network from its dynamic environment by incorporating only the environment's effect onto the network into a new model. More technically, we show how such fluctuating extrinsic components (e.g., chemical species) can be marginalized in order to obtain this decoupled model. We derive its corresponding process- and master equations and show how stochastic simulations can be performed. Using several case studies, we demonstrate the significance of the approach.
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Registered trials
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.