ArticleProceedings of the National Academy of Sciences of the United States of America2019
Markovian approaches to modeling intracellular reaction processes with molecular memory.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Gillespie-based simulation and inference for non-Markovian stochastic reaction networks.Briefings in bioinformatics · 2026Article
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- Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks.Nature communications · 2026Article
- Accelerated First-Passage Dynamics in a Non-Markovian Feedback Ornstein-Uhlenbeck Process.Journal of statistical physics · 2025Article
- Cellular forgetting, desensitisation, stress and ageing in signalling networks. When do cells refuse to learn more?Cellular and molecular life sciences : CMLS · 2024Review
- Power-law behavior of transcriptional bursting regulated by enhancer-promoter communication.Genome research · 2024Article
- Transcriptional bursting dynamics in gene expression.Frontiers in genetics · 2024Review
- 4D nucleome equation predicts gene expression controlled by long-range enhancer-promoter interaction.PLoS computational biology · 2023Article
- Stochastic modeling of the mRNA life process: A generalized master equation.Biophysical journal · 2023Article
- Theoretical and computational tools to model multistable gene regulatory networks.Reports on progress in physics. Physical Society (Great Britain) · 2023Review
- Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.Royal Society open science · 2023Article
- Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics.Nucleic acids research · 2023Article
- Spontaneous vortex formation by microswimmers with retarded attractions.Nature communications · 2023Article
- Dynamic survival analysis for non-Markovian epidemic models.Journal of the Royal Society, Interface · 2022Article
- Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number.Science advances · 2022Article
- Using average transcription level to understand the regulation of stochastic gene activation.Royal Society open science · 2022Article
- Neural network aided approximation and parameter inference of non-Markovian models of gene expression.Nature communications · 2021Article
- Improved bounds on entropy production in living systems.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Machine-learning model selection and parameter estimation from kinetic data of complex first-order reaction systems.PloS one · 2021Article
- Queuing Models of Gene Expression: Analytical Distributions and Beyond.Biophysical journal · 2020Article
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2 authors.
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Abstract
Many cellular processes are governed by stochastic reaction events. These events do not necessarily occur in single steps of individual molecules, and, conversely, each birth or death of a macromolecule (e.g., protein) could involve several small reaction steps, creating a memory between individual events and thus leading to nonmarkovian reaction kinetics. Characterizing this kinetics is challenging. Here, we develop a systematic approach for a general reaction network with arbitrary intrinsic waiting-time distributions, which includes the stationary generalized chemical-master equation (sgCME), the stationary generalized Fokker-Planck equation, and the generalized linear-noise approximation. The first formulation converts a nonmarkovian issue into a markovian one by introducing effective transition rates (that explicitly decode the effect of molecular memory) for the reactions in an equivalent reaction network with the same substrates but without molecular memory. Nonmarkovian features of the reaction kinetics can be revealed by solving the sgCME. The latter 2 formulations can be used in the fast evaluation of fluctuations. These formulations can have broad applications, and, in particular, they may help us discover new biological knowledge underlying memory effects. When they are applied to generalized stochastic models of gene-expression regulation, we find that molecular memory is in effect equivalent to a feedback and can induce bimodality, fine-tune the expression noise, and induce switch.
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