ArticleProceedings of the National Academy of Sciences of the United States of America2020
Stochastic reaction networks in dynamic compartment populations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Spatiotemporal modeling of GPCR signaling: The role of endosomal dynamics and receptor recycling.PLoS computational biology · 2026Article
- Stochastic Reaction Networks Within Interacting Compartments with Content-Dependent Fragmentation.Bulletin of mathematical biology · 2026Article
- Role of intercellular interactions on single cell and population level responses: considerations for multicellular bioreporter design.Frontiers in molecular biosciences · 2025Article
- Compartmental exchange regulates steady states and stochastic switching of a phosphorylation network.Biophysical journal · 2024Article
- Article
- Using single-cell models to predict the functionality of synthetic circuits at the population scale.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Beyond the chemical master equation: Stochastic chemical kinetics coupled with auxiliary processes.PLoS computational biology · 2021Article
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Authors and funding
2 authors.
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Abstract
Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.
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