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A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis

Spatial stochastic models of single cell kinetics are capable of capturing both fluctuations in molecular numbers and the spatial dependencies of the key steps of intracellular regulatory networks. The spatial stochastic model can be simulated both on a detailed microscopic level using particle trac...

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Detalles Bibliográficos
Autores principales: Coulier, Adrien, Hellander, Stefan, Hellander, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AIP Publishing LLC 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116061/
https://www.ncbi.nlm.nih.gov/pubmed/34241042
http://dx.doi.org/10.1063/5.0010764
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author Coulier, Adrien
Hellander, Stefan
Hellander, Andreas
author_facet Coulier, Adrien
Hellander, Stefan
Hellander, Andreas
author_sort Coulier, Adrien
collection PubMed
description Spatial stochastic models of single cell kinetics are capable of capturing both fluctuations in molecular numbers and the spatial dependencies of the key steps of intracellular regulatory networks. The spatial stochastic model can be simulated both on a detailed microscopic level using particle tracking and on a mesoscopic level using the reaction–diffusion master equation. However, despite substantial progress on simulation efficiency for spatial models in the last years, the computational cost quickly becomes prohibitively expensive for tasks that require repeated simulation of thousands or millions of realizations of the model. This limits the use of spatial models in applications such as multicellular simulations, likelihood-free parameter inference, and robustness analysis. Further approximation of the spatial dynamics is needed to accelerate such computational engineering tasks. We here propose a multiscale model where a compartment-based model approximates a detailed spatial stochastic model. The compartment model is constructed via a first-exit time analysis on the spatial model, thus capturing critical spatial aspects of the fine-grained simulations, at a cost close to the simple well-mixed model. We apply the multiscale model to a canonical model of negative-feedback gene regulation, assess its accuracy over a range of parameters, and demonstrate that the approximation can yield substantial speedups for likelihood-free parameter inference.
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spelling pubmed-81160612021-05-20 A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis Coulier, Adrien Hellander, Stefan Hellander, Andreas J Chem Phys ARTICLES Spatial stochastic models of single cell kinetics are capable of capturing both fluctuations in molecular numbers and the spatial dependencies of the key steps of intracellular regulatory networks. The spatial stochastic model can be simulated both on a detailed microscopic level using particle tracking and on a mesoscopic level using the reaction–diffusion master equation. However, despite substantial progress on simulation efficiency for spatial models in the last years, the computational cost quickly becomes prohibitively expensive for tasks that require repeated simulation of thousands or millions of realizations of the model. This limits the use of spatial models in applications such as multicellular simulations, likelihood-free parameter inference, and robustness analysis. Further approximation of the spatial dynamics is needed to accelerate such computational engineering tasks. We here propose a multiscale model where a compartment-based model approximates a detailed spatial stochastic model. The compartment model is constructed via a first-exit time analysis on the spatial model, thus capturing critical spatial aspects of the fine-grained simulations, at a cost close to the simple well-mixed model. We apply the multiscale model to a canonical model of negative-feedback gene regulation, assess its accuracy over a range of parameters, and demonstrate that the approximation can yield substantial speedups for likelihood-free parameter inference. AIP Publishing LLC 2021-05-14 2021-05-11 /pmc/articles/PMC8116061/ /pubmed/34241042 http://dx.doi.org/10.1063/5.0010764 Text en © 2021 Author(s). 0021-9606/2021/154(18)/184105/13/$0.00 https://creativecommons.org/licenses/by/4.0/All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle ARTICLES
Coulier, Adrien
Hellander, Stefan
Hellander, Andreas
A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
title A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
title_full A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
title_fullStr A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
title_full_unstemmed A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
title_short A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
title_sort multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis
topic ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116061/
https://www.ncbi.nlm.nih.gov/pubmed/34241042
http://dx.doi.org/10.1063/5.0010764
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