A method to coarse-grain multiagent stochastic systems with regions of multistability

Hybrid multiscale modeling has emerged as a useful framework for modeling complex biological phenomena. However, when accounting for stochasticity in the internal dynamics of agents, these models frequently become computationally expensive. Traditional techniques to reduce the computational intensit...

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Những tác giả chính: Stepanova, D, Byrne, HM, Maini, PK, Alarcón, T
Định dạng: Journal article
Ngôn ngữ:English
Được phát hành: Society for Industrial and Applied Mathematics 2022
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author Stepanova, D
Byrne, HM
Maini, PK
Alarcón, T
author_facet Stepanova, D
Byrne, HM
Maini, PK
Alarcón, T
author_sort Stepanova, D
collection OXFORD
description Hybrid multiscale modeling has emerged as a useful framework for modeling complex biological phenomena. However, when accounting for stochasticity in the internal dynamics of agents, these models frequently become computationally expensive. Traditional techniques to reduce the computational intensity of such models can lead to a reduction in the richness of the dynamics observed, compared to the original system. Here we use large deviation theory to decrease the computational cost of a spatially extended multiagent stochastic system with a region of multistability by coarse-graining it to a continuous time Markov chain on the state space of stable steady states of the original system. Our technique preserves the original description of the stable steady states of the system and accounts for noise-induced transitions between them. We apply the method to a bistable system modeling phenotype specification of cells driven by a lateral inhibition mechanism. For this system, we demonstrate how the method may be used to explore different pattern configurations and unveil robust patterns emerging on longer timescales. We then compare the full stochastic, coarse-grained, and mean-field descriptions via pattern quantification metrics and in terms of the numerical cost of each method. Our results show that the coarse-grained system exhibits the lowest computational cost while preserving the rich dynamics of the stochastic system. The method has the potential to reduce the computational complexity of hybrid multiscale models, making them more tractable for analysis, simulation, and hypothesis testing.
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spelling oxford-uuid:29b5c581-d707-411d-83e7-a574fb0c545c2022-04-04T15:53:35ZA method to coarse-grain multiagent stochastic systems with regions of multistabilityJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:29b5c581-d707-411d-83e7-a574fb0c545cEnglishSymplectic ElementsSociety for Industrial and Applied Mathematics2022Stepanova, DByrne, HMMaini, PKAlarcón, THybrid multiscale modeling has emerged as a useful framework for modeling complex biological phenomena. However, when accounting for stochasticity in the internal dynamics of agents, these models frequently become computationally expensive. Traditional techniques to reduce the computational intensity of such models can lead to a reduction in the richness of the dynamics observed, compared to the original system. Here we use large deviation theory to decrease the computational cost of a spatially extended multiagent stochastic system with a region of multistability by coarse-graining it to a continuous time Markov chain on the state space of stable steady states of the original system. Our technique preserves the original description of the stable steady states of the system and accounts for noise-induced transitions between them. We apply the method to a bistable system modeling phenotype specification of cells driven by a lateral inhibition mechanism. For this system, we demonstrate how the method may be used to explore different pattern configurations and unveil robust patterns emerging on longer timescales. We then compare the full stochastic, coarse-grained, and mean-field descriptions via pattern quantification metrics and in terms of the numerical cost of each method. Our results show that the coarse-grained system exhibits the lowest computational cost while preserving the rich dynamics of the stochastic system. The method has the potential to reduce the computational complexity of hybrid multiscale models, making them more tractable for analysis, simulation, and hypothesis testing.
spellingShingle Stepanova, D
Byrne, HM
Maini, PK
Alarcón, T
A method to coarse-grain multiagent stochastic systems with regions of multistability
title A method to coarse-grain multiagent stochastic systems with regions of multistability
title_full A method to coarse-grain multiagent stochastic systems with regions of multistability
title_fullStr A method to coarse-grain multiagent stochastic systems with regions of multistability
title_full_unstemmed A method to coarse-grain multiagent stochastic systems with regions of multistability
title_short A method to coarse-grain multiagent stochastic systems with regions of multistability
title_sort method to coarse grain multiagent stochastic systems with regions of multistability
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