Noise-induced mixing and multimodality in reaction networks

We analyse a class of chemical reaction networks under mass-action kinetics involving multiple time scales, whose deterministic and stochastic models display qualitative differences. The networks are inspired by gene-regulatory networks and consist of a slow subnetwork, describing conversions among...

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Главные авторы: Plesa, T, Erban, R, Othmer, H
Формат: Journal article
Опубликовано: Cambridge University Press 2018
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author Plesa, T
Erban, R
Othmer, H
author_facet Plesa, T
Erban, R
Othmer, H
author_sort Plesa, T
collection OXFORD
description We analyse a class of chemical reaction networks under mass-action kinetics involving multiple time scales, whose deterministic and stochastic models display qualitative differences. The networks are inspired by gene-regulatory networks and consist of a slow subnetwork, describing conversions among the different gene states, and fast subnetworks, describing biochemical interactions involving the gene products. We show that the long-term dynamics of such networks can consist of a unique attractor at the deterministic level (unistability), while the long-term probability distribution at the stochastic level may display multiple maxima (multimodality). The dynamical differences stem from a phenomenon we call noise-induced mixing, whereby the probability distribution of the gene products is a linear combination of the probability distributions of the fast subnetworks which are ‘mixed’ by the slow subnetworks. The results are applied in the context of systems biology, where noise-induced mixing is shown to play a biochemically important role, producing phenomena such as stochastic multimodality and oscillations.
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spelling oxford-uuid:b10e6250-9782-40d9-b34a-05f51ae5e9ed2022-03-27T04:01:09ZNoise-induced mixing and multimodality in reaction networksJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b10e6250-9782-40d9-b34a-05f51ae5e9edSymplectic Elements at OxfordCambridge University Press2018Plesa, TErban, ROthmer, HWe analyse a class of chemical reaction networks under mass-action kinetics involving multiple time scales, whose deterministic and stochastic models display qualitative differences. The networks are inspired by gene-regulatory networks and consist of a slow subnetwork, describing conversions among the different gene states, and fast subnetworks, describing biochemical interactions involving the gene products. We show that the long-term dynamics of such networks can consist of a unique attractor at the deterministic level (unistability), while the long-term probability distribution at the stochastic level may display multiple maxima (multimodality). The dynamical differences stem from a phenomenon we call noise-induced mixing, whereby the probability distribution of the gene products is a linear combination of the probability distributions of the fast subnetworks which are ‘mixed’ by the slow subnetworks. The results are applied in the context of systems biology, where noise-induced mixing is shown to play a biochemically important role, producing phenomena such as stochastic multimodality and oscillations.
spellingShingle Plesa, T
Erban, R
Othmer, H
Noise-induced mixing and multimodality in reaction networks
title Noise-induced mixing and multimodality in reaction networks
title_full Noise-induced mixing and multimodality in reaction networks
title_fullStr Noise-induced mixing and multimodality in reaction networks
title_full_unstemmed Noise-induced mixing and multimodality in reaction networks
title_short Noise-induced mixing and multimodality in reaction networks
title_sort noise induced mixing and multimodality in reaction networks
work_keys_str_mv AT plesat noiseinducedmixingandmultimodalityinreactionnetworks
AT erbanr noiseinducedmixingandmultimodalityinreactionnetworks
AT othmerh noiseinducedmixingandmultimodalityinreactionnetworks