Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks

Abstract Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular networks. However, standard approaches based on solving s...

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Autores principales:	Jongrae Kim, Mathias Foo, Declan G. Bates
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9f77f51e347544e5b1b8246289f35554
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spelling	oai:doaj.org-article:9f77f51e347544e5b1b8246289f355542021-12-02T15:08:14ZComputationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks10.1038/s41598-018-21826-82045-2322https://doaj.org/article/9f77f51e347544e5b1b8246289f355542018-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-21826-8https://doaj.org/toc/2045-2322Abstract Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular networks. However, standard approaches based on solving stochastic reaction-diffusion equations are computationally intractable for large-scale networks. We present a novel method for modeling stochastic and spatial dynamics in biomolecular networks using a simple form of the Langevin equation with noisy kinetic constants. Spatial heterogeneity in molecular interactions is decoupled into a set of compartments, where the distribution of molecules in each compartment is idealised as being uniform. The reactions in the network are then modelled by Langevin equations with correcting terms, that account for differences between spatially uniform and spatially non-uniform distributions, and that can be readily estimated from available experimental data. The accuracy and extreme computational efficiency of the approach is demonstrated on a model of the epidermal growth factor receptor network in the human mammary epithelial cell.Jongrae KimMathias FooDeclan G. BatesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-7 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jongrae Kim Mathias Foo Declan G. Bates Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks
description	Abstract Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular networks. However, standard approaches based on solving stochastic reaction-diffusion equations are computationally intractable for large-scale networks. We present a novel method for modeling stochastic and spatial dynamics in biomolecular networks using a simple form of the Langevin equation with noisy kinetic constants. Spatial heterogeneity in molecular interactions is decoupled into a set of compartments, where the distribution of molecules in each compartment is idealised as being uniform. The reactions in the network are then modelled by Langevin equations with correcting terms, that account for differences between spatially uniform and spatially non-uniform distributions, and that can be readily estimated from available experimental data. The accuracy and extreme computational efficiency of the approach is demonstrated on a model of the epidermal growth factor receptor network in the human mammary epithelial cell.
format	article
author	Jongrae Kim Mathias Foo Declan G. Bates
author_facet	Jongrae Kim Mathias Foo Declan G. Bates
author_sort	Jongrae Kim
title	Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks
title_short	Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks
title_full	Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks
title_fullStr	Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks
title_full_unstemmed	Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks
title_sort	computationally efficient modelling of stochastic spatio-temporal dynamics in biomolecular networks
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/9f77f51e347544e5b1b8246289f35554
work_keys_str_mv	AT jongraekim computationallyefficientmodellingofstochasticspatiotemporaldynamicsinbiomolecularnetworks AT mathiasfoo computationallyefficientmodellingofstochasticspatiotemporaldynamicsinbiomolecularnetworks AT declangbates computationallyefficientmodellingofstochasticspatiotemporaldynamicsinbiomolecularnetworks
_version_	1718388203797348352

Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks

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