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

Jongrae Kim, Mathias Foo, Declan G. Bates

Research output: Research - peer-reviewArticle

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.
LanguageEnglish
Article number3498
Number of pages1
JournalScientific Reports
Volume8
Issue number1
Early online date22 Feb 2018
DOIs
StatePublished - 22 Feb 2018

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Molecules
Molecular interactions
Computational efficiency
Trajectories
Kinetics
Epidermal Growth Factor

Bibliographical note

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Cite this

Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks. / Kim, Jongrae; Foo, Mathias; Bates, Declan G.

In: Scientific Reports, Vol. 8, No. 1, 3498, 22.02.2018.

Research output: Research - peer-reviewArticle

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