Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks.
Sci Rep
; 8(1): 3498, 2018 02 22.
Article
en En
| MEDLINE
| ID: mdl-29472589
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesos Estocásticos
/
Redes Reguladoras de Genes
/
Modelos Biológicos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2018
Tipo del documento:
Article
Pais de publicación:
Reino Unido