Multi-Grid Reaction-Diffusion Master Equation: Applications to Morphogen Gradient Modelling

The multi-grid reaction-diffusion master equation (mgRDME) provides a generalization of stochastic compartment-based reaction-diffusion modelling described by the standard reaction-diffusion master equation (RDME). By enabling different resolutions on lattices for biochemical species with different diffusion constants, the mgRDME approach improves both accuracy and efficiency of compartment-based reaction-diffusion simulations. The mgRDME framework is examined through its application to morphogen gradient formation in stochastic reaction-diffusion scenarios, using both an analytically tractable first-order reaction network and a model with a second-order reaction. The results obtained by the mgRDME modelling are compared with the standard RDME model and with the (more detailed) particle-based Brownian dynamics simulations. The dependence of error and numerical cost on the compartment sizes is defined and investigated through a multi-objective optimization problem.

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Publication Details

Journal
Bulletin of Mathematical Biology
Published
2024-11-27
DOI
https://doi.org/10.1007/s11538-024-01377-y
Citations
1
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
0.23

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article

Multi-Grid Reaction-Diffusion Master Equation: Applications to Morphogen Gradient Modelling

Stefanie Winkelmann, Radek Erban
1 citations
Bulletin of Mathematical Biology
Gene Regulatory Network Analysis
0.23
article

Multi-Grid Reaction-Diffusion Master Equation: Applications to Morphogen Gradient Modelling

Stefanie Winkelmann, Radek Erban
article en
1 citations

Abstract

The multi-grid reaction-diffusion master equation (mgRDME) provides a generalization of stochastic compartment-based reaction-diffusion modelling described by the standard reaction-diffusion master equation (RDME). By enabling different resolutions on lattices for biochemical species with different diffusion constants, the mgRDME approach improves both accuracy and efficiency of compartment-based reaction-diffusion simulations. The mgRDME framework is examined through its application to morphogen gradient formation in stochastic reaction-diffusion scenarios, using both an analytically tractable first-order reaction network and a model with a second-order reaction. The results obtained by the mgRDME modelling are compared with the standard RDME model and with the (more detailed) particle-based Brownian dynamics simulations. The dependence of error and numerical cost on the compartment sizes is defined and investigated through a multi-objective optimization problem.

Bulletin of Mathematical BiologyVol. 87(1)
Zuse Institute Berlin (DE), University of Oxford (GB), Mathematical Institute of the Slovak Academy of Sciences (SK)
Deutsche Forschungsgemeinschaft, Berlin Mathematics Research Center MATH+, Engineering and Physical Sciences Research Council
Life in Land
Openalex Percentile: Top 41%
Gene Regulatory Network Analysis
0.23
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