ArticleBulletin of mathematical biology2017
Multiscale Modeling of Diffusion in a Crowded Environment.
Article in Bulletin of mathematical biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Chemical interactions in polyethylene glycol-induced condensates lead to an anomalous FRET response from a flexible linker-fluorescent protein crowding sensor.bioRxiv : the preprint server for biology · 2026Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis.The Journal of chemical physics · 2021Article
- From membrane receptors to protein synthesis and actin cytoskeleton: Mechanisms underlying long lasting forms of synaptic plasticity.Seminars in cell & developmental biology · 2019Review
- Spatial Stochastic Intracellular Kinetics: A Review of Modelling Approaches.Bulletin of mathematical biology · 2019Review
- Excluded volume effects in on- and off-lattice reaction-diffusion models.IET systems biology · 2017Article
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Abstract
We present a multiscale approach to model diffusion in a crowded environment and its effect on the reaction rates. Diffusion in biological systems is often modeled by a discrete space jump process in order to capture the inherent noise of biological systems, which becomes important in the low copy number regime. To model diffusion in the crowded cell environment efficiently, we compute the jump rates in this mesoscopic model from local first exit times, which account for the microscopic positions of the crowding molecules, while the diffusing molecules jump on a coarser Cartesian grid. We then extract a macroscopic description from the resulting jump rates, where the excluded volume effect is modeled by a diffusion equation with space-dependent diffusion coefficient. The crowding molecules can be of arbitrary shape and size, and numerical experiments demonstrate that those factors together with the size of the diffusing molecule play a crucial role on the magnitude of the decrease in diffusive motion. When correcting the reaction rates for the altered diffusion we can show that molecular crowding either enhances or inhibits chemical reactions depending on local fluctuations of the obstacle density.
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