Scalable population-level modelling of biological cells incorporating mechanics and kinetics in continuous time

Royal Society Open Science
Stefan EngblomRuth E Baker

Abstract

The processes taking place inside the living cell are now understood to the point where predictive computational models can be used to gain detailed understanding of important biological phenomena. A key challenge is to extrapolate this detailed knowledge of the individual cell to be able to explain at the population level how cells interact and respond with each other and their environment. In particular, the goal is to understand how organisms develop, maintain and repair functional tissues and organs. In this paper, we propose a novel computational framework for modelling populations of interacting cells. Our framework incorporates mechanistic, constitutive descriptions of biomechanical properties of the cell population, and uses a coarse-graining approach to derive individual rate laws that enable propagation of the population through time. Thanks to its multiscale nature, the resulting simulation algorithm is extremely scalable and highly efficient. As highlighted in our computational examples, the framework is also very flexible and may straightforwardly be coupled with continuous-time descriptions of biochemical signalling within, and between, individual cells.

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Citations

Jun 22, 2018·Bulletin of Mathematical Biology·Stefan Engblom
Dec 12, 2019·PLoS Computational Biology·Elisabeth G Rens, Leah Edelstein-Keshet
Mar 10, 2020·PLoS Computational Biology·Kiara C EldredElijah Roberts
Jun 12, 2019·Journal of Mathematical Biology·Mark A J ChaplainFiona R Macfarlane
Feb 15, 2019·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Ruth NussinovHyunbum Jang

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Software Mentioned

PDE Toolbox
M atlab
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