Adaptive time integration of mechanical forces in center-based models for biological cell populations
Per L\"otstedt, Sonja Mathias

TL;DR
This paper introduces adaptive time stepping methods for center-based biological cell models, improving simulation efficiency and accuracy during tissue growth and cell division.
Contribution
It proposes adaptive single rate and multirate time stepping schemes, enhancing robustness and reducing manual parameter tuning in cell population simulations.
Findings
Adaptive forward Euler reduces simulation time significantly.
Multirate scheme effectively handles regions with high force gradients.
Eliminates manual time step size selection.
Abstract
Center-based models are used to simulate the mechanical behavior of biological cells during embryonic development or cancer growth. To allow for the simulation of biological populations potentially growing from a few individual cells to many thousands or more, these models have to be numerically efficient, while being reasonably accurate on the level of individual cell trajectories. In this work, we increase the robustness, accuracy, and efficiency of the simulation of center-based models by choosing the time steps adaptively in the numerical method. We investigate the gain in using single rate time stepping for the forward and backward Euler methods, based on local estimates of the numerical errors and the stability of the method in the case of the explicit forward Euler method. Furthermore, we propose a multirate time stepping scheme that simulates regions with high local force…
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Taxonomy
TopicsMathematical Biology Tumor Growth · Cellular Mechanics and Interactions · Numerical methods for differential equations
