Numerical Analysis of the Immersed Boundary Method for Cell-Based Simulation

Abstract

Abstract. Mathematical modelling provides a useful framework within which to investigate the organization of biological tissues. With advances in experimental biology leading to increasingly detailed descriptions of cellular behavior, models that consider cells as individual objects are becoming a common tool to study how processes at the single-cell level affect collective dynamics and determine tissue size, shape, and function. However, there often remains no comprehensive account of these models, their method of solution, computational implementation, or analysis of parameter scaling, hindering our ability to utilize and accurately compare different models. Here we present an efficient, open-source implementation of the immersed boundary (IB) method, tailored to simulate the dynamics of cell populations. This approach considers the dynamics of elastic membranes, representing cell boundaries, immersed in a viscous Newtonian fluid. The IB method enables complex and emergent cell shape dynamics, spatially heterogeneous cell properties, and precise control of growth mechanisms. We solve the model numerically using an established algorithm, based on the fast Fourier transform, providing full details of all technical aspects of our implementation. The implementation is undertaken within Chaste, an open-source C++ library that allows one to easily change constitutive assumptions. Our implementation scales linearly with time step, and subquadratically with mesh spacing and immersed boundary node spacing. We identify the relationship between the immersed boundary node spacing and fluid mesh spacing required to ensure fluid volume conservation within immersed boundaries, and the scaling of cell membrane stiffness and cell-cell interaction strength required when refining the immersed boundary discretization. Finally, we present a simulation study of a growing epithelial tissue to demonstrate the applicability of our implementation to relevant biological questions, highlighting several features of the IB method that make it well suited to address certain questions in epithelial morphogenesis.

Chaste: Cancer, Heart and Soft Tissue Environment

“…Publications enabled by Cell-based Chaste have focused on: the cellular mechanisms and dynamics of intestinal homeostasis and carcinogenesis; the mechanisms underlying vascular tumour growth and response to therapy in the Microvessel Chaste project (Grogan et al, 2017); the biomechanical characterization of epithelial tissue development and wound healing; the organisation and proliferation of stem and pluripotent cells in development; the spread of sexually-transmitted infections; vascular remodelling (Osborne & Bernabeu, 2018); cell-based model calibration and parameterisation (Kursawe, Baker, & Fletcher, 2018); and their efficient numerical solution (Cooper, Baker, & Fletcher, 2017).…”

Adhesion-regulated junction slippage controls cell intercalation dynamics in an Apposed-Cortex Adhesion Model

“…The immersed boundary method successfully models apposed cortices as continuum structures [ 37 ], providing insight to tumour growth and limb bud morphogenesis [ 38 , 39 ]. However, the model requires the presence of an unphysical incompressible fluid between cell cortices, requiring artificial source and sink terms to control growth [ 40 ]. A fluid-free cell boundary model, that is more faithful to existing physical quantities, is likely to be more appropriate for simulating epithelial tissue behaviour.…”

Seven challenges in the multiscale modeling of multicellular tissues

“…For deterministic systems, traditional approaches make use of forward Euler integration schemes (Osborne et al, 2017); however, higher-order explicit (or implicit) numerical solvers are more computationally intensive, and require modifications to account for cell death and division changing the size of the dynamical system over each time step, but allow larger time steps to be used while maintaining stability (Atwell, 2016). For some cell-based models, numerical methods must be carefully chosen to respect physical conservation laws (Cooper et al, 2017). Efficient numerical techniques have also been developed to simulate stochastic reaction-diffusion processes in spatially complex or evolving domains (e.g., Drawert et al, 2012).…”