Multicellular simulations with shape and volume constraints using optimal transport
This provides a new modeling tool for developmental biologists and computational biologists studying self-organization in cell aggregates.
The authors introduce a new computational framework based on optimal transport theory for modeling multicellular systems with arbitrary shapes and volume constraints. The method handles volume exclusion automatically at affordable cost and reproduces classical biological systems.
Many living and physical systems such as cell aggregates, tissues or bacterial colonies behave as unconventional systems of particles that are strongly constrained by volume exclusion and shape interactions. Understanding how these constraints lead to macroscopic self-organized structures is a fundamental question in e.g. developmental biology. To this end, various types of computational models have been developed. Here, we introduce a new framework based on optimal transport theory to model particle systems with arbitrary dynamical shapes and deformability properties. Our method builds upon the pioneering work of Brenier on incompressible fluids and its recent applications to materials science. It lets us specify the shapes and volumes of individual cells and supports a wide range of interaction mechanisms, while automatically taking care of the volume exclusion constraint at an affordable numerical cost. We showcase the versatility of this approach by reproducing several classical systems in computational biology. Our Python code is freely available at https://iceshot.readthedocs.io/.