A notoriously difficult challenge in biology is to understand how cells can be directed to grow and spontaneously arrange themselves in a desired spatial pattern. In this study, we leverage recent advances in automatic differentiation and gradient-based optimization to discover local interaction rules that yield some desired emergent, system-level characteristics in a complex biology-inspired model. We consider a model where cell-to-cell interactions are mediated by physical processes such as morphogen diffusion, cell adhesion and mechanical stress. Cells take internal decisions — such as whether to divide or not — based on their local environment, with learnable policies parametrized with feed-forward neural networks. We present here some preliminary results that showcase how this approach can discover cell interactions that break symmetry in a growing cluster, create emergent chemical gradients and homogenize cluster growth via mechanical stress response.

This content is only available as a PDF.

Author notes

author deceased

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.