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.

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