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Engineering morphogenesis of cell clusters with differentiable programming
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DOI：10.1038/s43588-025-00851-4.png)
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Abstract
En 中文
Understanding the fundamental rules of organismal development is a central, unsolved problem in biology. These rules dictate how individual cellular actions coordinate over macroscopic numbers of cells to grow complex structures with exquisite functionality. We use recent advances in automatic differentiation to discover local interaction rules and genetic networks that yield emergent, systems-level characteristics in a model of development. We consider a growing tissue with cellular interactions mediated by morphogen diffusion, cell adhesion and mechanical stress. Each cell has an internal genetic network that is used to make decisions based on the cell’s local environment. Here we show that one can learn the parameters governing cell interactions in the form of interpretable genetic networks for complex developmental scenarios. When combined with recent experimental advances measuring spatio-temporal dynamics and gene expression of cells in a growing tissue, the methodology outlined here offers a promising path to unraveling the cellular bases of development. This work uses differentiable simulations and reinforcement learning to design interpretable genetic networks, enabling simulated cells to self-organize into emergent developmental patterns by responding to local chemical and mechanical cues.
Keywords:
morphogenesis
differentiable programming
genetic networks
cell interactions
developmental biology
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K