Engineering morphogenesis of cell clusters with differentiable programming
<|reference_start|>Engineering morphogenesis of cell clusters with differentiable programming: Understanding the rules underlying organismal development is a major unsolved problem in biology. Each cell in a developing organism responds to signals in its local environment by dividing, excreting, consuming, or reorganizing, yet how these individual actions coordinate over a macroscopic number of cells to grow complex structures with exquisite functionality is unknown. Here 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, differential 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. We show that one can simultaneously learn parameters governing the cell interactions and the genetic network for complex developmental scenarios, including the symmetry breaking of an embryo from an initial cell by creation of emergent chemical gradients, homogenization of growth via mechanical stress, programmed growth into a pre-specified shape, and the ability to repair from damage. 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 unravelling the cellular bases of development.<|reference_end|>
@article{deshpande2024engineering,
    title={Engineering morphogenesis of cell clusters with differentiable
  programming},
    author={Ramya Deshpande, Francesco Mottes, Ariana-Dalia Vlad, Michael P.
  Brenner, Alma dal Co},
    journal={arXiv preprint arXiv:2407.06295},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06295},
    primaryClass={q-bio.CB cs.LG}
}