Engineering morphogenesis of cell clusters with differentiable programming
arxiv(2024)
Abstract
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 are mediated by morphogen diffusion, differential cell adhesion
and mechanical stress. Each cell has an internal genetic network that it uses
to make decisions based on its 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, the creation of emergent chemical
gradients,homogenization of growth via mechanical stress, programmed growth
into a prespecified 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 basis of development.
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