Engineering morphogenesis of cell clusters with differentiable programming

TL;DR

This paper uses differentiable programming to discover local interaction rules and genetic networks that lead to complex tissue development, advancing understanding of organismal morphogenesis.

Contribution

It introduces a method to learn interpretable genetic networks governing cell interactions in developmental models using automatic differentiation.

Findings

Learned genetic networks can produce directed axial elongation.

Discovered mechanisms for cell type homeostasis via chemical signaling.

Achieved homogenization of growth through mechanical stress modeling.

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 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. We show that one can learn the parameters governing cell interactions in the form of interpretable genetic networks for complex developmental scenarios, including directed axial elongation, cell type homeostasis via chemical signaling and homogenization of growth via mechanical stress. 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.