Engineering morphogenesis of cell clusters with differentiable programming

2024-07-08

arXiv.org

AI Summary

In developmental biology, a fundamental challenge remains understanding how cells collectively orchestrate complex morphogenesis through local interactions—such as morphogen diffusion, cell adhesion, and mechanical stress. This work introduces differentiable programming to developmental modeling for the first time, proposing a continuum-based cellular model that tightly couples reaction–diffusion dynamics with mechanical deformation. The framework enables interpretable inference of gene regulatory network parameters directly from macroscopic phenotypic outcomes via gradient-based optimization. It supports multiscale simulation and end-to-end differentiable training. We demonstrate its efficacy in three canonical scenarios: directed axial elongation, maintenance of chemical homeostasis, and mechanics-driven tissue homogenization—each yielding quantitatively accurate emergent morphologies. By bridging phenotype-level observations with mechanistic hypotheses, our approach provides computationally testable models of development and advances the “phenotype-to-mechanism” inverse paradigm in synthetic developmental biology.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Evolutionary Learning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Discover local interaction rules in cell clusters.
• Model genetic networks for complex developmental scenarios.
• Understand coordination of cell actions in tissue growth.

Innovation

• Differentiable programming for morphogenesis modeling
• Genetic networks interpret cell interaction rules
• Combines morphogen diffusion, adhesion, mechanical stress