Summary
Scientists want to understand how changes in genetic regulation lead to big changes in body shape and features. They use a computer model inspired by cells growing and working together, called neural cellular automata, to study this. The researchers found that complex changes in 2D shapes can be controlled by very simple adjustments to a shared regulatory process, which can be reused across different shapes. This means there are basic, low-dimensional controls for size and style that apply broadly, not just to one specific shape. Their work gives a computational example of classical ideas about how biological shapes can transform through simple underlying rules.
What this means in practice
- •For computational biologists: Model how genetic regulation changes can produce different organism shapes using low-dimensional controls over developmental processes.
- •For graphics software developers: Generate scalable and deformable 2D shapes by manipulating simple parameters in a shared developmental-inspired system.
Authors
Benedikt Hartl, Milton L. Montero, Marcello Barylli, Sebastian Risi, Michael Levin
Abstract
How phenotypic transformations are implemented by changes in underlying regulatory dynamics remains a central question in developmental biology. Inspired by D'Arcy Thompson's 1917 "On Growth and Form", we ask whether coherent large-scale transformations of morphology can be encoded as low-dimensional modulations of a self-organizing developmental system. We use neural cellular automata (NCAs) as bio-inspired models of distributed development, in which a shared local regulatory network grows target morphologies from a single cell. We apply low-rank adaptation (LoRA) to pretrained NCAs, representing each adapted developmental program as a low-rank modulation of a fixed regulatory scaffold. Horizontal and vertical scaling of a fully grown 2D emoji phenotype can each be implemented by rank-one adaptations. Their linear combinations parametrically control phenotype size, generalize beyond the training distribution, and compose with target-specific adapters. Strikingly, adaptations learned for one phenotype transfer zero-shot across structurally and semantically diverse phenotypes sharing the same reference scaffold, while largely preserving internal features. This suggests reusable system-level hyper-directions of scale rather than morphology-specific transformations. From approximately 25,000 independently trained phenotype-specific NCA adapters with a shared scaffold, we further identify latent low-dimensional directions that functionally control phenotypic variation including scaling, style, and symmetrical fission. Together, our results provide a computational realization of D'Arcy Thompson's remarkable grid transformations in a 2D NCA---a minimal cybernetic tissue in which variations of fully grown emoji phenotypes can be encoded, combined, and controlled through low-dimensional directions in regulatory weight space.