A gene network model accounting for development and evolution of mammalian teeth

TLDR

Morphological diversity generation is challenging because the integration of a finite set of pattern‑forming genes with morphogenesis remains unclear. The study aims to develop models that capture both developmental processes and evolutionary changes to uncover simple principles of gene‑driven form. The authors present a morphodynamic model that integrates gene interaction and growth data, allowing developing morphology to influence patterning. The model accurately predicts tooth‑shape development across species, reproduces key evolutionary transitions and gene expression dynamics, shows that small genetic changes can yield large morphological effects, and suggests that similar morphologies can arise from different genetic alterations, highlighting the challenges of predicting morphological outcomes and the potential for linking genotypes to phenotypes.

Abstract

Generation of morphological diversity remains a challenge for evolutionary biologists because it is unclear how an ultimately finite number of genes involved in initial pattern formation integrates with morphogenesis. Ideally, models used to search for the simplest developmental principles on how genes produce form should account for both developmental process and evolutionary change. Here we present a model reproducing the morphology of mammalian teeth by integrating experimental data on gene interactions and growth into a morphodynamic mechanism in which developing morphology has a causal role in patterning. The model predicts the course of tooth-shape development in different mammalian species and also reproduces key transitions in evolution. Furthermore, we reproduce the known expression patterns of several genes involved in tooth development and their dynamics over developmental time. Large morphological effects frequently can be achieved by small changes, according to this model, and similar morphologies can be produced by different changes. This finding may be consistent with why predicting the morphological outcomes of molecular experiments is challenging. Nevertheless, models incorporating morphology and gene activity show promise for linking genotypes to phenotypes.