Back

Cooperative short- and long-range interactions enable robust symmetry breaking and axis formation

Guan, G.; Wang, S.; Shields, T. G.; Pahng, S. H.; Shao, C. X.; Ye, J.; Budjan, C.; Hormoz, S.

2025-09-29 systems biology
10.1101/2025.09.27.678924 bioRxiv
Show abstract

The establishment of the anterior-posterior (A-P) axis is the first symmetry-breaking event in mammalian development, transforming initially uniform cell populations into a polarized body plan. Gastruloids, aggregates of embryonic stem cells, recapitulate this transition by reproducibly forming a posterior primitive-streak-like pole. To investigate the underlying physical principles, we constructed a coarse-grained agent-based model representing two radially differentiated cell populations -- outer/peripheral and inner/core -- interacting via short-range adhesion/surface tension and optional long-range, chemotaxis-like forces. Systematic exploration of this morphogenetic landscape revealed that adhesion alone cannot robustly generate a single axis, often leading to weak or unstable asymmetries. By contrast, introducing long-range attraction among peripheral cells markedly broadened the parameter space for robust symmetry breaking, yielding high morphological asymmetry with minimal cell loss. We further implement a minimal, modular gene regulatory network that partitions cells into outer vs. inner states and gates adhesion and peripheral long-range attraction, converting an inside-outside bias into a stable axis. To facilitate further exploration, we developed DevSim, a user-friendly platform for simulating coupled genetic-mechanical rules in multicellular systems. Our results suggest that cooperative short- and long-range interactions are necessary design principles for reliable A-P axis formation in gastruloids and provide a framework for dissecting and engineering self-organizing developmental systems.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.