Global modules robustly emerge from local interactions and smooth gradients.

Area of Science:

• Developmental Biology
• Neuroscience
• Ecology

Background:

• Biological systems exhibit ubiquitous modularity, yet mechanisms for module emergence from non-modular precursors are poorly understood.
• Understanding self-organization is key to explaining biological complexity from molecular to ecosystem levels.

Purpose of the Study:

• To introduce and elucidate the principle of peak selection as a mechanism for the self-organization of discrete biological modules.
• To demonstrate the applicability of peak selection in explaining modularity in neural and ecological systems.
• To provide a unified framework for understanding module emergence across biological scales.

Main Methods:

• Introduced the peak selection principle, integrating positional information and Turing pattern formation.
• Developed a computational model for morphogenesis based on local interactions and smooth gradients.
• Applied the model to simulate module self-organization in the brain's grid-cell system and ecological scenarios.

Main Results:

• Peak selection drives the self-organization of discrete global modules from local interactions and smooth gradients.
• The model accurately predicts the emergence of functionally distinct grid-cell modules with specific spatial periods.
• Demonstrated self-scaling and topological robustness, making module emergence insensitive to most parameters.
• Ecological applications include discrete multispecies niches and synchronized coral spawning events.

Conclusions:

• Peak selection offers a parsimonious explanation for modularity in diverse biological systems.
• The principle resolves fine-tuning requirements for attractor dynamics in grid-cell modules.
• Provides testable predictions for grid-cell properties at molecular, connectomic, and physiological levels.