Explaining the empirical success of random-network models in biology

Establish theoretical principles that explain why random-network models (ensembles with randomly drawn interactions subject to biologically motivated constraints) accurately capture dynamical and statistical features of high-dimensional biological systems, and determine whether universal principles analogous to universality classes guarantee typicality of core observables across such ensembles.

Background

Across neuroscience, ecology, systems biology, and soft matter biophysics, constrained random-network models have shown quantitative agreement with experimental data, often without fine-tuning. This raises a fundamental question about the source of their success.

The discussion suggests several potential explanations to investigate, including the existence of universality-like principles ensuring typical behaviors across ensembles, alignment between environmental statistics and random-feature models, evolutionary processes yielding robust solutions without precise parameter tuning, and dominance of non-specific interactions in large-scale measurements. The open question seeks a unifying theoretical explanation and criteria for selecting constraints.