Inferring scaling laws of branching networks from their geometry by global optimization

Weighted-length optimality best matches vascular branch points, with each vessel having its own fitted weight, and inferred scaling laws outperform all tested laws in 1,679 of 1,744 held-out hepatic and pulmonary trees.

• Why it matters: Local branch-point tests can’t reliably separate competing scaling laws, leaving a gap between optimality principles and what network geometry actually measures.
• What they did: The framework maps each candidate scaling law to predicted branch points and fits them via global optimization; it also uses an exact discrete model for coronary arteries and evaluates multiple network types.
• The result: Inferred, type-specific geometric signatures emerge that beat all tested laws, reject weighted-length optimality for neuronal axons, and enable quantitative testing of branching-network principles from geometry.

AI summary of the abstract · may contain errors · not medical advice · read the paper.