# Branching principles of animal and plant networks identified by combining extensive data, machine learning, and modeling

## TL;DR

This study combines extensive data, machine learning, and modeling to classify and understand the principles of branching networks in animals and plants, revealing key ratios that distinguish different biological systems.

## Contribution

It introduces a novel classification method for diverse branching networks by integrating machine learning with new theoretical insights relating vascular form to metabolic function.

## Key findings

- Ratios of limb radii effectively distinguish different branching networks.
- Vascular and branching geometry variation persists despite convergent metabolic relationships.
- A new framework links vascular form to metabolic function across species.

## Abstract

Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi, and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks--mouse lung, human head and torso, angiosperm and gymnosperm plants. We find that ratios of limb radii--which dictate essential biologic functions related to resource transport and supply--are best at distinguishing branching networks. We also show how variation in vascular and branching geometry persists despite observing a convergent relationship across organisms for how metabolic rate depends on body mass.