Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling: Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling

Alexander B. Brummer, Panagiotis Lymperopoulos, Jocelyn Shen, Elif Tekin, Lisa P. Bentley, Vanessa Buzzard, Andrew Gray, Imma Oliveras, Brian J. Enquist, Van M. Savage

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number20200624
JournalJournal of the Royal Society Interface
Volume18
Issue number174
DOIs
StatePublished - Jan 2021

Keywords

  • branching networks
  • machine learning
  • metabolic scaling
  • vascular biology

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering

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