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
  • Biochemistry
  • Biomaterials
  • Biomedical Engineering

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this