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

Journal: Journal of the Royal Society, Interface
Published Date: Jan 1, 2021

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.

Authors

  • Alexander B Brummer
    Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.
  • Panagiotis Lymperopoulos
    Department of Computer Science, Tufts University, Medford, MA, USA.
  • Jocelyn Shen
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Elif Tekin
    Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.
  • Lisa P Bentley
    Department of Biology, Sonoma State University, Rohnert Park, CA, USA.
  • Vanessa Buzzard
    School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA.
  • Andrew Gray
    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.
  • Imma Oliveras
    Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK.
  • Brian J Enquist
    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.
  • Van M Savage
    Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.

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