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

doi:10.1098/rsif.2020.0624

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

J R Soc Interface. 2021 Jan;18(174):20200624. doi: 10.1098/rsif.2020.0624. Epub 2021 Jan 6.

Authors

Alexander B Brummer^{1

2

3}, Panagiotis Lymperopoulos⁴, Jocelyn Shen⁵, Elif Tekin^{1

3}, Lisa P Bentley⁶, Vanessa Buzzard⁷, Andrew Gray⁸, Imma Oliveras⁹, Brian J Enquist^{8

10}, Van M Savage^{1

2

3

10}

Affiliations

¹ Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.
² Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.
³ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
⁴ Department of Computer Science, Tufts University, Medford, MA, USA.
⁵ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁶ Department of Biology, Sonoma State University, Rohnert Park, CA, USA.
⁷ School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA.
⁸ Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.
⁹ Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK.
¹⁰ Santa Fe Institute, Santa Fe, NM, USA.

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.

Keywords: branching networks; machine learning; metabolic scaling; vascular biology.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Machine Learning*
Mathematics
Plants*