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

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 divers...

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Veröffentlicht in:Journal of the Royal Society interface 2021-01, Vol.18 (174), p.20200624-20200624
Hauptverfasser: Brummer, Alexander B, Lymperopoulos, Panagiotis, Shen, Jocelyn, Tekin, Elif, Bentley, Lisa P, Buzzard, Vanessa, Gray, Andrew, Oliveras, Imma, Enquist, Brian J, Savage, Van M
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container_end_page 20200624
container_issue 174
container_start_page 20200624
container_title Journal of the Royal Society interface
container_volume 18
creator Brummer, Alexander B
Lymperopoulos, Panagiotis
Shen, Jocelyn
Tekin, Elif
Bentley, Lisa P
Buzzard, Vanessa
Gray, Andrew
Oliveras, Imma
Enquist, Brian J
Savage, Van M
description 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.
doi_str_mv 10.1098/rsif.2020.0624
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title Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling
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