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 language | English (US) |
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Article number | 20200624 |
Journal | Journal of the Royal Society Interface |
Volume | 18 |
Issue number | 174 |
DOIs | |
State | Published - 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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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.Datasets
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Supplementary Material: R Code from Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling
Brummer, A. B. (Creator), Lymperopoulos, P. (Creator), Shen, J. (Creator), Tekin, E. (Creator), Bentley, L. P. (Creator), Buzzard, V. (Creator), Gray, A. (Creator), Oliveras, I. (Creator), Enquist, B. J. (Creator) & Savage, V. M. (Creator), The Royal Society, 2020
DOI: 10.6084/m9.figshare.13370345, https://rs.figshare.com/articles/dataset/Supplementary_Material_R_Code_from_Branching_principles_of_animal_and_plant_networks_identified_by_combining_extensive_data_machine_learning_and_modelling/13370345
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Supplementary Material: R Code from Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling
Brummer, A. B. (Creator), Lymperopoulos, P. (Creator), Shen, J. (Creator), Tekin, E. (Creator), Bentley, L. P. (Creator), Buzzard, V. (Creator), Gray, A. (Creator), Oliveras, I. (Creator), Enquist, B. J. (Creator) & Savage, V. M. (Creator), The Royal Society, 2020
DOI: 10.6084/m9.figshare.13370345.v1, https://rs.figshare.com/articles/dataset/Supplementary_Material_R_Code_from_Branching_principles_of_animal_and_plant_networks_identified_by_combining_extensive_data_machine_learning_and_modelling/13370345/1
Dataset
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Supplementary Material: Python Code from Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling
Brummer, A. B. (Creator), Lymperopoulos, P. (Creator), Shen, J. (Creator), Tekin, E. (Creator), Bentley, L. P. (Creator), Buzzard, V. (Creator), Gray, A. (Creator), Oliveras, I. (Creator), Enquist, B. J. (Creator) & Savage, V. M. (Creator), The Royal Society, 2020
DOI: 10.6084/m9.figshare.13370339.v1, https://rs.figshare.com/articles/software/Supplementary_Material_Python_Code_from_Branching_principles_of_animal_and_plant_networks_identified_by_combining_extensive_data_machine_learning_and_modelling/13370339/1
Dataset