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Scarcity of scale-free topology is universal across biochemical networks
Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many re...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985396/ https://www.ncbi.nlm.nih.gov/pubmed/33753807 http://dx.doi.org/10.1038/s41598-021-85903-1 |
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author | Smith, Harrison B. Kim, Hyunju Walker, Sara I. |
author_facet | Smith, Harrison B. Kim, Hyunju Walker, Sara I. |
author_sort | Smith, Harrison B. |
collection | PubMed |
description | Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as “scale-free”. Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks. |
format | Online Article Text |
id | pubmed-7985396 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79853962021-03-25 Scarcity of scale-free topology is universal across biochemical networks Smith, Harrison B. Kim, Hyunju Walker, Sara I. Sci Rep Article Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as “scale-free”. Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks. Nature Publishing Group UK 2021-03-22 /pmc/articles/PMC7985396/ /pubmed/33753807 http://dx.doi.org/10.1038/s41598-021-85903-1 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Smith, Harrison B. Kim, Hyunju Walker, Sara I. Scarcity of scale-free topology is universal across biochemical networks |
title | Scarcity of scale-free topology is universal across biochemical networks |
title_full | Scarcity of scale-free topology is universal across biochemical networks |
title_fullStr | Scarcity of scale-free topology is universal across biochemical networks |
title_full_unstemmed | Scarcity of scale-free topology is universal across biochemical networks |
title_short | Scarcity of scale-free topology is universal across biochemical networks |
title_sort | scarcity of scale-free topology is universal across biochemical networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985396/ https://www.ncbi.nlm.nih.gov/pubmed/33753807 http://dx.doi.org/10.1038/s41598-021-85903-1 |
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