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The hidden simplicity of metabolic networks is revealed by multireaction dependencies

  • Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceivedUnderstanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.show moreshow less

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Metadaten
Author details:Anika KükenORCiDGND, Damoun LangaryORCiD, Zoran NikoloskiORCiDGND
DOI:https://doi.org/10.1126/sciadv.abl6962
ISSN:2375-2548
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/35353565
Title of parent work (English):Science Advances
Publisher:American Assoc. for the Advancement of Science
Place of publishing:Washington
Publication type:Article
Language:English
Date of first publication:2022/03/30
Publication year:2022
Release date:2023/06/01
Volume:8
Issue:13
Article number:eabl6962
Number of pages:10
Funding institution:Human Frontier Science Program [RGP0046/2018]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie
DDC classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Peer review:Referiert
Publishing method:Open Access / Gold Open-Access
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License (German):License LogoCC-BY-NC - Namensnennung, nicht kommerziell 4.0 International
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