Evolution of metabolic network organization

dc.contributor.authorMazurie, Aurélien J.
dc.contributor.authorBonchev, Danail
dc.contributor.authorSchwikowski, Benno
dc.contributor.authorBuck, Gregory A.
dc.date.accessioned2019-04-17T20:57:01Z
dc.date.available2019-04-17T20:57:01Z
dc.date.issued2010-05
dc.description.abstractBackground Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints. Results We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints. Conclusions Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems.en_US
dc.description.sponsorshipNational Institutes of Health grants R01AI050196, R01AI055347; European Union grant LSHG-CT-2006-037469en_US
dc.identifier.citationMazurie, Aurélien J., Danail Bonchev, Benno Schwikowski, and Gregory A. Buck. “Evolution of Metabolic Network Organization.” BMC Systems Biology 4, no. 1 (May 11, 2010). doi:10.1186/1752-0509-4-59.en_US
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/15442
dc.language.isoenen_US
dc.rightsCC BY: This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleEvolution of metabolic network organizationen_US
dc.typeArticleen_US
mus.citation.issue59en_US
mus.citation.journaltitleBMC Systems Biologyen_US
mus.citation.volume4en_US
mus.data.thumbpage4en_US
mus.identifier.categoryEngineering & Computer Scienceen_US
mus.identifier.doi10.1186/1752-0509-4-59en_US
mus.relation.collegeOther Departments & Programsen_US
mus.relation.departmentIT Center.en_US
mus.relation.researchgroupMT INBRE Bioinformatics and Biostatistics Core.en_US
mus.relation.universityMontana State University - Bozemanen_US

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