Large-scale network connectivity of Synechococcus elongatus PCC7942 metabolism

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

From the topological perspective, the availability of genome-scale metabolic network models assists to the large-scale analysis of the metabolites connections, and thus, the evaluation of the cell metabolic capabilities to produce high added-value molecules. In this study, a comprehensive connectivity analysis of the published genome-scale metabolic model of Synechococcus elongatus PCC7942 (iSyf715) is presented, highlighting the most connected metabolites of this biological system. To get a suitable fit, the connectivity distribution of the metabolic model is evaluated using the cumulative distribution function (Pareto's law), verifying a power-law distribution in iSyf715 metabolic network (γ=2.203). Additionally, through the comparison of the connectivity distributions in different microbial metabolic network models, the scale-free behavior of these metabolic networks is verified. The prediction of the metabolic network connectivity could supports the determination of the underlying functioning principles of certain cellular processes.

Original languageEnglish
Title of host publication2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509016297
DOIs
StatePublished - 21 Nov 2016
Event2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 - Guayaquil, Ecuador
Duration: 12 Oct 201614 Oct 2016

Publication series

Name2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016

Conference

Conference2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
Country/TerritoryEcuador
CityGuayaquil
Period12/10/1614/10/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Connectivity analysis
  • hub metabolites
  • metabolic network topology
  • Synechococcus elongatus PCC7942

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