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 language | English |
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Title of host publication | 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509016297 |
DOIs | |
State | Published - 21 Nov 2016 |
Event | 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 - Quito, Ecuador Duration: 12 Oct 2016 → 14 Oct 2016 |
Publication series
Name | 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 |
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Conference
Conference | 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 |
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Abbreviated title | ETCM 2016 |
Country/Territory | Ecuador |
City | Quito |
Period | 12/10/16 → 14/10/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Connectivity analysis
- hub metabolites
- metabolic network topology
- Synechococcus elongatus PCC7942