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.
|Title of host publication||2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 21 Nov 2016|
|Event||2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 - Quito, Ecuador|
Duration: 12 Oct 2016 → 14 Oct 2016
|Name||2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016|
|Conference||2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016|
|Abbreviated title||ETCM 2016|
|Period||12/10/16 → 14/10/16|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
- Connectivity analysis
- hub metabolites
- metabolic network topology
- Synechococcus elongatus PCC7942