Large-scale network connectivity of Synechococcus elongatus PCC7942 metabolism

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 (3=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.