Modeling Gene Networks in Saccharomyces cerevisiae

Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function

Novel Model for Cascading Failure Based on Degree Strength and Its Application in Directed Gene Logic Networks

A novel model for cascading failures in a directed logic network based on the degree strength at a node was proposed. The definitions of in-degree and out-degree strength of a node were initially reconsidered, and the load at a nonisolated node was proposed as the ratio of in-degree strength to out-degree strength of the node. The cascading failure model based on degree strength was applied to the logic network for three types of cancer including adenocarcinoma of lung, prostate cancer, and colon cancer based on their gene expression profiles. In order to highlight the differences between the three networks by the cascading failure mechanism, we used the largest-scale cascades and the cumulative cascade probability to depict the damage. It was found that the cascading failures caused by hubs are usually larger. Furthermore, the result shows that propagations against the networks were correlated with the structures motifs of connected logical doublets. Finally, some genes were selected based on cascading failure mechanism. We believe that these genes may be involved in the occurrence and development of three types of cancer