Insect diapause (dormancy) synchronizes an insect's life cycle to seasonal changes in the abiotic and biotic resources required for development and reproduction. Transcription analysis of diapause to post‐diapause quiescent transition in the alfalfa leafcutting bee Megachile rotundata Fabricius identifies 643 post‐diapause up‐regulated gene transcripts and 242 post‐diapause down‐regulated transcripts. The log2 fold change in gene expression levels ranges from −5 to 7. Transcripts from several pivotal diapause‐related processes, including chromatin remodelling, cellular signalling pathways, microRNA processing, anaerobic glycolysis, cell cycle arrest and neuroendocrine control, are identified as being differentially expressed during the diapause to post‐diapause transition. In conjunction with studies from other insect species, the data indicate that there are several common mechanisms of diapause control and maintenance.
With the availability of vast amounts of protein-protein, protein-DNA interactions, and genome-wide mRNA expression data for several organisms, identifying biological complexes has emerged as a major task in systems biology. Most of the existing approaches for complex identification have focused on utilizing one source of data. Recent research has shown that systematic integration of gene profile data with interaction data yields significant patterns. In this paper, we introduce the problem of mining maximal cohesive subnetworks that satisfy user-defined constraints defined over the gene profiles of the reported subnetworks. Moreover, we introduce the problem of finding maximal cohesive patterns which are sets of cohesive genes. Experiments on Yeast and Human datasets show the effectiveness of the proposed approach by assessing the overlap of the discovered subnetworks with known biological complexes. Moreover, GO enrichment analysis shows that the discovered subnetworks are biologically significant.
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