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Volumes and issues | Journal of Clinical Bioinformatics | Springer Nature Link

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Title:Volumes and issues | Journal of Clinical Bioinformatics | Springer Nature Link Description:Background Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during *** in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and *** protein interaction networks can be a good way to understand *** more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard. Methods In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major *** signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and *** conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively. Results In case of all tissues, *** PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in *** PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the *** networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in *** conditions. Conclusion The study predicts some major molecular complexes that might act as the important regulators in *** progression. The crucial nodes identified in this study can be potential drug targets to combat ***.

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journal of clinical bioinformatics

biomedicine

general

health informatics

bioinformatics

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