Clustering analysis of tumor metabolic networks
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RESEARCH
Open Access
Clustering analysis of tumor metabolic networks Ichcha Manipur1 , Ilaria Granata1 , Lucia Maddalena1 and Mario R. Guarracino1,2* From 13th Bioinformatics and Computational Biology Conference - BBCC 2018 Naples, Italy. 19-21 November 2018 *Correspondence: [email protected] 1 National Research Council, Institute for High-Performance Computing and Networking, Via P. Castellino 111, 80131 Naples, Italy 2 HSE - National Research University Higher School of Economics, LATNA Laboratory, 13 Rodionova Ulitsa, Nizhny Novgorod, Russia
Abstract Background: Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. Results: We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. Conclusions: We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism. Keywords: Metabolic networks, Network simplification, Networks clustering
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