Construction of Biological Association Network for Connected Components Analysis Using Graph Theory

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Title

Construction of Biological Association Network for Connected Components Analysis Using Graph Theory

Abstract

The concept of big data has been around for years. Big data analysis approaches can play an imperative part in health research. This can be a helpful asset for researchers because it can reveal veiled acquaintance from a massive sum of data. In order to get insights of metabolic pathways at molecular level there is a need to present a unit framework model that serves as the cutting edge technology of system biology. In this thesis data mining techniques were used to extract data from online databases automatically by developing a tool (PubMed Info Extractor). On inputing queries, relevant information about the association and interactions among biological entities have been found in the retrieved collection. The approach has been applied on the case study of T2DM. Cell boundaries of the found gene components were also identified. Only those components were selected that show highly expressed genes in the pancreatic endocrine cells and skeletal muscle cell for determining the disrupted pathway after analyzing the normal functional pathway of T2DM. Further, bioinformatics tools were used for the topological analysis of obtained patterns. From a network generated by using the association rule mining technique showing associations, their nature of relationship, seven strongly connected components have been identified. These components represent P53, HNF1Alpha, HNF1Beta, INSR, INS, IL-6 and GnRH as the regulators or initiators of seven different biological regulatory pathways. This research is an evidence for the association of T2DM with the genes that are involved in different pathways of cancer cell metabolism, growth regulation, proliferation control etc along with insulin signaling pathway, mTOR pathway, MODY pathway, glycolysis, lipid homeostasis, Age-rage signaling pathway, MAPK pathway, p53 pathway. Self inhibition of ngn3 is also acknowledged in these components. In diabetic patients, pancreatic islets in case of fasting lessen PKA and mTOR activity and induce Sox2 and Ngn3 expression and insulin production. Self inhibition of Ngn3 can therefore affect the insulin production. This research gives a unit framework model of system biology which gives better understanding of intrinsic disease mechanism. This research regarding T2DM which will facilitate the researchers to comprehend the system of T2DM disease mechanism and how to cure it with respect to personalized drugs.

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