Analysis method of omics data for metabolic interactions modeling in predictive carcinogenesis

SESSION 1 - EPIDEMIOLOGY - NUTRITIONAL PREVENTION AND CANCER RISK<br/>SESSION 1 - EPIDEMIOLOGY - NUTRITIONAL PREVENTION AND CANCER RISK

Английский. Background:Currently research focus on the identification of precoce biomarkers is important in the field of Health especially in carcinogenesis. Indeed, specific biomarkers could be crucial to predict the first steps of cancer appearance and to improve cancer prevention and care. In that goal, omic approaches are massively used generating a large amount of data. Our understanding of these data flows become difficult, so it is necessary to develop adaptive tools in order to treat and apprehend them. In many omic studies on cancer-metabolism profiling, discriminant compounds are implicated in energetic pathways such as lipids, glutamin and creatin. For example, lipids are largely used for cancer cell membranes building and undergo a lesser energetic oxidation. Thus, lipids could be used to help for detection of precancer steps.Objective and Methods:This study aims to discern the role of metabolic biomarkers of interest in the first step ofcarcinogenesis. After identification of biomolecules from MS and NMR metabolomic data, we will modelize using softwares allowing dynamic view such as Metaboflux which analyzes flux distribution in metabolic networks, Cytoscape, an open source software, or MetExplore a web server linking metabolomic experiments and genome-scale metabolic networks. We will compare the limits of each software in order to implement the most appropriate to modelize metabolic interactions in predictive carcinogenesis. In this aim, three file formats i.e. Systems Biology Markup Language, JavaScript Object Notation and MAT a format for MATLAB, will be tested.Conclusion:Despite of the existing softwares, most of them do not allow a dynamic visualization of metabolic pathways. That’s why our study plans to implement the most appropriate tool in order to optimize the dynamic modeling of metabolic interactions