Clustering analysis of tumor metabolic networks

摘要

Biological data produced by high throughput experiments, and in particular by Next Generation Sequencing technologies are being accumulated in publicly available databases. Multi-year research projects, such as The Cancer Genome Atlas (TCGA) [ ], are producing petabytes of data. Besides this type of initiatives, there are many research projects focused on extracting knowledge from experiments, and they are storing resulting metadata in knowledge-based repositories. One of these projects is the Human Metabolic Atlas (HMA) [ ], which has been accumulating genome-scale metabolic models for different healthy and cancer tissues. Such models describe in analytic format the knowledge about specific tissues metabolism. From the integration of such different sources, it is possible to obtain a knowledge-based characterization of patients with different cancer sub-types.