Integrated Contextualization and Analysis of Metabolic Networks

The development of new methods to integrate experimental data into metabolic models has become more important. In particular, the generation of context-specific metabolic networks from generic reconstructions allows to analyze a metabolic network which is closer to biological conditions. FASTCORE (Vlassis, Pires Pacheco and Sauter, 2014) and its extension, FASTCORMICS (Pires Pacheco, John et al., in revision), provide an efficient way to integrate experimental data to obtain consistent models that reflect the current state of the metabolic network. FASTCORE generates consistent models in seconds using a linear problem based on a provided core set, whereas FASTCORMICS allows the generation of consistent and specific metabolic networks in just a few minutes. FASTCORMICS algorithm makes use of the solid statistics provided by The Gene Expression Barcode to compute core sets. In addition, a set of penalized and non-penalized reactions are introduced to further restrict the specific models. By applying FASTCORMICS, differences in the metabolism of 63 primary human cell types were captured.

Another important point is the proper visualization of the generated metabolic networks and the integration of multiomics data. Therefore, our group developed the IDARE Cytoscape app along with a web application to tackle this issue. IDARE allows the visualization of multiple datasets of omics data on a network structure, and also provides functionalities to divide the large networks into subnetworks of predefined pathways.