RAGNO group page

CV

Research description

The focus of this group is on developing algorithms for gene network inference by integrative analysis of gene expression and genotyping data, as well as metabolic and protein interaction networks from metabolomics and proteomics data, respectively. Inferred network topologies will be investigated using tools from complex network analysis. Dynamic capabilities of these large networks will be studied using general kinetic models. The goal of these studies is to gain insight into organizational principles governing the fundamental properties of living systems, such as robustness and evolvability, and eventually to understand macroscopic phenotypes, such as disease susceptibility and resistance, in terms of regulatory networks.

• Network inference tools: Graphical Gaussian Modeling (GGM), Structural Equation Modeling (SEM).
• Network topology analysis: Basic statistical properties (degree distributions, clustering, path lengths), global components, local analysis (node degree correlations & motifs), centrality measures, visualization
• Dynamic analysis: Structural Kinetic Modeling (SKM).

Alberto de la Fuente's PhD thesis: Deciphering living networks

Peer reviewed publications