Exploiting transcriptomic data in genome-scale metabolic networks: new insights into obesity

Systems Biology is a holistic approach, based on the integration of multiscale models and different kinds of data, aimed at studying the underlying mechanisms of complex biological systems. A GEnome-scale metabolic Model (GEM) is the representation of the metabolic structure of a cell in terms of chemical reactions, involved metabolites, and associated genes. GEMs provide a functional scaffold for constraint-based mathematical methods aimed at simulating and predicting metabolic fluxes in living organisms. The most widely used constraint-based method is the Flux Balance Analysis (FBA), that exploits the stoichiometric matrix, a mathematical representation of the relations between substrates and products of all the reactions in the GEM. Recently, the increasing availability of large amounts of high-throughput sequencing data has fostered the research of new approaches in which the structural information described by GEMs is integrated with the knowledge coming from omics data, with the aim to build more accurate descriptions of metabolic states. Here we propose to use a recently published method, in which transcriptomic data are integrated into genome-scale metabolic models through the maximization of the correlation between the steady-state pattern of the predicted fluxes and the corresponding absolute gene expression data generated under the condition of interest. This approach has the interesting property that no cell growth function must be minimized to execute the model. We used this methodology to simulate a novel GEM of the human adipocyte (iAdipocytes1809), with the aim of getting new insights into the metabolic mechanisms underlying obesity and its relationships with cancer. Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, ranging from cardiovascular alterations to diabetes, hypertension and cancer. In particular, weight increase and obesity have been identified as the most important risk and prognostic factors for breast cancer, especially in postmenopausal women. We discuss some preliminary results obtained with this approach, hilighting the importance of data integration, and the need for developing new methods that could help in improving our interpretation of biological phenomena.