Our work concentrates on the molecular link between diet and health, with particular focus on diabetes and obesity. The main techniques that we employ are high throughput data analysis, network analysis and simulation, with the goal of integrating the three. As diabetes and obesity are multifactorial diseases with complex molecular pathology, we work with a range of biological data types, including genotyping, transcriptomic, and multivariate biomarkers. With integrated analysis of these data types, we aim to identify susceptibility markers of disease and networks of interacting genes/proteins that co-vary with health status and response to dietary intervention. Overlapping projects with systems pharmacology are an added value of our strategy, as they allow us to explicitly analyze disease onset/progression and response to clinical intervention in the context of genotype*environment (e.g., diet) interaction.

Our work concentrates on Alzheimer's and other neurodegenerative diseases. The application of high throughput data analysis, network analysis and simulation with the goals of integrating the three. We aim to clarify the basic mechanisms of the diseases and how to control or influence their progression and link preclinical and clinical studies of drugs and diseases. Overlapping projects with molecular nutrition are an added value of our strategy, as it allows us to explicitly analyze the complex effects of environment (e.g., diet) and genotype*environment interactions on neurodegenerative diseases.

Our work concentrates on the vulnerability of ecosystems and develop indicators to support biodiversity conservation and ecosystem management. By building dynamical food web simulation models, we quantify the sensitivity of the model to local disturbance and we identify key components (e.g. keystone species). Several systems are analyzed from agricultural to marine ones and fundamental problems are addressed from sustainability to overfishing. The primary data come from our collaborators.

We develop and apply network analysis for identifying key genes representing potential gatekeepers linking nutrition to disease. Network communities significantly enriched in proteins responding to drug pharmacology can highlight unexpected mechanisms of functioning, beyond the boundaries of canonical metabolic and regulatory pathways. Similarly, network analysis can shed light on the forces driving the evolution of food webs, focusing on the connection between ecosystem structure and dynamics.

We develop and apply simulation-based techniques that start from the list of components of the system, their (partial) wiring and their partial quantitative characterization. Signaling and metabolic networks are the main networks we model, simulate and analyze, linked to molecular nutrition and systems pharmacology. Once validated, models can be used as predictive tools by perturbing them with addition/deletion/modification of components.