Discover the MOBIL project in a new light
An original graphic novel by Guillaume Padilla
The Mobil project
Our lifestyle—including dietary habits and smoking—influences our health, yet the underlying biological mechanisms remain largely unknown. Metabolomic data describe the levels of thousands of metabolites (for example, in the blood), thereby covering a wide range of biological processes.
They have enabled the identification of biomarkers, and their combination into metabolic signatures, linked to lifestyle, as well as the description of biological mechanisms involved in the link between lifestyle and the development of cancer.
Metabolomic studies have hitherto implicitly assumed that a given lifestyle leads to the same average metabolic response in all individuals. The MOBiL project aims to address this gap.
In particular, we will develop statistical methods to simultaneously identify population subgroups and the metabolic signatures specific to those subgroups. We will apply these methods to derive lifestyle-related metabolic signatures in two large cohort studies and investigate their association with the risk of gastrointestinal cancers.
By reflecting interindividual variability, the signatures identified in MOBiL could enable the identification of subpopulations for which personalized interventions would be most beneficial.
Project leaders:
Vivian VIALLON
Vivian Viallon is a researcher at the Centre International de Rechercher sur le Cancer (WHO, Lyon). He leads the Biostatistics and Data Integration team within the Nutrition and Metabolism Branch. Working at the interface of biostatistics and cancer epidemiology, he develops and applies statistical methods to study the links between lifestyle and cancer risk, as well as the role of various types of molecular variables in this association.
Yohann DE CASTRO
Yohann De Castro is a Professor at École Centrale Lyon and site head at the Institut Camille Jordan. A member of the Institut Universitaire de France, his research focuses on machine learning, statistics, and optimization, with applications in biostatistics, time series, and networks.
