Prof.Dr. Ilja Arts

Prof. Dr. Ilja C.W. Arts is Professor of Molecular Epidemiology of Chronic Diseases at Maastricht University, Scientific Director of the Maastricht Centre for Systems Biology (MaCSBio), and Principal Investigator at CARIM (Cardiovascular Research Institute Maastricht). My research focusses on the integration of omics and complex phenotypic data from epidemiological studies using systems biology approaches in the fields of cardiovascular and metabolic disease.

My research aims to elucidate biological mechanisms that play a role in the etiology and prognosis of chronic diseases such as obesity, type 2 diabetes and cardiovascular disease. Such research will lead to novel biomarkers, new leads for prevention and treatment, and better risk stratification, thus enabling a biology-based approach to personalized health for chronic diseases.

To attain this I use methods from molecular epidemiology and systems biology. Within the Maastricht Centre for Systems Biology we apply and further develop mathematical and computational models and approaches to modelling. We are mainly interested in the integration of large, multi-dimensional data (genomics, transcriptomics, metabolomics, clinical, lifestyle) from deep-phenotyped cohorts with genome scale metabolic and dynamical models, and pathway and network models. We use both data-driven (machine learning) and biology-based approaches.

My research is conducted in close collaboration with clinical departments and laboratory groups that generate the data under well-controlled conditions. We use publicly available data and collaborate with several observational cohort studies that span the entire lifecycle and include both healthy people, people at increased risk of disease, for example those with obesity, and people with chronic diseases.

Keywords: Molecular epidemiology, chronic diseases, diabetes, cardiometabolic risk factors, metabolomics, integrative -omics, systems biology, personalized health, nutrition.

Role
Professor
Domain
Life Sciences
Specialism
Life Sciences
Machine Learning
Predictive Modelling
Application Area
Health care