Chronic liver diseases are long-term pathologies affecting a wide range of people nowadays. They can be caused by multiple factors including viral infection, alcohol overconsumption, metabolic disorders or genetic factors to which are added the impact of our lifestyles and environmental exposures. These diseases have become an important public health problem, but the impact of our environment is difficult to determine give the diversity of factors involved and the dynamics of these exposures. This internship project aims to identify environmental exposure factors associated to the occurrence of chronic liver diseases. The Comparative Toxicogenomics Database (CTD) is used as an information source. This database describes exposure events at several levels of associations (chemical-gene, chemical-disease and gene-disease associations). The conversion of the CTD tabulated chemical-gene interactions file into the BioPAX format (Biological Pathway Exchange) thanks to the development of a Python CTD-to-BioPAX converter allows to standardize it in accordance to Semantic Web standards. The obtained BioPAX file can be used as an input of the CadBiom application developed by the DYLISS and Dymec teams in order to build large-scale biological dynamic networks based on guarded transitions. The generated models could be analyzed thanks to reachability queries in order to identify environmental exposures causal signatures associated to the occurrence of chronic liver diseases.