Department Environmental Toxicology

Pollution of aquatic environments is a global threat to species diversity and ecosystem health. However, assessing the impacts of chemicals on freshwater ecosystems is challenging because of the many different chemicals in use and species exposed. Therefore, there is an urgent need for new computational methods and tools to assess the wide variety of chemicals in circulation and their potential impacts on diverse species.

We build new approach methodologies (NAMs) to assess multiscale biological impacts of stressors within and across species in aquatic ecosystems. Our lab is purely computational, highly explorative, and emphasizes a multidisciplinary approach. Through a combination of computational toxicology, bioinformatics, and quantitative sustainability assessment approaches, we aim to i) systematically assess adverse outcomes from exposure to stressors across diverse aquatic species with consideration of inter-individual variability, and ii) characterize the biological response patterns aquatic organisms share and uniquely express following exposure to stressors. To do this, we build high-throughput pipelines for multi-source data curation and integration and use a variety of data analysis techniques and methods across fields, including machine learning, adverse outcome pathways (AOPs), and the ecosystem carrying capacity concept. By forming data linkages across biological levels (e.g., from molecular perturbations to population-level responses), we aim to assess the impacts of chemical pollution on ecosystems in a fully integrated manner, with the ultimate goal of informing environmental protection initiatives.