The ability to predict rates and products of microbial biotransformation for a broad variety of chemical contaminants is essential not only for chemical risk assessment but also in the context of contaminated site remediation or the development of green chemical alternatives. Therefore, the development of improved algorithms for predicting transformation products and biotransformation half-lives in different environmental settings lies at the core of several projects in our team.
Since 2012, we maintain the former University of Minnesota Biodegradation/Biocatalysis Database and Pathway Prediction System (UM-BBD and UM-PPS), now Eawag-BBD/PPS . In a joint SNF/DFG project, we are further developing this system into enviPath, a new generation of biotransformation pathway database and prediction system. enviPath will allow for a more flexible and interactive usage and interfacing with other existing data resources. In the context of the enviPath project, we will also annotate available biotransformation pathway and rate data from relevant systems, i.e., agricultural soils and activated sludge, to make these data publically available and serve as a basis for development and validation of novel prediction engines within the project.