We focus on the computational study of gene expression using transcriptomic data and are particularly excited about the roles of non-coding RNAs in regulation of gene expression.
The group has a long history of applying computational methods (mainly docking) to identify promising leads in drug design projects, or ligands relevant to protein function. In the past, we have also explored ligand-binding and catalytic promiscuity and its evolutionary origins.
Selected publications: Proietti et al. (2016); Ashford et al. (2012); Patschull et al. (2012); Guzman et al. (2011); Favia et al. (2011); Gooptu et al. (2009); Nobeli et al. (2009); Macchiarulo et al. (2004).
We have a long-standing interest in the analysis of the collections of endogenous metabolites in model organisms from a structural and physicochemical point of view. We use chemoinformatics methods to reveal the relationships between metabolites within a species, compare metabolites from different species, and finally compare endogenous metabolites to exogenous (human made or environmental) small molecules.