|Rich Bonneau is an Associate Professor of Biology and Computer Science at NYU. Professor Bonneau’s laboratory is focused on two areas in computational and systems biology: 1) Predicting and designing protein and peptidomimetic structure and 2) Learning dynamic network models automatically from functional genomics data using scalable methods.
In both research areas Professor Bonneau has played key roles in achieving critical field-wide milestones. In the area of structure prediction he was one of the early authors on the Rosetta code, which was one of the first codes to demonstrate accurate and comprehensive ability to predict protein structure in the absence of detectable sequence homology to proteins with known structures. His lab continues to be a core contributor to the Rosetta research community, participating in the recent refactoring of the code and adding several new functionalities.
Professor Bonneau’s lab has also made key contributions to the area of genomics data analysis in a systems-biology context. His lab focuses on developing new methods for network inference that simultaneously learn dynamics and topology from data (the Inferelator), and methods that learn condition-dependent co-regulated gene groups from integrations of different genomics data- types (e.g. transcriptomic, proteomic, etc.) using approaches we have developed (cMonkey and multi-species-cMonkey integrative biclustering). In the DREAM3 and DREAM4 blind assessment of network inference methods they were top performers in the network inference category, and are currently contributing to a joint paper resulting from DREAM5 (the most current assessment of network inference methods).