Rich Bonneau
Ph.D., Biochemistry, Biomolecular Structure and Design Program
University of Washington, 2001


Email: rbonneau@systemsbiology.org

Dr. Bonneau is a faculty member at New York University's Center for Comparative Functional Genomics, where he is a joint member of both the Biology and Computer Science departments. His affiliate position with the Institute for Systems Biology offers many exciting possibilities for exchange of ideas between these two centers, both focused on applying functional genomics to a wide range of systems. His research efforts are focused on making computational tools, algorithms and methods for systems-wide elucidation of biological systems. His research aims to develop computational methods at the intersection of two interrelated fields: protein structure and functional genomics. Rich is also currently the technical lead on two grid computing collaborations with IBM -- the first and second phases of the Human Proteome Folding Project.

Rosetta de novo structure prediction: extracting function from de novo structure predictions.
Recent progress in de novo structure prediction methods has resulted in methods with increased accuracy that are applicable to greater numbers of proteins. When combined intelligently with other structure prediction methods, de novo structure prediction can contribute to systems biology in several ways. While still highly experimental such applications include 1) structural annotation on a genome wide scale and 2) synergy with experimental approaches to structural genomics such as the derivation of distance constraints from mass spectroscopy. I will describe the underlying methodologies common to current de novo prediction methods, focusing on core concepts rather than specific implementations, groups or methods. Possible applications of de novo structure prediction will also be reviewed. For more information on our latest results on 80 complete genomes, including many model organisms being actively studied at NYU click here. This work is being carried out in collaboration with David Baker and IBM WCGrid. WCGrid Message Board Posts by Dr. Bonneau, Description of HPF Project. Results from the Human Proteome Folding Project can be found at the Public Data Repository associated with the Yeast Resource Center.

Regulatory network inference: learning the structure of biological circuits from data:
We have developed a methodology for deriving transcriptional regulatory interactions on a genome-wide scale, and have applied the method to predict a large portion of the gene regulatory network of the archaea, Halobacterium NRC-1. The learned network is predictive, learned entirely from data de novo, and was used to successfully predict the global expression of Halobacterium under novel perturbations (not part of the original training set) with predictive power similar to that seen over the training set. Methodological advancements over earlier work include an explicit treatment of time such that the network model can be fit using both steady-state measurements and heterogeneous time series simultaneously. The method contains a novel means for learning binary logic interactions between regulators that requires no discretization of data. This work was done in tight collaboration with Nitin Baliga, Vestienn Thorsson , David Reiss and Lee Hood at the ISB. There are many interesting future directions on this project including: adding tighter control via user defined constraints and addition of known interactions to the current framework, use of proteomic and metabolomic data, using the networks for engineering and applying the method to subsequently more and more complex organisms. This program, the Inferelator, is a companion program to cMonkey.