The lab has been published (x2!) in the PLoS RosettaCon 2010 Special Collection. Check here: RosettaCon 2010 special issue: Macromolecular Prediction and Design Meets Reproducible Publishing
One of our latest papers has been accepted by Genome Research (print publication shortly)! See the full text here: The proteome folding project: Proteome-scale prediction of structure and function, or check it out on our Publications page.
Read an overview of the project on our Structure page.
My lab is focused on a number of computational biology problems that, if solved, would remove key bottlenecks in biology and systems biology. We focus on two main categories of computational biology: learning networks from functional genomics data and predicting and modeling protein structure. In both areas I have played key roles in solving unsolved problems and achieving critical field-wide milestones.
In the area of structure prediction we were early contributors to the Rosetta code; a platform for structure prediction, design and docking. In the area of network inference we worked on two computational methods that were used to demonstrate the first predictive genome-wide model of regulatory dynamics (i.e. the first case where a genome-wide model could predict the whole transcriptional state of cells at future time points not part of the training set). Both network inference and protein structure prediction remain grand challenges and in spite of our progress much exciting work remains to be done in the coming years as we continue to improve, scale and apply these methods.
The lab focuses on developing and implementing methods for modeling global regulatory circuits that are general and can be applied to many systems. I've also played a critical role in the development and deployment of "Rosetta", a state of the art protein folding program, and future work will include distilling functional information from genome-wide de novo predictions.
In a broader sense, we aim to train students to derive and implement novel computational approaches to biological problems (people in my lab are encouraged to build it themselves rather that download a not-quite-right tool from elsewhere). We can't perfectly anticipate tomorrow's computational challenges, but we can train students to be flexible and creative.
...Explore links above for more detailed information about the different research directions of the lab, the history of the efforts we're involved in, friends, etc...
Learning Biological Networks from Heterogeneous Data
Ever-improving genomics experiments have begun to make possible the reconstruction of large numbers of regulatory relationships from the analysis of large accumulated genomics data collections (protein-DNA interactions, genome wide mRNA, whole-genome sequencing, etc.). The work of collaborative teams of systems biologists and computational biologists has in several cases formed functional genomics projects that integrate computational analysis, experimental designs and data visualization to form highly productive multi-group consortia.
This review focuses on just one aspect of these coordinated systems biology efforts: the learning of genome-wide regulatory networks in a manner that enables prediction of unobserved cell states and modeling of dynamical regulatory responses to changes in cell state. As new technologies enable the more accurate, and cheeper, measurement of global metabolite, protein, non-coding RNAs and post-translational modifications many of the mathematical tools developed to learn and model transcriptional networks will prove powerful enough and general enough to incorporate these important additional informational levels.
Protein Structure and Design Using Rosetta
I was one of the initial authors on the Rosetta protein structure prediction code, the first method to demonstrate comprehensive ability to predict protein fold in the absence of sequence homology to a known structure. New developments and applications of Rosetta are ongoing in my lab, and via collaborations with other labs in the Rosetta-development community, IBM and biologists throughout the world.
The Human Proteome Folding Project on the World Community Grid is one of the largest bio-relevant computations undertaken to date; it will provide fold and function predictions for tens of thousands of proteins of unknown function, and be accessed by biologists in all sub-fields. I have an ongoing collaboration with IBMs World Community Grid and was the pilot project on this emerging global computing resource.
I was a founding member of the Rosetta commons, which is a non-profit entity that allows the continued development of Rosetta by over 10 institutions (including NYU) that simultaneously work on protein design, docking, prediction and using Rosetta to resolve experimental constraints (rosettacommons).
This is the ultimate in dissemination; we use money from licensing the code to industry to provide support to new academic developers, companies using the code, users, etc. Developments in all of these application domains are mutually synergistic, as bug fixes and improvements to core methods are quickly disseminated to the entire development community. All Rosetta commons members work off the same coordinated source code repository (managed by University of Washington). Yearly meetings are organized to coordinate research and code management issues. Additionally software will be available in frequent releases (source code freely available to academics). These mechanisms for collaboration, resource access, end-user inclusion, and technology export are in place, tested and exceptionally productive.
bonneau [AT] nyu [DOT] edu (NYU faculty page)
See the People page for more contact information.
Currently, we are generously funded by the following: