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Joel Bader, Ph.D.

Joel Bader, Ph.D.
Joel Bader, Ph.D.

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Research Program

Wiring diagrams for cells and organisms

Drosophila melanogaster is a proven model system for many aspects of human biology. The Drosophila genome provides a parts list for the organism, but does not directly indicate how the parts -- genes and proteins -- are wired together. Our lab provided bioinformatics and computational expertise for a large-scale experimental effort to generate a protein wiring diagram for Drosophila (Science 2003). Our map contains over 20,000 unique protein-protein interactions that touch over half of the predicted Drosophila proteins. A computational method of rating two-hybrid interaction confidence was developed to refine this draft map to a higher confidence map of 4679 proteins and 4780 interactions. Statistical modeling of the network showed two levels of organization: a short range organization, presumably corresponding to multiprotein complexes and a more global organization, presumably corresponding to inter-complex connections. The network recapitulated known pathways, extended pathways, and uncovered novel pathway components. This map serves as a starting point for a systems biology modeling of multicellular organisms including humans.

We are now working on a similar map for human, with interactions detected by large-scale two-hybrid screens conducted by CuraGen Corporation.

Anchoring protein pathways with genetic screens

Drosophila is an important model organism for human biology and disease. Cell-based RNAi screens now enable full-genome assays to identify genes relevant to human disease-related phenotypes and pharmaceutical intervention. Understanding the functional context of genes identified through RNAi loss-of-function assays (RNAi hits) and making quantitative predictions of the behavior of underlying biological pathways are fundamental challenges. Assembling genes identified as RNAi hits into pathways would permit interpretation of the primary data and assist further hypothesis-driven research.

We are developing algorithms to infer the topology of process-specific biological networks by merging information from full-genome RNAi screens with data from large-scale screens for protein-protein interactions. This includes enhancing existing protein-protein interaction databases; inferring interactions from cross-species comparison and network topology; and developing graph-theoretic network search techniques.

Ultimately, we aim to generate quantitative predictions of cellular pathways that can serve as the input for more detailed quantitative modeling of specific biological processes.

Evolution of biological networks

Biological networks are robust: small perturbations in environmental conditions, and even large perturbations generated by gene mutations and deletions, often have negligible effect on organism fitness. We are interested in understanding how biological networks have evolved to have these properties. We are collaborating with the Boeke lab at the Johns Hopkins School of Medicine to understand evolutionary motifs in genetic interaction networks, which reveal redundancy in biological pathways.