Center activities will involve a significant, multidisciplinary effort that will forge new relationships between the biological and computational sciences at the interface between several disciplines including biochemistry and molecular biophysics, biomedical informatics, computer science, engineering, biology, and applied physics. As such, the Center will encompass and integrate diverse research areas at varying degrees of granularity.
The Center will coordinate four important activities:
- Construct an evidence integration framework to collect and fuse a variety of diverse cellular interaction clues based on their statistical relevance.
- Assemble a comprehensive set of physics- and knowledge-based methodologies
to fill this framework, providing computational and experimental clues about
specific molecular interactions. These will be of several types:
- (a)
- protein-protein interactions, as revealed by sequence and structure analysis
- (b)
- interactions, discovered by a variety of novel reverse engineering algorithms,
- (c)
- interactions discovered by literature datamining, and finally
- (d)
- experimental interactions from a variety of existing databases.
- Provide a set of methodologies and filters, anchored in formal domain ontologies, to associate specific interactions to an organism, tissue, molecular, and cellular context (phenotype).
- Make all relevant tools accessible to the broader biomedical research community as components of a common, extensible, and interoperable software platform, geWorkbench (Genomic Workbench).
The latter will be based on the integration of two leading bioinformatics platforms, caWorkbench (Columbia University) and GenePattern (the Broad Institute), for the assembly of complex biomedical applications from simple interoperable components. Their fusion will combine the intuitive interoperability front-end (GUI) of the former (which will allow biomedical researchers to create complex applications without programming) with the scripting and workflow capabilities of the latter (which will allow the bioinformatically-trained researchers to create complex software pipelines).
