Our laboratory's long-range goal is to develop infrastructure to map and model molecular networks and to insert these models at key decision points in health care. We shall advance this goal along two general aims, seeking to better build network representations of the cell and seeking to use these networks to translate genotype to phenotype in cancer.
The first aim will pursue methods to address what we see as a ‘grand challenge’ of network biology for the next decade: Although networks have been very useful for representing molecular interactions and mechanism, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. We will thus pursue methods that move the field towards a fully hierarchical (multi-scale) view of cell structure and function. This type of model may be well-captured by moving from flat networks to hierarchical gene ontologies.
The second aim is to develop networks and ontologies as a major platform for translating genotype to phenotype, with a particular focus on personalized cancer therapeutics. We will argue that recent progress in computer science, embodied by intelligent agents such as Siri and Watson, inspires an approach for moving from networks and gene ontologies to predictive models able to predict a range of cellular phenotypes and answer biological questions. Ultimately, both aims of research will join to create hierarchical cell models able to integrate patient data to predict disease outcomes in response to specific therapies.
Figure 1. Axes of network-based research. A. Systematic approaches to assemble maps and models of networks of cancer cells. B. Systematic approaches to use these networks to translate patient genotype and other ‘omics profiles to prognostic and therapeutic outcomes.