​The overall objective of the Ideker Laboratory is to develop an artificially intelligent model of the cell able to translate a patient's data into precision diagnosis and treatment.  We shall advance this goal along two general aims, to develop better network representations of the cell and seeking to use these networks to translate genotype to phenotype.

The first aim will pursue methods to address what we see as a ‘grand challenge’ of network biology for the next decade: pursuing 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. Towards this goal, we are developing methods that learn how to structure cell models directly from genomics data sets (Dutkowski et al. Nature Biotechnology 31.1 pp. 38-45, 2013; Carvunis and Ideker, Cell 157.3 pp. 534-538 2014). For this purpose, we run an experimental facility for systematic measurement of gene and protein interaction networks (Bandyopadhyay et al. Science 330.6009 pp, 1385-1389 2010; Srivas, Shen, et al. Molecular Cell 2016).

A second challenge is to work out the functional logic by which these models process information, e.g., from genotype to phenotype. Here too, we have made recent progress (Yu et al. Cell Systems 2.2 pp. 77-88 2016) but much remains to be done before we have a cell model capable of making robust predictions about patients. We 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. As supporting software, we are developers of Cytoscape, a popular platform for visualization and modeling of biological networks which is supported by a consortium of many labs including our own (http://www.cytoscape.org/).

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, with a particular focus on personalized cancer therapeutics.

View Available Positions for current opportunities with the Ideker Laboratory.

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.

Research Focus Keywords: Biological Networks, Cellular Modeling, Cancer Genomics, Epigenetic Aging, Epigenomics, Gene Expression Control, Genetic and Molecular Networks.