Program Chairman and Director, Computational Biology Center
Dr. Sander combines theory and experiment to develop predictive models of biological systems and build tools that translate genomic data into biomedical knowledge and practice.
Lab members and collaborators infer quantitative models of signaling in cancer cells by performing combinatorial perturbation experiments with rich molecular readouts and then optimizing predictive accuracy and model simplicity. The resulting computational models can be used to design combinatorial interventions, identify novel drug synergies, or discover the specificity spectrum of drugs.
To discover molecular processes characteristic of cancer subtypes and indicative of prognosis and response to therapy, lab members use high-throughput data on genome structure, genetic and epigenetic variation, and gene expression. They map these molecular profiles to pathway and interaction networks for analysis. The group actively participates in The Cancer Genome Atlas project and the International Cancer Genome Consortium, which are delivering tremendously detailed information on the molecular characteristics of human cancers. When combined with clinical information, patient genomics becomes a powerful basis for the design of clinical trials and personalized therapy.
In protein science, Dr. Sander and collaborators have developed a powerful approach to calculate evolutionary couplings between amino acid residues in a protein family. The couplings can be used to identify functionally constrained residue interactions and to predict the three-dimensional structure of proteins and protein complexes to unprecedented accuracy (www.EVfold.org). A new theory of evolutionary couplings based on this work is under development.
Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Marks, D.S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS One 6, e28766 (2011).