Scientists in the Computational & Systems Biology Program at SKI combine findings in biology with computer algorithms and databases to conduct biological research. Work in so-called “dry” laboratories, consisting of powerful computers running sophisticated software, complements and strengthens traditional laboratory and clinical research. The aim is to build computer models that simulate biological processes from the molecular level up to the organism as a whole and to use these models to make useful predictions.
Scientists in the program are leaders in bringing the enormous power of single cell sequencing to an array of challenging problems in contemporary biomedicine. They use their mathematical expertise to help interpret the massive amounts of data that are emerging from genomic techniques.
Dana Pe'er, PhD
Chair, Computational & Systems Biology Program
Computational Biologist Dana Pe’er combines single cell technologies, genomic datasets and machine learning techniques to address fundamental questions addressing regulatory cell circuits, cellular development, tumor immune eco-system, genotype to phenotype relations and precision medicine.
Colin Begg, PhD
Biostatistician Colin Begg has research interests spanning cancer epidemiology and statistical methods that are applicable to clinical research more broadly.
John Chodera, PhD
Computational chemist John Chodera uses statistical mechanics, molecular modeling, and automated biophysical experiments to help identify new potential therapeutics and investigate mechanisms of drug resistance in cancer.
Kushal Dey, PhD
The Kushal Dey Lab builds computational models that integrate genetic and genomic data to prioritize variants, genes, and cell types, and to decode the causal functional architecture underlying heritable complex diseases — including immune-related diseases, like Alzheimer’s and inflammatory bowel disease, and heritable cancers, like breast cancer.
Christina Leslie, PhD
Computational biologist Christina Leslie focuses on developing machine learning algorithms for computational and systems biology.
Quaid Morris, PhD
Computational biologist Quaid Morris uses artificial intelligence techniques and develops machine learning algorithms to study gene regulation, cancer evolution, clinical informatics, and other topics in systems biology.
Thomas Norman, PhD
Systems biologist Thomas Norman develops new computational and functional genomics approaches for studying how genes interact to realize complex phenotypes.
Joao Xavier, PhD
Systems biologist Joao Xavier combines experimental and computational approaches to study diverse problems relevant to cancer: how the microbiome influences cancer and cancer treatment, how cancer cells metastasize, and how metabolic fluxes command the behavior of living cells.
Resources & Collaborations
SKI offers a wide array of core facilities and other technologies, as well as significant opportunity for collaboration. Members of the Computational & Systems Biology Program derive particular benefit from close ties to the following: