The Markowetz lab at Cancer Research UK's Cambridge Research Institute develops algorithms and statistics to leverage complex and heterogeneous data sources for biomedical research.
Our research is grounded in close collaborations with experimental partners. Together we approach pivotal questions in biology and medicine by computationally guided experimentation: biological and clinical questions motivate the development of novel statistical algorithms, which then guide the next round of experiments.
Research
Computational diagnostics > With our partners at CRI we develop systems approaches to dissect the genomic basis of cancer. We aim to identify disrupted signaling pathways in tumors, to discover novel biomarkers, and to diagnose disease sub-types by their molecular signatures.
Stem cell genomics > With international collaboration partners we work on defining regulatory networks of key pluripotency modulators, comparing them between different stem cell types, and characterizing their re-wiring during differentation.
Understanding the cell by breaking it > One of our key areas of interest is developing strategies to reconstruct cellular pathways and their condition-specific re-wiring from gene perturbation assays.
Software
- nem - an R/Bioconductor package to infer Nested Effects Models for phenotypic hierarchies
Events
- We organize the CRI Comp Bio Seminar series.
- We co-organize an ESF exploratory workshop 'From phenotypes to pathways' bringing together leading experimental and computational researchers from all of Europe.
Selected publications
- How to understand the cell by breaking it: network analysis of gene perturbation screens
F. Markowetz, PLoS Comp Bio 2010 6(2). [ PMID:20195495 ] - Systems-level dynamic analyses of fate change in murine embryonic stem cells. R. Lu, F. Markowetz, et al. Nature 2009; 462(7271):358-362
[ PMID:19924215 ] - Structure Learning in Nested Effects Models. A. Tresch, F. Markowetz. Statistical Applications in Genetics and Molecular Biology: Vol. 7: Iss. 1, Article 9, 2008. [ PMID:18312214 ]
