Precision medicine requires an understanding of cancer that goes beyond symptoms and identifies the drivers of disease. The Markowetz lab contributes to this goal by leveraging theory, experimentation and computation to analyse global portraits of cancer and dissect key disease mechanisms.
Research
We assemble global portraits of breast cancer that combine a quantitative view of tumour tissue architecture with geno- mic profiles and clinical data.
- Mello, Wang et al (2013), Poor prog- nosis colon cancer is defined by a dist- inct molecular subtype and develops from serrated precursor lesions, Nat Med 19(5)
- Yuan et al (2012), Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling, Science Transl Med 4
- Curtis et al (2012), The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel sub- groups, Nature 486, 7403
We uncover components, interactions and dynamics of key mechanisms from their response to experimental perturb- ations.
- Mulder et al (2012), Diverse epigenetic strategies interact to control epidermal differentiation, Nature Cell Bio 14(7)
- Wang et al (2012), Posterior associat- ion networks and functional modules inferred from rich phenotypes of gene perturbations, PLoS Comp Bio 8(6)
- Markowetz (2010), How to understand the cell by breaking it: network analy- sis of gene perturbation screens, PLoS Comp Bio 6(2)
Software
- nem - inferring Nested Effects Models from downstream perturbation effects
- RedeR - bridging the gap between statistical computing and network visualization
- PANR - posterior association networks and functional modules
- HTSanalyzeR - network analysis of high-throughput screens
Events
- Florian organizes a session at the CI symposium on Cancer Metabolism, Nov 2013
- Florian lectures in the NBIC/SIB International Winterschool 2013, March 10-15, 2013
- Florian speaks at Statistical Methods for (post)-Genomics Data (SMPGD 2013) in Amsterdam, Jan 24-25, 2013
PI