Computational cancer biology
We have developed statistical models for relating different layers of genomic, molecular and clinical data to extract the precise connections among variables to understand the connection of genotype and phenotype. Moreover we have been working on biostatistical models and informatics tools for predicting outcome based on comprehensive high-dimensional data sets.
Another area of our research are the evolutionary dynamics of cancer. The process of developing cancer is driven by mutation and selection; hence the language to quantify that process is that of evolutionary dynamics. Deep sequencing unmasks the clonal composition of a cancer, which sheds some light on its evolutionary history. Accurate detection of subclonal mutations and reconstruction of phylogenies requires, however, accurate bioinformatics tools that we are actively developing.
Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F (2015) Cancer evolution: mathematical models and computational inference. Systematic Biology, 64:e1-e25.
Gerstung M, Papaemmanuil E, Campbell PJ (2014) Subclonal variant calling with multiple samples and prior knowledge. Bioinformatics, 30:1198-1204.
Papaemmanuil E, Gerstung M, Malcovati L, et al. (2013) Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood, 122:3616-27.