Systems Medicine of Cancer: Bringing Together Clinical Data and Nonlinear Dynamics of Genetic Networks

The last few years have witnessed major developments in experimental techniques for analysis of cancer, including full genome sequencing, measurement of multiple oncomarkers, DNA methylation profile, genomic profile, or transcriptome of the pathological tissue. Whilst these advances have provided some additional understanding of cancer onset and development, the problem of cancer is far from solved. Since the amounts of data emerging from these are very substantial, and the data itself can be extremely heterogeneous (qualitative, quantitative, and verbal descriptions), this makes standard data analysis techniques not practically applicable. One promising direction of addressing the problem of analysis of cancer data is to use modern and sophisticated methods from systems biology and cybernetics. Some challenges of this approach are connecting the results of mathematical analysis with real clinical data, and bridging the existing gaps between the communities of clinicians and applied mathematicians.