Towards a quantitative and biophysical understanding of gene regulation: applications in evolution and cancer
Abstract: Regulation of gene expression (i.e. how the code in DNA is converted into functional molecules) is crucial to determine the diversity among the cells of an organism (e.g. brain vs. muscle, or healthy vs. cancer cells), individuals, populations and species. Thanks to new sequencing technologies, (maybe the first time in the history of biology) quantitative data are accumulating and becoming more available to set a framework for a quantitative understanding as is in physics. We already witness an enhanced predictive power in human disease studies with machine learning approaches. Yet, a better molecular understanding requires also solid and quantitative description of the physical mechanisms. In this talk, I will discuss some biophysical and mathematical models for protein binding to DNA and present my results on how we can get better insights for i-) the dynamics of regulatory DNA evolution, ii-) the diversity and interplay in bacterial lac promoters and LacZ protein expressions, iii-) and the epigenetic reprogramming in Ewing sarcoma disease (a soft tissue and bone childhood cancer).