Now is an exciting time for biology: the scope and accuracy of experimental techniques are increasing very rapidly, while their cost does the opposite. However, making the best use of those techniques is highly challenging. How to choose what experiments to do? How to make sense of the data we get? And how do we deal with reproducibility issues?
For me, the best way to go is to tightly integrate experiments with mathematical modeling, computing and data analysis. I think that we should try to do this at a whole new level compared to what is done traditionally. A long-term goal is to completely merge theory and experiment: to each piece of data corresponds a model explaining it, and to each model assumption/parameter corresponds an experiment to test/infer it.
Since the summer 2015, I am doing a postdoc at Imperial College London with Sam Marguerat and Vahid Shahrezaei. I combine quantitative experiments and modeling to understand the relationship between gene expression, cell growth and cell size at the single-cell level in the rod-shaped eukaryote S. pombe.
One main topic of my PhD work was to investigate in-silico the molecular mechanisms behind the resistance of cancer cells to the death ligand TRAIL. To do that, I developped a cell-based (each cell is represented individually) multi-scale (cellular decisions are controlled by biochemical reaction pathways simulated in each cell) approach to model the dynamics of cell populations.