Research

Approach. I use tools and concepts from statistical and soft-matter physics for a quantitative approach to fundamental questions in biology. My group seeks a full integration of data analysis and modeling. We work in close contact with experimentalists, and we consider all the available sources of empirical data (including bioinformatics, genomics, high-throughput biology, and biophysics). We mostly study microorganisms, where stringent and controlled experiments are possible (and where we developed the strongest collaborations), but most of the techniques and concepts we use can be extended to higher eukaryotes.

Single-cell Physiology. Growth and proliferation are central to many fields such as microbiology, ecology, and cancer, with implications for evolution. We focus on the interplay between the single-cell growth-division dynamics and key processes such as genome organization and dynamics, global transcription and the cell cycle. A lot of our work E. coli as a model system, but we also work on other systems, such as cancer cell lines. We aim to bridge the single-cell scale to the population scale, starting from single-cell behavior and exploring its consequences on large-scale growth. Such a predictive scenario for cell growth can be useful in complex situations, such as, e.g., microbial communities and cancer, where spatial degrees of freedom, ecosystem interactions and cell-to-cell communications play a role.

Quantitative Evolutionary Genomics. We study data from a large number of annotated genomes, to uncover invariants of genome architecture, and produce general theoretical descriptions. Such a complex systems approach to genomics sees the genome as a component system drawing its components (genes) from a toolbox (the universe of genes) based on functional requirements. The end goal is understanding the "recipe" by which the genome is built, quantifying the evolutionary impact of processes such as gene duplication, horizontal transfer, and structural variation of the genome. For example, we are interested in the interplay between genome instability and the evolutionary potential of major genome rearrangements, such as chromosomal or whole-genome duplications or ploidy changes. We apply the same concepts to ecosystem data, where simple tools are desperately needed to rationalize intricate interactions.