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.