Quantitative biology of microbes
We combine quantitative experiments and theoretical models to elucidate how robust properties seen in bacterial populations emerge from seemingly stochastic dynamics observed in individual cells.
Because bacteria are small and chemical reactions involved in gene expression are inherently stochastic, different individual bacteria display different properties – so called phenotypes – even when they are genetically identical and exposed to the same environment. We study the mechanistic origins as well as the functional and evolutionary implications of this phenotypic variability. To this end, our group develops new tools to study bacteria with single-cell resolution, in particular using microfluidics and image analysis.
Learning from fluctuations
Although intracellular processes are noisy, many events of the cell cycle must be tightly controlled in order to sustain growth and division. Analyzing the fluctuations between cells in constant environments allows us to learn more about how different aspects of bacterial physiology – for instance cell division and DNA replication – are coordinated. In parallel, we study how robust gene regulation can be achieved in fluctuating environments, despite noise in gene expression.
Coping with stress
Our group also studies responses to stress where rare phenotypes are expected to play a major role. First, when bacteria are starving, we aim at understanding how cellular physiology is reorganized, and in particular what drives the variability of the time required for bacteria to restart growing. But also, when bacteria are treated with antibiotics, we want to characterize the non-genetic determinants of sensitivity to drugs.
(2020). Subpopulations of sensorless bacteria drive fitness in fluctuating environments. PLoS biology, 18 (12), e3000952.
(2020). Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria. PLoS ONE, 15 (10), e0240233.
(2018). Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software. Nature Communications, 9 (1), 212.
(2014). Budding yeast as a model organism to study the effects of age. FEMS microbiology reviews, 38 (2), 300-25.
(2013). Cell-cell contacts confine public goods diffusion inside Pseudomonas aeruginosa clonal microcolonies. Proceedings of the National Academy of Sciences of the United States of America, 110 (31), 12577-82.
(2013). Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect. PLoS Computational Biology, 9 (4), e1003038.
(2012). Monitoring microbial population dynamics at low densities. Review of Scientific Instruments, 83 (7), 074301.
(2008). Some nonlinear challenges in biology. Nonlinearity, 21 (8), T131-T147.
(2005). Mixotrophy in orchids: insights from a comparative study of green individuals and nonphotosynthetic individuals of Cephalanthera damasonium. New Phytologist, 166 (2), 639-53.