Deciphering the relationship between molecules and cellular phenotypes is one of the most compelling challenges in life sciences, requiring the integration of approaches that span multiple biological scales.
We are still far from understanding the rules that control the interactions between biomolecules and the impact on the physiology of single cells or multicellular organisms. To close this gap, we apply and develop a wide-range of single-cell approaches that allow us to accurately measure, within individual cells, key biomolecules such as RNAs, proteins and metabolites. We use time-lapse fluorescence microscopy for single mRNA (MS2 system) and protein (Fluorescence-based FRET-sensors) detection in living cells, as well as single molecule RNA FISH (smFISH) in fixed cells, to investigate the spatial and temporal regulation of metabolism and gene expression and the consequences on cellular fitness. Methods from statistical biophysics, quantitative image analysis and machine learning are used to analyze raw data, and to predict and understand experimental outcomes.
A key biological question that we study is how cells optimize growth rates. We combine multiple single-cell model organisms such as E. coli, S. cerevisiae and S. pombe to unravel the conserved principles operating on molecular networks that coordinate gene expression regulation, metabolism and cell signaling.