Cells are incredibly complex out-of-equilibrium systems that constantly react to changing environments in an efficient and strategic manner.  As a consequence, it is necessary for cells to make fast and accurate decisions about their functional roles to fit their needs both at short and long timescales.  Throughout the decision-making process fluctuations in protein levels– called noise– play a pivotal role.  A high amount of noise allows probabilistic decision-making and enhances fitness when cells find themselves in variable environments.  However, noise can be detrimental for commitment to cellular (fate-)decisions, requiring cells to implement strategies to minimize noise when it is unfavourable.  Due to the prevalence and importance of cellular-decision making in healthy and pathogenic cells, it is important to identify the molecular events that drive and modulate noise throughout the decision-making process.   

In our research, we aim to identify the molecular drivers of gene expression noise and regulatory topologies that modulate (i.e., enhance or suppress) noise.  By combining single-molecule imaging, single-cell sequencing, and time-lapse microscopy with mathematical models we aim to map the mechanisms that modulate noise and drive cellular decision-making.  We focus on the role of gene-expression noise in cell-fate decisions of embryonic stem cells and cancer cells.