We aim to uncover how eukaryotes and bacteria carry out fundamental processes in order to survive. Our interests span the molecular mechanisms of proteins and their interaction networks all the way to cell-cell communication in microbes. Specific areas of interest include bacterial DNA repair, signalling, motility, transport and intercellular competition and yeast cell cycle and metabolic control. We explain these processes using a combination of experimental, modelling, computational and mathematical tools, at scales ranging from atomic resolution to whole cells and cell populations. We also develop synthetic biology approaches to enhance and exploit biological functions.
Our research centres on microbes and spans areas such as the molecular mechanisms of protein machines, bacterial pathogenesis, cellular homeostasis control and cell population dynamics. Colin Kleanthous’s group investigates how protein antibiotics known as bacteriocins kill Gram-negative bacteria and uses these molecules to understand the protective layers that envelope bacteria. Ben Berks’s lab studies complex nanomachines that allow selective movement of proteins and DNA across the different barriers of the bacterial cell envelope and how this movement is energized. Maike Bublitz’s group use structural methods to investigate transporter proteins embedded in the cytoplasmic membranes of pathogenic fungi and bacteria as a basis for the development of new drugs. Stephan Uphoff’s lab investigates the mechanisms underlying bacterial adaptation, DNA repair, and mutagenesis, using novel microscopy techniques developed to visualise these processes within cells. At the population level, Kevin Foster’s lab combines theory with experimental work to understand the complexity of microbial communities, how bacteria invade and persist in communities and how the gut microbiome of mammals is established and maintained. Bela Novak’s group use nonlinear differential equations to build accurate mathematical models that reproduce cell cycle dynamics in eukaryotes from which they can predict the effects of deleterious mutations.