The laws governing growth and division of microbes are subject of an intense debate. This is partly due to recently discovered empirical scaling laws challenging some of the long established growth laws in the field. These new laws, relating fundamental time- and size-scales at the population level, appear to be valid over a large range of growing conditions and for many different species. Thus, they suggest the existence of yet unknown universal principles governing cellular growth and division.
In this project, we want to combine methods from Statistical Physics and Bayesian Data Analysis to elucidate these principles. The first step is to express our current mechanistic understanding of microbial growth in the form of an individual-based growth-division model on trait space. The parameters of this model are the fundamental trait- and time-scales governing growth and division at the individual level. Then, we employ Bayesian inference to find the parameter ranges supported by the data. This will allow us to formulate and test a criticality hypothesis that could explain the observed scaling laws (see [Held et al., 2020] where such a hypothesis has been formulated for phytoplankton).
The project will be co-supervised by Carlo Albert (Swiss Federal Institute of Aquatic Science and Technology) and Samir Suweis (Unipd). We will collaborate with the labs of Martin Ackermann (ETH) and Marco Cosentino Lagomarsino (IFOM/University of Milan).
- [Held et al., 2020] Held, J., Lorimer, T., Pomati, F., Stoop, R., and Albert, C. (2020). Second-order Phase Transition in Phytoplankton Trait Dynamics. arXiv: 2004.00399.