Speaker
Description
The growth rate of a bacterial population is a fundamental quantity in microbiology, but we lack the tools to predict it for realistic mathematical models. Based on an emerging connection between branching processes and statistical physics, I present a new method to compute growth rates numerically in virtual "chemostats". These provide accurate estimates of growth rates and evolutionary fitness in quadratic time, bypassing the exponential complexity typically associated with growing populations. The underlying principle can be applied to design lineage-tracking experiments that measure bacterial fitness in vitro. I apply these results to two computational models that illustrate how bacteria optimally navigate their environment (chemotaxis) and investigate the role of noise in bacterial responses to antibiotics.
