An important property of any microbial community is its robustness – the ability to survive perturbations. Community robustness emerges from species interactions and is thus difficult to understand, especially in complex communities. Consequently, most work has been phenomenological, correlational, or theoretical. Robustness can also rapidly change as cells evolve. Understanding robustness and how it changes during evolution is critical in community design.
Using a model synthetic community, we will investigate the quantitative and genetic basis of community robustness. This community comprises two cooperative yeast strains engineered to exchange essential metabolites. We have already shown that this community exhibits “compositional robustness”: perturbed strain ratios returned to the steady-state ratio. Now, we will examine two other robustness metrics. One is community viability, the minimal total cell density required for the community to re-establish after a severe population reduction (e.g. antibiotic treatment). Another metric is community growth rate, which reflects the likelihood of an established community to withstand periodic dilutions such as those occurring in industrial fermenters. A mathematical model using experimentally measured strain phenotypes (e.g. metabolite release and consumption, and cell birth and death rates) will allow us to quantitatively predict how changes in strain phenotypes may impact community robustness. We will then experimentally test model predictions by assembling communities from phenotypic variants either isolated during community evolution or through genetic manipulations. Preliminary results suggest that different phenotypes drive robustness against different perturbations.
This project provides an excellent opportunity for tackling an outstanding yet solvable question while acquiring skills in experimentation and modelling.