Research

Microbes form essential components of every ecosystem on Earth. This includes the human body, where microbes sometimes cause disease but also regulate healthy processes such as digestion and immune response. The growth dynamics of microbial populations in these environments are complex, driven especially by the availability of nutrients and the ecological interactions between cells (e.g., sharing or competing for nutrients). These populations, however, are not genetically static: they can rapidly evolve when faced with a new environment such as drug treatment. The similarity of time scales for these ecological and evolutionary dynamics enables complex feedback between the two processes.

Our lab therefore aims to understand how fundamental evolutionary processes shape, and are shaped by, ecological and physiology aspects of microbial communities. We study this problem using a combination of laboratory experiments, computational models, and theoretical approaches.

Current major research directions:

1. Evolution of microbial population dynamics. Microbes rarely grow at steady state for very long, but rather they undergo frequent shifts in growth state as their physiology and environments change. We are especially focused on understanding how limitation by multiple nutrients and the presence of ecological interactions shape microbial population dynamics. We study these questions by combining high-throughput measurements of microbial growth in the lab with computational models.

2. Effect of ecological interactions on mutant fitness and evolution in microbial communities. Interactions are abundant in microbial communities, but their effects on mutant fitness and other aspects of adaptation remain poorly characterized. In particular, we want to know whether interactions tend to inhibit the rate of adaptation by muting the fitness effects of mutations. We are studying these questions experimentally by measuring fitness in large mutant libraries across ecological environments, as well as using both coarse-grained and genome-wide computational models