Our lab studies Quantitative Evolutionary Microbiology, which means we study how microbes grow, interact, and evolve using quantitative approaches. Here we break this down to show why it is an important area of science.
Microbes are microscopic organisms (all bacteria and archaea and some eukaryotes) and the most ancient and abundant form of life on Earth. They underlie every ecosystem on the planet, ranging from soil to the ocean to deep underground. In particular, they form critical ecosystems in and on the human body, sometimes causing disease but often mediating many healthy processes such as our digestive systems. Microbes also serve as powerful tools for biotechnology, making products including drugs, wine, and biofuels. Therefore the biology of microbes is important to just about everything in our world.
Evolution is the process of change in the genetic composition of a population. It is the organizing principle of biology — all biological systems are products of evolutionary processes, and therefore our understanding of those processes is crucial to making sense of those systems. While we often think of evolutionary processes occurring over millions of years, microbes can undergo significant evolutionary change in just a few days or weeks due to their rapid growth and large population sizes. For example, microbes exposed to antibiotics can evolve resistance to them in days. Therefore the dynamics of evolutionary change is an essential aspect of all microbial communities.
Numbers matter. For many important questions in biology, we care not just that X causes Y, but how much X causes how much Y. For example, it’s not good enough to know that a microbial population can evolve resistance to an antibiotic — we need to know how much resistance evolves and how rapidly. These quantitative questions are central to our lab’s research. As a corollary of this, we also invest significant effort in thinking about how to best quantify biological properties, such as fitness, interactions, and ecological diversity. We believe careful definitions are crucial for performing reliable experiments and rigorous statistical analyses, as well as establishing reproducibility of results across the scientific community.
