Our laboratory seeks to quantitatively predict microbial evolution, ecology and behavior. Our focus is on developing quantitative, first principles, predictive theory, using experiments in synthetic environments that can be directly compared with genome-scale computational models and analytical mathematical theory.
Microbes secrete many different kinds of molecules to their environment, from metabolic byproducts to extracellular enzymes. These molecules collectively modify the extracellular environment, and mediate either direct or indirect ecological interactions. Our lab is interested in how these excreted molecules structure microbial communities and determine the evolutionary trajectories of the species in these communities. Our work focuses on three questions:
1) Can we predict how microbes will evolve in a given environment? Evolution is governed many non-selective, stochastic processes. Moreover, microbial growth produces strong changes to the environment, through the secretion and uptake of metabolites, generation of spatial structure by biofilm or colony formation, modifications to the pH, and a long list. These modifications alter the selective landscape, and creates many opportunities for historical contingency. At the same time, experimental evolution has found strong phenotypic convergence in populations adapting to the same environment. Can we predict these convergent phenotypes if we know enough about the environment? We are trying to address this question by using a combination of theory (consumer-resource models), computation (dynamic FBA) and evolution experiments with model organisms such as E. coli (Bajic et al 2018). We are developing computational tools to simulate evolution in complex communities in silico, and to address questions such as (a) how horizontal gene transfer affects the stability and dynamics of microbial communities, (b) how metabolic networks evolve, or (c) how the complexity of a microbial community affects the strength and directionality of eco-evolutionary feedbacks.
2) Can we quantitatively predict which microbial communities will assemble in a given environment? Metabolic secretions produce new niches that can be occupied by other species, potentially leading to facilitation. By cultivating large numbers of environmental microbial communities in synthetic laboratory environments, we are investigating: (a) How environmental engineering by metabolic secretions affects microbial community assembly (Goldford et al 2018); (b) how environmental engineering affects the outcome of microbial invasions (Lu et al 2018); and (c) how environmental engineering leads to emergent properties of microbial ecosystems (Sanchez-Gorostiaga et al 2018).
3) Can we quantitatively predict microbial phenotypes in a given environment? By altering their shared environments through their metabolic activity, microbes can influence the phenotypes expressed by other cells in their proximity, e.g. which metabolites they choose to utilize or which enzymes to express. Evolutionary changes in the genetic and biochemical circuits that control microbial behavior may thus affect ecological interactions between species, and are an essential component of microbial communities. Our lab is interested understanding the relationship between the evolution of microbial behavior and microbial ecology. For instance, we ask questions such as: How conserved are metabolic decisions across the bacterial tree of life? How does the behavior of individuals from one species affect the behavior of other species in the community? (Rauch et al 2017);