Naturally occurring microbial consortia (microbiomes) evidence complicated networks of interactions between their myriad constituent species. These functional interactions give rise to emergent community phenomena, such as resilience, elasticity, and functional redundancy, that are difficult to predict a priori but must be considered to understand dynamic changes within microbiomes or to design interventions (e.g., modeling algal blooms, improving human health or crop yields, bioremediation). This project seeks is to develop measurement strategies to quantify and model these microbial interaction networks and emergent community phenomena.

 

Most interactions between microbes are nonspecific (with notable exceptions like quorum sensing) and are generally mediated through chemical changes in a shared soluble environment. Thus, we seek to employ metabolomics (e.g., LC-MS/MS) to measure extracellular metabolites and quantify the biochemical transformations produced by metabolically active microbes. Microbial abundances and growth rates (fitness), determined using NGS, allow predictions of changes to community structure over time. While these measurements and models will initially be developed and validated using pure cultures and simple mixtures of microbes, the goal is to move toward consortia of increasing complexity, eventually probing interaction networks within naturally occurring microbiomes (i.e., 10^2-10^3 constituent species).

 

This project lies solidly at the interface of microbiology and microbiome engineering, analytical and bioanalytical chemistry, and bioinformatic data analysis. We invite applicants from diverse backgrounds, and successful applicants should expect to collaborate closely across an interdisciplinary team.

key words

Microbiome; Microbiology; Microbial Community; Consortia; Microbiome Engineering; Metabolomics; NGS; Sequencing; Mass Spectrometry; Bioinformatics; Network Analysis; Population Dynamics

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants