CAREER: Synthetic approaches to unravel heterogeneous decision-making in individual microbes and populations

Microbes that are identical at the level of their genetic material can still behave differently (this is referred to as ‘phenotypic heterogeneity’) and one of the fundamental challenges in understanding and controlling the behavior of microbes is identifying the factors that control this heterogeneity. The goals of this project are to understand which specific factors control phenotypic heterogeneity and what the consequences of this heterogeneity are for populations of microbes.

We will use new tools that allow us to control cellular processes with light to determine the effect of different factors (specifically transcription factors) on phenotypic heterogeneity in a model microbe: yeast. To determine the consequences of phenotypic heterogeneity on outcomes for microbial communities we will measure how populations of microbes grow and survive under different stress and drug treatments.

In addition to this research, the project will focus on developing human resources at the interface of engineering and life sciences research starting at the middle-school level and reaching through graduate levels. This project will integrate research methods and findings from the research to develop new curricula and mentor trainees. Development of outreach and educational materials aimed specifically at middle-school students, including an innovative interactive modeling game, will enhance science literacy and maintain students in the STEM pipeline.

This project will utilize new approaches from synthetic and quantitative biology to understand (1) how the dynamics and heterogeneity of effector activity interact with additional cellular components to contribute to cell-to-cell variability in gene expression and (2) how heterogeneity in individual microbial decisions determines population-level phenotypes such as growth and stress resistance. The project will pursue these objectives using the yeast Saccharomyces cerevisiae and combine new optogenetic tools to control effector (specifically transcription factor, TF) activity with measurement of gene expression, single-cell decisions, and population-level outcomes.

Optogenetic control of transcription factor activity in combination with a computational model will allow us to (1) identify transcriptional targets that propagate or suppress fluctuations in their cognate TFs activity and (2) characterize how TF activity dynamics control transcriptional bursting. Furthermore, to characterize how heterogeneous decision-making determines population-level phenotypes in isogenic microbial populations we will quantify growth and stress survival while using optogenetic control of TF activity to modulate cell-to-cell variability in gene expression.

The results of this research will increase understanding of how effector activity contributes to observed heterogeneity in gene expression and phenotype between individual microbes and the effect of this heterogeneity on population-level outcomes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.