Abstract

Engineered microorganisms play an increasingly important role in the sustainable production of fuels, bioplastics, and other valuable biomaterials. Successful industrial deployment depends on maximizing product titers and ensuring process robustness at large scale. However, one of the major obstacles is non-genetic cell-to-cell heterogeneity arising from stochastic intracellular processes. Such heterogeneity can result in subpopulations that divert substrates toward growth rather than product biosynthesis, ultimately reducing yield and contributing to batch-to-batch inconsistency. To better understand the mechanisms by which cell-to-cell variation emerges, propagates, and impacts bioproduction, this dissertation investigated how intracellular energetic dynamics and stochastic biosynthetic noise contributed to metabolic heterogeneity and impact bioproduction, and developed strategies to enhance titer, yield, and productivity in both model and industrially relevant bioconversion systems. In Aim 1, we revealed how intracellular ATP dynamics influence microbial bioproduction by using an ATP biosensor to monitor energy levels across growth phases and carbon sources in Escherichia coli and Pseudomonas putida. We observed transient ATP accumulation during the transition to stationary phase that coincided with increased fatty acid (FA) and polyhydroxyalkanoate (PHA) production. We identified acetate and oleate as carbon sources that elevate steady-state ATP and enhance FA and PHA titers. ATP dynamics also served as a diagnostic indicator of metabolic burden, revealing pathway bottlenecks limiting limonene production, thereby demonstrating ATP’s utility as both a mechanistic and engineering handle. In Aim 2, we used time-lapse fluorescent microscopy and stochastic modeling to characterize noise propagation in a heterologous betaxanthin pathway. We found that over half of initially high-producing cells transitioned to medium or low production states within two doublings, demonstrating rapid phenotypic diversification. Model predictions identified a genetic circuit featuring enzyme-feedback-to-growth selection as the most effective for enhancing titer and productivity. We validated this prediction experimentally by coupling enzyme expression to nutrient availability, which enriched high-producing cells and increased overall betaxanthin titer by 4.4-fold. These results reveal how metabolic noise drives bioproduction heterogeneity and demonstrate that targeted circuit design can steer population dynamics toward improved production. In Aim 3, we examined the sources of heterogeneity in PHA production in P. putida by introducing a fluorescent reporter after additional copies of key enzymes phaC1 and phaJ4. Single-cell microscopy analysis revealed weak correlation between enzyme expression and PHA content in both genome-inserted strain and plasmid-bearing strain, suggesting other complex mechanisms behind cell-to-cell variation in PHA content. Based on the single-cell microscopy images, we speculated that the heterogeneity may stem from uneven PHA granule partitioning regulated by a family of proteins that are associated with the surface of intracellular PHA called phasin, identifying new regulatory targets for improving population-level uniformity. This dissertation establishes ATP dynamics, pathway-noise quantification, and growth-coupled circuit design as practical strategies to improve microbial production systems. By investigating and understanding sources of non-genetic cell-to-cell variation, these studies offer generalizable approaches to increase titer, yield, and batch consistency. The findings inform predictable strain engineering, identify new targets, and provide modeling and circuit-design frameworks adaptable to diverse industrial pathways.

Committee Chair

Fuzhong Zhang

Committee Members

Himadri Pakrasi; Joshua Yuan; Yinjie Tang; Zhen He

Degree

Doctor of Philosophy (PhD)

Author's Department

Energy, Environmental & Chemical Engineering

Author's School

McKelvey School of Engineering

Document Type

Dissertation

Date of Award

12-19-2025

Language

English (en)

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