Investigating Non-model Microorganisms for Biomanufacturing via Systems Biology and Machine Learning
Date of Award
Doctor of Philosophy (PhD)
Non-model microorganisms are becoming promising biomanufacturing chassis well-suited for production of sustainable oleochemicals. The expansion of synthetic biology tools has allowed for efficient redirection of carbon towards products, generating numerous production strains at the laboratory scale. However, selecting gene engineering targets is non-trivial, as lessons learned from previous microbes are not always applicable. Additionally, promising laboratory strains routinely fail to maintain performance during scale-up, requiring strain re-engineering and leading to significant commercialization risk. There is a need for characterization of the functional metabolism of non-model engineered strains as well as new computational design algorithms that can generate robust strains and link complex omics data to cellular regulatory processes. Current models inadequately capture the environmental stresses which contribute to loss of production during scale-up while the physiological responses over the course of long-cultivations in reactors remain poorly understood. This dissertation broadly focused on improving microbial systems for industrial applications and aimed to address current limitations by 1. identifying engineered strain bottlenecks using metabolomics and 13C-isotopic tracing, 2. developing a data-driven modeling framework integrated with mechanistic genome-scale modeling for engineered yeast performance prediction and strain design, and 3. expanding genome-scale modeling techniques for 13C flux analysis.
Yinjie J Tang
Available for download on Saturday, December 19, 2026