Date of Award
Doctor of Philosophy (PhD)
The long-held assumption of never-ending rapid growth in biotechnology and especially in synthetic biology has been recently questioned, due to lack of substantial return of investment. One of the main reasons for failures in synthetic biology and metabolic engineering is the metabolic burdens that result in resource losses. Metabolic burden is defined as the portion of a host cells resources either energy molecules (e.g., NADH, NADPH and ATP) or carbon building blocks (e.g., amino acids) that is used to maintain the engineered components (e.g., pathways). As a result, the effectiveness of synthetic biology tools heavily dependents on cell capability to carry on the metabolic burden. Although genetic modifications can effectively engineer cells and redirect carbon fluxes toward diverse products, insufficient cell ATP powerhouse is limited to support diverse microbial activities including product synthesis. Here, I employ an ancient Chinese philosophy (Yin-Yang) to describe two contrary forces that are interconnected and interdependent, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. To decipher Yin-Yang balance and its implication to microbial cell factories, this dissertation applied metabolic engineering, flux analysis, data mining tools to reveal cell physiological responses under different genetic and environmental conditions.
Firstly, a combined approach of FBA and 13C-MFA was employed to investigate several engineered isobutanol-producing strains and examine their carbon and energy metabolism. The result indicated isobutanol overproduction strongly competed for biomass building blocks and thus the addition of nutrients (yeast extract) to support cell growth is essential for high yield of isobutanol. Based on the analysis of isobutanol production, 'Yin-Yang' theory has been proposed to illustrate the importance of carbon and energy balance in engineered strains. The effects of metabolic burden and respiration efficiency (P/O ratio) on biofuel product were determined by FBA simulation. The discovery of energy cliff explained failures in bioprocess scale-ups. The simulation also predicted that fatty acid production is more sensitive to P/O ratio change than alcohol production. Based on that prediction, fatty acid producing strains have been engineered with the insertion of Vitreoscilla hemoglobin (VHb), to overcome the intracellular energy limitation by improving its oxygen uptake and respiration efficiency. The result confirmed our hypothesis and different level of trade-off between the burden and the benefit from various introduced genetic components. On the other side, a series of computational tools have been developed to accelerate the application of fluxomics research. Microbesflux has been rebuilt, upgraded, and moved to a commercial server. A platform for fluxomics study as well as an open source 13C-MFA tool (WUFlux) has been developed. Further, a computational platform that integrates machine learning, logic programming, and constrained programming together has been developed. This platform gives fast predictions of microbial central metabolism with decent accuracy. Lastly, a framework has been built to integrate Big Data technology and text mining to interpret concepts and technology trends based on the literature survey. Case studies have been performed, and informative results have been obtained through this Big Data framework within five minutes.
In summary, 13C-MFA and flux balance analysis are only tools to quantify cell energy and carbon metabolism (i.e., Yin-Yang Balance), leading to the rational design of robust high-producing microbial cell factories. Developing advanced computational tools will facilitate the application of fluxomics research and literature analysis.
Zi Chen, Pratim Biswas, Cynthia Lo, Tae Seok Moon,