Skip to main content
eScholarship
Open Access Publications from the University of California

UC San Diego

UC San Diego Electronic Theses and Dissertations bannerUC San Diego

Optimization of microbial cell factories with systems biology

  • Author(s): King, Zachary Andrew
  • Advisor(s): Palsson, Bernhard O
  • et al.
Abstract

Microbial cell factories can have a transformative impact the chemical industry, but, first, we must meet the challenges of designing and optimizing high-yield cell factory strains. The most popular conceptual model for cell factory optimization is the design-build-test-learn cycle. I present methods that use systems biology to improve the optimization process in each of these steps. First, the build step requires a parts list for a host organism and any heterologous pathways. I present BiGG Models, a database of more than 75 high-quality, manually-curated genome-scale metabolic models that comprise a standardized metabolic parts list. BiGG Models has become the most popular resource in the community for gold-standard genome-scale metabolic models. For the test step, contextualization of omics data is an enormous challenge, and I developed a visualization tool to address this challenge. Escher is a web application for visualizing data on biological pathways. With Escher, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP) and metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). For the learn step, genome-scale models can be used to identify general trends in cell factories performance. I introduce a computational method—OptSwap—to predict bioprocessing strain designs by identifying optimal modifications of the cofactor binding specificities of oxidoreductase enzyme and identifying complementary reaction knockouts. I also present an optimization procedure that identifies optimal cofactor- specificity “swaps” for improving theoretical yield in genome-scale metabolic models. Swapping the cofactor specificity of central metabolic enzymes is shown to increase NADPH production and increase theoretical yields for many native and non-native products. Last, the design step requires models that can successfully predict phenotype from genotype. I assess the predictive capabilities of existing models of E. coli through literature mining. I simulate strains from the literature in six historical genome-scale models of E. coli and report that the predictive power of the models has increased as they have expanded in size and scope. Together, these studies provide a path toward successfully applying systems biology methods to optimizing microbial cell factories.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View