Development of systematic and combinatorial approaches for the metabolic engineering of microorganisms

Explorations and optimizations through the genomic space are a daunting undertaking given the complexity and size of the possible search space. To approach this problem, systematic and combinatorial approaches were employed for the engineering of cellular phenotype in Escherichia coli. Initially, a computational method based on global cellular stoichiometry was employed to identify single and multiple gene knockout targets for lycopene production in E. coli. These targets led to substantial increases in lycopene production, but were limited in scope due to the nature of these models. Therefore, these approaches and targets were complemented with combinatorial searches to identify unknown and regulatory targets. When combined, these searches led to further increases of lycopene production and allowed for the visualization of the resulting metabolic landscape. A more exhaustive search was conducted in the background of eight genotypes which resulted in the formulation of the gene knockout search network. This network enables the investigation into how phenotype optimization is biased by search strategy. Collectively, these results demonstrated that despite the complexity and nonlinearity of genotype-phenotype spaces, most of the significant phenotypes were controlled and regulated by a small subset of key "gateway" nodes. Often, the mutations and genotypes incurred in altering global cellular phenotypes are not necessarily additive and can be quite non-linear. Effective probing of a metabolic landscape requires not only gene deletions, but also the varying (or tuning) of expression level for a gene of interest. Through promoter engineering, a library of promoters of varying strength were obtained through mutagenesis of a constitutive promoter. A multi-faceted characterization of the library, especially at the single-cell level to ensure homogeneity, permitted quantitative assessment correlating the effect of gene expression levels to improved growth and product formation phenotypes in E. coli. Integration of these promoters into the chromosome can allow for a quantitative, accurate assessment and tuning of genetic control. Collectively, quantitative phenotype-genotype analysis illustrated that optimal gene expression levels are variable and dependent on the genetic background of the strain.