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- All Subjects: Synthetic Biology
- Genre: Doctoral Dissertation
- Creators: Nielsen, David
- Status: Published
Description
Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.
ContributorsMenn, David J (Author) / Wang, Xiao (Thesis advisor) / Kiani, Samira (Committee member) / Haynes, Karmella (Committee member) / Nielsen, David (Committee member) / Marshall, Pamela (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation focuses on the biosynthetic production of aromatic fine chemicals in engineered Escherichia coli from renewable resources. The discussed metabolic pathways take advantage of key metabolites in the shikimic acid pathway, which is responsible for the production of the aromatic amino acids phenylalanine, tyrosine, and tryptophan. For the first time, the renewable production of benzaldehyde and benzyl alcohol has been achieved in recombinant E. coli with a maximum titer of 114 mg/L of benzyl alcohol. Further strain development to knockout endogenous alcohol dehydrogenase has reduced the in vivo degradation of benzaldehyde by 9-fold, representing an improved host for the future production of benzaldehyde as a sole product. In addition, a novel alternative pathway for the production of protocatechuate (PCA) and catechol from the endogenous metabolite chorismate is demonstrated. Titers for PCA and catechol were achieved at 454 mg/L and 630 mg/L, respectively. To explore potential routes for improved aromatic product yields, an in silico model using elementary mode analysis was developed. From the model, stoichiometric optimums maximizing both product-to-substrate and biomass-to-substrate yields were discovered in a co-fed model using glycerol and D-xylose as the carbon substrates for the biosynthetic production of catechol. Overall, the work presented in this dissertation highlights contributions to the field of metabolic engineering through novel pathway design for the biosynthesis of industrially relevant aromatic fine chemicals and the use of in silico modelling to identify novel approaches to increasing aromatic product yields.
ContributorsPugh, Shawn (Author) / Nielsen, David (Thesis advisor) / Dai, Lenore (Committee member) / Torres, Cesar (Committee member) / Lind, Mary Laura (Committee member) / Wang, Xuan (Committee member) / Arizona State University (Publisher)
Created2016