Lactam is an important class of commodity chemicals used in the manufacture of nylons, with millions of tons of production every year.

2-Pyrrolidone is a valuable bulk chemical with myriad applications as a solvent, polymer precursor and active pharmaceutical intermediate. A novel 2-pyrrolidone synthase, ORF27, from Streptomyces aizunensis was identified to catalyze the ring closing dehydration of γ-aminobutyrate. ORF27’s tendency to aggregate was resolved by expression at low temperature and fusion to the maltose binding protein (MBP). Recombinant Escherichia coli was metabolically engineered for the production of 2-pyrrolidone from glutamate by expressing both the genes encoding GadB, a glutamate decarboxylase, and ORF27. Incorporation of a GadB mutant lacking H465 and T466, GadB_ΔHT, improved the efficiency of one-pot 2-pyrrolidone biosynthesis in vivo. When the recombinant E. coli strain expressing the E. coli GadB_ΔHT mutant and the ORF27-MBP fusion was cultured in ZYM-5052 medium containing 9 g/L of L-glutamate, 7.7 g/L of L-glutamate was converted to 1.1 g/L of 2-pyrrolidone within 31 h, achieving 25% molar yield from the consumed substrate.

Caprolactam and valerolactam are important commodity chemicals used in the manufacture of nylons, with millions of tons produced every year. Biological production of these high valued chemicals has not been possible due to a lack of enzymes that will cyclize the ω-amino fatty acid precursors to the corresponding lactams under ambient conditions. In this study, we demonstrated proof of these bioconversions by in vitro enzyme assays. We found that ORF27, an enzyme involved in the biosynthesis of ECO-02301 in Streptomyces aizunensis, has a broad substrate spectrum and can not only cyclize γ-aminobutyric acid into butyrolactam, but also 5-aminovaleric acid (5-AVA) into valerolactam and 6-aminohexanoic acid (6-AHA) into caprolactam. The ORF27 lactam formation reaction was characterized by product analysis, and ORF27’s activity on the three ω-amino fatty acids were compared. Recombinant E. coli expressing ORF27 produced valerolactam and caprolactam when 5-AVA and 5-AHA, respectively, were added to the culture medium. Upon co-expressing ORF27 with a metabolic pathway that produced 5-aminovaleric acid from lysine, we were able to demonstrate production of -valerolactam from lysine or directly from glucose.

Biological production of caprolactam, valerolactam and butyrolactam were enabled by the recent discovery of lactam synthases that cyclize their ω-amino fatty acid precursors to the corresponding lactams. To facilitate strain optimization with regard to product yields, productivities, and titers, it is desirable to develop a high throughput screening system forspecific small-molecule detection and response. We took the chemical informatics concepts used in small molecule drug discovery, and adapted them into a metabolic engineering strategy for targeted scouting of protein sensor candidates, named “Analog Generation towards Catabolizable Chemicals”. We discovered a lactam biosensor based on the ChnR/Pb transcription factor-promoter pair. The microbial biosensor was engineered to be a single plasmid system, demonstrating dose dependent response for caprolactam, valerolactam and butyrolactam with great dynamic range (1.8-3.5) and wide linear range of 1-2 orders of magnitude. The biosensor also showed specificity against intermediates of lactam biosynthetic pathways and therefore could potentially be applied for high throughput metabolic engineering for industrially important lactam biosynthesis.

Urbanization accelerates greenhouse gas emissions and traffic congestion, endangering the environmental sustainability of cities and causing significant economic losses. In this thesis, we aim to tackle practical problems faced by public sector by leveraging tools including economic modeling, optimization, and data analytics to achieve sustainable urban mobility.

In Chapter 2, we focus on an incentive mechanism design problem to improve public transportation adoption. Due to a prolonged decline in public transit ridership over the last decade, transit agencies across the United States are in financial crisis. To entice commuters to travel by public transit instead of driving personal vehicles, municipal governments must address the “last mile” problem by providing convenient and affordable transportation between a commuter’s home and a transit station. This challenge raises an important question: Is there a cost-effective subsidy program that can improve public transit adoption by solving the last-mile problem? To address the question, we present and analyze two incentive mechanisms applied in practice for increasing commuter adoption of public transit. In a direct mechanism, the government provides a subsidy to commuters who adopt a “mixed mode”, which involves taking public transit and hailing rides to/from a transit station. The government funds the subsidy by imposing congestion fees on personal vehicles entering the city center. In an indirect mechanism, instead of levying congestion fees, the government secures funding for the subsidy from the private sector. These two mechanisms are especially relevant because several jurisdictions in the U.S. have begun piloting incentive programs in which commuters receive subsidies for ride-hailing trips that begin or end at a transit station. We then examine the implications of both mechanisms on five self-interested stakeholders (commuters, public transit agency, ride-hailing platform, municipal government, and local private enterprises), and provide conditions where either mechanism is superior with respect to total cost and commuter welfare. Our findings offer cost-effective prescriptions to municipal governments seeking to improve urban mobility and public transit ridership.

Chapter 3 of the thesis delves into improving cycling ridership through bike lane network planning. Sustainable urban infrastructure plays a pivotal role in the creation of livable cities. To meet the growing demand for cycling and reduce emissions, municipal governments worldwide have made significant investments in the expansion of bike lane networks. However, re-allocating road capacity from vehicles to cycling can often prove controversial due to the risk of exacerbating traffic congestion. This chapter presents a method for bike lane network planning that accounts for ridership and congestion effects. We first develop a game theoretic model that captures the traffic equilibrium and consumers’ transportation mode choice. We then present an estimator for recovering unknown parameters of a traffic equilibrium model from features of a road network and observed vehicle flows, which we show asymptotically recovers ground-truth parameters as the network grows large. We subsequently present a prescriptive model that recommends paths in a road network for bike lane construction while endogenizing cycling demand, driver route choice, and driving travel times. Our framework allows for a thorough assessment of bike lane network expansion on both cycling ridership and traffic congestion, which offers valuable insights and solutions for social planners seeking to promote urban mobility via urban infrastructure planning.

In Chapter 4, we build on the theoretical foundation of Chapter 3 and conduct a comprehensive empirical study on the City of Chicago. We bring together data on the road and bike lane networks, vehicle flows, travel mode choices, bike share trips, driving and cycling routes, and taxi trips to estimate the impact of expanding Chicago’s bike lane network. We estimate that adding 25 miles of bike lanes as prescribed by our model can lift ridership from 3.9% to 6.9%, with at most an 8% increase in driving times. We also find that three intuitive heuristics for bike lane planning can lead to lower ridership and worse congestion outcomes, which highlights the value of a holistic and data-driven approach to urban infrastructure planning.