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A central goal of biomolecular engineering is the construction of tools to manipulate nanoscale processes. DNA has proved to be a programmable material suited for this task. DNA strand displacement reactions can be designed to process chemical information in the form of concentrations and sequences. DNA nanotechnology has thus far produced devices for the detection of disease biomarkers, performed computation on chemical inputs, powered mechanical action at both the nanoscale and the macroscale, and assembled precise sub-micron structures from the bottom up. This dissertation addresses three main topics. First, we develop predictive models for non-canonical nucleic acid hybridization that enable rational design. Second, we show how rationally designed DNA strand displacement reactions can be used to perform computations on information stored in DNA. Third, we present nucleic acid computation with both strand displacement and transcription and discuss strategies for facilitating the scale up of networks. Finally, we discuss data storage in nucleic acid variants in the appendix. Rational design of DNA circuits and structures is possible because the thermodynamics of DNA and RNA hybridization can be approximated using a nearest-neighbor model. The parameters of this model are typically experimentally determined through the hyperchromism of denatured nucleic acids. This is measured through low-throughput UV-Vis spectrophotometry melting experiments that require a sizable amount of duplexes for a large set of sequences. For non-canonical nucleic acids or non-standard interactions, this characterization can be prohibitively costly and time consuming. Initially, we considered repurposing a next-generation sequencing (NGS) platform for high-throughput mapping of nucleic acid hybridization across a large sequence space; however, we found that the platform is suitable for mapping protein-nucleic acid interactions but not nucleic acid-nucleic acid hybridization due to its dynamic range. We then assessed whether high-resolution melting (HRM) can be used as a rapid method for determining approximate model parameters and found that HRM models can predict relative stabilities between duplexes of different sequences. Using this method, we developed a predictive model for phosphorothioate DNA which we then applied to the design of a phosphorothioate-modified catalytic hairpin assembly circuit. DNA strand displacement reactions can be used not only to manipulate chemical information in the form of concentration, but also to read and write to more permanent forms of information, such as sequence and secondary structure. We developed and demonstrated a DNA data storage scheme that enables in-memory computation. DNA is a promising data storage medium for meeting today's rapidly growing data storage needs; however, because computation on the stored data is usually performed in silico, strands must be sequenced and re-synthesized at every read-write cycle. Our scheme circumvents the bottleneck of de novo oligonucleotide synthesis by updating information using strand displacement cascades that result in sequence changes readable by NGS. We experimentally demonstrated two algorithms - binary counting and cellular automaton Rule 110 - and additionally showed that biologically-occurring DNA sequences without sequence design can be repurposed for storage and computation. Our scheme is capable of computation on multiple data in parallel, as well as random access and sequential computation, allowing for scaled up storage. Programmable chemical computation is also possible with enzymatic reactions such as transcription. Catalytic activity from enzymes has the potential to simplify circuit design and produce biologically potent signals. Practical concerns to expanding chemical computation circuits such as transcription networks include limited readout of signals and time-consuming purification. We addressed these concerns by expanding on previous efforts to build scalable in vitro transcription networks. We updated a single-stranded inhibitory transcription switch design for compatibility with multiplexed NGS readout and developed an analogous single-stranded switch that is activated by nucleic acid signals.

Mammalian artificial chromosomes, or MACs, have been studied as a potential avenue for hosting large numbers of transgenes in mammalian cells. MACs have several advantages over viral-based methods for transgene expression, including a lack of limits on loading capacity, which bypasses issues associated with integration into the genome. One area of research in which MACs can be applied is the biomanufacturing of protein-based therapeutics, where reported genome instability in Chinese hamster ovary (CHO) cells can lead to reduced product titer. MACs can potentially aid in solving this issue by providing alternate hosting sites for transgenes for integration of protein-based therapeutic production. However, some hurdles exist in the path of utilizing MACs as a biology tool, including the acquisition of sufficient mass and concentration of a MAC, the molecular cloning of a transgene into a MAC, and delivery of the cloned MAC to target mammalian cells. To address this, improvements were made at the steps of transformation of the MAC into E. coli, isolation of positive colonies, and subsequent kit purification to generate sufficient masses and concentrations for downstream applications. Using Gibson Assembly, a selectable marker, glutamine synthetase (GS), was successfully cloned onto the MAC, yielding the construct MAC-GS. MAC-GS was subsequently electroporated into suspension CHO cells, and selection by removal of L-glutamine demonstrated the functionality of GS. These results represent a positive step forward for the implementation of MACs as a useful synthetic biology tool.

Cancer is a profoundly devastating disease both globally and within the United States. Current standards of care for treating cancer often includes surgical resection, chemotherapy, and radiation, each of which associates with its own set of adversities. An emerging class of treatment, immunotherapy, aims to utilize a patient’s own functional immune system as the therapeutic agent. Adoptive T cell therapy, but more specifically, chimeric antigen receptor (CAR) expressing T cell transfer, has had notable clinical success particularly against hematological malignancies. Chimeric antigen receptors are synthetic immunoreceptors which can redirect T cells towards varying tumor associated antigens, and, as a living cell, have the aptitude to develop sustainable memory and anti-tumor efficacy. However, conventional CAR T cells lack clinical modularity afforded by other treatments because, once transfused into a patient, the modified immune cell cannot be further altered. This nuance has resulted in several adverse side-effects which can be lethal to a subset of patients. Several resolutions have been posed to solve these reported complications, one of which is genetic encoding CAR specificity towards a secondary, bispecific molecule. This split-CAR approach has the propensity to improve antigen specificity, resolve antigen loss, afford dose-able T cell activation, and more. However, while many bispecific molecules have been developed, many lack both tunability and developability, both of which are important for the complexities and ever-changing nature of cancer. To meet this demand for engineered ligands, several high-throughput ligand selection methods have been developed for discovering ligands with a desired specificity. Furthermore, the associated CAR T cells may have poor aptitude for activation and expansion due to insufficient antigen availability. Conversely, conventional anti-CD19 CAR T cells can harness both healthy or malignant CD19-positive B cells for activation and expansion and thus have an abundance of available antigen. To these points, we utilized yeast-surface display and directed evolution as a pipeline for developing an CD19-based bispecific molecule capable of harnessing the proliferative aptitude of anti-CD19 CAR T cells to target antigens conventionally associated with solid tumors. Human CD19 was evolved for improved structural integrity through conformational selections using anti-CD19 monoclonal antibodies. Improved mutants were sequenced and provided input for designing a stably expressing, generation 2 CD19 library (termed Frame2). The second-generation diversity applied experimentally determined, beneficial mutations in multi-site fashion to drive the enhanced CD19 framework towards a higher stability and/or functionality. The Frame2 CD19 library was constructed as several fusion constructs containing either an anti-EGFR fibronectin domain or an anti-HER2 scFv in both N-terminal and C-terminal orientations and selectively evolved with anti-CD19 antibodies and the ligand-respective antigen. A set of functional bispecific CD19-ligand fusions were successfully developed. In theory, because the anti-CD19 antibodies used for fusion development have an identical binding domain to several anti-CD19 CAR constructs, these fusions should be detectable by CD19-targetted CAR T cells. Moreover, if the ligand domain also retains specificity, the CD19-ligand bispecific molecule should be capable of redirecting anti-CD19 CAR T cells to EGFR or HER2 expressing tumors.