Browsing by Subject "Synthetic biology"

Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is `altruistic': the sacrifice of some cells can benefit the survivors through release of `public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, I determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. I further predicted that altruistic death could generate the `Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, I experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. These findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment.

The control of matter and phenomena at the nanoscale is fast becoming one of the most important challenges of the 21st century with wide-ranging applications from energy and health care to computing and material science. Conventional top-down approaches to nanotechnology, having served us well for long, are reaching their inherent limitations. Meanwhile, bottom-up methods such as self-assembly are emerging as viable alternatives for nanoscale fabrication and manipulation.

A particularly successful bottom up technique is DNA self-assembly where a set of carefully designed DNA strands form a nanoscale object as a consequence of specific, local interactions among the different components, without external direction. The final product of the self-assembly process might be a static nanostructure or a dynamic nanodevice that performs a specific function. Over the past two decades, DNA self-assembly has produced stunning nanoscale objects such as 2D and 3D lattices, polyhedra and addressable arbitrary shaped substrates, and a myriad of nanoscale devices such as molecular tweezers, computational circuits, biosensors and molecular assembly lines. In this dissertation we study multiple problems in the theory, simulations and experiments of DNA self-assembly.

We extend the Turing-universal mathematical framework of self-assembly known as the Tile Assembly Model by incorporating randomization during the assembly process. This allows us to reduce the tile complexity of linear assemblies. We develop multiple techniques to build linear assemblies of expected length N using far fewer tile types than previously possible.

We abstract the fundamental properties of DNA and develop a biochemical system, which we call meta-DNA, based entirely on strands of DNA as the only component molecule. We further develop various enzyme-free protocols to manipulate meta-DNA systems and provide strand level details along with abstract notations for these mechanisms.

We simulate DNA circuits by providing detailed designs for local molecular computations that involve spatially contiguous molecules arranged on addressable substrates via enzyme-free DNA hybridization reaction cascades. We use the Visual DSD simulation software in conjunction with localized reaction rates obtained from biophysical modeling to create chemical reaction networks of localized hybridization circuits that are then model checked using the PRISM model checking software.

We develop a DNA detection system employing the triggered self-assembly of a novel DNA dendritic nanostructure. Detection begins when a specific, single-stranded target DNA strand triggers a hybridization chain reaction between two distinct DNA hairpins. Each hairpin opens and hybridizes up to two copies of the other, and hence each layer of the growing dendritic nanostructure can in principle accommodate an exponentially increasing number of cognate molecules, generating a nanostructure with high molecular weight.

We build linear activatable assemblies employing a novel protection/deprotection strategy to strictly enforce the direction of tiling assembly growth to ensure the robustness of the assembly process. Our system consists of two tiles that can form a linear co-polymer. These tiles, which are initially protected such that they do not react with each other, can be activated to form linear co-polymers via the use of a strand displacing enzyme.

Bistable switches are commonly observed in the regulation of critical processes such as cell cycles and differentiation. The switches possess two fundamental properties: memory and bimodality. Once switched ON, the switches can remember their ON state despite a drastic drop in stimulus levels. Furthermore, at intermediate stimulus levels with cellular noise, the switches can cause a population to exhibit bimodal distribution of cell states. Till date, experimental studies have focused primarily on cellular mechanisms that generate bistable switches and their impact on cellular dynamics.

Here, I study emergent bistability due to bacterial interactions with either synthetic gene circuits or antibiotics. A synthetic gene circuit is often engineered by considering the host cell as an invariable "chassis". Circuit activation, however, may modulate host physiology, which in turn can drastically impact circuit behavior. I illustrate this point by a simple circuit consisting of mutant T7 RNA polymerase (T7 RNAP*) that activates its own expression in bacterium Escherichia coli. Although activation by the T7 RNAP* is noncooperative, the circuit caused bistable gene expression. This counterintuitive observation can be explained by growth retardation caused by circuit activation, which resulted in nonlinear dilution of T7 RNAP* in individual bacteria. Predictions made by models accounting for such effects were verified by further experimental measurements. The results reveal a novel mechanism of generating bistability and underscore the need to account for host physiology modulation when engineering gene circuits.

In the context of antibiotic treatment, I investigate bistability as the underlying mechanism of inoculum effect. The inoculum effect refers to the decreasing efficacy of an antibiotic with increasing bacterial density. Despite its implication for the design of antibiotic treatment strategies, its mechanism remains poorly understood. Here I show that, for antibiotics that target the core replication machinery, the inoculum effect can be explained by bistable bacterial growth. My results suggest that a critical requirement for this bistability is sufficiently fast turnover of the core machinery induced by the antibiotic via the heat shock response. I further show that antibiotics that exhibit the inoculum effect can cause a "band-pass" response of bacterial growth on the frequency of antibiotic treatment, whereby the treatment efficacy drastically diminishes at intermediate frequencies. The results have implications on optimal design of antibiotic treatment.

Interactions between nucleic acid substrates and the proteins and enzymes that bind and catalyze them are ubiquitous and essential for reading, writing, replicating, repairing, and regulating the genomic code by the proteomic machinery. In this dissertation, computational molecular engineering furthered the elucidation of spatial-temporal interactions of natural nucleic acid binding proteins and enzymes and the creation of synthetic counterparts with structure-function interactions at predictive proficiency. We examined spatial-temporal interactions to study how natural proteins can process signals and substrates. The signals, propagated by spatial interactions between genes and proteins, can encode and decode information in the temporal domain. Natural proteins evolved through facilitating signaling, limiting crosstalk, and overcoming noise locally and globally. Findings indicate that fidelity and speed of frequency signal transmission in cellular noise was coordinated by a critical frequency, beyond which interactions may degrade or fail. The substrates, bound to their corresponding proteins, present structural information that is precisely recognized and acted upon in the spatial domain. Natural proteins evolved by coordinating substrate features with their own. Findings highlight the importance of accurate structural modeling. We explored structure-function interactions to study how synthetic proteins can complex with substrates. These complexes, composed of nucleic acid containing substrates and amino acid containing enzymes, can recognize and catalyze information in the spatial and temporal domains. Natural proteins evolved by balancing stability, solubility, substrate affinity, specificity, and catalytic activity. Accurate computational modeling of mutants with desirable properties for nucleic acids while maintaining such balances extended molecular redesign approaches. Findings demonstrate that binding and catalyzing proteins redesigned by single-conformation and multiple-conformation approaches maintained this balance to function, often as well as or better than those found in nature. We enabled access to computational molecular engineering of these interactions through open-source practices. We examined the applications and issues of engineering nucleic acid binding proteins and enzymes for nanotechnology, therapeutics, and in the ethical, legal, and social dimensions. Findings suggest that these access and applications can make engineering biology more widely adopted, easier, more effective, and safer.

This research spans multiple scales—from the entire organism, down to the genes that created it.The first project, “Gene Dynamics in Tissue Development”, explores how stem cell differentiation depends on the dynamics of gene networks. In the Arabidopsis thaliana root, the SCARECROW (SCR) transcription factor is required for an asymmetric cell division of a stem cell, resulting in two daughter cells that acquire different fates and tissue identities. Although much research has developed the network topology for this division, the dynamics of this process remain unknown. A core feature of the GRN controlling this stem cell asymmetric division is the SCR positive feedback loop. This research develops a synthetic biology approach to systematically and precisely tune various dynamics of SCR protein accumulation. Thus, one can explore the role and function of this positive feedback loop in the developmental process of asymmetric division in the Arabidopsis root. The following project, “Organ Form for Function” details how organ function depends on cellular form and hormonal signals. As sessile organisms, plants must establish a firm foundation into the terrain wherever the seed lands. Roots, especially the primary root (a seed’s first root), are the only anchor into the terrain. With a multiscale investigation, we identified a molecular pathway required for circumnutation, the circular growth of the root tip. We found the cellular physiology and key hormonal cell signaling events driving this behavior.

Promising applications in the design of various biological systems hold critical implications as heralded in the rising field of synthetic biology. But, to achieve these goals, the ability to synthesize in situ DNA constructs of any size or sequence rapidly, accurately and economically is crucial. Today, the process of DNA oligonucleotide synthesis has been automated but the overall development of gene and genome synthesis technology has far lagged behind that of gene and genome sequencing. This has meant that numerous ideas go unfulfilled due to scale, cost and impediments in the quality of DNA due to synthesis errors.

This thesis presents the development of a multi-tool ensemble platform targeted at gene synthesis. An inkjet oligonucleotide synthesizer is constructed to synthesize DNA microarrays onto silica functionalized cylic olefin copolymer substrates. The arrays are married to microfluidic wells that provide a chamber to for enzymatic amplification and assembly of the DNA from the microarrays into a larger construct. Harvested product is then amplified off-chip and error corrected using a mismatch endonuclease-based reaction. This platform has the potential to be particularly low-cost since it employs standard phosphoramidite reagents and parts that are cheaper than optical and electrochemical systems. Genes sized 160 bp to 993 bp were successfully harvested and, after error correction, achieved up to 94% of intended functionality.

Voltage-gated sodium channels (VGSCs) enable generation and spread of action potentials in electrically excitable cells and tissues of all metazoans, from jellyfish to humans. The functional, pore-forming α-subunit of eukaryotic VGSCs is formed from a large polypeptide chain of ~2000 amino acids (~260 kDa), comprising four homologous domains. In humans, VGSC loss-of-function mutations are associated with various neuronal, cardiac, and skeletal muscle disorders characterized by a decrease or complete loss of tissue excitability. Similarly, permanent excitability loss due to acute tissue injuries (e.g. stroke, spinal cord injury, heart attack) could lead to long-term disability and death. Whilst an increase in sodium current through stable gene transfer could improve such conditions, eukaryotic VGSC genes are too large (>6 kbp) to be efficiently delivered to cells by existing viral vectors. In contrast, prokaryotic voltage-gated sodium channels (BacNav) consist of four identical subunits, individually transcribed and translated from single genes of only ~800 bp in size. Therefore, it is plausible that small BacNav genes can be efficiently packaged into viral vectors, either alone or with other ion channel genes, and used to stably introduce or modify electrical excitability of primary human cells. The objective of this thesis is thus to develop the methodology to screen, optimize, and assess BacNav channels as potential substitutes for eukaryotic VGSCs. Specifically, we sought to utilize engineered BacNav to create de novo excitable human tissues and to rescue impaired action potential conduction in vitro.

First, by using a monoclonal HEK293 line stably expressing the potassium channel Kir2.1 and gap junction channel Cx43, we were able to select, among various BacNav orthologs and variants, the channel NavRosD G217A that yielded action potential propagation with highest maximum capture rate. Lentiviral transduction of each of the three channels (NavRosD G217A, Kir2.1, and Cx43) into human fibroblasts yielded robust expression and expected electrical properties as confirmed by patch clamp recordings. By co-expressing all three channels, we were able for the first time to stably convert human fibroblasts into electrically excitable and actively conducting cells. However, the conduction velocity of engineered fibroblast tissue was low, largely due to the slow activation kinetics of NavRosD channel.

In order to improve the conduction properties of engineered fibroblasts, we shifted our focus to NavSheP channel, currently the fastest known BacNav ortholog. Due to the overly hyperpolarized voltage dependency of the wild-type NavSheP channel, we generated a library of NavSheP mutants exhibiting a wide range of shifts in voltage-dependent activation and inactivation and, with the guidance from computational modeling, identified three mutants that yielded ~2.5-fold increases in conduction velocity compared to NavRosD G217A. Importantly, we demonstrated that engineered fibroblasts retained stable functional properties despite extensive expansion or differentiation into myofibroblasts and exhibited strong viability while supporting AP propagation in 3D settings. Furthermore, in an in vitro model of interstitial fibrosis, engineered excitable and actively-conducting fibroblasts rescued impaired cardiac conduction to healthy level. These results strongly suggested that engineered fibroblasts could be used as a robust source for potential cell-based therapies for cardiac diseases.

In addition to the generation of excitable fibroblasts, BacNav channels could also serve as potential substitutes for impaired VGSC in various excitable tissue disorders. The channel NavSheP D60A (ShePA) was chosen for direct expression in mammalian excitable tissues as it yielded fastest conduction in previous studies. By performing codon optimization and adding appropriate endoplasmic-reticulum export signal, we were able to significantly improve membrane expression of ShePA channels. Expression of ShePA in excitable HEK293 tissue (Ex293) rescued impaired conduction upon membrane depolarization and decoupling. Furthermore, cultures of neonatal rat ventricular myocytes (NRVMs) transduced with ShePA virus exhibited enhanced conduction properties and increased resistance to conduction failure in an in vitro model of regional ischemia. Lastly, ShePA expression in highly-arrhythmogenic cardiomyocyte-fibroblast co-cultures led to significant reduction in incidence of reentry. Taken together, these results demonstrated the potential applications of engineered BacNav channels for cardiac gene therapies.

In summary, this dissertation presents the first experimental evidences supporting the use of prokaryotic sodium channels for the induction, control, and rescue of mammalian tissue excitability. The encouraging in vitro results shown in these studies will stimulate the development of BacNav-based therapies for the treatment of cardiac diseases. Furthermore, the experimental methodology developed in this work will serve as a useful framework for the screening, optimization, and assessment of engineered BacNav for specific therapeutic applications.

Various types of genetic constructs are widely used as diagnostic, prophylactic, and therapeutic tools for human diseases. They are also the workhorse in biotech and pharmaceutical industry for production of therapeutic antibodies and proteins. Since the majority of the genetic constructs encode protein products, it is therefore of tremendous value to human health and the society that we could find a way to fine-tune and optimize genetic constructs and hence protein expression for achieving maximal potency or long-lasting effects in therapeutics or for obtaining highest yields in pharmaceutical protein production. However, for protein-coding genes to be expressed in a heterologous host, the coding sequences need to be optimized by using synonymous codons to achieve reasonable levels of expression, if at all. Since codon optimization is done in a protein-by-protein basis with respect to specific host organisms, tissue/cell types, even health conditions, and there is no set of standard rules to follow, this process is still very unpredictable and time-consuming.

This thesis presents the development of a feasible platform for solving the problem of optimizing regular and long DNA constructs for academic or industrial purposes through the development of a novel cloning method for complex gene libraries, and based on the library expression system constructed in such manner, a platform for high-throughput screening of codon-optimized and error-corrected proteins, and a novel protocol for screening long gene constructs which could be extremely difficult to achieve by using regular screening methods. This multi-step platform has the potential for studying the natural systems: how codon bias correlates to protein expression efficiency, for generating improved pharmaceutical proteins and enhanced DNA vaccines and for constructing improved genome libraries.

Bacteria are found throughout the environment, from the air to the soil, but more importantly, they reside within the human body. Crucial to their survival in each of these environments is the constant interplay between these organisms and their surroundings. Inadvertently, the ways in which these stimuli are processed can have a profound impact on human health. With potentially negative or positive consequences, it becomes critical to understand how microorganisms encode and decode signals.

Understanding bacterial signal processing is crucial to tackling the treatment of infectious diseases, especially with the rise of antibiotic resistant organisms. Antibiotic resistance has become a global health issue as bacteria have developed or acquired genes that confer resistance to all antibiotics currently in use today. This has serious implications for the future treatment of infectious diseases, potentially limiting options to those from a pre-antibiotic era. However, as with other external factors, antibiotics are just another signal that bacteria need to decode and encode a response to. As such, it is of utmost importance to better understand how bacteria process stimuli.

In my dissertation, I analyzed the ways in which bacteria both encode and decode information. In particular, I focused on how information is processed from signals with a temporal domain. To start, I developed a computational framework to understand how organisms decode signals, specifically oscillatory signals. With this model, I examined the capability of an incoherent feedforward loop motif to exhibit temporal adaptation, in which a network becomes desensitized to sustained stimuli. I discovered that this property is crucial for networks to distinguish signals of varying temporal dynamics.

In terms of information encoding, I utilized the complexity of this process to predict bacterial characteristics of interest. The fundamental premise behind this work is to increase the information content of phenotypes for the prediction of bacterial characteristics. Specifically, I used the temporal domain of growth for the prediction of genetic identity and traits of interest. I demonstrated that temporal growth dynamics under standardized conditions can differentiate among hundreds of strains, even strains of the same species. While growth dynamics could, with high accuracy, differentiate between unique strains, it was insufficient to quantify how genetically different these strains were. This absence highlighted the challenges in using genomics to infer phenotypes and vice versa. Bypassing this complexity, I showed that growth dynamics alone could robustly predict antibiotic responses. Together, my findings demonstrate the ability to develop applications that take advantage of the complexity of bacterial information encoding.

This work highlights the importance of understanding how bacteria decode signals with temporal dynamics. Additionally, I demonstrated one application for utilizing bacterial signal encoding, the prediction of bacterial characteristics.

Bacteria are found throughout the environment, from the air to the soil, but more importantly, they reside within the human body. Crucial to their survival in each of these environments is the constant interplay between these organisms and their surroundings. Inadvertently, the ways in which these stimuli are processed can have a profound impact on human health. With potentially negative or positive consequences, it becomes critical to understand how microorganisms encode and decode signals.

Understanding bacterial signal processing is crucial to tackling the treatment of infectious diseases, especially with the rise of antibiotic resistant organisms. Antibiotic resistance has become a global health issue as bacteria have developed or acquired genes that confer resistance to all antibiotics currently in use today. This has serious implications for the future treatment of infectious diseases, potentially limiting options to those from a pre-antibiotic era. However, as with other external factors, antibiotics are just another signal that bacteria need to decode and encode a response to. As such, it is of utmost importance to better understand how bacteria process stimuli.

In my dissertation, I analyzed the ways in which bacteria both encode and decode information. In particular, I focused on how information is processed from signals with a temporal domain. To start, I developed a computational framework to understand how organisms decode signals, specifically oscillatory signals. With this model, I examined the capability of an incoherent feedforward loop motif to exhibit temporal adaptation, in which a network becomes desensitized to sustained stimuli. I discovered that this property is crucial for networks to distinguish signals of varying temporal dynamics.

In terms of information encoding, I utilized the complexity of this process to predict bacterial characteristics of interest. The fundamental premise behind this work is to increase the information content of phenotypes for the prediction of bacterial characteristics. Specifically, I used the temporal domain of growth for the prediction of genetic identity and traits of interest. I demonstrated that temporal growth dynamics under standardized conditions can differentiate among hundreds of strains, even strains of the same species. While growth dynamics could, with high accuracy, differentiate between unique strains, it was insufficient to quantify how genetically different these strains were. This absence highlighted the challenges in using genomics to infer phenotypes and vice versa. Bypassing this complexity, I showed that growth dynamics alone could robustly predict antibiotic responses. Together, my findings demonstrate the ability to develop applications that take advantage of the complexity of bacterial information encoding.

This work highlights the importance of understanding how bacteria decode signals with temporal dynamics. Additionally, I demonstrated one application for utilizing bacterial signal encoding, the prediction of bacterial characteristics.

Intrinsically disordered protein polymers (IDPPs) are repetitive biopolymers that, when enriched with prolines, glycines, and aliphatic amino acids, have observable lower critical solution temperature (LCST) phase transition behavior at physiologically relevant temperature and concentration ranges. This behavior is a striking feature of disordered proteins in nature, where chemical or physical stimuli lead to sharp conformational or phase transitions. Accordingly, protein-based polymers have been designed to mimic these behaviors, leading to a broad range of biotechnological applications. This work is driven by two approaches. In our science focused approach, we developed a polymer-physics based framework for understanding IDPP hydrophobicity using the relationship between phase transition temperature and globule surface tension. This physics-based framework has allowed us to better understand the unified contributions of chain length, concentration, temperature, and individual amino acid side chains to IDPP hydrophobicity by studying phase transition data. In our engineering focused approach, we developed novel tools that enable the high throughput discovery of new proteins that exhibit phase transitions, in order to increase the number of known stimuli responsive peptide sequence motifs beyond the limits of bioinspired design. The exhaustive discovery of new proteins that exhibit phase transitions consists of gene synthesis and protein screening. We developed two key technologies that has enabled (1) the scalable synthesis of repetitive gene libraries using a novel graph theoretic gene optimization approach (Codon Scrambling) and (2) the pooled synthesis of large complex gene libraries from libraries of oligonucleotides. Combined with pipelines for the screening of phase transition behavior, these technologies have enabled us to generate a diverse library of protein sequences necessary to validate our theoretical models. Finally, we developed an algorithm for the de novo design of nonrepetitive protein sequences that exhibit phase transition behavior, further broadening the sequence space of stimuli responsive synthetic IDPPs.

Driven by the development of new technologies and an ever expanding knowledge base of molecular and cellular function, Biology is rapidly gaining the potential to develop into a veritable engineering discipline - the so-called `era of synthetic biology' is upon us. Designing biological systems is advantageous because the engineer can leverage existing capacity for self-replication, elaborate chemistry, and dynamic information processing. On the other hand these functions are complex, highly intertwined, and in most cases, remain incompletely understood. Brazenly designing within these systems, despite large gaps in understanding, engenders understanding because the design process itself highlights gaps and discredits false assumptions.

Here we cover results from design projects that span several scales of complexity. First we describe the adaptation and experimental validation of protein functional assays on minute amounts of material. This work enables the application of cell-free protein expression tools in a high-throughput protein engineering pipeline, dramatically increasing turnaround time and reducing costs. The parts production pipeline can provide new building blocks for synthetic biology efforts with unprecedented speed. Tools to streamline the transition from the in vitro pipeline to conventional cloning were also developed. Next we detail an effort to expand the scope of a cysteine reactivity assay for generating information-rich datasets on protein stability and unfolding kinetics. We go on to demonstrate how the degree of site-specific local unfolding can also be determined by this method. This knowledge will be critical to understanding how proteins behave in the cellular context, particularly with regards to covalent modification reactions. Finally, we present results from an effort to engineer bacterial cell suicide in a population-dependent manner, and show how an underappreciated facet of plasmid physiology can produce complex oscillatory dynamics. This work is a prime example of engineering towards understanding.

This dissertation presents several novel architectures for DNA computing from different perspectives including analog DNA circuits, polymerase-based DNA logic circuits, and localized DNA-based biomolecular reaction networks on cancer cell membranes.

Chapter 2 presents an architecture for the systematic construction of DNA circuits for analog arithmetic computation based on DNA strand displacement. The elementary gates in our architecture include addition, subtraction, and multiplication gates. The input and output of these gates are analog, which means that they are directly represented by the concentrations of the input and output DNA strands respectively, without requiring a threshold for converting to Boolean signals. We provide detailed domain designs and kinetic simulations of the gates to demonstrate their expected performance. Based on these gates, we describe how DNA circuits to compute polynomial functions of inputs can be built. Using Taylor Series and Newton Iteration methods, functions beyond the scope of polynomials can also be computed by DNA circuits built upon our architecture.

Chapter 3 focuses on an architecture to build compact DNA strand displacement circuits to compute a broad scope of functions in an analog fashion. A circuit by this architecture is composed of three autocatalytic amplifiers, and the amplifiers interact to perform computation. We show DNA circuits to compute functions sqrt(x), ln(x) and exp(x) for x in tunable ranges with simulation results. A key innovation in our architecture, inspired by Napier’s use of logarithm transforms to compute square roots on a slide rule, is to make use of autocatalytic amplifiers to do logarithmic and exponential transforms in concentration and time. In particular, we convert from the input that is encoded by the initial concentration of the input DNA strand, to time, and then back again to the output encoded by the concentration of the output DNA strand at equilibrium. This combined use of strand-concentration and time encoding of computational values may have impact on other forms of molecular computation.

Chapter 4 introduces an architecture for fast diffusion-based DNA logic circuits based on Bst 2.0 DNA polymerase and single-stranded logic gates. Each gate consists of only single-stranded DNAs that are easy to design and robust to environmental changes. Large-scale logic circuits can be constructed from the gates by simple cascading strategies. The logic gates and circuits respond in minutes (in terms of half-completion time) compared to hours in prior architectures, providing a very substantial speed-up over prior DNA computing architectures. In particular, we have demonstrated a large-scale DNA logic circuit that computes (the floor of) the square root of 4-bit input numbers. The scale of this circuit is comparable to the largest DNA logic circuit to date (that circuit computes the same function as ours) in terms of the number of gates, and the half-completion time of computing by our circuit is only several minutes, compared to hours by the prior circuit.

Chapter 5 proposes an architecture to program localized DNA-based biomolecular reaction networks on cancer cell membranes. Each node in a network targets a designated cancer cell membrane receptor via aptamer-receptor binding. If all nodes find their corresponding receptors on a cancer cell, the network can start to function by adding initiator DNA strands. Various types of circuits have been experimentally demonstrated from simple linear cascades to reaction networks of more complex structures. These localized reaction networks can be used for medical applications such as cancer detection and therapies.

Patterns are ubiquitous in living organisms. However, the mechanisms driving self-organized pattern formations are not well understood. Due to the complexity of natural systems, many confounding factors complicate quantitative experiments and data interpretation, often making it difficult to draw definitive conclusions. Therefore, a limited number of experimental systems could enable precise perturbation and quantification of pattern formation. In comparison, the synthetic system serves as well-defined model systems to elucidate ‘‘design principles’’ of biological networks. In the past sixteen years, engineering pattern formation is a major endeavor in synthetic biology. However, there are only two studies about the generation of programmed self-organized pattern formation in growing cells based on coordinated dynamics in a population.

Intrigued by the challenge, my colleagues and I programmed E. coli with a synthetic gene circuit to generate self-organized pattern formation. Two implications of this engineered pattern-forming system were illustrated in my Ph.D. thesis.

First, the synthetic system provides a well-defined context to probe principles underlying the scaling property of self-organized pattern formation. Our mechanism underscores the importance of temporal control in generating scale-invariant patterns. The fundamental premise of this approach is that the principles defined in such engineered systems can be generally applicable to natural examples.

Second, the synthetic system serves as a foundation to generate structured materials with well-defined physical properties. Diverse natural biological systems can form structured materials with well-defined physical and chemical properties spontaneously. However, these natural processes are not readily programmable. By taking the synthetic biology approach, we demonstrate here the programmable, three-dimensional (3D) material fabrication using pattern-forming bacteria growing on top of permeable membranes as the structural scaffold. We equip the bacteria with an engineered protein that enables the assembly of gold nanoparticles into a hybrid organic-inorganic dome structure. The resulting hybrid structure functions as a pressure sensor that responds to touch. We show that the response dynamics are determined by the geometry of the structure, which is programmable by the membrane properties and the extent of circuit activation. Taking advantage of this property, we demonstrate signal sensing and processing using one or multiple bacterially assembled structures.

Cells can sense and respond to various environmental cues. In the past 25 years, this ability has been exploited in engineering many innovative applications, ranging from bioproduction and metabolic engineering, to living therapeutics and biosensing. Despite tremendous advancements in complex genetic circuit development, the field still suffers from several limitations. For instance, evolutionary pressure can hamper the long-term genetic stability and functionality of circuits. The long incubation times required for cell growth serve as a fundamental rate limiting step for routine microbiology experiments and circuit engineering. Available biological parts, such as promoters and ribosome binding sites, often confer a limited dynamic range of gene expression levels and are incompatible, exacerbating the construction of higher order circuits.

Plasmids are extrachromosomal DNAs, usually circular, that replicate independently of the host genome. Because they are easy to manipulate and engineer, plasmids have served as a popular workhorse for programming desired functions in microbial populations. Plasmids can maintain steady average copy numbers in hosts, and a specific plasmid type can be chosen to express genes at a desired level. However, recent studies have focused on the dynamic modulation of plasmid copy number as a new engineering strategy, which is still underexplored. Understanding plasmid dynamics can provide insights to harness powerful tools for engineering microbial communities and offer a new avenue to overcome the current challenges in synthetic biology.

In this dissertation, I used mathematical modeling and synthetic biology approaches to develop methods for engineering microbial communities by exploiting and manipulating plasmid dynamics. First, I examined the sources of circuit failure and studied design strategies for enhancing synthetic gene circuits’ stability in microbial hosts for robust long-term performance. I summarized the engineering strategies into two categories: (1) to suppress the chance of mutant emergence by reducing the evolutionary pressure; and (2) to suppress the relative fitness of mutants by selecting against genetic variants.

Applying some of the identified engineering strategies, I developed synthetic gene circuits, named Red Queen circuits, that can modulate the host cell viability according to its growth rate. Using the circuit, I achieved a 250% increase in host cell growth rates at the end of a 100-day long-term adversarial laboratory evolutionary experiment, during which the circuit continuously suppressed slow-growing cells. The results suggest that the circuit can serve as an effective strain engineering strategy to accelerate biotechnology and molecular biology research.

Next, I constructed another gene circuits, named ADEPT system, to regulate the collective gene expression of an engineered microbial community by modulating plasmid dynamics. By dynamically tuning the plasmid loss rate, horizontal gene transfer rate, and plasmid-mediated fitness effects, I demonstrated that the ADEPT system can tune the total gene expression with a significantly amplified dynamic range.

Finally, in the Appendix, I engineered gene circuits for targeted conjugative plasmid elimination from microbial communities. The results illustrate the potential of plasmid dynamics modulations in engineering complex microbial communities.

Antibiotic discovery and widespread usage has revolutionized the treatment of infectious diseases. However, this golden age of modern-day medicine is threatened by the increasing prevalence of antibiotic-resistant pathogens. As the antibiotic development pipeline increasingly slows, we find ourselves falling behind in the race between innovation and evolution.

Among the various means of bacterial evolution, horizontal gene transfer (HGT) – or the non-genealogical transmission of DNA between organisms – is the dominant mode responsible for the acquisition of antibiotic resistance genes. Combined with antibiotic overuse and misuse, HGT primarily via conjugation, has compromised the efficacy of nearly every single antimicrobial available. Tight coupling between HGT and antibiotic-mediated selection, along with a lack of quantitative experiments, has led to the general belief that antibiotics themselves promote gene transfer; however, antibiotic action could modulate the rate of gene transfer (as is assumed), the resulting population dynamics, or both. Therefore, it is critical to decouple these two processes to definitively determine the influence of antibiotics on conjugation.

In my dissertation, I quantified the extent to which antibiotics influence conjugation in the presence and absence of antibiotic-mediated selection dynamics. To do so, I implemented a synthetically engineered conjugation system, which facilitates precise quantification of conjugation dynamics. Using this platform, I quantified the rate of gene transfer, or the conjugation efficiency, in the absence of confounding selection dynamics. I discovered that, in contrast to conventional wisdom, antibiotics did not significantly increase the conjugation efficiency. This finding was general to 10 antibiotics, as well as nine native and clinically relevant plasmids. Instead, antibiotic selection dynamics alone could account for conjugation dynamics.

I next investigated the potential strategies to minimize, or ideally reverse, plasmid-mediated resistance. Traditionally, reducing overall antibiotic use has been the primary approach to reversing resistance; minimizing selection takes advantage of costly resistance genes to competitively displace resistant bacteria with their sensitive counterparts. However, despite widespread antibiotic stewardship initiatives, even costly resistance persists for long periods of time. One potential explanation is that sufficiently fast conjugation enables plasmid persistence in the absence of selection. Similar challenges in quantifying conjugation have prevented general conclusions, and overall, the extent to which conjugation enables persistence is unknown.

Using the same platform, I showed that conjugation enables the persistence of costly plasmids, even in the absence of selection. Conjugation-assisted persistence was true for nine common conjugal plasmids, and in microbial populations consisting of varying degrees of complexity. Finally, I showed that by reducing the conjugation efficiency and promoting plasmid loss, it is possible to reverse resistance. Together, these findings contribute to basic understanding of the propagation and persistence plasmids, and elucidate a novel therapeutic strategy by taking advantage of ecological/evolutionary dynamics to to reduce or even reverse the spread of resistance.

Diverse mechanisms have been proposed to explain natural pattern formation processes, such as slime mold aggregation, feather branching, and tissue stratification. Regardless of the specific molecular interactions, the vast majority of these mechanisms invoke morphogen gradients, which are either predefined or generated as part of the patterning processes. However, using E. coli programmed by a simple synthetic gene circuit, I demonstrate here the generation of robust, self-organized ring patterns of gene expression in the absence of an apparent morphogen gradient. Interestingly, modeling and experimental tests show that the temporal dynamics of the global morphogen concentration serve as a timing mechanism to trigger formation and maintenance of these ring patterns, which are readily tunable by experimentally controllable environmental factors. This mechanism represents a novel mode of pattern formation that has implications for understanding natural developmental processes. In addition, the system can be coupled with inkjet printing technology and metabolic engineering approaches to develop future complex patterned biomaterials.

Synthetic biology facilitates both the design and fabrication of biological components and systems that do not already exist in the natural world. From an engineering point of view, synthetic biology is akin to building a complex machine by assembling simpler parts. Complex genetic machines can also be built by a modular and rational assembly of simpler biological parts. These biological machines can profoundly affect various cellular processes including the transcriptional machinery. In this thesis I demonstrate the utilization of biological parts according to synthetic biology principles to solve three distinct transcription-level problems: 1) How to efficiently select for transgene excision in induced pluripotent stem cells (iPSCs)? 2) How to eliminate transposase expression following piggyBac-mediated transgenesis? 3) How to reprogram cell lineage specification by the dCas9/gRNA transactivator-induced expression of endogenous transcription factors?

Viral vectors remain the most efficient and popular in deriving induced pluripotent stem cells (iPSCs). For translation, it is important to silence or remove the reprogramming factors after induction of pluripotency. In the first study, we design an excisable loxP-flanked lentiviral construct that a) includes all the reprogramming elements in a single lentiviral vector expressed by a strong EF-1α promoter; b) enables easy determination of lentiviral titer; c) enables transgene removal and cell enrichment using LoxP-site-specific Cre-recombinase excision and Herpes Simplex Virus-thymidine kinase/ganciclovir (HSV-tk/gan) negative selection; and d) allows for transgene excision in a colony format. With our design, a reprogramming efficiency comparable to that reported in the literature without boosting molecules can be consistently obtained. To further demonstrate the utility of this Cre-loxP/HSV-tk/gan strategy, we incorporate a non-viral therapeutic transgene (human blood coagulation Factor IX) in the iPSCs, whose expression can be controlled by a temporal pulse of Cre recombinase. The robustness of this platform enables the implementation of an efficacious and cost-effective protocol for iPSC generation and their subsequent transgenesis for downstream studies.

Transgene insertion plays an important role in gene therapy and in biological studies. Transposon-based systems that integrate transgenes by transposase-catalyzed "cut-and-paste" mechanism have emerged as an attractive system for transgenesis. Hyperactive piggyBac transposon is particularly promising due to its ability to integrate large transgenes with high efficiency. However, prolonged expression of transposase can become a potential source of genotoxic effects due to uncontrolled transposition of the integrated transgene from one chromosomal locus to another. In the second study we propose a vector design to decrease post-transposition expression of transposase and to eliminate the cells that have residual transposase expression. We design a single plasmid construct that combines the transposase and the transpositioning transgene element to share a single polyA sequence for termination. Consequently, the transposase element is deactivated after transposition. We also co-express Herpes Simplex Virus thymidine kinase (HSV-tk) with the transposase. Therefore, cells having residual transposase expression can be eliminated by the administration of ganciclovir. We demonstrate the utility of this combination transposon system by integrating and expressing a model therapeutic gene, human coagulation Factor IX, in HEK293T cells.

Genome editing by the efficient CRISPR/Cas9 system shows tremendous promise with ease of customization and the capability to multiplex distinguishing it from other such technologies. Endogenous gene activation is another aspect of CRISPR/Cas9 technology particularly attractive for biotechnology and medicine. However, the CRISPR/Cas9 technology for gene activation leaves much room for improvement. In the final study of this thesis we show that the fusion of two transactivation (VP64) domains to Cas9 dramatically enhances gene activation to a level that is sufficient to achieve direct cell reprogramming. Targeted activation of the endogenous Myod1 gene locus with this system leads to stable and sustained reprogramming of mouse embryonic fibroblasts into skeletal myocytes.

In conclusion, this dissertation demonstrates the power of utilizing biological parts in a rational and systematic way to rectify problems associated with cell fate reprogramming and transposon-based gene delivery. Through design of genetic constructs aided by synthetic biology principles, I aspire to make contributions to the related fields of cellular reprogramming, stem cell differentiation, genomics, epigenetics, cell-based disease models, gene therapy, and regenerative medicine.