There is increasing concern regarding alternative, sustainable energy sources, such as biofuels, to replace declining oil reserves. The abundance of lignocellulosic biomass makes it the only imaginable resource that can potentially substitute a substantial portion of the fossil fuels we use today, but current methods for producing biofuels from non-food crops are cost intensive and not economically viable. Synthetic biology provides several potential approaches for developing biologically mediated processes for the conversion of lignocellulosic biomass into biofuels. Such systems are based on engineered microbes that produce enzymes for catalysing the conversion of cellulose into fermentable sugars and subsequently into high value products. Effective degradation of cellulose requires multiple classes of enzyme working together. In naturally occurring cellulose degrading microbes, bioconversion is catalysed by a battery of enzymes with different catalytic properties. However, naturally occurring cellulases with multiple catalytic domains seem to be rather rare in known cellulose-degrading organisms. Using synthetic biology approaches, seven cellulases with multiple catalytic domains were engineered and tested to determine the usefulness of such chimeric enzymes to replace cloning of multiple enzymes for biomass conversion. Catalytic domains were taken from Cellulomonas fimi endoglucanases CenA, CenB and CenD, exoglucanase Cex, and β-glucosidase, Cfbglu as well as Cytophaga hutchinsonii cellodextrinase CHU2268. All fusions retained both catalytic activities of the parental enzymes. To investigate the benefits of fusion, Citrobacter freundii NCIMB11490 was transformed with either fused or non-fused enzymes and cultured with cellulose blotting papers as main carbon source. Cells expressing fusions of Cex with CenA or CenD reproducibly showed higher growth than cells expressing non-fused versions, as well as more rapid physical destruction of paper. The opposite was observed for the other combinations. Comparing two different Cex and CenA fusions, CxnA2, which contains two carbohydrate binding modules (CBMs), degraded filter paper faster and led to better growth than CxnA1, which contains only one CBM. It was observed that CxnA1 was exported to the supernatant of E. coli and C. freundii cultures, as also seen for Cex and CenA, although there is no clear biological mechanism for this. Monitoring of growth using colony counts is laborious, but the use of optical density is not possible for cellulose-based cultures as it is affected by the insoluble cellulose particles. The SYBR Green I/propidium iodide live/dead staining protocol was therefore evaluated for growth measurements and was found to allow rapid measurements of large numbers of samples. In conclusion, these studies have demonstrated a simple and useful method for making chimeric proteins from libraries of multiple parts. The results demonstrate that use of fusion proteins can improve biomass conversion in vivo, and could potentially reduce the necessity for cloning of multiple enzymes and improve product yields. A simple and effective method for monitoring growth of bacteria in turbid cultures using a fluorimeter has also been developed.

A primary aim of Synthetic Biology is the design and implementation of biological systems that perform engineered functions. However, the assembly of double-stranded DNA molecules is a major barrier to this progress, as it remains time consuming and laborious. Here I present three improved methods for DNA assembly. The first is based on, and makes use of, BioBricks. The second method relies on overlap-extension PCR to assemble non-standard parts. The third method improves upon overlap extension PCR by reducing the number of steps and the time it takes to assemble DNA. Finally, I show how the PCR-based assembly methods presented here can be used, in concert, with in vivo homologous recombination in yeast to assemble as many as 19 individual DNA parts in one step. These methods will also be used to assemble an incoherent feedforward loop, gene regulatory network.

Individual cells in a genetically identical population, exposed to the same environment, can show great variation in their protein expression levels. This is due to noise, which is inherent in many biological processes, due in part to the low molecule numbers and probabilistic interactions which lead to stochasticity. Much of the work in the field of noise and its propagation in gene expression networks, whether it is experimental, modelling or theoretical, has been conducted on networks/systems that occur within a single cell. However, cells do not exist solely in isolation and understanding how cells are able to coordinate their behaviour despite this noise is an interesting area of expansion for the field. In this study, a synthetic intercellular communication system was designed that allows the investigation of how noise is propagated in intercellular communication. The communication system consists of separate sender and receiver cells incorporating components of the Lux quorum sensing system of Vibrio fischeri. The sender cell was designed so that the production of the signalling molecule, 3-oxohexanoyl homoserine lactone, is able to be controlled by addition of isopropyl-β-D-thio-galactoside (IPTG) and monitored via a reporter gene. The receiver cell was designed with a dual reporter system to enable the response of the cell to the signalling molecule to be monitored and the intrinsic and extrinsic noise contributions to the total noise to be calculated. Sender and the receiver cells were engineered in Escherichia coli. The functionality of the receiver cells was tested in the presence of known concentrations of the signalling molecule. The population response and the noise characteristics of the receiver cells in the homogeneous environment were determined from single cell measurements. The functionality of the sender cells was tested in the presence of a range of IPTG concentrations and the induction of expression from the LacI-repressible promoter was monitored. Mathematical models of the system were developed. Stochastic simulations of the models were used to investigate any unexplained behaviour seen in the characterisation of the cells. The full functionality of the intercellular communication system was then tested by growing the receiver in the collected media of the induced sender cells. The response of the receiver cells to the signalling molecule in the media was again characterised using single cell measurements of the reporter expression levels. The analysis of mixed populations of the sender and receiver cells was hampered by the technical limitations of the instruments used for the single cell measurements. Difficulties were encountered in simultaneous and specific measurement of the three reporter genes. Two methods for overcoming this issue were proposed using microscopy, and one of these methods was shown to have potential in overcoming the issue.

abstract: Gene circuit engineering facilitates the discovery and understanding of fundamental biology and has been widely used in various biological applications. In synthetic biology, gene circuits are often constructed by two main strategies: either monocistronic or polycistronic constructions. The Latter architecture can be commonly found in prokaryotes, eukaryotes, and viruses and has been largely applied in gene circuit engineering. In this work, the effect of adjacent genes and noncoding regions are systematically investigated through the construction of batteries of gene circuits in diverse scenarios. Data-driven analysis yields a protein expression metric that strongly correlates with the features of adjacent transcriptional regions (ATRs). This novel mathematical tool helps the guide for circuit construction and has the implication for the design of synthetic ATRs to tune gene expression, illustrating its potential to facilitate engineering complex gene networks. The ability to tune RNA dynamics is greatly needed for biotech applications, including therapeutics and diagnostics. Diverse methods have been developed to tune gene expression through transcriptional or translational manipulation. Control of RNA stability/degradation is often overlooked and can be the lightweight alternative to regulate protein yields. To further extend the utility of engineered ATRs to regulate gene expression, a library of RNA modules named degradation-tuning RNAs (dtRNAs) are designed with the ability to form specific 5’ secondary structures prior to RBS. These modules can modulate transcript stability while having a minimal interference on translation initiation. Optimization of their functional structural features enables gene expression level to be tuned over a wide dynamic range. These engineered dtRNAs are capable of regulating gene circuit dynamics as well as noncoding RNA levels and can be further expanded into cell-free system for gene expression control in vitro. Finally, integrating dtRNA with synthetic toehold sensor enables improved paper-based viral diagnostics, illustrating the potential of using synthetic dtRNAs for biomedical applications. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2020

Cell-free transcription and translation systems promise to accelerate and simplify the engineering of synthetic proteins, biological circuits or metabolic pathways. Microfluidic droplet platforms can generate millions of reactions in parallel. This allows cell-free reactions to be miniaturized down to picoliter volumes. Nevertheless, the true potential of microfluidics have not been reached for cell-free bioengineering. Better approaches are needed for reaching sufficient in-drop expression levels while efficiently creating DNA diversity among droplets. This work develops a droplet microfluidic platform for single or multiple protein expression from a single DNA coated bead per droplet. This opens up the possibility to diversify a million droplets for synthetic biology applications.

Strigolactones (SLs) are a class of plant hormones regulating several aspects of plant growth and development according to nutrient availability, particularly the modulation of root and shoot architectures. Under nutrient deficiency, SLs are abundantly released into the soil to recruit a plant-beneficial partner, arbuscular mycorrhizal fungi (AMF), and establish plant-AMF symbiosis that provides the plant with minerals and water. However, released SLs are also seed germination signals for the root parasitic plants Orobanchacea family and pave their way to the host plants’ roots. “New comers” in the field of plant hormones, their large structural variety intrigues and led to ask why plants produce many different types of SLs. In this work, we generated tools that can help to link the SL structural diversity with their biological function(s). The most common way to evaluate SL activity is based on their ability to be parasitic seeds’ germination stimulants. Despite being the most sensitive assay for SL quantification, parasitic seed-based bioassays are laborious and time-consuming as performed usually manually. Therefore, we developed a detection algorithm, SeedQuant, which identifies and counts germinated and non-germinated seeds 600 times faster than a trained human; thus, reducing the data processing from days down to minutes. To gain quantitative insights in SL perception, depending on the structural diversity, we developed a precise and detailed protocol for the use of a genetically encoded biosensor in Arabidopsis protoplast, StrigoQuant. StrigoQuant takes advantage of the SL-dependent degradation of the repressor protein AtSMXL6 coupled with luciferase reporter proteins. We also tried to adapt this molecular sensor to the rice repressor protein D53, but the use of rice protoplasts made it very challenging. Moreover, to better understand the later steps in SL biosynthesis in vivo, we generated CRISPR/Cas9-based rice mutants and shed light on the biological function of different SLs at the organismal level. MAX1-900 mutants defined the minor role of the canonical SL 4-deoxyorobanchol (4DO) - a major plant SL - in determining rice architecture, while being a crucial contributor to rhizospheric interactions. Finally, we reviewed other strategies to decipher plant signaling pathways in general.

Much of Nature is composed of highly modular and composable nested multicomponent living systems. Synthetic biology and bioengineering exploit this modularity to understand and engineer living things. This thesis explores two projects coupled by these principles, the first utilizing a synthetic genomics approach to probe the evolutionary history, flexibility, and modularity of core metabolism, and the second adapting and engineering components of a living material to generate living architecture and embed add program new behaviors into the living biocomposite. Chapter 1 details the synthetic resurrection of a core metabolic pathway lost from the metazoan lineage millions of years ago. All metazoans are auxotrophic for 9 of the 20 amino acids, the so-called “essential” amino acids. The pressures behind the loss of the 9 are a deep evolutionary puzzle. To investigate this event and probe the limits of core metabolic flexibility, we generated a synthetic valine prototrophic mammalian cell line, restoring valine self-sufficiency to the metazoan lineage. The restoration of this pathway implies the modern mammalian metabolism is still compatible with autogenous valine production, suggests profound modularity in core metabolism, and underscores the potential usefulness of large-scale synthetic genomics approaches in a answering deep evolutionary questions. Chapter 2 describes the engineering of a hybrid fungal-bacterial biocomposite by adapting and leveraging existing behaviors and microbial constituents of a living material. Fungal biocomposites are composed of a particulate lignocellulosic feedstock bound together into a bulk biocomposite by a network of dense fungal mycelium. Using a bioprospecting approach, we designed architectural and design strategies that relied on the natural substrate flexibility and growth patterns of the fungal component of the biocomposite to form origami-inspired human scale folding structures. Similarly, we isolated, characterized, and engineered a natural microbial component of the biocomposite’s own microbiome and used its pre-adapted ability to engraft in the growing biomaterial to embed new genetic functionalities in biocomposite objects. We believe that the strategy of bioprospecting useful components and behaviors holds promise for the development of future biomaterials adapted from living systems.

Synthetic biology endeavors to forward engineer genetic circuits with novel function. A major inspiration for the field has been the enormous success in the engineering of digital electronic circuits over the past half century. This dissertation approaches synthetic biology from the perspective of the engineering design cycle, a concept ubiquitous across many engineering disciplines. First, an analysis of the state of the engineering design cycle in synthetic biology is presented, pointing out the most limiting challenges currently facing the field. Second, a principle commonly used in electronics to weigh the tradeoffs between hardware and software implementations of a function, called co-design, is applied to synthetic biology. Designs to implement a specific logical function in three distinct domains are proposed and their pros and cons weighed. Third, automatic transitioning between an abstract design, its physical implementation, and accurate models of the corresponding system are critical for success in synthetic biology. We present a framework for accomplishing this task and demonstrate how it can be used to explore a design space. A major limitation of the aforementioned approach is that adequate parameter values for the performance of genetic components do not yet exist. Thus far, it has not been possible to uniquely attribute the function of a device to the function of the individual components in a way that enables accurate prediction of the function of new devices assembled from the same components. This lack presents a major challenge to rapid progression through the design cycle. We address this challenge by first collecting high time-resolution fluorescence trajectories of individual cells expressing a fluorescent protein, as well as snapshots of the number of corresponding mRNA molecules per cell. We then leverage the information embedded in the cell-cell variability of the population to extract parameter values for a stochastic model of gene expression more complex than typically used. Such analysis opens the door for models of genetic components that can more reliably predict the function of new combinations of these basic components. / Ph. D.

<p><a></a><a>Microbial chemical factories (MCFs) have become an attractive platform for producing valuable drugs, chemicals, and biofuels due to increasing environmental concerns, energy demands, and the difficulties associated with chemically synthesizing complex molecules. However, the potential of using microbes to produce many valuable products has not been fully realized due to low productivity and yields. Production may be enhanced through the dynamic control of metabolic pathways to alleviate metabolic imbalances and toxic product build-up, but very few tools are available to broadly implement this paradigm. I aim to expand this toolbox by developing tunable sensor-regulator devices that act as feedback controllers for toxic intermediate formation by responding to cues of cell health for improved production. Elastin-like polypeptide (ELP) will act as the sensing domain of the controller to indirectly sense toxic metabolite accumulation through changes in intracellular pH. ELPs make ideal sensors since they exhibit a sharp, inverse phase transition to indicators of cellular health such as pH and ionic strength, and external stimuli such as temperature. In this research, a library of ELPs that exhibit pH sensitivity and transition under various conditions was made and purified using a new organic solvent extraction method. It is hypothesized that fusion of ELP to orthogonal transcription factor will allow for the controlled expression of target genes in response to stimuli without disrupting native processes. As proof of concept, an ELP fusion to orthogonal sigma factor was designed to drive the expression of a fluorescent reporter protein. Initial designs successfully alter gene expression by 21% in response to temperature. To improve this response, an alternative feed-forward loop architecture was modelled, which predicted an improved response of 35% and increased ultrasensitivity. Refinement of this design </a>and combinatorial construct libraries will generate various regulators with diverse outputs that may be integrated in bioproduction pathways for improved performance.</p>

This thesis describes my work to develop methods and assays to study transgenerational inheritance in the widely used genetic model organism Caenorhabditis elegans (C. elegans). In the first chapter, I describe a novel method that uses an exogenous histamine-selective chloride channel (HisCl1) for negative selection in transgenesis. C. elegans transgenesis is a core technique used by most laboratories and often requires distinguishing between rare animals with a single-copy transgene inserted into the genome from more frequent animals that carry multiple copies of the transgene in extra-chromosomal arrays. I demonstrate that histamine-selection induces rapid and irreversible paralysis in only array animals thus allowing quick identification of the desired transgenic animals. In the second chapter, I develop a high-throughput assay for quantifying transgenerational epigenetic inheritance of endogenous gene silencing. Small RNA -mediated gene silencing leads to an increased incidence of males in the population which can be inherited for four to six generations. I identify a fluorescent marker that specifically fluoresces in males and show that I can use a large-particle particle sorter to quantify the frequency of males in a population. This automated system will allow me to follow inheritance patterns over at least ten generations in various mutant backgrounds in parallel to determine the genetic basis and the rules of epigenetic inheritance.