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Synthetic Biology Tools for Targeted Incorporation of Non-Canonical Amino Acids into Cellular Proteins
Get PDFProteins mediate many essential functions in cells, and methods to profile cellular proteins are of great interest for biological discovery. Whereas all of the cells in an organism share the same genome, the landscape of proteins (the proteome) varies between different cell types and over the lifetime of the organism. Rapid progress in mass spectrometers is enabling the detailed analysis of cellular proteomes. Whereas better instruments increase coverage, throughput, and measurement precision, new chemical reporters, metabolic tags, and synthetic biology techniques are required to enhance the specificity and spatiotemporal resolution of protein labeling and detection. This work introduces methods for cell-selective proteome analysis through the incorporation of non-canonical amino acids into newly synthesized proteins.
Chapter I provides an overview of current technologies for translational profiling and proteomic analysis in cells. Strategies for the residue-specific incorporation of non-canonical amino acids and bioorthogonal non-canonical amino acid tagging are discussed. Chapter II introduces a new approach for the identification of secreted bacterial proteins from infected host cells using non-canonical amino acid labeling. This work demonstrates an application of cell-selective proteome labeling. Selectivity is achieved through controlled expression of a mutant aminoacyl tRNA synthetase (aaRS) enzyme that enables the metabolic incorporation of a non-canonical amino acid.
Ideally, the activity of multiple genes should be used to genetically control the extent of proteome labeling in cells. This is useful because many cell states are characterized by the activity of multiple genes and identified based on the expression of several proteins. Therefore chapter III introduces a novel approach to control proteome labeling as a function of multiple promoters using a genetically encoded AND gate based on a bisected methionyl-tRNA synthetase, a class I aaRS. Cellular protein labeling occurs only upon activation of two different promoters that drive expression of the N- and C-terminal fragments of this bisected aaRS. The utility of this tool is demonstrated by the selective labeling of proteins in subpopulations of bacterial cells in a laminar-flow microfluidic channel.
Chapter IV extends the cell-selective incorporation of non-canonical amino acids from bacterial systems to mammalian cells by introducing a mutant mammalian methionyl-tRNA synthetase for cell-targeted proteome labeling. This enzyme is genetically encoded and can be conditionally activated for time-resolved and cell-targeted proteome analysis in a variety of different mammalian cell types. Chapter V uses this enzyme for lineage-specific proteomic analysis of mouse embryonic stem cells during differentiation to cardiac and mesoderm lineages. This approach for lineage-specific protein labeling enables the unbiased and comprehensive analysis of proteomic changes that occur during stem cell differentiation and cell-fate commitment.
Appendices A-G provide brief summaries of publications and research efforts during my PhD that are not directly related to this thesis. These publications are the result of a number of collaborations that I have been fortunate to be involved with during my graduate research.
The technologies and methods introduced in this thesis provide versatile tools for the comprehensive and unbiased detection and identification of newly synthesized proteins in complex multicellular systems. Time-resolved, genetically encoded, and spatially defined non-canonical amino acid incorporation enables the identification of proteins involved in cell-cell interactions and the proteins made during specific cell states.</p
New Catalysts for the Renewable Production of Monomers for Bioplastics
Get PDFTerephthalic acid (PTA) is one of the monomers used for the synthesis of the polyester, polyethylene terephthalate (PET), that is used for the large-scale manufacture of synthetic fibers and plastic bottles. PTA is largely produced from the liquid-phase oxidation of petroleum-derived p-xylene (PX). However, there are now ongoing worldwide efforts exploring alternative routes for producing PTA from renewable, biomass resources.
In this thesis, I present a new route to PTA starting from the biomass-derived platform chemical, 5-hydroxymethylfurfural (HMF). This route utilizes new, selective Diels-Alder-dehydration reactions involving ethylene and is advantageous over the previously proposed Diels-Alder-dehydration route to PTA from HMF via 2,5-dimethylfuran (DMF) since the H2 reduction of HMF to DMF is avoided. Specifically, oxidized derivatives of HMF are reacted as is, or after etherification-esterification with methanol, with ethylene over solid Lewis acid catalysts that do not contain strong Brønsted acids in order to synthesize intermediates of PTA and its equally important diester, dimethyl terephthalate (DMT). The partially oxidized HMF, 5-(hydroxymethyl)furoic acid (HMFA) is reacted with high pressure ethylene over a pure-silica molecular sieve catalyst containing framework tin (Sn-Beta) to produce the Diels-Alder-dehydration product, 4-(hydroxymethyl)benzoic acid (HMBA), with ~30% selectivity at ~20% yield. If HMFA is protected with methanol to form methyl 5-(methoxymethyl)furan-2-carboxylate (MMFC), MMFC can react with ethylene in the presence of a pure-silica molecular sieve containing framework zirconium (Zr-Beta) to produce methyl 4-(methoxymethyl)benzenecarboxylate (MMBC) with >70% selectivity at >20% yield. HMBA and MMBC can then be oxidized to produce PTA and DMT, respectively. When Lewis acid containing mesoporous silica (MCM-41) and amorphous silica, or Brønsted acid containing zeolites (Al-Beta), are used as catalysts, a significant decrease in selectivity/yield of the Diels-Alder-dehydration product is observed.
An investigation to elucidate the reaction network and side products in the conversion of MMFC to MMBC was performed, and the main side products are found to be methyl 4-formylcyclohexa-1,3-diene-1-carboxylate and the ethylene Diels-Alder adduct of this cyclohexadiene. These products presumably form by a different dehydration pathway of the MMFC/ethylene Diels-Alder adduct and should be included when determining the overall selectivity to PTA or DMT since, like MMBC, these compounds are precursors to PTA or DMT.
Fundamental physical and chemical information on the ethylene Diels-Alder-dehydration reactions catalyzed by the Lewis acid-containing molecular sieves was obtained. Madon-Boudart experiments using Zr-Beta as catalyst show that the reaction rates are limited by chemical kinetics only (physical transport limitations are not present), all the Zr4+ centers are incorporated into the framework of the molecular sieve, and the whole molecular sieve crystal is accessible for catalysis. Apparent activation energies using Zr-Beta are low, suggesting that the overall activation energy of the system may be determined by a collection of terms and is not the true activation energy of a single chemical step.</p
Convex Relaxation for Low-Dimensional Representation: Phase Transitions and Limitations
Get PDFThere is a growing interest in taking advantage of possible patterns and structures in data so as to extract the desired information and overcome the curse of dimensionality. In a wide range of applications, including computer vision, machine learning, medical imaging, and social networks, the signal that gives rise to the observations can be modeled to be approximately sparse and exploiting this fact can be very beneficial. This has led to an immense interest in the problem of efficiently reconstructing a sparse signal from limited linear observations. More recently, low-rank approximation techniques have become prominent tools to approach problems arising in machine learning, system identification and quantum tomography.
In sparse and low-rank estimation problems, the challenge is the inherent intractability of the objective function, and one needs efficient methods to capture the low-dimensionality of these models. Convex optimization is often a promising tool to attack such problems. An intractable problem with a combinatorial objective can often be "relaxed" to obtain a tractable but almost as powerful convex optimization problem. This dissertation studies convex optimization techniques that can take advantage of low-dimensional representations of the underlying high-dimensional data. We provide provable guarantees that ensure that the proposed algorithms will succeed under reasonable conditions, and answer questions of the following flavor:
For a given number of measurements, can we reliably estimate the true signal?
If so, how good is the reconstruction as a function of the model parameters?
More specifically, i) Focusing on linear inverse problems, we generalize the classical error bounds known for the least-squares technique to the lasso formulation, which incorporates the signal model. ii) We show that intuitive convex approaches do not perform as well as expected when it comes to signals that have multiple low-dimensional structures simultaneously. iii) Finally, we propose convex relaxations for the graph clustering problem and give sharp performance guarantees for a family of graphs arising from the so-called stochastic block model. We pay particular attention to the following aspects. For i) and ii), we aim to provide a general geometric framework, in which the results on sparse and low-rank estimation can be obtained as special cases. For i) and iii), we investigate the precise performance characterization, which yields the right constants in our bounds and the true dependence between the problem parameters.</p
From Lakes to Lungs: Assessing Microbial Activity in Diverse Environments
Get PDFAll major geochemical cycles on the Earth’s surface are mediated by microorganisms. Our understanding of how these microbes have interacted with their environments (and vice versa) throughout Earth's history, and how they will respond to changes in the future, is primarily based on studying their activity in different environments today. The overarching questions that motivate the research presented in the two parts of this thesis -- how do microorganisms shape their environment (and vice versa)? and how can we best study microbial activity in situ? -- have arisen from the ultimate goal of being able to predict microbial activity in response to changes within their environments both past and future.
Part one focuses on work related to microbial processes in iron-rich Lake Matano and, more broadly, microbial interactions with the biogeochemical cycling of iron. Primarily, we find that the chelation of ferrous iron by organic ligands can affect the role of iron in anoxic environmental systems, enabling photomixotrophic growth of anoxygenic microorganisms with ferrous iron, as well as catalyzing the oxidation of ferrous iron by denitrification intermediates. These results imply that the ability to grow photomixotrophically on ferrous iron might be more widespread than previously assumed, and that the co-occurrence of chemical and biological processes involved in the coupled biogeochemical cycling of iron and nitrogen likely dominate organic-rich environmental systems.
Part two switches focus to in situ measurements of growth activity and comprises work related to microbial processes in the Cystic Fibrosis lung, and more broadly, the physiology of slow growth. We introduce stable isotope labeling of microbial membrane fatty acids and whole cells with heavy water as a new technique to measure microbial activity in a wide range of environments, demonstrate its application in continuous culture in the laboratory at the population and single cell level, and apply the tool to measure the in situ activity of the opportunistic pathogen Staphylococcus aureus within the environment of expectorated mucus from cystic fibrosis patients. We find that the average in situ growth rates of S. aureus fall into a range of generation times between ~12 hours and ~4 days, with substantial heterogeneity at the single-cell level. These data illustrate the use of heavy water as a universal environmental tracer for microbial activity, and highlight the crucial importance of studying the physiology of slow growth in representative laboratory systems in order to understand the role of these microorganisms in their native environments.</p
Extraction of CP Properties of the H(125) Boson Discovered in Proton-Proton Collisions at sqrt(s) = 7 and 8 TeV with the CMS Detector at the LHC
Get PDFIn this thesis we build a novel analysis framework to perform the direct extraction of all possible effective Higgs boson couplings to the neutral electroweak gauge bosons in the H → ZZ(*) → 4l channel also referred to as the golden channel. We use analytic expressions of the full decay differential cross sections for the H → VV' → 4l process, and the dominant irreducible standard model qq ̄ → 4l background where 4l = 2e2μ,4e,4μ. Detector effects are included through an explicit convolution of these analytic expressions with transfer functions that model the detector responses as well as acceptance and efficiency effects. Using the full set of decay observables, we construct an unbinned 8-dimensional detector level likelihood function which is con- tinuous in the effective couplings, and includes systematics. All potential anomalous couplings of HVV' where V = Z,γ are considered, allowing for general CP even/odd admixtures and any possible phases. We measure the CP-odd mixing between the tree-level HZZ coupling and higher order CP-odd couplings to be compatible with zero, and in the range [−0.40, 0.43], and the mixing between HZZ tree-level coupling and higher order CP -even coupling to be in the ranges [−0.66, −0.57] ∪ [−0.15, 1.00]; namely compatible with a standard model Higgs. We discuss the expected precision in determining the various HVV' couplings in future LHC runs. A powerful and at first glance surprising prediction of the analysis is that with 100-400 fb-1, the golden channel will be able to start probing the couplings of the Higgs boson to diphotons in the 4l channel. We discuss the implications and further optimization of the methods for the next LHC runs
Distributed Load Control in Multiphase Radial Networks
Get PDFThe current power grid is on the cusp of modernization due to the emergence of distributed generation and controllable loads, as well as renewable energy. On one hand, distributed and renewable generation is volatile and difficult to dispatch. On the other hand, controllable loads provide significant potential for compensating for the uncertainties. In a future grid where there are thousands or millions of controllable loads and a large portion of the generation comes from volatile sources like wind and solar, distributed control that shifts or reduces the power consumption of electric loads in a reliable and economic way would be highly valuable.
Load control needs to be conducted with network awareness. Otherwise, voltage violations and overloading of circuit devices are likely. To model these effects, network power flows and voltages have to be considered explicitly. However, the physical laws that determine power flows and voltages are nonlinear. Furthermore, while distributed generation and controllable loads are mostly located in distribution networks that are multiphase and radial, most of the power flow studies focus on single-phase networks.
This thesis focuses on distributed load control in multiphase radial distribution networks. In particular, we first study distributed load control without considering network constraints, and then consider network-aware distributed load control.
Distributed implementation of load control is the main challenge if network constraints can be ignored. In this case, we first ignore the uncertainties in renewable generation and load arrivals, and propose a distributed load control algorithm, Algorithm 1, that optimally schedules the deferrable loads to shape the net electricity demand. Deferrable loads refer to loads whose total energy consumption is fixed, but energy usage can be shifted over time in response to network conditions. Algorithm 1 is a distributed gradient decent algorithm, and empirically converges to optimal deferrable load schedules within 15 iterations.
We then extend Algorithm 1 to a real-time setup where deferrable loads arrive over time, and only imprecise predictions about future renewable generation and load are available at the time of decision making. The real-time algorithm Algorithm 2 is based on model-predictive control: Algorithm 2 uses updated predictions on renewable generation as the true values, and computes a pseudo load to simulate future deferrable load. The pseudo load consumes 0 power at the current time step, and its total energy consumption equals the expectation of future deferrable load total energy request.
Network constraints, e.g., transformer loading constraints and voltage regulation constraints, bring significant challenge to the load control problem since power flows and voltages are governed by nonlinear physical laws. Remarkably, distribution networks are usually multiphase and radial. Two approaches are explored to overcome this challenge: one based on convex relaxation and the other that seeks a locally optimal load schedule.
To explore the convex relaxation approach, a novel but equivalent power flow model, the branch flow model, is developed, and a semidefinite programming relaxation, called BFM-SDP, is obtained using the branch flow model. BFM-SDP is mathematically equivalent to a standard convex relaxation proposed in the literature, but numerically is much more stable. Empirical studies show that BFM-SDP is numerically exact for the IEEE 13-, 34-, 37-, 123-bus networks and a real-world 2065-bus network, while the standard convex relaxation is numerically exact for only two of these networks.
Theoretical guarantees on the exactness of convex relaxations are provided for two types of networks: single-phase radial alternative-current (AC) networks, and single-phase mesh direct-current (DC) networks. In particular, for single-phase radial AC networks, we prove that a second-order cone program (SOCP) relaxation is exact if voltage upper bounds are not binding; we also modify the optimal load control problem so that its SOCP relaxation is always exact. For single-phase mesh DC networks, we prove that an SOCP relaxation is exact if 1) voltage upper bounds are not binding, or 2) voltage upper bounds are uniform and power injection lower bounds are strictly negative; we also modify the optimal load control problem so that its SOCP relaxation is always exact.
To seek a locally optimal load schedule, a distributed gradient-decent algorithm, Algorithm 9, is proposed. The suboptimality gap of the algorithm is rigorously characterized and close to 0 for practical networks. Furthermore, unlike the convex relaxation approach, Algorithm 9 ensures a feasible solution. The gradients used in Algorithm 9 are estimated based on a linear approximation of the power flow, which is derived with the following assumptions: 1) line losses are negligible; and 2) voltages are reasonably balanced. Both assumptions are satisfied in practical distribution networks. Empirical results show that Algorithm 9 obtains 70+ times speed up over the convex relaxation approach, at the cost of a suboptimality within numerical precision.</p
A Model For Residential Adoption of Photovoltaic Systems
Get PDFThe rapid rise in the residential photo voltaic (PV) adoptions in the past half decade has created a need in the electricity industry for a widely-accessible model that estimates PV adoption based on a combination of different business and policy decisions. This work analyzes historical adoption patterns and finds fiscal savings to be the single most important factor in PV adoption, with significantly greater predictive power compared to all other socioeconomic factors including income and education. We can create an application available on Google App Engine (GAE) based on our findings that allows all stakeholders including policymakers, power system researchers and regulators to study the complex and coupled relationship between PV adoption, utility economics and grid sustainability. The application allows users to experiment with different customer demographics, tier structures and subsidies, hence allowing them to tailor the application to the geographic region they are studying. This study then demonstrates the different type of analyses possible with the application by studying the relative impact of different policies regarding tier structures, fixed charges and PV prices on PV adoption
Effects of TI-299423 on Neuronal Nicotinic Acetylcholine Receptors
Get PDFNicotinic acetylcholine receptors (nAChRs) are pentameric, ligand-gated, cation channels found throughout the central and peripheral nervous system, whose endogenous ligand is acetylcholine, but which can also be acted upon by nicotine. The subunit compositions of nAChR determine their physiological and pharmacological properties, with different subunits expressed in different combinations or areas throughout the brain. The behavioral and physiological effects of nicotine are elicited by its agonistic and desensitizing actions selectively on neuronal nAChRs. The midbrain is of particular interest due to its population of nAChRs expressed on dopaminergic neurons, which are important for reward and reinforcement, and possibly contribute to nicotine dependence. The α6-subunit is found on dopaminergic neurons but very few other regions of the brain, making it an interesting drug target. We assayed a novel nicotinic agonist, called TI-299423 or TC299, for its possible selectivity for α6-containing nAChRs. Our goal was to isolate the role of α6-containing nAChRs in nicotine reward and reinforcement, and provide insight into the search for more effective smoking cessation compounds. This was done using a variety of in vitro and behavioral assays, aimed dually at understanding TI-299423’s exact mechanism of action and its downstream effects. Additionally, we looked at the effects of another compound, menthol, on nicotine reward. Understanding how reward is generated in the cholinergic system and how that is modulated by other compounds contributes to a better understand of our complex neural circuitry and provides insight for the future development of therapeutics
Computational Predictions of G Protein-Coupled Receptor Structures and Binding Sites
Get PDFG protein-coupled receptors (GPCRs) are the largest family of proteins within the human genome. They consist of seven transmembrane (TM) helices, with a N-terminal region of varying length and structure on the extracellular side, and a C-terminus on the intracellular side. GPCRs are involved in transmitting extracellular signals to cells, and as such are crucial drug targets. Designing pharmaceuticals to target GPCRs is greatly aided by full-atom structural information of the proteins. In particular, the TM region of GPCRs is where small molecule ligands (much more bioavailable than peptide ligands) typically bind to the receptors. In recent years nearly thirty distinct GPCR TM regions have been crystallized. However, there are more than 1,000 GPCRs, leaving the vast majority of GPCRs with limited structural information. Additionally, GPCRs are known to exist in a myriad of conformational states in the body, rendering the static x-ray crystal structures an incomplete reflection of GPCR structures. In order to obtain an ensemble of GPCR structures, we have developed the GEnSeMBLE procedure to rapidly sample a large number of variations of GPCR helix rotations and tilts. The lowest energy GEnSeMBLE structures are then docked to small molecule ligands and optimized. The GPCR family consists of five subfamilies with little to no sequence homology between them: class A, B1, B2, C, and Frizzled/Taste2. Almost all of the GPCR crystal structures have been of class A GPCRs, and much is known about their conserved interactions and binding sites. In this work we particularly focus on class B1 GPCRs, and aim to understand that family’s interactions and binding sites both to small molecules and their native peptide ligands. Specifically, we predict the full atom structure and peptide binding site of the glucagon-like peptide receptor and the TM region and small molecule binding sites for eight other class B1 GPCRs: CALRL, CRFR1, GIPR, GLR, PACR, PTH1R, VIPR1, and VIPR2. Our class B1 work reveals multiple conserved interactions across the B1 subfamily as well as a consistent small molecule binding site centrally located in the TM bundle. Both the interactions and the binding sites are distinct from those seen in the more well-characterized class A GPCRs, and as such our work provides a strong starting point for drug design targeting class B1 proteins. We also predict the full structure of CXCR4 bound to a small molecule, a class A GPCR that was not closely related to any of the class A GPCRs at the time of the work
Numerical Studies of Topological Phases
Get PDFThe topological phases of matter have been a major part of condensed matter physics research since the discovery of the quantum Hall effect in the 1980s. Recently, much of this research has focused on the study of systems of free fermions, such as the integer quantum Hall effect, quantum spin Hall effect, and topological insulator. Though these free fermion systems can play host to a variety of interesting phenomena, the physics of interacting topological phases is even richer. Unfortunately, there is a shortage of theoretical tools that can be used to approach interacting problems. In this thesis I will discuss progress in using two different numerical techniques to study topological phases.
Recently much research in topological phases has focused on phases made up of bosons. Unlike fermions, free bosons form a condensate and so interactions are vital if the bosons are to realize a topological phase. Since these phases are difficult to study, much of our understanding comes from exactly solvable models, such as Kitaev's toric code, as well as Levin-Wen and Walker-Wang models. We may want to study systems for which such exactly solvable models are not available. In this thesis I present a series of models which are not solvable exactly, but which can be studied in sign-free Monte Carlo simulations. The models work by binding charges to point topological defects. They can be used to realize bosonic interacting versions of the quantum Hall effect in 2D and topological insulator in 3D. Effective field theories of "integer" (non-fractionalized) versions of these phases were available in the literature, but our models also allow for the construction of fractional phases. We can measure a number of properties of the bulk and surface of these phases.
Few interacting topological phases have been realized experimentally, but there is one very important exception: the fractional quantum Hall effect (FQHE). Though the fractional quantum Hall effect we discovered over 30 years ago, it can still produce novel phenomena. Of much recent interest is the existence of non-Abelian anyons in FQHE systems. Though it is possible to construct wave functions that realize such particles, whether these wavefunctions are the ground state is a difficult quantitative question that must be answered numerically. In this thesis I describe progress using a density-matrix renormalization group algorithm to study a bilayer system thought to host non-Abelian anyons. We find phase diagrams in terms of experimentally relevant parameters, and also find evidence for a non-Abelian phase known as the "interlayer Pfaffian".</p