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The Bioenergetics of a Low-Power, Phenazine-Dependent Maintenance Metabolism in Pseudomonas aeruginosa
Get PDFA common feature of all life is the metabolic transformation of energy from the environment to biochemical energy in the organism. While this process is well-characterized in molecular detail for fast-growing or otherwise fast-metabolizing organisms such as humans, many microorganisms subsist in the environment around us with little to no exogenous energy for extended periods, and we have only vague ideas how. Questions about the metabolic mechanisms and rates underpinning these astounding survival capabilities speak to the fundamental question of the lower energetic limits of life. Motivated by this big-picture question in biology, this thesis represents one line of physiological inquiry into a specific anaerobic survival metabolism of Pseudomonas aeruginosa, an opportunistic bacterial pathogen. Pseudomonas is perhaps best known for its characteristic production of colorful, redox-active, secondary metabolites called phenazines that allow a metabolic process called extracellular electron transfer. Phenazine extracellular electron transfer has been previously shown to unlock a slow, anaerobic glucose catabolism that facilitates the survival of energy-limited populations of cells. My thesis work has elucidated the predominant membrane-bound protein complexes involved in phenazine reduction and the predominant subcellular location of reduction for each of the main phenazines produced by Pseudomonas. I show that the survival metabolism powered by these phenazines places them in a true maintenance state where there is no detectable growth in the population at the single-cell level. The metabolic rate of this maintenance was measured and found to be 1,000 times slower than when the cells are growing in aerobic culture, 100 times slower than estimates of maintenance rates made in continuous culture, and 10 times slower than the mean basal metabolic rate estimated across all life on the planet. These results open the door to investigations of metabolic attenuation, a physiological state that underpins microbial survival in nature and disease. In pursuit of these discoveries, various new experimental assays that allow further investigation into the bioenergetics and biochemistry of phenazine metabolism were developed. Finally, intellectual frameworks are presented that, in conjunction with the discoveries made and methods developed, collectively bring us steps closer to understanding the bioenergetic basis of microbial resiliency
Energy Correlators, Dispersive Sum Rules, and Modular Bootstrap in Conformal Field Theories
Get PDFIn this thesis, we study various observables and constraints in conformal field theories. First, we consider a product of two null-integrated operators on the same null plane and derive an operator product expansion in the direction transverse to the null plane. We then generalize to the case of three null-integrated operators, and study constraints from Lorentz symmetry. We generate a class of dispersive CFT sum rules using commutativity of null-integrated operators, and build a dictionary between the CFT sum rules and the flat space dispersion relations. Finally, we consider two-dimensional conformal field theories and derive a scalar crossing equation using modular invariance
Bootstrapping the Gross-Neveu-Yukawa Archipelago and Skydiving Algorithm
Get PDFThe goal of the conformal bootstrap is to solve conformal field theories (CFTs) by imposing physical constraints including symmetries and unitarity. It has been a powerful tool to rigorously constrain CFT data, especially for strongly-coupled theories where traditional perturbative methods fail. Based solely on unitarity, symmetry, and assumptions about the spectrum of scaling dimensions, the bootstrap method has produced stringent bounds on critical exponents of several universality classes describing real-world statistical and quantum phase transitions.
The numerical bootstrap method combines the physical constraints with convex optimization. Specifically, the physics problems are converted into semidefinite programs and solved numerically. Such methods have led to precise and rigorous predictions on critical exponents of condensed-matter systems, such as 3d Ising models and the models. In my first research project, we perform a bootstrap analysis of a mixed system of four-point functions of bosonic and fermionic operators in parity-preserving 3d CFTs with O(N) global symmetry. Our results provide rigorous bounds on scaling dimensions and OPE coefficients of the O(N) symmetric Gross-Neveu-Yukawa (GNY) fixed-points, constraining these theories to live in isolated islands in the space of CFT data. We delivered the bounds on the critical points with N = 1, 2, 4, and 8, which have applications to phase transitions in condensed matter systems. We were also able to demonstrate the existence of the supercurrent when supersymmetry emerges at N=1 without prior assumptions of the symmetry.
On the other hand, as we progress towards larger systems to study and to obtain more precise bounds on various CFTs, the limits on computational resources cannot be overlooked. To tackle the numerical challenges and improve efficiency, my second research project studies families of semidefinite programs (SDPs) that depend nonlinearly on a small number of “external” parameters. Such families appear universally in numerical bootstrap computations. The traditional method for finding an optimal point in parameter space works by first solving an SDP with fixed external parameters, then moving to a new point in parameter space and repeating the process. Instead, we unify solving the SDP and moving in parameter space in a single algorithm that we call “skydiving”. We test skydiving on some representative problems in the conformal bootstrap, finding significant speedups compared to traditional methods.</p
Investigation of Quantum Computers for Quantum Simulation and Machine Learning
Get PDFThe use of quantum mechanical phenomena for information processing has the potential to solve computational problems which are believed to be intractable for classical computers. Inspired by this potential, the last several decades has seen rapid development in both the theory and practice of quantum information processing. In this thesis, we explore three applications of quantum computing for the physical and computational sciences.
The first potential application is for the simulation of open quantum systems. We introduce two algorithms for the simulation of open quantum systems governed by a Lindblad equation. Based on adaptations of the quantum imaginary time evolution algorithm, these methods transform non-unitary open system evolution into unitary evolution which can be implemented on contemporary quantum hardware. We demonstrate these algorithms on IBM's quantum hardware via the simulation of the spontaneous emission of a two-level system and the dissipative transverse field Ising model.
Next, we explore efficient methods to probe measurement induced phase transitions using superconducting circuits. These phase transitions occur in monitored quantum systems as the measurement rate of randomized single qubit measurements increases. We overcome two exponential bottlenecks which limited the system sizes of previous experiments on superconducting circuits by employing a cross-entropy benchmarking protocol and Clifford based circuit compression techniques. We observed measurement induced phase transitions on systems of up to 22 physical qubits.
Finally, we switch our attention to machine learning, where we prove rigorous quantum advantages for adversarially robust classification. By constructing a learning task based on widely accepted cryptographic assumptions, we show a necessary condition for the utility of quantum computers for robust classification. In particular, we show that for the learning task we construct, any efficient classical learner cannot robustly classify better than chance, whereas a quantum learner can efficiently and robustly classify data with high accuracy.
Through these studies, we show that quantum computers have potential application in the physical and information sciences in both the near and long term.</p
Accelerating Biological Discovery with Deep Learning and Spatial Optical Barcodes
Get PDFMethodological advances in biology have given us a powerful suite of tools for measuring the state of the cell. Among these methods, next-generation sequencing, including single-cell methods, enables comprehensive measurement of gene expression; however, sequencing-based methods often preclude the collection of other visible phenotypic information. In contrast, light microscopy supports many different measurements that can be acquired in sequential rounds of labeling and imaging because light microscopy does not destroy the sample. Furthermore, light microscopy supports live cell imaging, including the use of fluorescent reporters to observe signaling dynamics in real time. In order to fully understand cellular function, multimodal data collection is needed that encompasses live cell response, end-point phenotypes, and finally perturbations to test the components of relevant signaling networks. In this thesis, I present key advances to create a unified experimental platform for interrogating the cell state. This platform uses light microscopy to collect multimodal measurements of cell state while supporting high-throughput perturbation screening. This platform is supported by a suite of deep learning analysis tools to enable quantitative analysis of these high-dimensional datasets. In Chapter 2, I introduce Caliban, our deep learning method for nuclear segmentation and tracking. In Chapter 3, I present a new method of optical barcodes to enable microscopy-based pooled perturbation screens. Finally, in Chapter 4, I describe preliminary work that leverages the previously described cell tracking and barcoding methodologies to explore the interdependencies of signaling pathway dynamics
Improvement of Microbial Detection and Analysis Techniques in Complex Biological Environments
No full textHuman bodies are home to a vast assortment of microbes, including bacteria, fungi, and viruses. These microbes live within their human hosts, interacting with each other and influencing states of health and disease. Despite their prevalence and importance, studying host-microbe interactions has been limited by the dearth of appropriate tools and approaches, and an underappreciation for the role of biophysics.
This thesis describes the development and application of novel tools and approaches for studying bacteria, fungi, and viruses to uncover their potential roles in human health and disease.
In my first project, we investigated bacterial aggregation, a phenomenon related to important host-microbe interactions such as biofilm formation and the clearance of pathogens from the gastrointestinal tract. We found that bacteria aggregate in the presence of polymers (such as dietary fiber) via a mechanism that is qualitatively consistent with depletion-type forces under gut-like conditions. Surprisingly, motile bacteria aggregate more than nonmotile bacteria in viscous, high-polymer concentrations due to the higher effective diffusivity and inter-bacterial collisions enabled by motility. These two results give insight on how the foods (such as fiber) that we consume can physically affect the structure of microbes and other matter in the gut.
In my next projects, we investigated viral-load kinetics to understand the best testing modality for early detection of SARS-CoV-2 via a large community-based household transmission study. By collecting longitudinal, paired saliva and nasal-swab specimens from SARS-CoV-2 patients starting from the incident of infection, we quantified the viral-load trajectories of COVID-19-positive participants in each specimen type over time. Our results revealed that viral loads increased quickly and reached a higher peak in nasal-swab specimens, whereas viral loads were detectable earlier but reached a lower maximum in saliva. Both specimen types exhibited a temporal trend whereby viral loads were higher in specimens collected in the morning compared with the evening. In samples where infectious viral titer was measured, we found that the ratio of N gene viral load and infectious viral titer did not remain consistent throughout the course of infection. These three results help us understand the heterogeneity of SARS-CoV-2 disease progression in different individuals, and how the analytical sensitivity of a diagnostic, the specimen type, and time of sampling can be crucial in conducting community surveillance programs during a pandemic.
Finally, we extended and co-validated for fungi a novel sample-preparation method that enriches fungal cells in host-rich samples to enable the first demonstration of deep metagenomic sequencing of fungal communities directly from clinical samples (without a culture step). Our results show that this method depletes host DNA by over 1000-fold by mass, improving taxonomic classification and gene calling, as well as enabling de novo metagenome assembled genome (MAG) assembly in samples dominated by human biomass.</p
Probing Active Nanophotonic Materials Phenomena Under Electrostatic Modulation
Get PDFNanophotonic metastructured devices have gained significant attention due to their ability to manipulate properties of light such as the wavelength, amplitude, and phase. For photonic metastructures, these properties are typically fixed at the time of fabrication, as they depend on the geometrical parameters of resonant structures. Therefore, there is a growing interest in active nanophotonic devices, which can dynamically control the properties of light by incorporating active materials and applying external stimuli, in operation after fabrication.
This thesis investigates the dynamic control of light through electrostatic modulation of metastructures containing indium tin oxide (ITO) and monolayer transition metal dichalcogenides (1L-TMDs) materials. Specifically, we analyze the dynamic behavior of these materials, characterizing their morphological, electrical, and optical properties within devices.
In the first two chapters, we discuss the effects of ion migration on the electro-optic response of ITO-based active nanophotonic devices. Initially, we investigated uniformly deposited silver/dielectric/ITO heterostructures. Under electrical bias, silver ions and oxygen vacancies in the ITO actively migrate changing the device operating characteristics, resulting in hysteretic current-voltage curve behavior. Although optical modulation was barely observed, we explored the thermodynamic instability giving rise to electrical hysteresis in this volatile device. Furthermore, we investigated the impact of oxygen vacancy ion migration on the frequency response and phase modulation of ITO-based active metasurfaces. By annealing the devices, we were able to reduce the oxygen vacancy concentration, thereby improving the device high frequency performance.
In the latter two chapters, we explore the electro-optic response of field effect heterostructures comprised of 1L-TMDs in high-Q resonators. We designed and simulated two distinct types of high-Q resonators: Fabry-Perot resonators and silicon pillar resonators. We optimized the geometrical parameters of these resonant structures embedded with 1L-TMDs to enhance device amplitude and phase modulation. Subsequently, we examined the potential for electro-optic modulation of TMDs in the telecommunication band, beyond their excitonic resonance wavelengths, by integrating them with Fabry-Perot resonators. We also discussed the compatibility of 1L-TMDs with gated heterostructure fabrication methods.
Overall, this thesis presents the application of electrical bias as a tool for the dynamic control of light in ITO and 1L-TMDs-based nanophotonic devices, with potential future applications in adaptive and reconfigurable photonic technologies.</p
Developments in Mössbauer Spectrometry: From Instrumentation to High Pressure Studies on Spins and Phonons
Get PDFThe well-established technique of 57Fe Mössbauer spectrometry is used to investigate the local chemical environment in iron-containing materials. This technique relies on the recoil-free emission and absorption of γ-rays by resonant nuclei within a solid. The key component of a Mössbauer spectrometer is the velocity Doppler drive, which modulates the energy of the incident γ-rays to detect the hyperfine structure of resonant nuclei. Since the 1970s, the conventional velocity Doppler drive has been constructed using a pair of electromagnetic coils, one for power and the second for feedback. An alternative Mössbauer spectrometer was developed, utilizing an amplified piezoelectric actuator as the Doppler velocity drive under feedback control. The actuator, driven with a quadratic displacement waveform, produced a linear velocity profile and was optimized using measurements from a laser Doppler vibrometer (LDV). In transmission geometry, 57Fe Mössbauer spectra of α-iron display minimal peak distortions, enabling Mössbauer spectrometry in applications requiring compact size and low mass, such as geochemical studies on the Moon, Mars, or asteroids.
Synchrotron radiation is used for numerous experimental techniques, including X-ray diffraction (XRD), nuclear resonant inelastic X-ray scattering (NRIXS), and nuclear forward scattering (NFS), also known as synchrotron Mössbauer spectrometry. Diamond-anvil cells, capable of reaching high pressures at various temperatures, combined with synchrotron experimental methods, provide the means to investigate the vibrational, magnetic, and thermophysical properties of materials. Measurements on 57Fe55Ni45 were conducted using synchrotron XRD, NRIXS, and NFS under various pressures and temperatures. XRD measurements at 298 K and 392 K under pressures up to 20 GPa confirmed a pressure-induced Invar effect between 7 GPa and 13 GPa, where the coefficient of thermal expansion is nearly zero. NFS measurements revealed a decrease in the magnetic moment of 57Fe under pressure, indicating an increase in magnetic entropy. The 57Fe phonon density of states (DOS) was measured with NRIXS from which a phonon entropy was extracted. Using thermodynamic Maxwell relations, magnetic and phonon contributions to thermal expansion were determined, demonstrating that the low thermal expansion in the pressure-induced Invar region stems from a competition between the thermal expansion from spins and from phonons.</p
Sexual Dimorphism and Evolutionary Innovation in piRNA-Guided Genome Defense
Get PDFThe genome is a battleground, where different genetic elements vie for inheritance. In particular, selfish genetic elements enhance their own transmission at the expense of host fitness, causing intragenomic conflicts that must be resolved to protect host reproduction. To keep selfish genes in check, animals employ several genome defense mechanisms, including the PIWI-interacting RNA (piRNA) pathway, where small non-coding piRNAs guide PIWI proteins to find complementary RNAs for silencing. While selfish genes are found across the tree of life, they are often sexually dimorphic and lineage-specific. Yet, it remains poorly understood how sex- and lineage-specific selfish genes are tamed by conserved genome defense mechanisms. To address this, I used the piRNA pathway in Drosophila melanogaster as the model system to study sexual dimorphism and evolutionary innovation in genome defense.
In this thesis, I first described my discovery of piRNA sexual dimorphism, which evolved in response to the sex-specific selfish gene landscape. Next, I dissected the genetic basis and molecular mechanisms that underpin piRNA sexual dimorphism, gaining mechanistic insights into how the biological sex modifies the piRNA pathway to tame distinct selfish genes in two sexes. Pivoting to evolutionary innovation, I discovered a novel piRNA locus on the Y chromosome, which I named petrel, that silences the expression of an X-linked host gene, which I named pirate, implicating piRNAs in resolving X-versus-Y sex chromosome conflicts. petrel piRNAs evolved very recently after the split of D. melanogaster from its sibling species, highlighting a recent piRNA innovation against a lineage-specific target. Finally, I described my discovery of a novel genome defense protein factor, which I named Trailblazer, that tames a radically expanded selfish gene family, Stellate. Mechanistically, Trailblazer is a germline transcription factor that, via recent innovation of its DNA-binding domain, up-regulates the expression of two piRNA pathway effectors to quantitatively match Stellate in abundance, indicating a new mode of defense innovation beyond target-specific repressors.
Collectively, my thesis shows that the genomic battleground against selfish genes differs substantially between sexes and across lineages, which selects for distinct innovations in the piRNA pathway to control different selfish genes, thereby safeguarding genome integrity, animal fertility, and species continuity.</p
Do Robots Dream of Random Trees? Monte Carlo Tree Search for Dynamical, Partially Observable, and Multi-Agent Systems
Get PDFAutonomous robots are poised to transform various aspects of society, spanning transportation, labor, and scientific space exploration. A critical component to enable their capabilities is the algorithm that interprets sensor data to generate intelligent planned behavior. Although reinforcement learning methods that train parameterized policies offline from data have shown recent success, they are inherently limited when robots inevitably encounter situations outside their training domain. In contrast, optimal control techniques, which compute trajectories in real-time using numerical optimization, typically yield only locally optimal solutions.
This research endeavors to bridge the gap by developing algorithms that compute trajectories in real-time while converging towards globally optimal solutions. Building upon the Monte Carlo Tree Search (MCTS) framework—a stochastic tree search method that simulates future trajectories while balancing exploration and exploitation—the research focus is twofold: (i) constructing an efficient discrete representation of continuous systems in a decision trees, and (ii) searching on the resulting tree while balancing exploration and exploitation to achieve global optimality.
The study spans theoretical analysis, algorithmic design, and hardware demonstrations across dynamical, partially observable, and multi-agent systems. By addressing these critical questions, this research aims to advance the field of autonomous robotics, enabling the deployment of intelligent robots in complex and diverse environments.</p