Emerging Infectious Disease: Host and Parasite Perspectives

Avian malaria and related haematozoa are nearly ubiquitous parasites that can impose fitness costs of variable severity and may, in some cases, cause substantial mortality in their host populations. One example of the latter, the emergence of avian malaria in the endemic avifauna of Hawaii, has become a model for understanding the consequences of human-mediated disease introduction. The drastic declines of native Hawaiian birds due to avian malaria provided the impetus for examining more closely several aspects of host-parasite interactions in this system. Host-specificity is an important character determining the extent to which a parasite may emerge. Traditional parasite classification, however, has used host information as a character in taxonomical identification, potentially obscuring the true host range of many parasites. To improve upon previous methods, I first developed molecular tools to identify parasites infecting a particular host. I then used these molecular techniques to characterize host-specificity of parasites in the genera Plasmodium and Haemoproteus. I show that parasites in the genus Plasmodium exhibit low specificity and are therefore most likely to emerge in new hosts in the future. Subsequently, I characterized the global distribution of the single lineage of P. relictum that has emerged in Hawaii. I demonstrate that this parasite has a broad host distribution worldwide, that it is likely of Old World origin and that it has been introduced to numerous islands around the world, where it may have been overlooked as a cause of decline in native birds. I also demonstrate that morphological classification of P. relictum does not capture differences among groups of parasites that appear to be reproductively isolated based on molecular evidence. Finally, I examined whether reduced immunological capacity, which has been proposed to explain the susceptibility of Hawaiian endemics, is a general feature of an "island syndrome" in isolated avifauna of the remote Pacific. I show that, over multiple time scales, changes in immune response are not uniform and that observed changes probably reflect differences in genetic diversity, parasite exposure and life history that are unique to each species.

Bioenergetic and ecological consequences of diet variability in Atlantic croaker Micropogonias undulatus

Atlantic croaker Micropogonias undulatus is a commercially and ecologically important bottom-associated fish that occurs in marine and estuarine systems from Cape Cod, MA to Mexico. I documented the temporal and spatial variability in the diet of Atlantic croaker in Chesapeake Bay and found that in the summer fish, particularly bay anchovies Anchoa mitchilli, make up at least 20% of the diet of croaker by weight. The use of a pelagic food source seems unusual for a bottom-associated fish such as croaker, but appears to be a crepuscular feeding habit that has not been previously detected. Thus, I investigated the bioenergetic consequences of secondary piscivory to the distribution of croaker, to the condition of individuals within the population and to the ecosystem. Generalized additive models revealed that the biomass of anchovy explained some of the variability in croaker occurrence and abundance in Chesapeake Bay. However, physical factors, specifically temperature, salinity, and seasonal dynamics were stronger determinants of croaker distribution than potential prey availability. To better understand the bioenergetic consequences of diet variability at the individual level, I tested the hypothesis that croaker feeding on anchovies would be in better condition than those feeding on polychaetes using a variety of condition measures that operate on multiple time scales, including RNA:DNA, Fulton's condition factor (K), relative weight (Wr), energy density, hepatosomatic index (HSI), and gonadosomatic index (GSI). Of these condition measures, several morphometric measures were significantly positively correlated with each other and with the percentage (by weight) of anchovy in croaker diets, suggesting that the type of prey eaten is important in improving the overall condition of individual croaker. To estimate the bioenergetic consequences of diet variability on growth and consumption in croaker, I developed and validated a bioenergetic model for Atlantic croaker in the laboratory. The application of this model suggested that croaker could be an important competitor with weakfish and striped bass for food resources during the spring and summer when population abundances of these three fishes are high in Chesapeake Bay. Even though anchovies made up a relatively small portion of croaker diet and only at certain times of the year, croaker consumed more anchovy at the population level than striped bass in all simulated years and nearly as much anchovy as weakfish. This indicates that weak trophic interactions between species are important in understanding ecosystem processes and should be considered in ecosystem-based management.

Laser beam control, combining, and propagation in atmospheric turbulence

This dissertation is concerned with the control, combining, and propagation of laser beams through a turbulent atmosphere. In the first part we consider adaptive optics: the process of controlling the beam based on information of the current state of the turbulence. If the target is cooperative and provides a coherent return beam, the phase measured near the beam transmitter and adaptive optics can, in principle, correct these fluctuations. However, for many applications, the target is uncooperative. In this case, we show that an incoherent return from the target can be used instead. Using the principle of reciprocity, we derive a novel relation between the field at the target and the scattered field at a detector. We then demonstrate through simulation that an adaptive optics system can utilize this relation to focus a beam through atmospheric turbulence onto a rough surface. In the second part we consider beam combining. To achieve the power levels needed for directed energy applications it is necessary to combine a large number of lasers into a single beam. The large linewidths inherent in high-power fiber and slab lasers cause random phase and intensity fluctuations occurring on sub-nanosecond time scales. We demonstrate that this presents a challenging problem when attempting to phase-lock high-power lasers. Furthermore, we show that even if instruments are developed that can precisely control the phase of high-power lasers; coherent combining is problematic for DE applications. The dephasing effects of atmospheric turbulence typically encountered in DE applications will degrade the coherent properties of the beam before it reaches the target. Finally, we investigate the propagation of Bessel and Airy beams through atmospheric turbulence. It has been proposed that these quasi-non-diffracting beams could be resistant to the effects of atmospheric turbulence. However, we find that atmospheric turbulence disrupts the quasi-non-diffracting nature of Bessel and Airy beams when the transverse coherence length nears the initial aperture diameter or diagonal respectively. The turbulence induced transverse phase distortion limits the effectiveness of Bessel and Airy beams for applications requiring propagation over long distances in the turbulent atmosphere.

THE PHYSICS OF IDEAS: INFERRING THE MECHANICS OF OPINION FORMATION FROM MACROSCOPIC STATISTICAL PATTERNS

In a microscopic setting, humans behave in rich and unexpected ways. In a macroscopic setting, however, distinctive patterns of group behavior emerge, leading statistical physicists to search for an underlying mechanism. The aim of this dissertation is to analyze the macroscopic patterns of competing ideas in order to discern the mechanics of how group opinions form at the microscopic level. First, we explore the competition of answers in online Q&A (question and answer) boards. We find that a simple individual-level model can capture important features of user behavior, especially as the number of answers to a question grows. Our model further suggests that the wisdom of crowds may be constrained by information overload, in which users are unable to thoroughly evaluate each answer and therefore tend to use heuristics to pick what they believe is the best answer. Next, we explore models of opinion spread among voters to explain observed universal statistical patterns such as rescaled vote distributions and logarithmic vote correlations. We introduce a simple model that can explain both properties, as well as why it takes so long for large groups to reach consensus. An important feature of the model that facilitates agreement with data is that individuals become more stubborn (unwilling to change their opinion) over time. Finally, we explore potential underlying mechanisms for opinion formation in juries, by comparing data to various types of models. We find that different null hypotheses in which jurors do not interact when reaching a decision are in strong disagreement with data compared to a simple interaction model. These findings provide conceptual and mechanistic support for previous work that has found mutual influence can play a large role in group decisions. In addition, by matching our models to data, we are able to infer the time scales over which individuals change their opinions for different jury contexts. We find that these values increase as a function of the trial time, suggesting that jurors and judicial panels exhibit a kind of stubbornness similar to what we include in our model of voting behavior.

Coarse Graining to Invesitigate Peptide-Lipid Interactions

Experimental characterization of molecular details is challenging, and although single molecule experiments have gained prominence, oligomer characterization remains largely unexplored. The ability to monitor the time evolution of individual molecules while they self assemble is essential in providing mechanistic insights about biological events. Molecular dynamics (MD) simulations can fill the gap in knowledge between single molecule experiments and ensemble studies like NMR, and are increasingly used to gain a better understanding of microscopic properties. Coarse-grained (CG) models aid in both exploring longer length and time scale molecular phenomena, and narrowing down the key interactions responsible for significant system characteristics. Over the past decade, CG techniques have made a significant impact in understanding physicochemical processes. However, the realm of peptide-lipid interfacial interactions, primarily binding, partitioning and folding of amphipathic peptides, remains largely unexplored compared to peptide folding in solution. The main drawback of existing CG models is the inability to capture environmentally sensitive changes in dipolar interactions, which are indigenous to protein folding, and lipid dynamics. We have used the Drude oscillator approach to incorporate structural polarization and dipolar interactions in CG beads to develop a minimalistic peptide model, WEPPROM (Water Explicit Polarizable PROtein Model), and a lipid model WEPMEM (Water Explicit Polarizable MEmbrane Model). The addition of backbone dipolar interactions in a CG model for peptides enabled us to achieve alpha-beta secondary structure content de novo, without any added bias. As a prelude to studying amphipathic peptide-lipid membrane interactions, the balance between hydrophobicity and backbone dipolar interactions in driving ordered peptide aggregation in water and at a hydrophobic-hydrophilic interface, was explored. We found that backbone dipole interactions play a crucial role in driving ordered peptide aggregation, both in water and at hydrophobic-hydrophilic interfaces; while hydrophobicity is more relevant for aggregation in water. A zwitterionic (POPC: 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and an anionic lipid (POPS: 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine) are used as model lipids for WEPMEM. The addition of head group dipolar interactions in lipids significantly improved structural, dynamic and dielectric properties of the model bilayer. Using WEPMEM and WEPPROM, we studied membrane-induced peptide folding of a cationic antimicrobial peptide with anticancer activity, SVS-1. We found that membrane-induced peptide folding is driven by both (a) cooperativity in peptide self interaction and (b) cooperativity in membrane-peptide interactions. The dipolar interactions between the peptide and the lipid head-groups contribute to stabilizing folded conformations. The role of monovalent ion size and peptide concentration in driving lipid domain formation in anionic/zwitterionic lipid mixtures was also investigated. Our study suggest monovalent ion size to be a crucial determinant of interaction with lipid head groups, and hence domain formation in lipid mixtures. This study reinforces the role of dipole interactions in protein folding, lipid membrane properties, membrane induced peptide folding and lipid domain formation. Therefore, the models developed in this thesis can be used to explore a multitude of biomolecular processes, both at longer time-scales and larger system sizes.

Engineering a Quantum Many-body Hamiltonian with Trapped Ions

While fault-tolerant quantum computation might still be years away, analog quantum simulators offer a way to leverage current quantum technologies to study classically intractable quantum systems. Cutting edge quantum simulators such as those utilizing ultracold atoms are beginning to study physics which surpass what is classically tractable. As the system sizes of these quantum simulators increase, there are also concurrent gains in the complexity and types of Hamiltonians which can be simulated. In this work, I describe advances toward the realization of an adaptable, tunable quantum simulator capable of surpassing classical computation. We simulate long-ranged Ising and XY spin models which can have global arbitrary transverse and longitudinal fields in addition to individual transverse fields using a linear chain of up to 24 Yb+ 171 ions confined in a linear rf Paul trap. Each qubit is encoded in the ground state hyperfine levels of an ion. Spin-spin interactions are engineered by the application of spin-dependent forces from laser fields, coupling spin to motion. Each spin can be read independently using state-dependent fluorescence. The results here add yet more tools to an ever growing quantum simulation toolbox. One of many challenges has been the coherent manipulation of individual qubits. By using a surprisingly large fourth-order Stark shifts in a clock-state qubit, we demonstrate an ability to individually manipulate spins and apply independent Hamiltonian terms, greatly increasing the range of quantum simulations which can be implemented. As quantum systems grow beyond the capability of classical numerics, a constant question is how to verify a quantum simulation. Here, I present measurements which may provide useful metrics for large system sizes and demonstrate them in a system of up to 24 ions during a classically intractable simulation. The observed values are consistent with extremely large entangled states, as much as ~95% of the system entangled. Finally, we use many of these techniques in order to generate a spin Hamiltonian which fails to thermalize during experimental time scales due to a meta-stable state which is often called prethermal. The observed prethermal state is a new form of prethermalization which arises due to long-range interactions and open boundary conditions, even in the thermodynamic limit. This prethermalization is observed in a system of up to 22 spins. We expect that system sizes can be extended up to 30 spins with only minor upgrades to the current apparatus. These results emphasize that as the technology improves, the techniques and tools developed here can potentially be used to perform simulations which will surpass the capability of even the most sophisticated classical techniques, enabling the study of a whole new regime of quantum many-body physics.

Guided Migration and Collective Behavior: Cell Dynamics during Cancer Progression

This dissertation focuses on gaining understanding of cell migration and collective behavior through a combination of experiment, analysis, and modeling techniques. Cell migration is a ubiquitous process that plays an important role during embryonic development and wound healing as well as in diseases like cancer, which is a particular focus of this work. As cancer cells become increasingly malignant, they acquire the ability to migrate away from the primary tumor and spread throughout the body to form metastatic tumors. During this process, changes in gene expression and the surrounding tumor environment can lead to changes in cell migration characteristics. In this thesis, I analyze how cells are guided by the texture of their environment and how cells cooperate with their neighbors to move collectively. The emergent properties of collectively moving groups are a particular focus of this work as collective cell dynamics are known to change in diseases such as cancer. The internal machinery for cell migration involves polymerization of the actin cytoskeleton to create protrusions that---in coordination with retraction of the rear of the cell---lead to cell motion. This actin machinery has been previously shown to respond to the topography of the surrounding surface, leading to guided migration of amoeboid cells. Here we show that epithelial cells on nanoscale ridge structures also show changes in the morphology of their cytoskeletons; actin is found to align with the ridge structures. The migration of the cells is also guided preferentially along the ridge length. These ridge structures are on length scales similar to those found in tumor microenvironments and as such provide a system for studying the response of the cells' internal migration machinery to physiologically relevant topographical cues. In addition to sensing surface topography, individual cells can also be influenced by the pushing and pulling of neighboring cells. The emergent properties of collectively migrating cells show interesting dynamics and are relevant for cancer progression, but have been less studied than the motion of individual cells. We use Particle Image Velocimetry (PIV) to extract the motion of a collectively migrating cell sheet from time lapse images. The resulting flow fields allow us to analyze collective behavior over multiple length and time scales. To analyze the connection between individual cell properties and collective migration behavior, we compare experimental flow fields with the migration of simulated cell groups. Our collective migration metrics allow for a quantitative comparison between experimental and simulated results. This comparison shows that tissue-scale decreases in collective behavior can result from changes in individual cell activity without the need to postulate the existence of subpopulations of leader cells or global gradients. In addition to tissue-scale trends in collective behavior, the migration of cell groups includes localized dynamic features such as cell rearrangements. An individual cell may smoothly follow the motion of its neighbors (affine motion) or move in a more individualistic manner (non-affine motion). By decomposing individual motion into both affine and non-affine components, we measure cell rearrangements within a collective sheet. Finally, finite-time Lyapunov exponent (FTLE) values capture the stretching of the flow field and reflect its chaotic character. Applying collective migration analysis techniques to experimental data on both malignant and non-malignant human breast epithelial cells reveals differences in collective behavior that are not found from analyzing migration speeds alone. Non-malignant cells show increased cooperative motion on long time scales whereas malignant cells remain uncooperative as time progresses. Combining multiple analysis techniques also shows that these two cell types differ in their response to a perturbation of cell-cell adhesion through the molecule E-cadherin. Non-malignant MCF10A cells use E-cadherin for short time coordination of collective motion, yet even with decreased E-cadherin expression, the cells remain coordinated over long time scales. In contrast, the migration behavior of malignant and invasive MCF10CA1a cells, which already shows decreased collective dynamics on both time scales, is insensitive to the change in E-cadherin expression.

Guided Migration and Collective Behavior: Cell Dynamics during Cancer Progression

This dissertation focuses on gaining understanding of cell migration and collective behavior through a combination of experiment, analysis, and modeling techniques. Cell migration is a ubiquitous process that plays an important role during embryonic development and wound healing as well as in diseases like cancer, which is a particular focus of this work. As cancer cells become increasingly malignant, they acquire the ability to migrate away from the primary tumor and spread throughout the body to form metastatic tumors. During this process, changes in gene expression and the surrounding tumor environment can lead to changes in cell migration characteristics. In this thesis, I analyze how cells are guided by the texture of their environment and how cells cooperate with their neighbors to move collectively. The emergent properties of collectively moving groups are a particular focus of this work as collective cell dynamics are known to change in diseases such as cancer. The internal machinery for cell migration involves polymerization of the actin cytoskeleton to create protrusions that---in coordination with retraction of the rear of the cell---lead to cell motion. This actin machinery has been previously shown to respond to the topography of the surrounding surface, leading to guided migration of amoeboid cells. Here we show that epithelial cells on nanoscale ridge structures also show changes in the morphology of their cytoskeletons; actin is found to align with the ridge structures. The migration of the cells is also guided preferentially along the ridge length. These ridge structures are on length scales similar to those found in tumor microenvironments and as such provide a system for studying the response of the cells' internal migration machinery to physiologically relevant topographical cues. In addition to sensing surface topography, individual cells can also be influenced by the pushing and pulling of neighboring cells. The emergent properties of collectively migrating cells show interesting dynamics and are relevant for cancer progression, but have been less studied than the motion of individual cells. We use Particle Image Velocimetry (PIV) to extract the motion of a collectively migrating cell sheet from time lapse images. The resulting flow fields allow us to analyze collective behavior over multiple length and time scales. To analyze the connection between individual cell properties and collective migration behavior, we compare experimental flow fields with the migration of simulated cell groups. Our collective migration metrics allow for a quantitative comparison between experimental and simulated results. This comparison shows that tissue-scale decreases in collective behavior can result from changes in individual cell activity without the need to postulate the existence of subpopulations of leader cells or global gradients. In addition to tissue-scale trends in collective behavior, the migration of cell groups includes localized dynamic features such as cell rearrangements. An individual cell may smoothly follow the motion of its neighbors (affine motion) or move in a more individualistic manner (non-affine motion). By decomposing individual motion into both affine and non-affine components, we measure cell rearrangements within a collective sheet. Finally, finite-time Lyapunov exponent (FTLE) values capture the stretching of the flow field and reflect its chaotic character. Applying collective migration analysis techniques to experimental data on both malignant and non-malignant human breast epithelial cells reveals differences in collective behavior that are not found from analyzing migration speeds alone. Non-malignant cells show increased cooperative motion on long time scales whereas malignant cells remain uncooperative as time progresses. Combining multiple analysis techniques also shows that these two cell types differ in their response to a perturbation of cell-cell adhesion through the molecule E-cadherin. Non-malignant MCF10A cells use E-cadherin for short time coordination of collective motion, yet even with decreased E-cadherin expression, the cells remain coordinated over long time scales. In contrast, the migration behavior of malignant and invasive MCF10CA1a cells, which already shows decreased collective dynamics on both time scales, is insensitive to the change in E-cadherin expression.