Physics
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Item Analyzing the Dynamics of Biological and Artificial Neural Networks with Applications to Machine Learning(2024) Srinivasan, Keshav; Girvan, Michelle; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The study of the brain has profoundly shaped the evolution of computational learning models and the history of neural networks. This journey began in the 1940s with Warren McCulloch and Walter Pitts’ groundbreaking work on the first mathematical model of a neuron, laying the foundation for artificial neural networks. The 1950s and 60s witnessed a significant milestone with Frank Rosenblatt’s development of the perceptron, showcasing the potential of neural networks for complex computational tasks. Since then, the field of neural networks has witnessed explosive growth, and terms like “Artificial Intelligence” and “Machine Learning” have become commonplace across diverse fields, including finance,medicine, and science. This dissertation explores the symbiotic parallels between neuroscience and machine learning, focusing on the dynamics of biological and artificial neural networks. We begin by examining artificial neural networks, particularly in predicting the dynamics of large, complex networks—a paradigm where traditional machine learning algorithms often struggle. To address this, we propose a novel approach utilizing a parallel architecture that mimics the network’s structure, achieving scalable and accurate predictions. Shifting our focus to biological neuronal networks, we delve into the theory of critical systems. This theory posits that the brain, when viewed as a complex dynamical system, operates near a critical point, a state ideal for efficient information processing. A key experimental observation of this type of criticality is neuronal avalanches—scale-free cascades of neuronal activity—which have been documented both in vitro (in neuronal cultures and acute brain slices) and in vivo (in the brains of awake animals). Recent advancements in experimental techniques, such as multi-photon imaging and genetically encoded fluorescent markers, allow for the measurement of activity in living organisms with unparalleled single-cell resolution. Despite these advances, significant challenges remain when only a fraction of neurons can be recorded with sufficient resolution, leading to inaccurate estimations of power-law relationships in size, duration, and scaling of neuronal avalanches. We demonstrate that by analyzing simulated critical neuronal networks alongside real 2-photon imaging data, temporal coarse-graining can recover the critical value of the mean size vs. duration scaling of neuronal avalanches, allowing for more accurate estimations of critical brain dynamics even from subsampled data. Finally, we bridge the gap between machine learning and neuroscience by exploring the concept of excitatory-inhibitory balance, a crucial feature of neuronal networks in the brain, within the framework of reservoir computing. We emphasize the stabilizing role of inhibition in reservoir computers (RCs), mirroring its function in the brain. We propose a novel inhibitory adaptation mechanism that allows RCs to autonomously adjust inhibitory connections to achieve a specific firing rate target, motivated by the firing rate homeostasis observed in biological neurons. Overall, this dissertation strives to deepen the ongoing collaboration between neuroscience and machine learning, fostering advancements that will benefit both fields.Item THE ROLE OF THE PROTEIN-LIPID BOUNDARY IN THE GATING OF THE MECHANOSENSITIVE CHANNEL MSCS, AND THE THERMODYNAMICS OF ARGININE-PHOSPHATE INTERACTIONS(2024) Britt, Madolyn; Sukharev, Sergei; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bacteria are exceptionally adaptive to a wide range of conditions. The E. coli mechanosensitive channel MscS is a low-threshold osmolyte release valve that provides environmental stability by regulating turgor pressure to prevent cell swelling and lysis in response to hypoosmotic shock. MscS is an adaptive multi-state channel that gates directly in response to tension in the surrounding lipid bilayer. Although MscS has three functional states (closed, open, inactivated), there are only two classes of structures: (1) nonconductive, characterized by splayed lipid-facing helices, kinked pore-lining helices, and lipid perturbations at the cytoplasmic interface and (2) semi-open conductive, characterized by an expanded pore that does not fully satisfy the experimental conductance. Currently, there is no consensus on how to relate these structural states to functional states. By default, the nonconductive structure is regularly assumed closed in the literature. In this thesis, I contribute to the body of existing experimental evidence that strongly suggests that the nonconductive structure corresponds to the inactivated state, rather than the closed state. Specifically, I focus on the channel as a membrane-embedded physical object and look to examine how lipids mediate tension-driven conformational dynamics. I use mutagenesis and patch-clamp electrophysiology to determine how MscS mutants with different protein-lipid interactions alter functional state distributions and transition rates. I then leverage these data to inform structure interpretation. Correctly identifying the structures of MscS that correspond to each functional state and the physical factors that stabilize them is critical towards understanding the underlying mechanism for MscS mechanosensitivity and its adaptive functional cycle. Chapter 2 explores how mutations of conserved anchor residues R46 and R74, interacting with lipid phosphates, affect gating transitions. We find that mutations at these positions predominantly alter the kinetics and voltage dependence of slow inactivation transitions, suggesting that extensive lipid rearrangement around these residues is a structural feature of inactivation. We also identify membrane potential as a factor regulating MscS state distribution. Chapter 3 investigates the role of protein-lipid interactions at both cytoplasmic and periplasmic interfaces in MscS functional behavior. Results indicate that MscS requires TM helix mobility at the periplasmic interface, but helix stability at the cytoplasmic interface for proper state transitions. We also find an interesting mutant, R46L/R74L, that is highly predisposed toward the inactivated state in giant spheroplasts, but apparently distributed normally into the closed state in actively metabolizing bacteria, providing evidence that the MscS population is under metabolic control. Finally, Chapter 4 aims to improve methods for examining these interactions in silico. The thermodynamics of arginine small peptide interactions with POPC, POPA, and POPG phospholipids is determined using ITC, and the affinity is found to depend on the accessibility of the lipid phosphate group. I also identify the ensemble of peptide-membrane bound states by constructing Markov state models from clustered trajectory data, revealing discrepancies between experimental and simulation results. These data are the first steps toward improving FF descriptions of arginine-phosphate interactions within membranes.Item Understanding Allosteric Communication in Biological Systems using Molecular Dynamics Simulations(2024) Samanta, Riya; Matysiak, Silvina; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Allostery is critical to survival in living organisms due to its biological relevance in signal transduction, metabolism, and drug discovery. However, the molecular details of this phenomenon remain unclear. In this thesis, I present my work on two allosteric protein systems, each representative of structure-based (E. coli Biotin Protein Ligase) and dynamics-based (B. taurus S100B) allostery. I examined the structural and dynamic features of the proteins and associated variants subjected to various allosteric triggers (ligand/salt/mutations) to study how external/internal perturbations transmit across large distances using Molecular Dyanmic simulations in conjunction with the experiments carried out by our collaborators. Additionally, I carried out Network analyses on the two systems to characterize communication pathways on the protein/ protein family levels. Together, the structural and dynamic features would help us elucidate the underlying mechanism of allostery. The first chapter introduces the two systems with a brief dive into the history of allostery. In the second chapter, my work on E. coli Biotin Protein Ligase and its variants reveal one possible mechanism by which disorder-to-order transitions at the functional surfaces transmit via local changes around the critical residues in the allosteric network. The third chapter explores how the protein network reconfigures to adopt a new allosteric function by studying the allosteric and non-allosteric Biotin Protein Ligases. The fourth chapter elucidates the structural and dynamical markers in bovine S100B, which help to relay information about an allosteric signal by varying two allosteric triggers - ionic strength and target peptide. The final chapter sums up my conclusions, where I propose additional experiments and computational analyses that could be carried out to further our understanding of allostery.Item Analyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence(2024) Wang, Dedi; Tiwary, Pratyush; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rapid advances in computational power have made all-atom molecular dynamics (MD) a powerful tool for studying systems in biophysics, chemical physics and beyond. By solving Newton's equations of motion in silico, MD simulations allow us to track the time evolution of complex molecular systems in an all-atom, femtosecond resolution, enabling the evaluation of both their thermodynamic and kinetic properties. Though MD simulations are powerful, their effectiveness is often hampered by the large amount of data they produce. For instance, a standard microsecond-long simulation of a protein can easily generate hundreds of gigabytes of data, which can be difficult to analyze. Moreover, the time required to conduct these simulations can be prohibitively long. Microsecond-long simulations often take weeks to complete, whereas the processes of interest may occur on the timescale of milliseconds or even hundreds of seconds. These factors collectively pose significant challenges in leveraging MD simulations for comprehensive analysis and exploration of chemical and biological systems. In this thesis, I address these challenges by leveraging physics-inspired insights to learn unique, useful, and also meaningful low-dimensional representations of complex molecular systems. These representations enable effective analysis and interpretation of the vast amount of data generated from experiments and simulations. These representations have proven to be valuable in providing mechanistic insights into some fundamental problems within theoretical chemistry and biophysics, such as understanding the interplay between long-range and short-range forces in ion pair dissociation and the transformation of proteins from unstable random coils to structured forms. Furthermore, these physics-informed representations play a crucial role in enhancing MD simulations. They facilitate the construction of simplified kinetic models, enabling the generation of dynamical trajectories spanning significantly longer time scales than those accessible by conventional MD simulations. Additionally, they can serve as blueprints to guide the sampling process in combination with existing enhanced sampling methods. Through this thesis, I showcase how the synergy between physics and AI can advance our understanding of molecular systems and facilitate more efficient and insightful analysis in the fields of computational chemistry and biophysics.Item TOWARD ENSEMBLE-BASED DRUG DISCOVERY THOUGH ENHANCED SAMPLING(2023) Smith, Zachary; Tiwary, Pratyush; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Quantitatively assessing protein conformational dynamics and ligand dissociation are two problems of critical importance for computer-aided drug discovery. Both of these problems involve larger shifts in the protein conformation than are ordinarily considered in drug discovery efforts. Even though it is well known that proteins are best described as a dynamic ensemble of states, actually acquiring a representative ensemble, especially one with probabilities attached to states, has remained an elusive problem. Molecular dynamics can in theory capture the full ensemble with a long enough simulation but it would take millions of years to simulate the timescale needed to study drug binding or unbinding. Given this timescale problem, it is necessary to develop software solutions to accelerate the sampling of these important rare events. A number of enhanced sampling methods such as metadynamics have arisen to deal with this problem but the methods that are able to attain the fastest speedup also require a low-dimensional description of the system's dynamics. In this thesis, I will develop methods to describe protein dynamics with low-dimensional functions that can be used with enhanced sampling and apply these methods in an enhanced sampling pipeline. The methods developed will both perform variable selection finding a small set of descriptors for the protein dynamics and perform manifold learning to find a low-dimensional representation of the dynamics using this set of descriptions. This pipeline will be used to tackle both problems of conformational dynamics and ligand dissociation in a relatively automated manner. I will then describe how solving these problems in a high throughput manner could impact structure-based drug design efforts, and the work remaining to attain that goal.Item UNCOVERING THE MOLECULAR BASIS OF ACTIVITY-DEPENDENT RETINOFUGAL SYNAPSE PLASTICITY(2023) Zhang, Chenghang; Speer, Colenso; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Activity-dependent synapse plasticity is important for the establishment of neuron wiring in the central nervous system, particularly in the context of sensory processing. In the visual system, image-forming and non-image-forming retinal input into the brain is a popular model for studying activity-dependent plasticity due to the well-characterized neural activity and bulk-level innervation pattern. However, investigation of synaptic connection during early development has been impeded by the limited resolution of conventional fluorescent microscopy or lack of profile tagging in electron microscopy (EM) images. To overcome these challenges, we employed volumetric STochastic Optical Reconstruction Microscopy, immunohistochemistry synaptic protein labeling, and anterograde retinal tract tracing to investigate the activity-dependent retinogeniculate and retinohypothalamic synapse plasticity. Through our findings, we uncover the developmental pattern of retinofugal innervation and shed light on the impact of spontaneous activity on retinal synapse maturation at the synaptic level. During the first postnatal week in mice, the dorsal lateral geniculate nucleus (dLGN) initially receives overlapping input from the two eyes before the binocular innervation segregated. The changes in individual synapse properties during the eye-specific segregation process have remained unknown. In Chapter 2, we uncovered eye-specific differences in presynaptic vesicle pool size and vesicle association with the active zone at the earliest stages of retinogeniculate refinement but found no evidence of eye-specific differences in subsynaptic domain number, size, or transsynaptic alignment across development. Genetic disruption of spontaneous retinal activity decreased retinogeniculate synapse density, delayed the emergence of eye-specific differences in vesicle organization, and disrupted subsynaptic domain maturation. These results suggest that activity-dependent eye-specific presynaptic maturation underlies synaptic competition in the mammalian visual system. The dLGN relays visual information from the retina to the visual cortex through parallel processing pathways. In adult mice, such processing is achieved through spatial clustering of several retinal ganglion cells (RGCs) boutons to integrate convergent or divergent visual information. It is unknown whether such RGC synapse clustering occurs during the early developmental stage. In Chapter 3, we identified a subset of complex retinogeniculate synapses with larger presynaptic vesicle pools and multiple AZs that simultaneously promote the clustering of like-eye synapses (synaptic stabilization) and prevent synapse formation from the opposite eye (synaptic punishment). In mutant mice with disrupted spontaneous retinal wave activity, complex synapses are formed but fail to drive eye-specific synaptic clustering and punishment. These results reveal the early formation of a unique synaptic subset that regulates activity-dependent eye-specific synaptic competition and may serve as substrates for later synapse clustering formation. A subset of RGCs that express the photopigment melanopsin (OPN4) innervate the suprachiasmatic nucleus (SCN), which serves as the central pacemaker responsible for controlling circadian rhythm in mammals. The function of OPN4 is important for SCN photoentrainment, but its impact on retinal synapse maturation during early development is unknown. In Chapter 4, we found that OPN4 plays an important role in retinal synapse formation and activation in the SCN during the early developmental stage. Loss of OPN4 leads to reduced retinal synapse density, and increased variability in the ratio of synapses with few or no docking vesicles, but has not effect on total vesicle pool volume. Meanwhile, the subsequent maturation of retinohypothalamic tract (RHT) synapses after the first postnatal week shows diminished reliance on OPN4 function and further compensates for the early defects in the absence of OPN4. This study reveals a moderate influence of OPN4 on early RHT synapse development and sheds light on the role of photopigment in regulating SCN synapse plasticity. This dissertation introduces a novel approach using super-resolution fluorescent imaging in the thalamus and hypothalamus tissue. Our work has yielded insights into the activity-dependent maturation in synapse properties and spatial distribution in the dLGN, as well as the impact of OPN4 on retinohypothalamic synapses in the SCN. By revealing the synapse development at the molecular level, our study demonstrates presynaptic mechanisms that underlie activity-dependent retinal synapse plasticity during the early developmental stage. Furthermore, our application of super-resolution fluorescent microscopy highlights its potential as a valuable tool for future in situ studies on brain development.Item REGULATING GENE EXPRESSION: THE ROLE OF TRANSCRIPTION FACTOR DYNAMICS(2023) Wagh, Kaustubh; Upadhyaya, Arpita; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The genetic information encoded within our DNA is converted into RNA in a process called transcription. This is a tightly regulated process where multiple proteins act in concert to activate appropriate gene expression programs. Transcription factors (TFs) are key players in this process, with TF binding being the first step in the assembly of the transcriptional machinery. TFs are sequence-specific DNA binding proteins that bind specific motifs within chromatin. How TFs navigate the complex nuclear microenvironment to rapidly find their target sites remains poorly understood. Technological advances over the past 20 years have enabled us to follow single TF molecules within live cells as they interact with chromatin. Most TFs have been shown to exhibit power law distributed residence times, which arise from the broad distribution of binding affinities within the nucleus. This blurs the line between specific and non-specific binding and renders it impossible to distinguish between different binding modes based on residence times alone. In this dissertation, I combine single molecule tracking (SMT) with statistical algorithms to identify two distinct low-mobility states for chromatin (histone H2B) and bound transcriptional regulators within the nucleus. On our timescales, the TF mobility states represent the mobility of the piece of chromatin that they are bound to. Ligand activation results in a dramatic increase in the proportion of steroid receptors in the lowest mobility state. Mutational analysis revealed that only chromatin interactions in the lowest mobility state require an intact DNA-binding domain as well as oligomerization domains. Importantly, these states are not spatially separated as previously believed but in fact, individual H2B and chromatin-bound TF molecules can dynamically switch between them. Single molecules presenting different mobilities exhibit different residence time distributions, suggesting that the mobility of a TF is intimately coupled with their temporal dynamics. This provides a way to identify different binding modes that cannot be detected by measuring residence times alone. Together, these results identify two unique and distinct low-mobility states of chromatin that appear to represent common pathways for transcription activation in mammalian cells. Next, I demonstrate how SMT can complement genome wide assays to paint a complete picture of gene regulation by TFs using two case studies: corticosteroid signaling and endocrine therapy resistance in breast cancer. Finally, I conclude with a roadmap for future work on examining the role of mechanical cues within the cellular microenvironment (such as stiffness and topography) in regulating TF dynamics and gene expression.Item MULTISCALE MEASUREMENTS OF ELECTRICAL & MECHANICAL CELLULAR DYNAMICS(2023) Alvarez, Phillip; Losert, Wolfgang; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on the study and measurement of coupled electrical and mechanical responses in mammalian cells, tissues, and organs. Cellular biophysics often studies forces and their impact on biochemical pathways. These forces can be electrical, resulting in neuronal action potentials or cardiac cell contractions, or mechanical, driving e.g., a cell’s ability to recognize physical probing or surface texture. These forces and their responses, though, are frequently coupled through interlinked cellular mechanisms which result in emergent responses that take both electrical and mechanical signals into account. One challenge in capturing these emergent responses is that they occur on multiple scales, from the intracellular scale to the organ scale, limiting the ability of commercial microscopes to image these responses simultaneously. In this work I use surface texture, optical imaging, and multiscale-capable image analysis algorithms across these scales to elicit and measure electrical and mechanical responses. To image emergent responses from electrical and mechanical coupling, I developed two custom microscopes that can image at multiple length scales and timescales simultaneously. The Multiscale Microscope can capture slow intracellular mechanical dynamics concurrently with fast tissue scale electrical dynamics, while the BEAMM microscope links fast tissue scale electrical dynamics with both intracellular mechanical dynamics and slower organ-scale mechanical and electrical responses. Finally, I describe ongoing and future studies which exploit these new capabilities for multiscale measurements of electrical and mechanical dynamics.Item PROBING BIOPHYSICAL INTERACTIONS TO UNDERSTAND VIRAL DIFFUSION AND PARTICLE FATE IN THE AIRWAY MUCOSAL BARRIER(2023) Kaler, Logan; Duncan, Gregg A; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The mucus barrier in the airway is the first line of defense against inhaled particulates and pathogens. Within the mucus barrier, large, heavily glycosylated gel-forming mucin proteins form a network to trap particles for removal. Influenza A virus (IAV) must first cross the mucus barrier before reaching the underlying airway epithelial cells to cause infection. On the IAV envelope, hemagglutinin (HA) binds sialic acid on the surface of the cell to initiate viral entry. However, HA preferentially binds sialic acid attached to galactose by either an ⍺2,3 or ⍺2,6 linkage. In addition to the cell surface, sialic acid is found on mucins and is thought to act as a decoy receptor to entrap the IAV within the mucus layer. However, neuraminidase (NA) on the envelope of IAV cleaves the bond between HA and sialic acid, releasing the virus. While the mechanism of IAV infection has been characterized, the interplay between mucus biophysical properties and the binding of IAV within the mucus network prior to infection requires further investigation. The overall objective of this dissertation is to understand how IAV moves through the mucosal barrier to subsequently cause infection. We hypothesize the structural features of the mucus gel network are responsible for the changes in IAV movement, rather than the binding and unbinding of the virus. To investigate this, we first analyzed the movement of IAV in ex vivo mucus from human endotracheal tubes. In order to further analyze this movement, we developed a novel analysis to calculate the dissociation constant of IAV-mucus binding in a 3D gel network environment. Using this data, we established a pipeline for estimating the passage of particles, including IAV, through the airway mucosal barrier. A machine learning-based trajectory analysis was employed to classify individual trajectories in order to calculate the percentage of particles able to cross the mucus barrier within a physiologically relevant time frame. Lastly, we investigated the effect of sialic acid binding preference on diffusion of IAV through mucus collected from different in vitro human airway epithelial cell cultures. The combined results of these studies confirmed our hypothesis that the mucus microstructure rather than the adhesive interactions of IAV to the mucins was responsible for the differences in IAV diffusion. This work provides further insight into role of the mucosal barrier in IAV infection and identifies the mucus gel network microstructure as a target for the development of therapeutics against IAV.Item A UNITED-ATOM REPRESENTATION FOR SPHINGOLIPIDS IN THE CHARMM MOLECULAR DYNAMICS FORCE FIELD(2023) Lucker, Joshua; Klauda, Jeffery B; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The development of the CHARMM force field (FF) in the late 1970’s and early 1980’s was groundbreaking at the time. For the first time, a computer program was created that could simulate biological systems on a macromolecular scale. Starting with the simulation of simple proteins, CHARMM has since expanded to include such macromolecules as nucleic acids and lipids, now being able to model complex biological systems and processes. Force fields like CHARMM can be represented in different ways. For example, force fields can be represented through an all-atom representation, in which all atoms in a system are modeled as distinct interaction units. This representation can be simplified into a united-atom representation, which shall be the primary focus of this thesis. A united atom FF has no explicit interaction sites for hydrogen. Instead, the hydrogens are lumped onto the atoms they are connected to, termed ‘heavy atoms’ as these atoms have a greater atomic weight than hydrogen. The CHARMM FF originally had a united-atom representation for proteins, which was abandoned to focus on all-atom representations. However, in certain cases, such as lipid tails, united-atom representations are often useful in certain situations; as compared to all-atom representations, united-atom models often speed up simulation times, which is useful in the simulation of large enough systems of molecules. Although there are currently united-atom representations for many types of biomolecules in the CHARMM FF, including multiple types of membrane lipids, there has yet to be a united-atom model for sphingolipids, a type of membrane lipid most commonly found in the myelin sheath of neurons, although its presence has been noted in many types of eukaryotic cells. The goal of this thesis is thus to develop such a model and implement it in the CHARMM FF.