Biophysics, Rockets, and the State: the Making of a Scientific Discipline in Twentieth-Century China

Luk, Yi Lai Christine Arizona State University 2014 해외박사(DDOD)

This study takes biophysics---a relatively new field with complex origins and contested definitions---as the research focus and investigates the history of disciplinary formation in twentieth-century China. The story of building a scientific discipline in modern China illustrates how a science specialty evolved from an ambiguous and amorphous field into a full-fledged academic discipline in specific socio-institutional contexts. It focuses on archival sources and historical writings concerning the constitution and definition of biophysics in order to examine the relationship between particular scientific styles, national priorities, and institutional opportunities in the People's Republic of China. It argues that Chinese biophysicists exhibited a different style of conceiving and organizing their discipline by adapting to the institutional structure and political economy that had been created since 1949. The eight chapters demonstrate that biophysics as a scientific discipline flourished in China only where priorities of science were congruent with political and institutional imperatives. Initially consisting of cell biologists, the Chinese biophysics community redirected their disciplinary priorities toward rocket science in the late 1950s to accommodate the national need of the time. Biophysicists who had worked on biological sounding rockets were drawn to the military sector and continued to contribute to human spaceflight in post-Mao China. Besides the rocket-and-space missions which provided the material context for biophysics to expand in the late 1950s and early 1960s, Chinese biophysicists also created research and educational programs surrounding biophysics by exploiting the institutional opportunities afforded by the policy emphasis on science's role to drive modernization. Biophysics' tie to nationalistic and utilitarian goals highlights the merits of approaching modern Chinese history from disciplinary, material, and institutional perspectives.

The Molecular Biophysics of Perception: How Force Sensitive Proteins Transform External Input into Useful Work

Nisler, Collin The Ohio State University ProQuest Dissertations & 2021 해외박사(DDOD)

A defining characteristic of complex life is the capacity to integrate a variety of stimuli from the environment and, through the interaction of a dizzying array of biomolecules, to properly interpret and respond to such stimuli. Gene duplications and random mutations, when filtered through the sieve of natural selection, provides living organisms with the creative power needed to produce the suite of highly specialized molecules required to perform the vital task of sensing environmental cues. From maintaining balance by harnessing the power of gravity in vertebrates, to the detection of a dangerous shift from diffusive equilibrium in yeast cells, proteins play a vital role in detecting changes in the environment of all living organisms.Central to the function of many of these proteins is the transmission or detection of mechanical forces applied to the proteins themselves, or to surrounding cellular structures and tissues in which they are embedded. Such force sensitive proteins are thus ideal subjects of single-molecule, quantitative biophysical approaches to better understand the molecular basis of force transduction. Examples of such proteins are cadherin-23 (CDH23) and protocadherin-15 (PCDH15), two proteins required for vertebrate hearing; desmoglein (DSG) and desmocollin (DSC), two proteins that are vital for maintaining tissue integrity in the presence of constant mechanical stress; and transient receptor potential yeast 1 (TRPY1), a mechanically-sensitive ion channel in yeast that restores osmotic balance in response to hyperosmotic shock. While these proteins are involved in unrelated biological processes, they all have evolved the ability to detect and respond to force generated by environmental sources, and their proper function is vital to the survival of the host organism. Here, a multidisciplinary approach is used to provide fundamental insights into the structure, mechanical properties, and dynamics of these specific proteins, as well as into protein-protein interactions and the biophysics of force transduction in general.CDH23 and PCDH15 interact in the inner ear of all vertebrates to form the tip link, a fine protein filament essential for transducing the force generated by sound waves. Their interaction must be strong enough to withstand the constant and repeated application of force, but must also be able to break and re-form in response to potentially damaging stimuli. Additionally, this complex is likely under varying selection pressures depending on the lifestyle and environment of each organism, and thus presents an ideal system to study the evolutionary biophysics of force-transducing protein-protein interactions. High-resolution structures obtained through X-ray crystallography reveal a high degree of structural similarity in these proteins from evolutionarily distant vertebrates, indicating that their architecture has been maintained throughout evolution despite significant sequence divergence. Through the use of all-atom and coarse-grained molecular dynamics (MD) simulations, it was discovered that these evolutionary sequence changes result in significant differences in the dynamics, forces, and energies of unbinding involved in this complex, and that these differences are supported by affinities obtained through surface plasmon resonance experiments. It is hypothesized here that such differences reflect a varying degree of purifying selection in the different vertebrate lineages, and this is supported by biophysics-based molecular sequence evolution simulations as well as evolutionary rate analyses. These results are likely not specific to CDH23 and PCDH15, and may shape the biophysics-based evolutionary landscape of other protein-protein complexes as well.As with CDH23 and PCDH15, DSG and DSC are members of the cadherin superfamily of proteins and interact with one another to form a cell-cell contact that must withstand force. That is where the similarity ends between these systems, however, as DSC and DSG not only exhibit different overall structures from CDH23 and PCDH15, but they coalesce in a lattice of DSG and DSC molecules to form a robust junction between cells called the desmosome. Additionally, their mode of interaction is entirely distinct from CDH23 and PCDH15. Found in cardiac and epithelial tissue, these proteins are vital for maintaining a robust connection between cells despite abrasions and stretching forces that these tissues are subjected to. Through MD simulations, the mechanical properties of these proteins has been described both in dimers, as well as in a hypothetical model of the desmosomal lattice. Through comparison of these two systems, the simulations reveal the role that parallel cis-interactions play in the forces and mechanics of the lattice, and provide a model for how the desmosome may function in vivo. Results of these simulations demonstrate how desmosomal proteins exhibit a similar mechanical response to E-cadherins but a distinct response from the clustered protocadherins, provide insight into the role of cell-cell junctions in tissue morphogenesis and wound healing as well as into the molecular basis of disease mutations, and can be used as a guide for future experimental design.While the ion channel TRPY1 is not directly involved in force transduction as with the CDH23-PCDH15 and DSG-DSC systems, it has evolved to respond to forces generated through the stretching or compression of the membrane in which it is embedded. TRPY1 has been characterized experimentally, and its activity has been found to be modulated by the binding of both cytoplasmic and luminal calcium (Ca2+), binding of lipid molecules, and membrane stretch. However, the molecular mechanisms involved in these processes are not well understood. The first structure of TRPY1, obtained by collaborators, facilitated MD simulations that revealed varying degrees of stability and dynamics in essential structural domains depending on whether Ca2+ and/or the regulatory lipids were bound to the protein. Analysis of these simulations also reveals the paths of allosteric communication between the transmembrane and cytoplasmic regions, and how removal of inhibitory ligands affect them. To ensure these results are not an effect of insufficient sampling, ?s long simulations were ran, and the results of these longer simulations corroborate the conclusions of the shorter simulations. Finally, to obtain a model of the open state, the pore was forced open by an expanding cylinder, applying forces to the protein radially from the center of the pore, after which an external electric field was applied to simulate a membrane voltage. Permeation of K+ across the pore was observed from which a conductivity of 480 pS was calculated, which is similar to experimentally obtained values. These results provide an insight into the function and modulation of the TRPY1 protein as well as molecular insight into the activation of mechanically-gated ion channels in general.

Biophysical models of transcriptional regulation from sequence data

Kinney, Justin Block Princeton University 2008 해외박사(DDOD)

In the post-genomics era, DNA sequence itself is becoming a medium by which to probe biological phenomena. With the advent of microarray technology, and ultra-high-throughput sequencing more recently, large sequence data sets are becoming standard products of day-to-day research. Yet as software for analyzing such data proliferates, a fundamental understanding of how DNA sequence should be used to gain biological insight is missing from the literature. The focus of this thesis is on developing tools for characterizing the biophysical interactions underlying transcriptional regulation---the ability of cells to control which genes they transcribe to mRNA, and thus express as protein. We begin by presenting basic principles for the analysis of DNA sequence data---specifically, data for which each sequence sigma is accompanied by a (perhaps very noisy) measurement z of biophysical functionality. A salient feature of experiments which produce such sigma z data is the difficulty of characterizing experimental noise a priori. We overcome this obstacle by introducing error-model-averaged (EMA) likelihood, which allows biophysical models of arbitrary functional form to be rigorously fit to sigmaz data. EMA likelihood is closely related to mutual information, but its probabilistic interpretation provides some advantages. We demonstrate EMA likelihood's utility on previously published microarray data, using Metropolis Monte Carlo sampling to infer models for the DNA-binding energy of transcription factor proteins. The ability to properly analyze sigmaz data leads us to propose a new experimental assay, called Sort-Seq. This technique uses ultra-high-throughput sequencing to probe the protein-DNA and protein-protein interactions underlying transcriptional regulation at specific genomic loci. We present data from a proof-of-principle Sort-Seq experiment probing the lacZ promoter of E. coli, data we use to characterize the sequence-dependent binding energy of the transcription factor CRP. We then discuss what one can, in principle, infer from large Sort-Seq data sets. We show that, with enough sigmaz data probing the binding of multiple proteins per sequence, one should be able to infer both protein-DNA and protein-protein interaction energies in absolute thermal units. We conclude that, with the advent of ultra-high-throughput sequencing, DNA sequence itself might provide a very sensitive means by which to probe in vivo biophysics.

Studies of Membrane Biophysics Using Fluorescence Correlation Spectroscopy

Jiang, Yanfei Washington University in St. Louis 2013 해외박사(DDOD)

This work is dedicated to developing theories and applications of techniques related to Fluorescence Correlation Spectroscopy (FCS) to study membrane biophysics. One of the theories presented in this work is inverse FCS, which provides a way to study the diffusion rate, size and number of dark particles immersed in a high concentration of fluorescent probes. The size determination using inverse FCS can be diffusion-independent. By using inverse FCS, we found two different domain growth pathways in DLPC/DSPC membranes. In one of these two pathways, nanoscopic domains appear at first and then gradually grow to micron size. In the other pathway, the domains reach micron size quickly and their number gradually increases. The second theory we have developed is an FCS theory for closed systems for both periodic and reflective boundaries. We demonstrate that this theory can be applied to the study the diffusion in the nanotube membranes by providing accurate diffusion coefficients. The developed methodology is also useful in the single molecule studies. We also used FCS and fluorescence microscopy to investigate the composition heterogeneity in GUV (giant unilamellar vesicle) membranes made by electroformation. We found that there is a large composition heterogeneity among GUVs and this heterogeneity is caused by a phase separation in the depositing step in the preparation. This heterogeneity can be reduced by a pre-heating (annealing) treatment after the deposition. Finally, by using FCS and fluorescence microscopy, we found two different gel phases in DLPC/DSPC GUVs. These two gel phases have different affinities for a variety of fluorescent lipid probes. We also found the gel domains in the GUVs are preserved after the GUVs rupture on a cover glass. Our AFM (atomic force microscopy) studies on the ruptured GUVs indicate that the two different gel phases behave differently during the rupture.

Analyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence

Wang, Dedi University of Maryland, College Park ProQuest Diss 2024 해외박사(DDOD)

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.

Differentiable Programming for Problems in Statistical Mechanics and Biophysics

Krueger, Ryan Kirkman Harvard University ProQuest Dissertations & Theses 2025 해외박사(DDOD)

This thesis explores how differentiable programming -- a paradigm that leverages automatic differentiation (AD) for scientific computing -- can be used to advance modeling and design in soft matter and biophysics. Traditional applied mathematics relies on hand-crafted models, approximations, and domain-specific numerics. However, recent advances in hardware acceleration and AD frameworks originally developed for deep learning have transformed the landscape of scientific computing, enabling exact and efficient computation of gradients in complex models. I demonstrate how this computational shift enables novel capabilities across several domains. I first show how AD enables otherwise intractable analytical calculations. Specifically, I use AD to efficiently evaluate partition functions and assembly yields in systems of anisotropically interacting particles. I then apply this framework to compare calculations under existing models of protein-protein interactions with experimentally-determined assembly yields of de novo proteins. Inspired by this example of poor model generalization, I then focus on AD as an optimization tool for physics-based models. I devise a framework for directly differentiating the aforementioned assembly yield calculation, allowing me to fit protein force fields to target assembly yields via gradient-based optimization. I then extend these techniques to thermodynamic models of nucleic acids, showing that parameters in the popular "nearest neighbor" model describing secondary structure thermodynamics can be fit to data via gradient descent, enhancing predictive power. I also apply differentiable molecular dynamics to design functional colloidal systems. For some physics-based calculations, direct differentiation for modeling or design is infeasible. One such cause is that a calculation is differentiable in principle but computationally prohibitive to unroll. In the face of this, I leverage and extend novel methods for stochastic gradient estimation to develop a framework for fitting coarse-grained force fields to experimental data. In the second limiting case, a calculation may be inherently discontinuous due to discrete control variables. One example of this is designing RNA sequences with respect to the aforementioned nearest neighbor model, for which I introduce an algorithm to compute the expected partition function over a probability distribution of RNA sequences, enabling gradient-based RNA design. Building on these advanced methods for stochastic gradient estimation and this probabilistic sequence representation, I develop a general method for inverse design in molecular simulations by introducing a notion of expected Hamiltonians. I demonstrate how this enables the rational design of intrinsically disordered proteins, DNA sequences, and even improved particle linking algorithms. I conclude with a forward-looking perspective on promising applications of these methods, opportunities for future methods development, and proposed directions for novel interfaces between computation, mathematics, and physics.

Optogenetics and computer vision for Caenorhabditis elegans neuroscience and other biophysical applications

Leifer, Andrew Michael Harvard University 2011 해외박사(DDOD)

This work presents optogenetics and real-time computer vision techniques to non-invasively manipulate and monitor neural activity with high spatiotemporal resolution in awake behaving Caenorhabditis elegans. These methods were employed to dissect the nematode's mechanosensory and motor circuits and to elucidate the neural control of wave propagation during forward locomotion. Additionally, similar computer vision methods were used to automatically detect and decode fluorescing DNA origami nanobarcodes, a new class of fluorescent reporter constructs. An optogenetic instrument capable of real-time light delivery with high spatiotemporal resolution to specified targets in freely moving C. elegans, the first such instrument of its kind, was developed. The instrument was used to probe the nematode's mechanosensory circuit, demonstrating that stimulation of a single mechanosensory neuron suffices to induce reversals. The instrument was also used to probe the motor circuit, demonstrating that inhibition of regions of cholinergic motor neurons blocks undulatory wave propagation and that muscle contractions can persist even without inputs from the motor neurons. The motor circuit was further probed using optogenetics and microfluidic techniques. Undulatory wave propagation during forward locomotion was observed to depend on stretch-sensitive signaling mediated by cholinergic motor neurons. Specifically, posterior body segments are compelled, through stretch-sensitive feedback, to bend in the same direction as anterior segments. This is the first explicit demonstration of such feedback and serves as a foundation for understanding motor circuits in other organisms. A real-time tracking system was developed to record intracellular calcium transients in single neurons while simultaneously monitoring macroscopic behavior of freely moving C. elegans. This was used to study the worm's stereotyped reversal behavior, the omega turn. Calcium transients corresponding to temporal features of the omega turn were observed in interneurons AVA and AVB. Optics and computer vision techniques similar to those developed for the C. elegans experiments were also used to detect DNA origami nanorod barcodes. An optimal Bayesian multiple hypothesis test was deployed to unambiguously classify each barcode as a member of one of 216 distinct barcode species. Overall, this set of experiments demonstrates the powerful role that optogenetics and computer vision can play in behavioral neuroscience and quantitative biophysics.

Holographic Microscopy for Soft Matter and Biophysics

Dimiduk, Thomas G Harvard University ProQuest Dissertations & Theses 2016 해외박사(DDOD)

I discuss a series of advancements I have made towards making digital holographic microscopy into a useful tool for experimental scientists in soft-matter physics and biophysics. Digital holograms can be recorded with simple hardware at high speed to capture three-dimensional information about the dynamics of aqueous suspensions of colloidal particles, cells, viruses or other microscopic objects. The challenge of working with holograms is that they map information about the objects non-locally onto an interference pattern. Therefore, post processing is needed to extract the information from a hologram. Traditionally this has been done by reconstructions, effectively shining light back through the hologram to obtain a representation of the recorded objects. More recently Ovryn and Izen (JOSA A 2000) and Lee, Grier and coworkers (Opt. Express 2012) have shown that more precise information can be recovered by physically modelling the light scattering that creates the hologram and solving a constrained inverse-scattering problem to obtain information about the scatterers such as their position or size. This technique gives precise results but requires a scattering model for the objects under observation. It therefore requires significant expertise to set up and implement. In this dissertation I present several advances that improve upon this state-of-the art. First, I present a simple, inexpensive, portable, battery-powered holographic microscope that is suitable for imaging biological samples inside an incubator. Next, I describe a method using a general scattering model called the discrete dipole approximation to analyze holograms of non-spherical particles. Because this analysis is computationally expensive, I present a new method based on analyzing a random subset of the pixels of a hologram. This method, which significantly speeds up computation, is the basis for a framework based on Bayesian inference that gives a more intuitive and rigorous way of specifying prior information and presenting uncertainty in results, which I present at the end. The motivating thread through this thesis is building tools to enable new experiments using holography and making it easier for scientists who are not experts in holography and light scattering to use holography as a tool to do the science that interests them. In support of these goals, I have implemented all of the computational techniques and physical models in an open source library, HoloPy, to make it as easy as possible for other scientists to use them.

Neuron Growth and Neurodevelopmental Disorders Modeling With Isogeometric Analysis, Biophysics, and Machine Learning

Qian, Kuanren Carnegie Mellon University ProQuest Dissertations 2025 해외박사(DDOD)

Neurons are the fundamental component of the brain, consisting of the cell body, dendrites, and an axon. Multiple neurons form neurite networks to perform complex tasks. Neurological disorders exist when neurons are damaged or lose connections. Studying the neurodevelopmental process and associated neuron growth factors (NGF) could significantly strengthen our understanding and potentially lead to effective treatment strategies. Neuron growth is a complex, multi-stage process in which neurons develop sophisticated morphologies and interwoven neurite networks. This makes conventional computational neuron growth modeling challenging and necessitates sophisticated modeling techniques to accurately represent the diverse morphologies and growth patterns. A computational model that can simulate these diverse and dynamic growth processes is essential for advancing our understanding of neurodevelopmental processes and function. These models could substantially benefit neuron culturing experiments by offering researchers a platform to test hypotheses and protocols in silico before progressing to in vitro or clinical studies. By studying disruptions in these processes through the models, researchers can gain insights into how such disturbances might lead to neurological disorders. This approach enhances our understanding of neuronal dynamics and aids in developing interventions for neurological disorders, necessitating a robust computational framework that emphasize the importance of integrating advanced mathematical and computational techniques to capture the intricacies of neuron growth accurately and efficiently.Recent advances in experimental research have allowed us to examine the effects of various neuron growth factors, such as NGF and neurotrophin concentrations, that play crucial roles in neurite outgrowth, survival, and differentiation. Additionally, these studies have provided insights into the potential causes of neurological diseases, such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis. Despite these advances, there is a need for computational tools that can accurately simulate the neuron growth process. Existing bio-phenomenon-based models often overlook NGFs. On the other hand, biophysics-based models require extensive and computationally expensive governing equations, limiting the practicality of large-scale simulations. Moreover, these models often struggle to capture the dynamic transitions between different stages of neuron growth. These transitions are critical for understanding how neurons develop their complex morphologies and how disruptions in these processes may lead to neurodevelopmental disorders (NDDs). NDDs are among the most prevalent chronic diseases in the U.S., severely impacting the formation of central and peripheral nervous systems. Consisting of a wide array of disorders, such as autism spectrum disorder, attention deficit hyperactivity disorder, and epilepsy, NDDs are characterized by progressive impairments in cognitive, speech, memory, motor, and other neurological functions. The heterogeneous nature of NDDs makes it significantly challenging to identify the exact cause, impeding accurate diagnosis and the development of targeted treatments. A computational model for NDDs could enhance our understanding of the various factors involved and assist in identifying root causes to expedite treatment development.Despite significant advances in experimental neuroscience that have identified factors influencing neuron growth and mechanisms underlying neurological disorders, computational tools are still needed to simulate neuron growth and NDDs biomimetically and efficiently. This thesis introduces a novel computational framework to enhance the understanding of neuron growth processes and associated NDDs. The framework leverages the convergence of spline modeling, computational mechanics, neurophysiology, and data-driven modeling techniques. The computational framework includes: (1) The development of an IGA-collocation-based phase field model that simulates neuron growth behaviors; (2) Integration of experimental data for biomimetic simulation of neurite morphological evolution to capture dynamic transitions in growth stages and accurately reflect complex neuronal behaviors, along with a customized convolutional neural network (CNN) based on an autoencoder to enhance computational efficiency and reduce simulation time; (3) The implementation of the IGA neuron growth model in C++ using truncated T-spline with local refinements for computational efficiency and accuracy by reducing necessary degrees of freedom (DOFs) with a thorough investigation of factors contributing to abnormal morphological transformation, including retraction and atrophy associated with NDDs, as well as a MetaFormer-based ML model that offers fast and accurate predictions of NDDs based on healthy neurite growth; and (4) Extension of the neuron growth model to 3D using truncated hierarchical B-splines (THB-splines) with multiple levels of local refinements. This computational framework seeks to bridge the gap in high-fidelity mathematical modeling and efficient simulation tools. These tools are crucial for elucidating the complex biophysics of NDDs and for shaping future targeted therapeutic strategies and treatments.

An Investigation of Microtubule-Kinetochore Attachment Mechanisms

Murray, Lucas Edward University of Washington ProQuest Dissertations & 2024 해외박사(DDOD)

The ability to replicate is a defining feature of life. At the center of eukaryotic cell division are a set of protein machines responsible for pulling apart the chromosomes before cells divide. Spindle microtubules grow from the poles of the cell and connect to chromosomes via protein complexes called kinetochores. Kinetochores must maintain tenacious attachments to microtubule tips, even as they assemble and disassemble underneath their grip. Additionally, kinetochores mediate an error correction process to ensure the proper attachments to microtubules are formed before separation of the chromosomes commences. Here, I work to understand how the proteins in the kinetochore work together to maintain attachments to microtubules. I investigate two different mechanisms for microtubule-kinetochore attachment: the conformational wave mechanism and the biased diffusion mechanism. I developed a new optical trapping assay, using it to show that microtubule protofilament morphological and energetic properties can be measured and changed. I investigate the role of protofilament curl enlargement in the attachment and motility of the kinetochore. I develop theoretical models that show that the biased diffusion mechanism can fit experimentally measured detachment rates for assembling and disassembling kinetochores. Finally, I show kinetochores exhibit asymmetry in their sliding friction when they are dragged along microtubule lattices, a new phenomenon for microtubule-kinetochore biophysics. I argue this sliding friction forms the basis for a new mode of error correction during cell division, one that likely holds across most eukaryotic organisms.