Browsing by Subject "Computational modeling"
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Item Embargo A Multiplexed, Multi-scale Optical Imaging Platform to Quantify Tumor Metabolic Heterogeneity(2023) Deutsch, Riley JosephThe American Cancer Society reported an estimated 300,000 new cases of breast cancer and 44,000 new breast-cancer related deaths in 2022 in the United States alone. With each new successfully treated primary tumor, there is a subsequent risk of disease recurrence. Recurrence poses a risk to 10% of patients within the first 5 years post treatment and a lifetime risk of 30% across all patients. While new tools are being developed to better understand and mitigate the risk of recurrence, triple negative breast cancers, which exhibit no targetable surface markers, offer little in the way of recurrence prediction or treatment. It is understood that tumor heterogeneity is a driving force in tumor recurrence. Temporal heterogeneity is associated with therapeutic treatment, where the administration either selects for resistance subpopulations of tumor cells that are able to recur or a de novo resistant phenotype arises that leads to recurrence. Additionally, it has been well documented that tumors vary spatially across a primary tumor. This heterogeneity takes the form of genetic, epigenetic, and phenotypic heterogeneity. One such phenotype of interest is metabolic heterogeneity. Metabolism is classified as a ‘Hallmark of Cancer’ and has been studied as a driver of tumor progression for almost a century since Otto Warburg first described the phenomenon of tumors exhibiting high rates of aerobic glycolysis. Optical imaging is well poised to study metabolic heterogeneity due to its ability to image cellular level features, to multiplex multiple endpoints, and the ability to image longitudinally. Endogenous fluorescence contrast of coenzymes NADH and FAD have been used to report on the redox state of in vivo tissue and distinguish cancerous from benign lesions. The Center for Global Women’s Health Technologies (GWHT) has employed the use of exogenous fluorescent contrast agents to provide substrate-specific metabolic information. Three fluorescent agents have been validated including: 2-[N-(7-nitrobenz-2-oxa-1, 3-diazol-4-yl) amino]-2-deoxy-D-glucose (2-NBDG), a glucose derivative that is able to report on glycolysis; Tetramethylrhodamine ethyl ester (TMRE), a cation that is selectively attracted to the charge gradient generated by the mitochondria during ATP synthesis, making it a reporter of OXPHOS; and Difluoro-5,7-Dimethyl-4-Bora-3a,4a-Diaza-s-Indacene-3-Hexadecanoic Acid (Bodipy FL C16), a long chain saturated fatty acid is taken up by the cell and undergoes beta oxidation similar to native fatty acids. More recently, GWHT has begun combing these fluorescence agents for in vivo use to provide a wholistic understanding of cancer metabolism. The work here sets out to develop a novel optical imaging platform that is capable of imaging multiplexed metabolic endpoints, for quantitative intra-image analysis of metabolic gradients. This technology is built on the use of exogenous fluorescence contrast agents to report on substrate or pathway specific axes of metabolism. By simultaneously introducing multiple contrast agents, it is possible to capture a more wholistic snapshot of tissue metabolism. To encourage the adoption of this technology, a novel low-cost instrument will also be developed. Leveraging a consumer grade CMOS camera and variable focus lens, it is possible to image over multiple length scales, capturing both bulk tumor features and also single cell features. The flexibility offered by this simple innovation will allow for metabolic imaging to be applied over a variety sample type. Three specific aims were proposed to realize this goal by developing methods of multi-parametric exogenous contrast and low-cost instrumentation for multi-scale imaging of tumor metabolic heterogeneity in preclinical models. Aim 1 validated and demonstrated a method for the simultaneous injection and measurement of Bodipy FL C16 and TMRE to report on lipid uptake and mitochondrial activity, two potentially interrelated axes of metabolism. To validate this method, three sets of experiments were performed to establish that the two probes do not exhibit chemical, optical, or biological crosstalk. Chemical compatibility was established using liquid chromatography. Briefly, high molar concentration solutions of each individual probe (Bodipy FL C16, TMRE, and 2-NBDG) were created alongside a solution of all three probes at the same concentration. Chromatograms were collected immediately upon mixing, after 1 hour and after 24 hours. The area under the curve for each probe at each time point displayed an area under the curve (AUC) within 2% of the AUC of the single probe solutions, suggesting no chemical reactions. Optical crosstalk was assessed using optical spectroscopy and tissue mimicking phantoms. Optical phantoms were created with tissue mimicking optical properties and various concentration of Bodipy FL C16, TMRE, polystyrene microspheres (tissue scattering mimic), and hemoglobin (tissue absorption mimic). Leveraging an inverse Monte Carlo algorithm, we demonstrated that accurate values for each fluorescent probe could be measured regardless of the concentration of the other optical probe or level of optical scattering or absorption, indicating optical compatibility. To address biological crosstalk, two sets of 4T1 tumor bearing mice were subject to optical spectroscopy with either 1) Bodipy FL C16 alone, 2) TMRE alone, 3) a dual injection of Bodipy FL C16 and TMRE. Fluorescence spectra were measured 2-, 4-, 6-, 8-, 10-, 20-, 30-, 40-, 50-, and 60-minutes post-injection to establish uptake kinetics. It was found that the uptake kinetics of the dual probe group were not statistically different from the single probe group, indicating biological compatibility. With no observable crosstalk between Bodipy FL C16 and TMRE, the two probes method was applied to characterize murine mammary gland and two tumor of differing metastatic potential (4T1 and 67NR). In addition, to Bodipy FL C16 and TMRE, oxygen saturation and total hemoglobin were extracted from estimates of optical absorption, and these 4 endpoints were used to attempt to cluster groups of tumor and normal tissue. Difficulty clustering tumor groups of varying metastatic potential suggest a need for imaging technology. In Aim 2 a low-cost fluorescence microscope was developed capable of performing quantitative fluorescence imaging over a variety of samples. The goal of this work was to design a system that could be adapted to image a number of different sample types include core-needle tissue biopsies, preclinical window chambers, and in vitro organoids. To accomplish this a low-cost CMOS detector was used with a variable magnification lens allowing for imaging at multiple length scales. Uniform illumination was a necessary criterion for quantitative imaging. To generate uniform illumination that could be scaled across multiple length scales, an LED coupled 1:4 fanout optical fiber was employed alongside a computational model to determine the positioning of each fiber. To automate the design of illumination, a computational model was employed where each optical fiber was modeled as a Lambertian emitter in a spherical coordinate system. To determine the ideal placement of each fiber such that the individual illumination contributions of all fibers summed to a uniform distribution, a global optimizer was employed. A genetic pattern search allowed for the selection of coordinates to produce uniform illumination that could be feasibly employed at the benchtop. This integrated system is referred to as the CapCell microscope. Using this computational approach, two uniform illumination profiles were designed, one with a high aspect ratio (length ≫ width) and one with a low aspect ratio (length = width). To demonstrate the utility of optimized illumination, core needle biopsies from 4T1 tumors were stained with a tumor-specific fluorescent contrast agent, HS-27 and imaged with either optimized or unoptimized gaussian illumination. The repeatability of intra-image features was compared for the two illumination scenarios, and it was found that uniform illumination repeatedly revealed the same fluorescent features across the sample. These features were further confirmed with standard histology. Window chamber imaging demonstrated the importance of designing application specific illumination. 4T1 mammary tumors were grown orthotopically before a window chamber was surgically implanted. Animals were injected with either Bodipy FL C16, 2-NBDG, or HS-27 and imaged with both the high AR and low AR illumination platforms. As expected, the low AR, designed for window chambers, had a higher power density at the sample site and thus increased contrast compared to the low AR images. With a method and a system in place, the goal of Aim 3 was to apply the optical imaging platform to observe spatiotemporal metabolic heterogeneity. To achieve this, the CapCell microscope was upgraded to enhance contrast and improve resolution for the visualization of capillaries and single cells. This was demonstrated using 4T1 window chamber models stained with acridine orange, a nucleus specific stain, and green light reflectance to highlight hemoglobin absorption in microvessels. Given the interplay between metabolism and vasculature it was desirable to employ a vessel segmentation approach to describe vascular features within an image. A Gabor filter and Djikstra segmentation approach was employed on metabolic images to enable metabolic and vascular comparisons across an image field of view. To test the improved CapCell system, 4T1 tumors were treated with combretastatin A-1, a vascular disrupting agent. Across the course of treatment, the CapCell was able to observe bulk changes in metabolism and vascular density. Additionally, by employing high resolution imaging, it was possible to observe relationships between each metabolic probe and vessel tortuosity. This analysis allowed for the identification of metabolically unique regions within each group of animals, demonstrating the ability of this technology to parse metabolically distinct regions of tumor. In total, the work outlined here describes the development of a novel optical imaging platform capable of quantifying intratumor metabolic heterogeneity of multiple metabolic endpoints over multiple length scales. The system expands on previous work developing methods for simultaneous measurement of exogenous fluorescent contrast agents to report on lipid uptake and mitochondrial activity. The system also introduced a novel computational approach to design uniform illumination for a low-cost microscope capable of imaging across multiple sample types. Together these technologies were used to observe metabolic heterogeneity in preclinical window chamber models following chemical perturbation. The technology introduced here, is primed for future exploration. First, it would be desirable to integrate all three exogenous contrast agents for simultaneous imaging of three axes of metabolism in vivo. Once accomplished, the sample technology could be applied to study metabolic and vascular changes associated with residual disease and tumors that are entering recurrence.
Item Open Access Bridging Scales: How Microstructural Features Impact Macroscopic Cardiac Propagation(2018) Gokhale, Tanmay AnilCardiac arrhythmias such as atrial fibrillation and ventricular tachycardia are closely associated with microscopic fibrotic changes in cardiac structure that result in a heterogeneous myocardium. While the incidence of fibrosis is correlated with arrhythmia burden and recurrence, the mechanisms linking the two remain poorly understood. Previous experimental and simulation studies have identified changes in local conduction due to micron-scale structural heterogeneities. However, because of the limited ability to simultaneously study conduction over a range of spatial scales, it remains unclear how numerous microheterogeneities act in aggregate to alter conduction on the macroscopic scale. The overall objective of this dissertation is to elucidate and characterize the effect of microfibrosis on cardiac conduction, through the use of computational models and directly paired experimental studies.
The impact of fibrotic collagen deposition on reentrant conduction was first examined in a model of cardiac tissue. The presence of collagenous septa was shown to prolong the cycle length of reentry; the magnitude of reentry prolongation is correlated with the overall degree of fibrosis and the length of individual collagenous septa. Mechanistically, cycle length prolongation is caused by lengthening of the reentrant tip trajectory and alteration of restitution properties. An equivalent homogenized model of fibrosis is unable to recapitulate the observed cycle length prolongation, suggesting that the details of the microstructure greatly impact the observed macroscale behavior. A hybrid model generated by adding discrete heterogeneities to the coarse, homogenized model is able to partially reproduce cycle length prolongation by replicating the lengthened tip trajectory.
In order to examine the mechanisms by which cardiac microstructure influences global conduction, a new framework for paired computational and experimental studies using the engineered-excitable Ex293 cell line was developed. The Ex293 mathematical model incorporates several measures of variation in cellular and tissue electrophysiological properties, and is novel in its use of stochastic variation in a multidimensional model of tissue. Replicating the range of experimentally observed single-cell and macro-scale behavior requires introducing ionic conductance variation between individual cells and between tissues, as well as conductivity variation between tissues.
This framework was then utilized for paired studies in a geometry of idealized fibrosis to examine fibrosis-induced changes in micro- and macro-scale behavior. The presence of microscopic heterogeneities slows conduction and alters the curvature of the macroscopic wavefront. On the microscale, branching of tissue around heterogeneities leads to conduction slowing due to imbalances of electrical source and load, while wavefront collisions at sites of tissue convergence lead to acceleration of propagation. The observed macroscopic behavior is directly attributable to the combination of these microscopic effects and the tortuosity of propagation around heterogeneities. Under diseased conditions involving reduced excitability, alteration of these microscale behaviors leads to reversal of changes in wavefront curvature.
These findings advance our knowledge of the role of myocardial micro-heterogeneities in conduction. Further application of these techniques to examine how the effects of microstructure are dynamically modulated may help improve our understanding of the factors giving rise to cardiac arrhythmia.
Item Open Access Computational Modeling of Multi-Agent, Continuous Decision Making in Competitive Contexts(2021) McDonald, KelseyHumans are able to make adaptive decisions with the goal of obtaining a goal, earning a reward, or avoiding punishment. While much is known about the behavior and corresponding underlying neural mechanism relating to this aspect of decision-making, the field of cognitive neuroscience has focused almost exclusively on how these types of decisions are made in discrete choices where the set of possible actions is comparatively much smaller. We know much less about how human brains are able to make similar types of goal-directed decisions in continuous contexts which are more akin to the types of choices humans make in real-life. Further, how these processes are modified by the presence of other humans whose goals might influence one's own future behavior is currently unknown. Across three empirical studies, I address some of these gaps in the literature by studying human competitive decision-making in a dynamic, control paradigm in which humans interacted with both social and non-social opponents (Chapter 2 and Chapter 4). In Chapter 3, I show that brain regions heavily implicated in social cognition and value-based decision-making also play a role in tracking continuous decision metrics involved in monitoring instantaneous coupling between opponents, advantageous decision timing, and constructing social context. Collectively, the results in this dissertation demonstrate the utility in studying decision-making in less-constrained paradigms with the overall goal of gaining further understanding of how humans make complex, goal-directed decisions closer to real-world conditions.
Item Embargo Development and Validation of Software for Modeling Vagus Nerve Stimulation Across Species(2023) Musselman, Eric DavidElectrical stimulation and block of peripheral nerves hold great promise for treatment of a range of disease and disorders, but promising results from preclinical studies often fail to translate to successful clinical therapies. Differences in neural anatomy across species require different electrodes and stimulation parameters to achieve equivalent nerve responses, and accounting for the consequences of these factors is difficult. In Chapter 2, we describe the implementation, validation, and application of a standardized, modular, and scalable computational modeling pipeline for biophysical simulations of electrical activation and block of nerve fibers within peripheral nerves. The ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds) pipeline provides a suite of built-in capabilities for user control over the entire workflow, including libraries for parts to assemble electrodes, electrical properties of biological materials, previously published fiber models, and common stimulation waveforms. We validated the accuracy of ASCENT calculations, verified usability in beta release, and provide several compelling examples of ASCENT-implemented models. ASCENT will enable the reproducibility of simulation data, and it will be used as a component of integrated simulations with other models (e.g., organ system models), to interpret experimental results, and to design experimental and clinical interventions for the advancement of peripheral nerve stimulation therapies.
Next, in Chapter 3 we demonstrated how ASCENT can be applied to simulate accurately nerve responses to electrical stimulation. We simulated vagus nerve stimulation (VNS) for humans, pigs, and rats. We informed our models using histology from sample-specific or representative nerves, device design features (i.e., cuff, waveform), published material and tissue conductivities, and realistic fiber models. Despite large differences in nerve size, cuff geometry, and stimulation waveform, the models predicted accurate activation thresholds across species and myelinated fiber types. However, our C fiber model thresholds overestimated thresholds across pulse widths, suggesting that improved models of unmyelinated nerve fibers are needed. Our models of human VNS yielded accurate thresholds to activate laryngeal motor fibers and captured the inter-individual variability for both acute and chronic implants. For B fibers, our small-diameter fiber model underestimated threshold and saturation for pulse widths >0.25 ms. Our models of pig VNS consistently captured the range of in vivo thresholds across all measured nerve and physiological responses (i.e., heart rate, Aδ/B fibers, Aγ fibers, EMG, and Aα fibers). In rats, our smallest diameter myelinated fibers accurately predicted fast fiber thresholds across short and intermediate pulse widths; slow unmyelinated fiber thresholds overestimated thresholds across shorter pulse widths, but there was overlap for pulse widths >0.3 ms. We elevated standards for models of peripheral nerve stimulation in populations of models across species, which enabled us to model accurately nerve responses, demonstrate that individual-specific differences in nerve morphology produce variability in neural and physiological responses, and predict mechanisms of VNS therapy and side effect.
Lastly, in Chapter 4 we investigated how previous efforts to translate VNS therapies (e.g., for stroke, heart failure, and rheumatoid arthritis) have not accounted for individual and species-specific differences in nerve responses while selecting stimulation parameters, which could explain why clinical outcomes have not reproduced promising results from preclinical animal studies. We used previously validated computational models of VNS based on individual-specific nerve morphologies for populations of rats, pigs, and humans from Chapter 3 to show that a range of thresholds exists to achieve a target nerve response within and across species. We found that applying the same parameters across individuals of a species and recycling or linear scaling of stimulation parameters across species produces a large range of nerve responses. Our work highlights the need for systematic approaches to select stimulation parameters that account for individual- and species-specific differences in nerve responses to stimulation, which may be required to achieve higher response rates and greater therapeutic benefit from VNS therapies.
Item Open Access Investigating Potential Mechanisms of Subperception Spinal Cord Stimulation(2022) Titus, NathanSpinal cord stimulation (SCS) is a surgically implanted therapy for chronic pain that delivers electrical stimulation to the spinal cord. Recently, SCS paradigms have rapidly expanded to include more frequencies, amplitudes, and indications, but the therapeutic success of SCS remains stagnant(Titus et al., 2020). Many of the new SCS paradigms treat pain by applying stimulation at amplitudes less than the amount required for patients to perceive the treatments. However, there is little information regarding how these “subperception” SCS therapies effect analgesia. The work in this dissertation uses computational modeling and in vivo neural recordings to investigate and explore the potential analgesic mechanisms of subperception SCS.The combination of computational modeling and preclinical experimental data displayed in this dissertation provides many insights into the mechanisms of subperception SCS. A tool was built to design temporal patterns of SCS which predicted features of effective SCS patterns observed during in vivo single-unit recordings. A model was built to explain dorsal column responses to 10 kHz SCS, and this model accurately predicted in vivo single-unit recordings of dorsal column responses to low-amplitude, low-frequency SCS. This model was used to build a population of responses which was applied to a network model of SCS, and this modeling combination predicted in vivo multi-unit recordings of responses to a novel subperception SCS paradigm. Finally, models were developed which predicted the direct response of dorsal horn neurons to SCS. Feeding these responses into a network model of dorsal horn circuitry yielded similar changes in neural activity to measurements of neural activity obtained from post-mortem tissue of animals which had undergone SCS. Overall, this thesis work improved understanding of the mechanisms of action underlying multiple subperception SCS paradigms, provided models to predict and explain responses of neural elements to novel SCS paradigms, and developed a tool for designing new, effective patterns of SCS.
Item Open Access Investigating the Influence of Heterogeneity Within Cell Types on Microvessel Network Transport.(Cellular and molecular bioengineering, 2023-12) Nan, Junyu; Roychowdhury, Sayan; Randles, AmandaBackground
Current research on the biophysics of circulating tumor cells often overlooks the heterogeneity of cell populations, focusing instead on average cellular properties. This study aims to address the gap by considering the diversity of cell biophysical characteristics and their implications on cancer spread.Methods
We utilized computer simulations to assess the influence of variations in cell size and membrane elasticity on the behavior of cells within fluid environments. The study controlled cell and fluid properties to systematically investigate the transport of tumor cells through a simulated network of branching channels.Results
The simulations revealed that even minor differences in cellular properties, such as slight changes in cell radius or shear elastic modulus, lead to significant changes in the fluid conditions that cells experience, including velocity and wall shear stress (p < 0.001).Conclusion
The findings underscore the importance of considering cell heterogeneity in biophysical studies and suggest that small variations in cellular characteristics can profoundly impact the dynamics of tumor cell circulation. This has potential implications for understanding the mechanisms of cancer metastasis and the development of therapeutic strategies.Item Open Access Model Based Investigation of Virtual Electrodes of the Multi-Electrode Array for Epiretinal Prostheses(2010) Mueller, JerelVisual prostheses are an emerging technology to restore vision in blind
individuals. The level of vision currently attainable with these prostheses is crude and
far from the level of normal vision though. Epiretinal prostheses work by using a multi-
electrode array implanted within the eye on the inner layer of the retina to electrically
stimulate the neural elements beneath the electrodes and produce punctate visual
percepts of light called phosphenes. Stimulation by serially delivering a cathodic
monopolar pulse of current with each electrode in the array would require the least
power to construct pixilated images of the visual scene. There is the possibility of
complex stimulation schemes that may be able to preferentially stimulate the neural
elements between the electrodes of the multi-electrode array by utilizing multiple
electrodes of the array at once though. Although this would require more power, this
would effectively increase the resolution capabilities of the epiretinal prosthesis without
the need to increase the number of electrodes on the multi-electrode array. To
investigate the possibility of such a stimulation scheme, a computational model of the
inner layers of the human retina including the nerve fiber layer and ganglion cells was
constructed. The model response was validated against studies of biological ganglion
cells, and under comparable conditions reproduced features of epiretinal stimulation
seen clinically. The response of the computational model of the inner retinal layers to
stimulation by up to two electrodes at once in the multi-electrode array was then
determined to evaluate the possibility of producing phosphenes between the electrodes.
The investigation found that disk electrodes using rectangular pulses of equal
magnitude could not produce a distinct phosphene between the electrodes of the model.
Item Open Access Quantifying the Effects of Kilohertz Frequency Electrical Signals on Small Autonomic Nerve Fibers: Computational Modeling and In Vivo Electrophysiology AKA Applying Fast Signals to Slow Nerves(2019) Pelot, Nicole AmeliaThere is growing interest in treating diseases through electrical stimulation and block of peripheral autonomic nerves, especially the vagus nerve (VN). Applications include treatment of epilepsy, depression, obesity, heart failure, rheumatoid arthritis, and Crohn’s disease. Development of these neuromodulation therapies requires understanding the excitation properties of the small myelinated and unmyelinated autonomic fibers that constitute the VN. Further, effective neuromodulatory therapies require the capability to block unwanted neural activity, as well as the ability to generate controlled activation. The studies herein quantify autonomic axon responses to conventional pulse stimulation and to kilohertz frequency (KHF) signals, which can produce neural conduction block. Through this examination of axonal responses to a range of electrical signals, we strengthen the foundation upon which neuromodulatory therapies are built.
Item Open Access Study of Lorentz Effect Imaging and Neuronal Current MRI Using Electromagnetohydrodynamic Models(2013) Pourtaheri, NavidNeuronal current MRI (ncMRI) is a field of study to directly map electrical activity in the brain using MRI, which has many benefits over functional MRI. One potential ncMRI method, Lorentz effect imaging (LEI), has shown promise but needs a better theoretical understanding to improve its use.
We develop three computational models to simulate the LEI experiments of an electrolyte filled phantom subject to a current dipole based on: ion flow, particle drift, and electromagnetohydrodynamics (EMHD). With comparative experimental results, we use the EMHD model to better understand the Lorentz effect over a range of current strengths. We also quantify the LEI experimental images and assess ways to measure the underlying current strength, which would greatly benefit comparative brain mapping.
EMHD is a good predictor of LEI signal loss. We can measure the underlying current strength and polarity in the phantom using LEI images. We can also use trends from the EMHD model results to predict the required current density for signal detection in future LEI experiments. We can also infer the electric field strength, flow velocity, displacement, and pressure from the predicted current magnitude in an LEI experiment.
The EMHD model provides information that greatly improves the utility and understanding of LEI. Future study with our EMHD model should be performed using shorter dipole lengths, higher density and lower strength of current sources, and varying current source frequencies to understand LEI in the setting of mapping brain activity.