Optimizing Temporal Waveform Analysis: A Novel Pipeline for Efficient Characterization of Left Coronary Artery Velocity Profiles

Continuously measured arterial blood velocity can provide insight into physiological parameters and potential disease states. The efficient and effective description of the temporal profiles of arterial velocity is crucial for both clinical practice and research. We propose a pipeline to identify the minimum number of points of interest to adequately describe a velocity profile of the left coronary artery. This pipeline employs a novel operation that "stretches" a baseline waveform to quantify the utility of a point in fitting other waveforms. Our study introduces a comprehensive pipeline specifically designed to identify the minimal yet crucial number of points needed to accurately represent the velocity profile of the left coronary artery. Additionally, the only location-dependent portion of this pipeline is the first step, choosing all of the possible points of interest. Hence, this work is broadly applicable to other waveforms. This versatility paves the way for a novel non-frequency domain method that can enhance the analysis of physiological waveforms. Such advancements have potential implications in both research and clinical treatment of various diseases, underscoring the broader applicability and impact.Comment: 4 pages, 4 figure

Modeling Cardiovascular Hemodynamics Using the Lattice Boltzmann Method on Massively Parallel Supercomputers

Accurate and reliable modeling of cardiovascular hemodynamics has the potential to improve understanding of the localization and progression of heart diseases, which are currently the most common cause of death in Western countries. However, building a detailed, realistic model of human blood flow is a formidable mathematical and computational challenge. The simulation must combine the motion of the fluid, the intricate geometry of the blood vessels, continual changes in flow and pressure driven by the heartbeat, and the behavior of suspended bodies such as red blood cells. Such simulations can provide insight into factors like endothelial shear stress that act as triggers for the complex biomechanical events that can lead to atherosclerotic pathologies. Currently, it is not possible to measure endothelial shear stress in vivo, making these simulations a crucial component to understanding and potentially predicting the progression of cardiovascular disease. In this thesis, an approach for efficiently modeling the fluid movement coupled to the cell dynamics in real-patient geometries while accounting for the additional force from the expansion and contraction of the heart will be presented and examined. First, a novel method to couple a mesoscopic lattice Boltzmann fluid model to the microscopic molecular dynamics model of cell movement is elucidated. A treatment of red blood cells as extended structures, a method to handle highly irregular geometries through topology driven graph partitioning, and an efficient molecular dynamics load balancing scheme are introduced. These result in a large-scale simulation of the cardiovascular system, with a realistic description of the complex human arterial geometry, from centimeters down to the spatial resolution of red-blood cells. The computational methods developed to enable scaling of the application to 294,912 processors are discussed, thus empowering the simulation of a full heartbeat. Second, further extensions to enable the modeling of fluids in vessels with smaller diameters and a method for introducing the deformational forces exerted on the arterial flows from the movement of the heart by borrowing concepts from cosmodynamics are presented. These additional forces have a great impact on the endothelial shear stress. Third, the fluid model is extended to not only recover Navier-Stokes hydrodynamics, but also a wider range of Knudsen numbers, which is especially important in micro- and nano-scale flows. The tradeoffs of many optimizations methods such as the use of deep halo level ghost cells that, alongside hybrid programming models, reduce the impact of such higher-order models and enable efficient modeling of extreme regimes of computational fluid dynamics are discussed. Fourth, the extension of these models to other research questions like clogging in microfluidic devices and determining the severity of co-arctation of the aorta is presented. Through this work, a validation of these methods by taking real patient data and the measured pressure value before the narrowing of the aorta and predicting the pressure drop across the co-arctation is shown. Comparison with the measured pressure drop in vivo highlights the accuracy and potential impact of such patient specific simulations. Finally, a method to enable the simulation of longer trajectories in time by discretizing both spatially and temporally is presented. In this method, a serial coarse iterator is used to initialize data at discrete time steps for a fine model that runs in parallel. This coarse solver is based on a larger time step and typically a coarser discretization in space. Iterative refinement enables the compute-intensive fine iterator to be modeled with temporal parallelization. The algorithm consists of a series of prediction-corrector iterations completing when the results have converged within a certain tolerance. Combined, these developments allow large fluid models to be simulated for longer time durations than previously possible.Engineering and Applied Science

Performance Analysis of the Lattice Boltzmann Model Beyond Navier-Stokes

The lattice Boltzmann method is increasingly important in facilitating large-scale fluid dynamics simulations. To date, these simulations have been built on discretized velocity models of up to 27 neighbors. Recent work has shown that higher order approximations of the continuum Boltzmann equation enable not only recovery of the Navier-Stokes hydrodynamics, but also simulations for a wider range of Knudsen numbers, which is especially important in micro- and nanoscale flows. These higher-order models have significant impact on both the communication and computational complexity of the application. We present a performance study of the higherorder models as compared to the traditional ones, on both the IBM Blue Gene/P and Blue Gene/Q architectures. We study the tradeoffs of many optimizations methods such as the use of deep halo level ghost cells that, alongside hybrid programming models, reduce the impact of extended models and enable efficient modeling of extreme regimes of computational fluid dynamics.Physic

Massively Parallel Model of Extended Memory Use in Evolutionary Game Dynamics

To study the emergence of cooperative behavior, we have developed a scalable parallel framework for evolutionary game dynamics. This is a critical computational tool enabling large-scale agent simulation research. An important aspect is the amount of history, or memory steps, that each agent can keep. When six memory steps are taken into account, the strategy space spans $2^{4096}$ potential strategies, requiring large populations of agents. We introduce a multi-level decomposition method that allows us to exploit both multi-node and thread-level parallel scaling while minimizing communication overhead. We present the results of a production run modeling up to six memory steps for populations consisting of up to 1018 agents, making this study one of the largest yet undertaken. The high rate of mutation within the population results in a non-trivial parallel implementation. The strong and weak scaling studies provide insight into parallel scalability and programmability trade-offs for large-scale simulations, while exhibiting near perfect weak and strong scaling on 16,384 tasks on Blue Gene/Q. We further show 99% weak scaling up to 294,912 processors 82% strong scaling efficiency up to 262,144 processors of Blue Gene/P. Our framework marks an important step in the study of game dynamics with potential applications in fields ranging from biology to economics and sociology.Engineering and Applied Science

Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity.

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution

A Spatio-temporal Coupling Method to Reduce the Time-to-Solution of Cardiovascular Simulations

We present a new parallel-in-time method designed to reduce the overall time-to-solution of a patient-specific cardiovascular flow simulation. Using a modified Para real algorithm, our approach extends strong scalability beyond spatial parallelism with fully controllable accuracy and no decrease in stability. We discuss the coupling of spatial and temporal domain decompositions used in our implementation, and showcase the use of the method on a study of blood flow through the aorta. We observe an additional 40% reduction in overall wall clock time with no significant loss of accuracy, in agreement with a predictive performance model

Replication Data for' Role of Deformable Cancer Cells on Wall Shear Stress-Associated-VEGF Secretion by Endothelium in Microvasculature'

This is the raw data generated from HARVEY simulations. These were conducted to investigate the relationship between cell size, deformability, and wall shear stress. The work is described in detail in 'Replication Data for Role of Deformable Cancer Cells on Wall Shear Stress-Associated-VEGF Secretion by Endothelium in Microvasculature' in PLOS One

Role of deformable cancer cells on wall shear stress-associated-VEGF secretion by endothelium in microvasculature.

Endothelial surface layer (glycocalyx) is the major physiological regulator of tumor cell adhesion to endothelium. Cancer cells express vascular endothelial growth factor (VEGF) which increases the permeability of a microvessel wall by degrading glycocalyx. Endothelial cells lining large arteries have also been reported, in vitro and in vivo, to mediate VEGF expression significantly under exposure to high wall shear stress (WSS) > 0.6 Pa. The objective of the present study is to explore whether local hemodynamic conditions in the vicinity of a migrating deformable cancer cell can influence the function of endothelial cells to express VEGF within the microvasculature. A three-dimensional model of deformable cancer cells (DCCs) migrating within a capillary is developed by applying a massively parallel hemodynamics application to simulate the fluid-structure interaction between the DCC and fluid surrounding the DCC. We study how dynamic interactions between the DCC and its local microenvironment affect WSS exposed on endothelium, under physiological conditions of capillaries with different diameters and flow conditions. Moreover, we quantify the area of endothelium affected by the DCC. Our results show that the DCC alters local hemodynamics in its vicinity up to an area as large as 40 times the cancer cell lateral surface. In this area, endothelium experiences high WSS values in the range of 0.6-12 Pa. Endothelial cells exposed to this range of WSS have been reported to express VEGF. Furthermore, we demonstrate that stiffer cancer cells expose higher WSS on the endothelium. A strong impact of cell stiffness on its local microenvironment is observed in capillaries with diameters <16 μm. WSS-induced-VEGF by endothelium represents an important potential mechanism for cancer cell adhesion and metastasis in the microvasculature. This work serves as an important first step in understanding the mechanisms driving VEGF-expression by endothelium and identifying the underlying mechanisms of glycocalyx degradation by endothelium in microvasculature. The identification of angiogenesis factors involved in early stages of cancer cell-endothelium interactions and understanding their regulation will help, first to develop anti-angiogenic strategies applied to diagnostic studies and therapeutic interventions, second to predict accurately where different cancer cell types most likely adhere in microvasculature, and third to establish accurate criteria predisposing the cancer metastasis

Effect of constitutive law on the erythrocyte membrane response to large strains

Three constitutive laws, that is the Skalak, neo-Hookean and Yeoh laws, commonly employed for describing the erythrocyte membrane mechanics are theoretically analyzed and numerically investigated to assess their accuracy for capturing erythrocyte deformation characteristics and morphology. Particular emphasis is given to the nonlinear deformation regime, where it is known that the discrepancies between constitutive laws are most prominent. Hence, the experiments of optical tweezers and micropipette aspiration are considered here, for which relationships between the individual shear elastic moduli of the constitutive laws can also be established through analysis of the tension-deformation relationship. All constitutive laws were found to adequately predict the axial and transverse deformations of a red blood cell subjected to stretching with optical tweezers for a constant shear elastic modulus value. As opposed to Skalak law, the neo-Hookean and Yeoh laws replicated the erythrocyte membrane folding, that has been experimentally observed, with the trade-off of sustaining significant area variations. For the micropipette aspiration, the suction pressure-aspiration length relationship could be excellently predicted for a fixed shear elastic modulus value only when Yeoh law was considered. Importantly, the neo-Hookean and Yeoh laws reproduced the membrane wrinkling at suction pressures close to those experimentally measured. None of the constitutive laws suffered from membrane area compressibility in the micropipette aspiration case