Amanda Randles

As computational power increases and systems with millions of red blood cells can be simulated, it is important to note that varying spatial distributions of cells may affect simulation outcomes. Since a single simulation may not represent the ensemble behavior, many different configurations may need to be sampled to adequately assess the entire collection of potential cell arrangements. In order to determine both the number of distributions needed and which ones to run, we must first establish methods to identify well-generated, randomly placed cell distributions and to quantify distinct cell configurations. We utilize metrics to assess (1) the presence of any underlying structure to the initial cell distribution and (2) similarity between cell configurations. We propose the use of the radial distribution function to identify long-range structure in a cell configuration and apply it to a randomly distributed and structured set of red blood cells. To quantify spatial similarity between two configurations, we make use of the Jaccard index, and characterize sets of red blood cell and sphere initializations. As an extension to our work submitted to the International Conference on Computational Science (Roychowdhury et al., 2022), we significantly increase our data set size from 72 to 1048 cells, include a similar set of studies using spheres, compare the effects of varying sphere size, and utilize the Jaccard index distribution to probe sets of extremely similar configurations. Our results show that the radial distribution function can be used as a metric to determine long-range structure in both distributions of spheres and RBCs. We determine that the ideal case of spheres within a cube versus bi-concave shaped cells within a cylinder affects the shape of the Jaccard index distributions, as well as the range of Jaccard values, showing that both the shape of particle and the domain may play a role. We also find that the distribution is able to capture very similar configurations through Jaccard index values greater than 95% when appending several nearly identical configurations into the data set.

Parallel 3D cellular automaton models of tumor growth can efficiently capture emergent morphology. We extended a 2D growth model to 3D to examine the influence of symmetric division in heterogeneous tumors on growth dynamics. As extending to 3D severely increased time-to-solution, we parallelized the model using N-body, lattice halo exchange, and adaptive communication schemes. Supplementing prior work from Tanade et al. (2022), we demonstrated over 55x speedup and evaluated performance on ≤30 nodes of Stampede2. This work established a framework to parametrically study 3D growth dynamics, and of the cancer phenotypes we studied, the parallel model better scaled when tumor boundaries were radially symmetric.

<h4>Background</h4>Personalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR baseline ), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines.<h4>Aims</h4>To determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR invasive ) as gold standard.<h4>Materials and methods</h4>Personalized coronary geometries ( N=50 ) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR baseline , was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR baseline was validated against FFR invasive and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR streamlined was created by only incorporating the most sensitive inputs and FFR semi-streamlined additionally included patient-specific distal location.<h4>Results</h4>FFR baseline was validated against FFR invasive via correlation ( r=0.714 , p<0.001 ), agreement (mean difference: 0.01±0.09 ), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR semi-streamlined provided identical diagnostic performance with FFR baseline . Compared to FFR baseline vs. FFR invasive , FFR streamlined vs. FFR invasive had decreased correlation ( r=0.64 , p<0.001 ), improved agreement (mean difference: 0.01±0.08 ), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90).<h4>Conclusion</h4>Streamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement.

The original version of this chapter was revised. The spelling of the last author’s name was corrected to Amanda Randles.

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.