Amanda Randles

Overview:

My research in biomedical simulation and high-performance computing focuses on the development of new computational tools that we use to provide insight into the localization and development of human diseases ranging from atherosclerosis to cancer. 

Positions:

Alfred Winborne and Victoria Stover Mordecai Assistant Professor of Biomedical Sciences

Biomedical Engineering
Pratt School of Engineering

Assistant Professor of Biomedical Engineering

Biomedical Engineering
Pratt School of Engineering

Assistant Professor of Computer Science

Computer Science
Trinity College of Arts & Sciences

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2013

Harvard University

Grants:

Student Support: IEEE Cluster 2018 Conference

Administered By
Biomedical Engineering
Awarded By
National Science Foundation
Role
Principal Investigator
Start Date
End Date

3D Bioprinted Aneurysm for Intervention Modeling Validation

Administered By
Biomedical Engineering
Awarded By
Lawrence Livermore National Laboratory
Role
Principal Investigator
Start Date
End Date

Toward coupled multiphysics models of hemodynamics on leadership systems

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Interactive virtual reality cardiovascular visualizations: User study for clinicians - Harvey Shi award

Administered By
Biomedical Engineering
Awarded By
Sigma Xi
Role
Principal Investigator
Start Date
End Date

ORNL Joint Faculty Appointment for Amanda Randles

Administered By
Biomedical Engineering
Awarded By
UT-Battelle, LLC
Role
Principal Investigator
Start Date
End Date

Publications:

Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma.

Glioblastoma stem-like cells dynamically transition between a chemoradiation-resistant state and a chemoradiation-sensitive state. However, physical barriers in the tumour microenvironment restrict the delivery of chemotherapy to tumour compartments that are distant from blood vessels. Here, we show that a massively parallel computational model of the spatiotemporal dynamics of the perivascular niche that incorporates glioblastoma stem-like cells and differentiated tumour cells as well as relevant tissue-level phenomena can be used to optimize the administration schedules of concurrent radiation and temozolomide-the standard-of-care treatment for glioblastoma. In mice with platelet-derived growth factor (PDGF)-driven glioblastoma, the model-optimized treatment schedule increased the survival of the animals. For standard radiation fractionation in patients, the model predicts that chemotherapy may be optimally administered about one hour before radiation treatment. Computational models of the spatiotemporal dynamics of the tumour microenvironment could be used to predict tumour responses to a broader range of treatments and to optimize treatment regimens.
Authors
Randles, A; Wirsching, H-G; Dean, JA; Cheng, Y-K; Emerson, S; Pattwell, SS; Holland, EC; Michor, F
MLA Citation
Randles, Amanda, et al. “Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma.Nature Biomedical Engineering, vol. 5, no. 4, Apr. 2021, pp. 346–59. Epmc, doi:10.1038/s41551-021-00710-3.
URI
https://scholars.duke.edu/individual/pub1481023
PMID
33864039
Source
epmc
Published In
Nature Biomedical Engineering
Volume
5
Published Date
Start Page
346
End Page
359
DOI
10.1038/s41551-021-00710-3

Non-invasive characterization of complex coronary lesions.

Conventional invasive diagnostic imaging techniques do not adequately resolve complex Type B and C coronary lesions, which present unique challenges, require personalized treatment and result in worsened patient outcomes. These lesions are often excluded from large-scale non-invasive clinical trials and there does not exist a validated approach to characterize hemodynamic quantities and guide percutaneous intervention for such lesions. This work identifies key biomarkers that differentiate complex Type B and C lesions from simple Type A lesions by introducing and validating a coronary angiography-based computational fluid dynamic (CFD-CA) framework for intracoronary assessment in complex lesions at ultrahigh resolution. Among 14 patients selected in this study, 7 patients with Type B and C lesions were included in the complex lesion group including ostial, bifurcation, serial lesions and lesion where flow was supplied by collateral bed. Simple lesion group included 7 patients with lesions that were discrete, [Formula: see text] long and readily accessible. Intracoronary assessment was performed using CFD-CA framework and validated by comparing to clinically measured pressure-based index, such as FFR. Local pressure, endothelial shear stress (ESS) and velocity profiles were derived for all patients. We validates the accuracy of our CFD-CA framework and report excellent agreement with invasive measurements ([Formula: see text]). Ultra-high resolution achieved by the model enable physiological assessment in complex lesions and quantify hemodynamic metrics in all vessels up to 1mm in diameter. Importantly, we demonstrate that in contrast to traditional pressure-based metrics, there is a significant difference in the intracoronary hemodynamic forces, such as ESS, in complex lesions compared to simple lesions at both resting and hyperemic physiological states [n = 14, [Formula: see text]]. Higher ESS was observed in the complex lesion group ([Formula: see text] Pa) than in simple lesion group ([Formula: see text] Pa). Complex coronary lesions have higher ESS compared to simple lesions, such differential hemodynamic evaluation can provide much the needed insight into the increase in adverse outcomes for such patients and has incremental prognostic value over traditional pressure-based indices, such as FFR.
Authors
Vardhan, M; Gounley, J; Chen, SJ; Chi, EC; Kahn, AM; Leopold, JA; Randles, A
MLA Citation
Vardhan, Madhurima, et al. “Non-invasive characterization of complex coronary lesions.Scientific Reports, vol. 11, no. 1, Apr. 2021, p. 8145. Epmc, doi:10.1038/s41598-021-86360-6.
URI
https://scholars.duke.edu/individual/pub1480885
PMID
33854076
Source
epmc
Published In
Scientific Reports
Volume
11
Published Date
Start Page
8145
DOI
10.1038/s41598-021-86360-6

Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm

GPU performance of the lattice Boltzmann method (LBM) depends heavily on memory access patterns. When implemented with GPUs on complex domains, typically, geometric data is accessed indirectly and lattice data is accessed lexicographically. Although there are a variety of other options, no study has examined the relative efficacy between them. Here, we examine a suite of memory access schemes via empirical testing and performance modeling. We find strong evidence that semi-direct is often better suited than the more common indirect addressing, providing increased computational speed and reducing memory consumption. For the layout, we find that the Collected Structure of Arrays (CSoA) and bundling layouts outperform the common Structure of Array layout; on V100 and P100 devices, CSoA consistently outperforms bundling, however the relationship is more complicated on K40 devices. When compared to state-of-the-art practices, our recommendations lead to speedups of 10-40 percent and reduce memory consumption up to 17 percent. Using performance modeling and computational experimentation, we determine the mechanisms behind the accelerations. We demonstrate that our results hold across multiple GPUs on two leadership class systems, and present the first near-optimal strong results for LBM with arterial geometries run on GPUs.
Authors
Herschlag, G; Lee, S; Vetter, JS; Randles, A
MLA Citation
Herschlag, G., et al. “Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm (Accepted).” Ieee Transactions on Parallel and Distributed Systems, vol. 32, no. 10, Oct. 2021, pp. 2400–14. Scopus, doi:10.1109/TPDS.2021.3061895.
URI
https://scholars.duke.edu/individual/pub1476509
Source
scopus
Published In
Ieee Transactions on Parallel and Distributed Systems
Volume
32
Published Date
Start Page
2400
End Page
2414
DOI
10.1109/TPDS.2021.3061895

Computational models of cancer cell transport through the microcirculation.

The transport of cancerous cells through the microcirculation during metastatic spread encompasses several interdependent steps that are not fully understood. Computational models which resolve the cellular-scale dynamics of complex microcirculatory flows offer considerable potential to yield needed insights into the spread of cancer as a result of the level of detail that can be captured. In recent years, in silico methods have been developed that can accurately and efficiently model the circulatory flows of cancer and other biological cells. These computational methods are capable of resolving detailed fluid flow fields which transport cells through tortuous physiological geometries, as well as the deformation and interactions between cells, cell-to-endothelium interactions, and tumor cell aggregates, all of which play important roles in metastatic spread. Such models can provide a powerful complement to experimental works, and a promising approach to recapitulating the endogenous setting while maintaining control over parameters such as shear rate, cell deformability, and the strength of adhesive binding to better understand tumor cell transport. In this review, we present an overview of computational models that have been developed for modeling cancer cells in the microcirculation, including insights they have provided into cell transport phenomena.
Authors
Puleri, DF; Balogh, P; Randles, A
MLA Citation
Puleri, Daniel F., et al. “Computational models of cancer cell transport through the microcirculation.Biomechanics and Modeling in Mechanobiology, Mar. 2021. Epmc, doi:10.1007/s10237-021-01452-6.
URI
https://scholars.duke.edu/individual/pub1477016
PMID
33765196
Source
epmc
Published In
Biomechanics and Modeling in Mechanobiology
Published Date
DOI
10.1007/s10237-021-01452-6

Multiscale modeling of blood flow to assess neurological complications in patients supported by venoarterial extracorporeal membrane oxygenation.

Computational blood flow models in large arteries elucidate valuable relationships between cardiovascular diseases and hemodynamics, leading to improvements in treatment planning and clinical decision making. One such application with potential to benefit from simulation is venoarterial extracorporeal membrane oxygenation (VA-ECMO), a support system for patients with cardiopulmonary failure. VA-ECMO patients develop high rates of neurological complications, partially due to abnormal blood flow throughout the vasculature from the VA-ECMO system. To better understand these hemodynamic changes, it is important to resolve complex local flow parameters derived from three-dimensional (3D) fluid dynamics while also capturing the impact of VA-ECMO support throughout the systemic arterial system. As high-resolution 3D simulations of the arterial network remain computationally expensive and intractable for large studies, a validated, multiscale model is needed to compute both global effects and high-fidelity local hemodynamics. In this work, we developed and demonstrated a framework to model hemodynamics in VA-ECMO patients using coupled 3D and one-dimensional (1D) models (1D→3D). We demonstrated the ability of these multiscale models to simulate complex flow patterns in specific regions of interest while capturing bulk flow throughout the systemic arterial system. We compared 1D, 3D, and 1D→3D coupled models and found that multiscale models were able to sufficiently capture both global and local hemodynamics in the cerebral arteries and aorta in VA-ECMO patients. This study is the first to develop and compare 1D, 3D, and 1D→ 3D coupled models on the larger arterial system scale in VA-ECMO patients, with potential use for other large scale applications.
Authors
Feiger, B; Adebiyi, A; Randles, A
MLA Citation
Feiger, Bradley, et al. “Multiscale modeling of blood flow to assess neurological complications in patients supported by venoarterial extracorporeal membrane oxygenation.Computers in Biology and Medicine, vol. 129, Feb. 2021, p. 104155. Epmc, doi:10.1016/j.compbiomed.2020.104155.
URI
https://scholars.duke.edu/individual/pub1469650
PMID
33333365
Source
epmc
Published In
Computers in Biology and Medicine
Volume
129
Published Date
Start Page
104155
DOI
10.1016/j.compbiomed.2020.104155

Research Areas:

Aortic Coarctation
Atherosclerosis
Biomechanical Phenomena
Biomechanics
Biophysics
Cancer
Cancer cells
Cardiovascular Diseases
Computational Biology
Computational fluid dynamics
Computer Simulation
Fluid mechanics
Hemodynamics
High performance computing
Lattice Boltzmann methods
Metastasis
Multiscale modeling
Parallel algorithms
Parallel computers