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

Examining metastatic behavior within 3D bioprinted vasculature for the validation of a 3D computational flow model.

Understanding the dynamics of circulating tumor cell (CTC) behavior within the vasculature has remained an elusive goal in cancer biology. To elucidate the contribution of hydrodynamics in determining sites of CTC vascular colonization, the physical forces affecting these cells must be evaluated in a highly controlled manner. To this end, we have bioprinted endothelialized vascular beds and perfused these constructs with metastatic mammary gland cells under physiological flow rates. By pairing these in vitro devices with an advanced computational flow model, we found that the bioprinted analog was readily capable of evaluating the accuracy and integrated complexity of a computational flow model, while also highlighting the discrete contribution of hydrodynamics in vascular colonization. This intersection of these two technologies, bioprinting and computational simulation, is a key demonstration in the establishment of an experimentation pipeline for the understanding of complex biophysical events.
Authors
Hynes, WF; Pepona, M; Robertson, C; Alvarado, J; Dubbin, K; Triplett, M; Adorno, JJ; Randles, A; Moya, ML
MLA Citation
Hynes, W. F., et al. “Examining metastatic behavior within 3D bioprinted vasculature for the validation of a 3D computational flow model.Science Advances, vol. 6, no. 35, Aug. 2020, p. eabb3308. Epmc, doi:10.1126/sciadv.abb3308.
URI
https://scholars.duke.edu/individual/pub1461063
PMID
32923637
Source
epmc
Published In
Science Advances
Volume
6
Published Date
Start Page
eabb3308
DOI
10.1126/sciadv.abb3308

Computational Framework to Evaluate the Hydrodynamics of Cell Scaffold Geometries.

The fluid dynamics of microporous materials are important to many biomedical processes such as cell deposition in scaffold materials, tissue engineering, and bioreactors. Microporous scaffolds are frequently composed of suspensions of beads that have varying topology which, in turn, informs their hydrodynamic properties. Previous work has shown that shear stress distributions can affect the response of cells in microporous environments. Using computational fluid dynamics, we characterize localized differences in fluid flow attributes such wall shear stress and velocity to better understand the fluid dynamics underpinning microporous device function. We evaluated whether bead packings with similar void fractions had different fluid dynamics as characterized by the distribution of velocity magnitudes and wall shear stress and found that there are differences despite the similarities in void fraction. We show that another metric, the average distance to the nearest wall, can provide an additional variable to measure the porosity and susceptibility of microporous materials to high shear stress. By increasing our understanding of the impact of bead size on cell scaffold fluid dynamics we aim to increase the ability to predict important attributes such as loading efficiency in these devices.
Authors
Puleri, DF; Roychowdhury, S; Ames, J; Randles, A
MLA Citation
Puleri, Daniel F., et al. “Computational Framework to Evaluate the Hydrodynamics of Cell Scaffold Geometries.Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference, vol. 2020, 2020, pp. 2299–302. Epmc, doi:10.1109/embc44109.2020.9176313.
URI
https://scholars.duke.edu/individual/pub1461062
PMID
33018467
Source
epmc
Published In
Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference
Volume
2020
Published Date
Start Page
2299
End Page
2302
DOI
10.1109/embc44109.2020.9176313

Evaluating the Influence of Hemorheological Parameters on Circulating Tumor Cell Trajectory and Simulation Time

Extravasation of circulating tumor cells (CTCs) occurs primarily in the microvasculature, where flow and cell interactions significantly affect the blood rheology. Capturing cell trajectory at this scale requires the coupling of several interaction models, leading to increased computational cost that scales as more cells are added or the domain size is increased. In this work, we focus on micro-scale vessels and study the influence of certain hemorheological factors, including the presence of red blood cell aggregation, hematocrit level, microvessel size, and shear rate, on the trajectory of a circulating tumor cell. We determine which of the aforementioned factors significantly affect CTC motion and identify those which can potentially be disregarded, thus reducing simulation time. We measure the effect of these elements by studying the radial CTC movement and runtime at various combinations of these hemorheological parameters. To accurately capture blood flow dynamics and single cell movement, we perform high-fidelity hemodynamic simulations at a sub-micron resolution using our in-house fluid dynamics solver, HARVEY. We find that increasing hematocrit increases the likelihood of tumor cell margination, which is exacerbated by the presence of red blood cell aggregation. As microvessel diameter increases, there is no major CTC movement towards the wall; however, including aggregation causes the CTC to marginate quicker as the vessel size increases. Finally, as the shear rate is increased, the presence of aggregation has a diminished effect on tumor cell margination.
Authors
Roychowdhury, S; Gounley, J; Randles, A
MLA Citation
Roychowdhury, S., et al. “Evaluating the Influence of Hemorheological Parameters on Circulating Tumor Cell Trajectory and Simulation Time.” Proceedings of the Platform for Advanced Scientific Computing Conference, Pasc 2020, 2020. Scopus, doi:10.1145/3394277.3401848.
URI
https://scholars.duke.edu/individual/pub1448925
Source
scopus
Published In
Proceedings of the Platform for Advanced Scientific Computing Conference, Pasc 2020
Published Date
DOI
10.1145/3394277.3401848

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