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:

A data-driven approach to modeling cancer cell mechanics during microcirculatory transport.

In order to understand the effect of cellular level features on the transport of circulating cancer cells in the microcirculation, there has been an increasing reliance on high-resolution in silico models. Accurate simulation of cancer cells flowing with blood cells requires resolving cellular-scale interactions in 3D, which is a significant computational undertaking warranting a cancer cell model that is both computationally efficient yet sufficiently complex to capture relevant behavior. Given that the characteristics of metastatic spread are known to depend on cancer type, it is crucial to account for mechanistic behavior representative of a specific cancer's cells. To address this gap, in the present work we develop and validate a means by which an efficient and popular membrane model-based approach can be used to simulate deformable cancer cells and reproduce experimental data from specific cell lines. Here, cells are modeled using the immersed boundary method (IBM) within a lattice Boltzmann method (LBM) fluid solver, and the finite element method (FEM) is used to model cell membrane resistance to deformation. Through detailed comparisons with experiments, we (i) validate this model to represent cancer cells undergoing large deformation, (ii) outline a systematic approach to parameterize different cell lines to optimally fit experimental data over a range of deformations, and (iii) provide new insight into nucleated vs. non-nucleated cell models and their ability to match experiments. While many works have used the membrane-model based method employed here to model generic cancer cells, no quantitative comparisons with experiments exist in the literature for specific cell lines undergoing large deformation. Here, we describe a phenomenological, data-driven approach that can not only yield good agreement for large deformations, but explicitly detail how it can be used to represent different cancer cell lines. This model is readily incorporated into cell-resolved hemodynamic transport simulations, and thus offers significant potential to complement experiments towards providing new insights into various aspects of cancer progression.
Authors
Balogh, P; Gounley, J; Roychowdhury, S; Randles, A
MLA Citation
Balogh, Peter, et al. “A data-driven approach to modeling cancer cell mechanics during microcirculatory transport.Scientific Reports, vol. 11, no. 1, July 2021, p. 15232. Epmc, doi:10.1038/s41598-021-94445-5.
URI
https://scholars.duke.edu/individual/pub1492760
PMID
34315934
Source
epmc
Published In
Scientific Reports
Volume
11
Published Date
Start Page
15232
DOI
10.1038/s41598-021-94445-5

Localization of Rolling and Firm-Adhesive Interactions Between Circulating Tumor Cells and the Microvasculature Wall.

<h4>Introduction</h4>The adhesion of tumor cells to vessel wall is a critical stage in cancer metastasis. Firm adhesion of cancer cells is usually followed by their extravasation through the endothelium. Despite previous studies identifying the influential parameters in the adhesive behavior of the cancer cell to a planer substrate, less is known about the interactions between the cancer cell and microvasculature wall and whether these interactions exhibit organ specificity. The objective of our study is to characterize sizes of microvasculature where a deformable circulating cell (DCC) would firmly adhere or roll over the wall, as well as to identify parameters that facilitate such firm adherence and underlying mechanisms driving adhesive interactions.<h4>Methods</h4>A three-dimensional model of DCCs is applied to simulate the fluid-structure interaction between the DCC and surrounding fluid. A dynamic adhesion model, where an adhesion molecule is modeled as a spring, is employed to represent the stochastic receptor-ligand interactions using kinetic rate expressions.<h4>Results</h4>Our results reveal that both the cell deformability and low shear rate of flow promote the firm adhesion of DCC in small vessels ( <10μm ). Our findings suggest that ligand-receptor bonds of PSGL-1-P-selectin may lead to firm adherence of DCC in smaller vessels and rolling-adhesion of DCC in larger ones where cell velocity drops to facilitate the activation of integrin-ICAM-1 bonds.<h4>Conclusions</h4>Our study provides a framework to predict accurately where different DCC-types are likely to adhere firmly in microvasculature and to establish the criteria predisposing cancer cells to such firm adhesion.
Authors
Dabagh, M; Gounley, J; Randles, A
MLA Citation
Dabagh, Mahsa, et al. “Localization of Rolling and Firm-Adhesive Interactions Between Circulating Tumor Cells and the Microvasculature Wall.Cellular and Molecular Bioengineering, vol. 13, no. 2, Apr. 2020, pp. 141–54. Epmc, doi:10.1007/s12195-020-00610-7.
URI
https://scholars.duke.edu/individual/pub1431691
PMID
32175027
Source
epmc
Published In
Cellular and Molecular Bioengineering
Volume
13
Published Date
Start Page
141
End Page
154
DOI
10.1007/s12195-020-00610-7

Predicting aneurysmal degeneration of type B aortic dissection with computational fluid dynamics

Stanford Type B aortic dissection (TBAD) is a deadly cardiovascular disease with mortality rates as high as 50% in complicated cases. Patients with TBAD are often medically managed, but in ∼20-40% of cases, patients experience aneurysmal degeneration in the dissected aorta, and surgical intervention is required. In this work, we simulated blood flow using computational fluid dynamics (CFD) to determine relationships between hemodynamics and aneurysmal degeneration, providing an important step towards predicting the need for intervention prior to significant aneurysm occurrence. Currently, surgeons intervene in TBAD cases based on the aneurysms growth rate and overall size, as well as a variety of other factors such as malperfusion, thrombosis, and pain, but predicting future risk of aneurysmal degeneration would allow earlier intervention leading to improved patient outcomes. Here, we hypothesized that hemodynamic metrics play an important role in the formation of aneurysms and that these metrics could be used to predict future aneurysmal degeneration in this patient population. Our retrospective dataset consisted of 16 patients with TBAD where eight required intervention due to aneurysmal degeneration and eight were medically managed. The patients with surgical intervention were examined in our study prior to the formation of an aneurysm. For each patient, we segmented and reconstructed the aortic geometry and simulated blood flow using the lattice Boltzmann method. We then compared hemodynamic metrics between to the two groups of patients, including time-averaged wall shear stress, oscillatory shear index, relative residence time, and flow fractions to the true and false lumen. We found significant differences in each metric between the true and false lumen. We also showed that flow fractions to the false lumen was higher in patients with aneurysmal degeneration (p = 0.02). These results are an important step towards developing more precise methods to predict future aneurysmal degeneration and the need for intervention in TBAD patients.
Authors
Feiger, B; Lorenzana, E; Ranney, D; Bishawi, M; Doberne, J; Vekstein, A; Voigt, S; Hughes, C; Randles, A
MLA Citation
Feiger, B., et al. “Predicting aneurysmal degeneration of type B aortic dissection with computational fluid dynamics.” Proceedings of the 12th Acm Conference on Bioinformatics, Computational Biology, and Health Informatics, Bcb 2021, 2021. Scopus, doi:10.1145/3459930.3469563.
URI
https://scholars.duke.edu/individual/pub1493373
Source
scopus
Published In
Proceedings of the 12th Acm Conference on Bioinformatics, Computational Biology, and Health Informatics, Bcb 2021
Published Date
DOI
10.1145/3459930.3469563

Propagation Pattern for Moment Representation of the Lattice Boltzmann Method

A propagation pattern for the moment representation of the regularized lattice Boltzmann method (LBM) in three dimensions is presented. Using effectively lossless compression, the simulation state is stored as a set of moments of the lattice Boltzmann distribution function, instead of the distribution function itself. An efficient cache-aware propagation pattern for this moment representation has the effect of substantially reducing both the storage and memory bandwidth required for LBM simulations. This article extends recent work with the moment representation by expanding the performance analysis on central processing unit (CPU) architectures, considering how boundary conditions are implemented, and demonstrating the effectiveness of the moment representation on a graphics processing unit (GPU) architecture.
Authors
Gounley, J; Vardhan, M; Draeger, EW; Valero-Lara, P; Moore, SV; Randles, A
MLA Citation
Gounley, J., et al. “Propagation Pattern for Moment Representation of the Lattice Boltzmann Method (Accepted).” Ieee Transactions on Parallel and Distributed Systems, vol. 33, no. 3, Mar. 2022, pp. 642–53. Scopus, doi:10.1109/TPDS.2021.3098456.
URI
https://scholars.duke.edu/individual/pub1492761
Source
scopus
Published In
Ieee Transactions on Parallel and Distributed Systems
Volume
33
Published Date
Start Page
642
End Page
653
DOI
10.1109/TPDS.2021.3098456

Moments-based method for boundary conditions in the lattice Boltzmann framework: A comparative analysis for the lid driven cavity flow

Dealing with boundary conditions (BC) was ever considered a puzzling question in the lattice Boltzmann (LB) method. The most popular BC models are based on Ad-Hoc rules and, although these BC models were shown to be suitable for low-order LB equations, their extension to high-order LB was shown to be a very difficult problem and, at authors knowledge, never solved with satisfaction. The main question to be solved is how to deal with a problem when the number of unknowns (the particle populations coming from the outside part of the numerical domain) is greater than the number of equations at our disposal at each boundary site. Recently, BC models based on the regularization of the LB equation, or moments-based models, were proposed. These moments replace the discrete populations as unknowns, independently of the number of discrete velocities that are needed for solving a given problem. The full set of moments-based BC leads, nevertheless, to an overdetermined system of equations, and what distinguishes one model from another is the way this system is solved. In contrast with previous work, we base our approach on second-order moments. Four versions of this model are compared with previous moments-based models considering, in addition to the accuracy, some main model attributes such as global and local mass conservation, rates of convergence, and stability. For this purpose, the complex flow patterns displayed in a two-dimensional lid-driven cavity are investigated.
Authors
Bazarin, RLM; Philippi, PC; Randles, A; Hegele, LA
MLA Citation
Bazarin, R. L. M., et al. “Moments-based method for boundary conditions in the lattice Boltzmann framework: A comparative analysis for the lid driven cavity flow (Accepted).” Computers and Fluids, vol. 230, Nov. 2021. Scopus, doi:10.1016/j.compfluid.2021.105142.
URI
https://scholars.duke.edu/individual/pub1497425
Source
scopus
Published In
Computers & Fluids
Volume
230
Published Date
DOI
10.1016/j.compfluid.2021.105142

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