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Collier N, Ozik J. DISTRIBUTED AGENT-BASED SIMULATION WITH REPAST4PY. PROCEEDINGS OF THE ... WINTER SIMULATION CONFERENCE. WINTER SIMULATION CONFERENCE 2022; 2022:192-206. [PMID: 36777718 PMCID: PMC9912342 DOI: 10.1109/wsc57314.2022.10015389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The increasing availability of high-performance computing (HPC) has accelerated the potential for applying computational simulation to capture ever more granular features of large, complex systems. This tutorial presents Repast4Py, the newest member of the Repast Suite of agent-based modeling toolkits. Repast4Py is a Python agent-based modeling framework that provides the ability to build large, MPI-distributed agent-based models (ABM) that span multiple processing cores. Simplifying the process of constructing large-scale ABMs, Repast4Py is designed to provide an easier on-ramp for researchers from diverse scientific communities to apply distributed ABM methods. We will present key Repast4Py components and how they are combined to create distributed simulations of different types, building on three example models that implement seven common distributed ABM use cases. We seek to illustrate the relationship between model structure and performance considerations, providing guidance on how to leverage Repast4Py features to develop well designed and performant distributed ABMs.
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Affiliation(s)
- Nicholson Collier
- Decision and Infrastructure Sciences, Argonne National Laboratory,Lemont,IL,USA,60439
| | - Jonathan Ozik
- Decision and Infrastructure Sciences, Argonne National Laboratory,Lemont,IL,USA,60439
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An G, Döllinger M, Li-Jessen NYK. Editorial: Integration of Machine Learning and Computer Simulation in Solving Complex Physiological and Medical Questions. Front Physiol 2022; 13:949771. [PMID: 35864898 PMCID: PMC9294637 DOI: 10.3389/fphys.2022.949771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 06/07/2022] [Indexed: 12/03/2022] Open
Affiliation(s)
- Gary An
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT, United States
| | - Michael Döllinger
- Department of Otorhinolaryngology Head and Neck Surgery, Medical School, Division of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Nicole Y. K. Li-Jessen
- School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
- Department of Otolaryngology-Head and Neck Surgery, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- *Correspondence: Nicole Y. K. Li-Jessen,
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Alarid-Escudero F, Knudsen AB, Ozik J, Collier N, Kuntz KM. Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models. Front Physiol 2022; 13:780917. [PMID: 35615677 PMCID: PMC9124835 DOI: 10.3389/fphys.2022.780917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/04/2022] [Indexed: 11/24/2022] Open
Abstract
Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of −0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters’ posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold. Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.
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Affiliation(s)
- Fernando Alarid-Escudero
- Division of Public Administration, Center for Research and Teaching in Economics (CIDE), Aguascalientes, Mexico
- *Correspondence: Fernando Alarid-Escudero,
| | - Amy B. Knudsen
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, United States
| | - Jonathan Ozik
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Argonne, IL, United States
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, United States
| | - Nicholson Collier
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Argonne, IL, United States
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, United States
| | - Karen M. Kuntz
- Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, United States
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Ponce-de-Leon M, Montagud A, Akasiadis C, Schreiber J, Ntiniakou T, Valencia A. Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth. Front Mol Biosci 2022; 9:836794. [PMID: 35463947 PMCID: PMC9019571 DOI: 10.3389/fmolb.2022.836794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.
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Affiliation(s)
- Miguel Ponce-de-Leon
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- *Correspondence: Miguel Ponce-de-Leon,
| | | | - Charilaos Akasiadis
- Institute of Informatics and Telecommunications, NCSR “Demokritos”, Agia Paraskevi, Greece
| | | | | | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- ICREA, Pg. Lluís Companys, Barcelona, Spain
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Getz M, Wang Y, An G, Asthana M, Becker A, Cockrell C, Collier N, Craig M, Davis CL, Faeder JR, Ford Versypt AN, Mapder T, Gianlupi JF, Glazier JA, Hamis S, Heiland R, Hillen T, Hou D, Islam MA, Jenner AL, Kurtoglu F, Larkin CI, Liu B, Macfarlane F, Maygrundter P, Morel PA, Narayanan A, Ozik J, Pienaar E, Rangamani P, Saglam AS, Shoemaker JE, Smith AM, Weaver JJA, Macklin P. Iterative community-driven development of a SARS-CoV-2 tissue simulator. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.04.02.019075. [PMID: 32511322 PMCID: PMC7239052 DOI: 10.1101/2020.04.02.019075] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.
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Lindau ST, Makelarski JA, Kaligotla C, Abramsohn EM, Beiser DG, Chou C, Collier N, Huang ES, Macal CM, Ozik J, Tung EL. Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention. PLoS Comput Biol 2021; 17:e1009471. [PMID: 34695116 PMCID: PMC8568099 DOI: 10.1371/journal.pcbi.1009471] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 11/04/2021] [Accepted: 09/23/2021] [Indexed: 11/18/2022] Open
Abstract
CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical "doses" of the HealtheRx shared their information with others ("social doses"). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population ("agents") using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.
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Affiliation(s)
- Stacy Tessler Lindau
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois, United States of America
- Comprehensive Cancer Center, University of Chicago, Chicago, Illinois, United States of America
- Department of Medicine, Section of Geriatrics & Palliative Medicine, University of Chicago, Chicago, Illinois, United States of America
- Bucksbaum Institute for Clinical Excellence, University of Chicago, Chicago, Illinois, United States of America
| | - Jennifer A. Makelarski
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois, United States of America
| | - Chaitanya Kaligotla
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, Illinois, United States of America
- Beedie School of Business, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Emily M. Abramsohn
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois, United States of America
| | - David G. Beiser
- Section of Emergency Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Chiahung Chou
- Department of Health Outcomes Research and Policy, Auburn University, Auburn, Alabama, United States of America
| | - Nicholson Collier
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, Illinois, United States of America
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, United States of America
| | - Elbert S. Huang
- Section of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Charles M. Macal
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, Illinois, United States of America
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, United States of America
| | - Jonathan Ozik
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, Illinois, United States of America
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, United States of America
| | - Elizabeth L. Tung
- Section of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
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Duggan B, Metzcar J, Macklin P. DAPT: A package enabling distributed automated parameter testing. GIGABYTE 2021; 2021:gigabyte22. [PMID: 36824329 PMCID: PMC9631979 DOI: 10.46471/gigabyte.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/01/2021] [Indexed: 11/09/2022] Open
Abstract
Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches), as well as storing simulation data, requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster with the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here, we describe DAPT and provide an example demonstrating its use.
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Affiliation(s)
- Ben Duggan
- Indiana University Luddy School of Informatics, Computing and Engineering, 107 S Indiana Ave, Bloomington, IN 47405, USA
| | - John Metzcar
- Indiana University Luddy School of Informatics, Computing and Engineering, 107 S Indiana Ave, Bloomington, IN 47405, USA
| | - Paul Macklin
- Indiana University Luddy School of Informatics, Computing and Engineering, 107 S Indiana Ave, Bloomington, IN 47405, USA, Corresponding author. E-mail:
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Souza R, Silva V, Lima AAB, de Oliveira D, Valduriez P, Mattoso M. Distributed in-memory data management for workflow executions. PeerJ Comput Sci 2021; 7:e527. [PMID: 34013039 PMCID: PMC8114816 DOI: 10.7717/peerj-cs.527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/14/2021] [Indexed: 06/12/2023]
Abstract
Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since such executions usually last for hours or days, some WMSs provide user steering support, i.e., they allow users to run data analyses and, depending on the results, adapt the workflows at runtime. A challenge in the parallel execution control design is to manage workflow data for efficient executions while enabling user steering support. Data access for high scalability is typically transaction-oriented, while for data analysis, it is online analytical-oriented so that managing such hybrid workloads makes the challenge even harder. In this work, we present SchalaDB, an architecture with a set of design principles and techniques based on distributed in-memory data management for efficient workflow execution control and user steering. We propose a distributed data design for scalable workflow task scheduling and high availability driven by a parallel and distributed in-memory DBMS. To evaluate our proposal, we develop d-Chiron, a WMS designed according to SchalaDB's principles. We carry out an extensive experimental evaluation on an HPC cluster with up to 960 computing cores. Among other analyses, we show that even when running data analyses for user steering, SchalaDB's overhead is negligible for workloads composed of hundreds of concurrent tasks on shared data. Our results encourage workflow engine developers to follow a parallel and distributed data-oriented approach not only for scheduling and monitoring but also for user steering.
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Affiliation(s)
- Renan Souza
- COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Current Affiliation: IBM Research, Rio de Janeiro, Brazil
| | - Vitor Silva
- COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Current Affiliation: Snap, Inc., Los Angeles, CA, USA
| | - Alexandre A. B. Lima
- COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Campus Duque de Caxias/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | | | - Marta Mattoso
- COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
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Cockrell C, Ozik J, Collier N, An G. Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models. SIMULATION 2021; 97:287-296. [PMID: 34744189 PMCID: PMC8570577 DOI: 10.1177/0037549720975075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model's rule set. The model's internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.
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Affiliation(s)
| | | | | | - Gary An
- Department of Surgery, University of Vermont, USA
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Ozik J, Collier N, Heiland R, An G, Macklin P. Learning-accelerated discovery of immune-tumour interactions. MOLECULAR SYSTEMS DESIGN & ENGINEERING 2019; 4:747-760. [PMID: 31497314 PMCID: PMC6690424 DOI: 10.1039/c9me00036d] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 04/18/2019] [Indexed: 05/04/2023]
Abstract
We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.
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Affiliation(s)
- Jonathan Ozik
- Decision and Infrastructure Sciences , Argonne National Laboratory , 9700 S. Cass Ave , Lemont , IL 60439 , USA .
- Consortium for Advanced Science and Engineering , University of Chicago , 5801 S. Ellis Ave. , Chicago , IL 60637 , USA
| | - Nicholson Collier
- Decision and Infrastructure Sciences , Argonne National Laboratory , 9700 S. Cass Ave , Lemont , IL 60439 , USA .
- Consortium for Advanced Science and Engineering , University of Chicago , 5801 S. Ellis Ave. , Chicago , IL 60637 , USA
| | - Randy Heiland
- Intelligent Systems Engineering , Indiana University , 700 N. Woodlawn Avenue Bloomington , IN 47408 , USA
| | - Gary An
- The University of Vermont Medical Center , 111 Colchester Avenue , Burlington , VT 05401 , USA
| | - Paul Macklin
- Intelligent Systems Engineering , Indiana University , 700 N. Woodlawn Avenue Bloomington , IN 47408 , USA
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