1
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Zhu X, Hager ER, Huyan C, Sgro AE. Leveraging the model-experiment loop: Examples from cellular slime mold chemotaxis. Exp Cell Res 2022; 418:113218. [PMID: 35618013 DOI: 10.1016/j.yexcr.2022.113218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/19/2022] [Indexed: 11/04/2022]
Abstract
Interplay between models and experimental data advances discovery and understanding in biology, particularly when models generate predictions that allow well-designed experiments to distinguish between alternative mechanisms. To illustrate how this feedback between models and experiments can lead to key insights into biological mechanisms, we explore three examples from cellular slime mold chemotaxis. These examples include studies that identified chemotaxis as the primary mechanism behind slime mold aggregation, discovered that cells likely measure chemoattractant gradients by sensing concentration differences across cell length, and tested the role of cell-associated chemoattractant degradation in shaping chemotactic fields. Although each study used a different model class appropriate to their hypotheses - qualitative, mathematical, or simulation-based - these examples all highlight the utility of modeling to formalize assumptions and generate testable predictions. A central element of this framework is the iterative use of models and experiments, specifically: matching experimental designs to the models, revising models based on mismatches with experimental data, and validating critical model assumptions and predictions with experiments. We advocate for continued use of this interplay between models and experiments to advance biological discovery.
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Affiliation(s)
- Xinwen Zhu
- Department of Biomedical Engineering and the Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - Emily R Hager
- Department of Biomedical Engineering and the Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - Chuqiao Huyan
- Department of Biomedical Engineering and the Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - Allyson E Sgro
- Department of Biomedical Engineering and the Biological Design Center, Boston University, Boston, MA, 02215, USA.
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2
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Corti A, Colombo M, Migliavacca F, Rodriguez Matas JF, Casarin S, Chiastra C. Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models. Front Bioeng Biotechnol 2021; 9:744560. [PMID: 34796166 PMCID: PMC8593007 DOI: 10.3389/fbioe.2021.744560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/04/2021] [Indexed: 12/20/2022] Open
Abstract
The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.
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Affiliation(s)
- Anna Corti
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Monika Colombo
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Jose Felix Rodriguez Matas
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Stefano Casarin
- Department of Surgery, Houston Methodist Hospital, Houston, TX, United States.,Center for Computational Surgery, Houston Methodist Research Institute, Houston, TX, United States.,Houston Methodist Academic Institute, Houston, TX, United States
| | - Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
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3
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Ye D, Veen L, Nikishova A, Lakhlili J, Edeling W, Luk OO, Krzhizhanovskaya VV, Hoekstra AG. Uncertainty quantification patterns for multiscale models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200072. [PMID: 33775139 PMCID: PMC8059643 DOI: 10.1098/rsta.2020.0072] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/26/2020] [Indexed: 05/06/2023]
Abstract
Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale coupling toolkit Multiscale Coupling Library and Environment 3. Potential speed-up for UQPs has been derived as well. As a proof of concept, two examples of multiscale applications using UQPs are presented. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
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Affiliation(s)
- D. Ye
- Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - L. Veen
- Netherlands eScience Center, Amsterdam, The Netherlands
| | - A. Nikishova
- Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - J. Lakhlili
- Max Planck Institute for Plasma Physics, Garching, Germany
| | - W. Edeling
- Scientific Computing Group, Centrum Wiskunde and Informatica, Amsterdam, The Netherlands
| | - O. O. Luk
- Max Planck Institute for Plasma Physics, Garching, Germany
| | - V. V. Krzhizhanovskaya
- Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
- ITMO University, Saint Petersburg, Russia
| | - A. G. Hoekstra
- Computational Science Lab, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
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4
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McCullough JWS, Richardson RA, Patronis A, Halver R, Marshall R, Ruefenacht M, Wylie BJN, Odaker T, Wiedemann M, Lloyd B, Neufeld E, Sutmann G, Skjellum A, Kranzlmüller D, Coveney PV. Towards blood flow in the virtual human: efficient self-coupling of HemeLB. Interface Focus 2021; 11:20190119. [PMID: 33335704 PMCID: PMC7739917 DOI: 10.1098/rsfs.2019.0119] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/23/2020] [Indexed: 11/12/2022] Open
Abstract
Many scientific and medical researchers are working towards the creation of a virtual human—a personalized digital copy of an individual—that will assist in a patient’s diagnosis, treatment and recovery. The complex nature of living systems means that the development of this remains a major challenge. We describe progress in enabling the HemeLB lattice Boltzmann code to simulate 3D macroscopic blood flow on a full human scale. Significant developments in memory management and load balancing allow near linear scaling performance of the code on hundreds of thousands of computer cores. Integral to the construction of a virtual human, we also outline the implementation of a self-coupling strategy for HemeLB. This allows simultaneous simulation of arterial and venous vascular trees based on human-specific geometries.
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Affiliation(s)
- J W S McCullough
- Centre for Computational Science, Department of Chemistry, University College London, London, UK
| | - R A Richardson
- Centre for Computational Science, Department of Chemistry, University College London, London, UK
| | - A Patronis
- Centre for Computational Science, Department of Chemistry, University College London, London, UK.,Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - R Halver
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - R Marshall
- SimCenter, University of Tennessee at Chattanooga, Chattanooga, TN, USA
| | - M Ruefenacht
- SimCenter, University of Tennessee at Chattanooga, Chattanooga, TN, USA
| | - B J N Wylie
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - T Odaker
- Leibniz Supercomputing Centre, Leibniz-Rechenzentrum (LRZ), Garching, Germany
| | - M Wiedemann
- Leibniz Supercomputing Centre, Leibniz-Rechenzentrum (LRZ), Garching, Germany
| | - B Lloyd
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - E Neufeld
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - G Sutmann
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany.,ICAMS, Ruhr-University Bochum, Bochum, Germany
| | - A Skjellum
- SimCenter, University of Tennessee at Chattanooga, Chattanooga, TN, USA
| | - D Kranzlmüller
- Leibniz Supercomputing Centre, Leibniz-Rechenzentrum (LRZ), Garching, Germany
| | - P V Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London, UK.,Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
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5
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Wan S, Bhati AP, Zasada SJ, Coveney PV. Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction. Interface Focus 2020; 10:20200007. [PMID: 33178418 PMCID: PMC7653346 DOI: 10.1098/rsfs.2020.0007] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2020] [Indexed: 02/06/2023] Open
Abstract
A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.
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Affiliation(s)
- Shunzhou Wan
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Agastya P. Bhati
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Stefan J. Zasada
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Peter V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, 1098XH Amsterdam, The Netherlands
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6
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Coveney PV, Highfield RR. From digital hype to analogue reality: Universal simulation beyond the quantum and exascale eras. JOURNAL OF COMPUTATIONAL SCIENCE 2020; 46:101093. [PMID: 33312270 PMCID: PMC7709487 DOI: 10.1016/j.jocs.2020.101093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/03/2020] [Indexed: 05/23/2023]
Abstract
Many believe that the future of innovation lies in simulation. However, as computers are becoming ever more powerful, so does the hyperbole used to discuss their potential in modelling across a vast range of domains, from subatomic physics to chemistry, climate science, epidemiology, economics and cosmology. As we are about to enter the era of quantum and exascale computing, machine learning and artificial intelligence have entered the field in a significant way. In this article we give a brief history of simulation, discuss how machine learning can be more powerful if underpinned by deeper mechanistic understanding, outline the potential of exascale and quantum computing, highlight the limits of digital computing - classical and quantum - and distinguish rhetoric from reality in assessing the future of modelling and simulation, when we believe analogue computing will play an increasingly important role.
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Affiliation(s)
- Peter V. Coveney
- Centre for Computational Science, University College London, Gordon Street, London, WC1H 0AJ, UK
- Institute for Informatics, Science Park 904, University of Amsterdam, 1098 XH, Amsterdam, Netherlands
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7
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Uleman JF, Melis RJF, Quax R, van der Zee EA, Thijssen D, Dresler M, van de Rest O, van der Velpen IF, Adams HHH, Schmand B, de Kok IMCM, de Bresser J, Richard E, Verbeek M, Hoekstra AG, Rouwette EAJA, Olde Rikkert MGM. Mapping the multicausality of Alzheimer's disease through group model building. GeroScience 2020; 43:829-843. [PMID: 32780293 PMCID: PMC8110634 DOI: 10.1007/s11357-020-00228-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 07/06/2020] [Indexed: 12/02/2022] Open
Abstract
Alzheimer’s disease (AD) is a complex, multicausal disorder involving several spatiotemporal scales and scientific domains. While many studies focus on specific parts of this system, the complexity of AD is rarely studied as a whole. In this work, we apply systems thinking to map out known causal mechanisms and risk factors ranging from intracellular to psychosocial scales in sporadic AD. We report on the first systemic causal loop diagram (CLD) for AD, which is the result of an interdisciplinary group model building (GMB) process. The GMB was based on the input of experts from multiple domains and all proposed mechanisms were supported by scientific literature. The CLD elucidates interaction and feedback mechanisms that contribute to cognitive decline from midlife onward as described by the experts. As an immediate outcome, we observed several non-trivial reinforcing feedback loops involving factors at multiple spatial scales, which are rarely considered within the same theoretical framework. We also observed high centrality for modifiable risk factors such as social relationships and physical activity, which suggests they may be promising leverage points for interventions. This illustrates how a CLD from an interdisciplinary GMB process may lead to novel insights into complex disorders. Furthermore, the CLD is the first step in the development of a computational model for simulating the effects of risk factors on AD.
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Affiliation(s)
- Jeroen F Uleman
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Reinier Postlaan 4, 6525GC, Nijmegen, The Netherlands. .,Institute for Advanced Study, Amsterdam, The Netherlands.
| | - René J F Melis
- Institute for Advanced Study, Amsterdam, The Netherlands.,Department of Geriatric Medicine, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rick Quax
- Computational Science Lab, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Eddy A van der Zee
- Molecular Neurobiology, Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, Groningen, The Netherlands
| | - Dick Thijssen
- Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Liverpool John Moores University, Liverpool, United Kingdom
| | - Martin Dresler
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ondine van de Rest
- Division of Human Nutrition and Health, Wageningen University, Research, Wageningen, The Netherlands
| | - Isabelle F van der Velpen
- Department of Epidemiology, Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Hieab H H Adams
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ben Schmand
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Inge M C M de Kok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeroen de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Edo Richard
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Marcel Verbeek
- Departments of Neurology and Laboratory Medicine, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Alfons G Hoekstra
- Institute for Advanced Study, Amsterdam, The Netherlands.,Computational Science Lab, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Marcel G M Olde Rikkert
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Reinier Postlaan 4, 6525GC, Nijmegen, The Netherlands
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8
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Leopold JA, Maron BA, Loscalzo J. The application of big data to cardiovascular disease: paths to precision medicine. J Clin Invest 2020; 130:29-38. [PMID: 31895052 DOI: 10.1172/jci129203] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Advanced phenotyping of cardiovascular diseases has evolved with the application of high-resolution omics screening to populations enrolled in large-scale observational and clinical trials. This strategy has revealed that considerable heterogeneity exists at the genotype, endophenotype, and clinical phenotype levels in cardiovascular diseases, a feature of the most common diseases that has not been elucidated by conventional reductionism. In this discussion, we address genomic context and (endo)phenotypic heterogeneity, and examine commonly encountered cardiovascular diseases to illustrate the genotypic underpinnings of (endo)phenotypic diversity. We highlight the existing challenges in cardiovascular disease genotyping and phenotyping that can be addressed by the integration of big data and interpreted using novel analytical methodologies (network analysis). Precision cardiovascular medicine will only be broadly applied to cardiovascular patients once this comprehensive data set is subjected to unique, integrative analytical strategies that accommodate molecular and clinical heterogeneity rather than ignore or reduce it.
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9
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Barnes BC, Leiter KW, Larentzos JP, Brennan JK. Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials. PROPELLANTS EXPLOSIVES PYROTECHNICS 2019. [DOI: 10.1002/prep.201900187] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Brian C. Barnes
- Energetic Materials Science Branch, FCDD-RLW-LB U.S. Army Research Laboratory Aberdeen Proving Ground MD 21005-5066
| | - Kenneth W. Leiter
- Simulation Sciences Branch, FCDD-RLC-NB U.S. Army Research Laboratory Aberdeen Proving Ground MD 21005-5066
| | - James P. Larentzos
- Energetic Materials Science Branch, FCDD-RLW-LB U.S. Army Research Laboratory Aberdeen Proving Ground MD 21005-5066
| | - John K. Brennan
- Energetic Materials Science Branch, FCDD-RLW-LB U.S. Army Research Laboratory Aberdeen Proving Ground MD 21005-5066
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10
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Vassaux M, Sinclair RC, Richardson RA, Suter JL, Coveney PV. Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation. ADVANCED THEORY AND SIMULATIONS 2019. [DOI: 10.1002/adts.201900122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Maxime Vassaux
- Centre for Computational SciencesUniversity College London20 Gordon Street London WC1H 0AJ UK
| | - Robert C. Sinclair
- Centre for Computational SciencesUniversity College London20 Gordon Street London WC1H 0AJ UK
| | - Robin A. Richardson
- Centre for Computational SciencesUniversity College London20 Gordon Street London WC1H 0AJ UK
| | - James L. Suter
- Centre for Computational SciencesUniversity College London20 Gordon Street London WC1H 0AJ UK
| | - Peter V. Coveney
- Centre for Computational SciencesUniversity College London20 Gordon Street London WC1H 0AJ UK
- Computational Science LaboratoryInstitute for InformaticsFaculty of ScienceUniversity of Amsterdam Amsterdam 1098XH The Netherlands
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11
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Hoekstra AG, Portegies Zwart S, Coveney PV. Multiscale modelling, simulation and computing: from the desktop to the exascale. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180355. [PMID: 30967039 PMCID: PMC6388007 DOI: 10.1098/rsta.2018.0355] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/11/2018] [Indexed: 05/02/2023]
Abstract
This short contribution introduces a theme issue dedicated to 'Multiscale modelling, simulation and computing: from the desktop to the exascale'. It holds a collection of articles presenting cutting-edge research in generic multiscale modelling and multiscale computing, and applications thereof on high-performance computing systems. The special issue starts with a position paper to discuss the paradigm of multiscale computing in the face of the emerging exascale, followed by a review and critical assessment of existing multiscale computing environments. This theme issue provides a state-of-the-art account of generic multiscale computing, as well as exciting examples of applications of such concepts in domains ranging from astrophysics, via material science and fusion, to biomedical sciences. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Affiliation(s)
- Alfons G. Hoekstra
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
- ITMO University, Saint Petersburg, Russia
| | | | - Peter V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London, England
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