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Bomberna T, Maleux G, Debbaut C. Adaptive design of experiments to fit surrogate Gaussian process regression models allows fast sensitivity analysis of the input waveform for patient-specific 3D CFD models of liver radioembolization. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 252:108234. [PMID: 38823206 DOI: 10.1016/j.cmpb.2024.108234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/14/2024] [Accepted: 05/16/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND AND OBJECTIVE Patient-specific 3D computational fluid dynamics (CFD) models are increasingly being used to understand and predict transarterial radioembolization procedures used for hepatocellular carcinoma treatment. While sensitivity analyses of these CFD models can help to determine the most impactful input parameters, such analyses are computationally costly. Therefore, we aim to use surrogate modelling to allow relatively cheap sensitivity analysis. As an example, we compute Sobol's sensitivity indices for three input waveform shape parameters. METHODS We extracted three characteristic shape parameters from our input mass flow rate waveform (peak systolic mass flow rate, heart rate, systolic duration) and defined our 3D input parameter space by varying these parameters within 75 %-125 % of their nominal values. To fit our surrogate model with a minimal number of costly CFD simulations, we developed an adaptive design of experiments (ADOE) algorithm. The ADOE uses 100 Latin hypercube sampled points in 3D input space to define the initial design of experiments (DOE). Subsequently, we re-sample input space with 10,000 Latin Hypercube sampled points and cheaply estimate the outputs using the surrogate model. In each of 27 equivolume bins which divide our input space, we determine the most uncertain prediction of the 10,000 points, compute the true outputs using CFD, and add these points to the DOE. For each ADOE iteration, we calculate Sobol's sensitivity indices, and we continue to add batches of 27 samples to the DOE until the Sobol indices have stabilized. RESULTS We tested our ADOE algorithm on the Ishigami function and showed that we can reliably obtain Sobol's indices with an absolute error <0.1. Applying ADOE to our waveform sensitivity problem, we found that the first-order sensitivity indices were 0.0550, 0.0191 and 0.407 for the peak systolic mass flow rate, heart rate, and the systolic duration, respectively. CONCLUSIONS Although the current study was an illustrative case, the ADOE allows reliable sensitivity analysis with a limited number of complex model evaluations, and performs well even when the optimal DOE size is a priori unknown. This enables us to identify the highest-impact input parameters of our model, and other novel, costly models in the future.
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Affiliation(s)
- Tim Bomberna
- IBiTech-BioMMedA, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, Ghent, Belgium; Cancer Research Institute Ghent, Corneel Heymanslaan 10, Ghent, Belgium.
| | - Geert Maleux
- Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven, Belgium; Department of Imaging and Pathology, KU Leuven, Herestraat 49, Leuven, Belgium
| | - Charlotte Debbaut
- IBiTech-BioMMedA, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, Ghent, Belgium; Cancer Research Institute Ghent, Corneel Heymanslaan 10, Ghent, Belgium
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2
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Lo SCY, McCullough JWS, Xue X, Coveney PV. Uncertainty quantification of the impact of peripheral arterial disease on abdominal aortic aneurysms in blood flow simulations. J R Soc Interface 2024; 21:20230656. [PMID: 38593843 PMCID: PMC11003782 DOI: 10.1098/rsif.2023.0656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/05/2024] [Indexed: 04/11/2024] Open
Abstract
Peripheral arterial disease (PAD) and abdominal aortic aneurysms (AAAs) often coexist and pose significant risks of mortality, yet their mutual interactions remain largely unexplored. Here, we introduce a fluid mechanics model designed to simulate the haemodynamic impact of PAD on AAA-associated risk factors. Our focus lies on quantifying the uncertainty inherent in controlling the flow rates within PAD-affected vessels and predicting AAA risk factors derived from wall shear stress. We perform a sensitivity analysis on nine critical model parameters through simulations of three-dimensional blood flow within a comprehensive arterial geometry. Our results show effective control of the flow rates using two-element Windkessel models, although specific outlets need attention. Quantities of interest like endothelial cell activation potential (ECAP) and relative residence time are instructive for identifying high-risk regions, with ECAP showing greater reliability and adaptability. Our analysis reveals that the uncertainty in the quantities of interest is 187% of that of the input parameters. Notably, parameters governing the amplitude and frequency of the inlet velocity exert the strongest influence on the risk factors' variability and warrant precise determination. This study forms the foundation for patient-specific simulations involving PAD and AAAs which should ultimately improve patient outcomes and reduce associated mortality rates.
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Affiliation(s)
- Sharp C. Y. Lo
- Centre for Computational Science, University College London, London, UK
| | | | - Xiao Xue
- Centre for Computational Science, University College London, London, UK
| | - Peter V. Coveney
- Centre for Computational Science, University College London, London, UK
- Advanced Research Computing Centre, University College London, London, UK
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
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3
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Wan S, Bhati AP, Wade AD, Coveney PV. Ensemble-Based Approaches Ensure Reliability and Reproducibility. J Chem Inf Model 2023; 63:6959-6963. [PMID: 37965695 PMCID: PMC10685440 DOI: 10.1021/acs.jcim.3c01654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Indexed: 11/16/2023]
Abstract
It is increasingly widely recognized that ensemble-based approaches are required to achieve reliability, accuracy, and precision in molecular dynamics calculations. The purpose of the present article is to address a frequently raised question: what is the optimal way to perform ensemble simulation to calculate quantities of interest?
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Affiliation(s)
- Shunzhou Wan
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U. K
| | - Agastya P. Bhati
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U. K
| | - Alexander D. Wade
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U. K
| | - Peter V. Coveney
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, U. K
- Advanced
Research Computing Centre, University College
London, London WC1H 0AJ, U.K.
- Institute
for Informatics, Faculty of Science, University
of Amsterdam, 1098XH Amsterdam, The Netherlands
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4
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Suter JL, Vassaux M, Coveney PV. Large-Scale Molecular Dynamics Elucidates the Mechanics of Reinforcement in Graphene-Based Composites. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2302237. [PMID: 37376866 DOI: 10.1002/adma.202302237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/10/2023] [Indexed: 06/29/2023]
Abstract
Using very large-scale classical molecular dynamics, the mechanics of nano-reinforcement of graphene-based nanocomposites are examined. Simulations show that significant quantities of large, defect-free, and predominantly flat graphene flakes are required for successful enhancement of materials properties in excellent agreement with experimental and proposed continuum shear-lag theories. The critical lengths for enhancement are approximately 500 nm for graphene and 300 nm and for graphene oxide (GO). The reduction of Young's modulus in GO results in a much smaller enhancement of the composite's Young's modulus. The simulations reveal that the flakes should be aligned and planar for optimal reinforcement. Undulations substantially degrade the enhancement of materials properties.
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Affiliation(s)
- James L Suter
- Centre for Computational Science - University College London, 20 Gordon Street, London, WC1H 0AJ, UK
| | - Maxime Vassaux
- Centre for Computational Science - University College London, 20 Gordon Street, London, WC1H 0AJ, UK
- Institut de Physique de Rennes - UMR 6251 CNRS, Université de Rennes, Rennes, 35000, France
| | - Peter V Coveney
- Centre for Computational Science - University College London, 20 Gordon Street, London, WC1H 0AJ, UK
- Advanced Research Computing Centre, University College London, London, WC1E 6BT, UK
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, 1098XH, The Netherlands
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5
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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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6
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Principles governing control of aggregation and dispersion of aqueous graphene oxide. Sci Rep 2021; 11:22460. [PMID: 34789770 PMCID: PMC8599484 DOI: 10.1038/s41598-021-01626-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/28/2021] [Indexed: 11/09/2022] Open
Abstract
Controlling the structure of graphene oxide (GO) phases and their smaller analogues, graphene (oxide) quantum dots (GOQDs), is vitally important for any of their widespread intended applications: highly ordered arrangements of nanoparticles for thin-film or membrane applications of GO, dispersed nanoparticles for composite materials and three-dimensional porous arrangements for hydrogels. In aqueous environments, it is not only the chemical composition of the GO flakes that determines their morphologies; external factors such as pH and the coexisting cations also influence the structures formed. By using accurate models of GO that capture the heterogeneity of surface oxidation and very large-scale coarse-grained molecular dynamics that can simulate the behaviour of GO at realistic sizes of GOQDs, the driving forces that lead to the various morphologies in aqueous solution are resolved. We find the morphologies are determined by a complex interplay between electrostatic, [Formula: see text]-[Formula: see text] and hydrogen bonding interactions. Assembled morphologies can be controlled by changing the degree of oxidation and the pH. In acidic aqueous solution, the GO flakes vary from fully aggregated over graphitic domains to partial aggregation via hydrogen bonding between hydroxylated domains, leading to the formation of planar extended flakes at high oxidation ratios and stacks at low oxidation ratios. At high pH, where the edge carboxylic acid groups are deprotonated, electrostatic repulsion leads to more dispersion, but a variety of aggregation behaviour is surprisingly still observed: over graphitic regions, via hydrogen bonding and "face-edge" interactions. Calcium ions cause additional aggregation, with a greater number of "face-face" and "edge-edge" aggregation mechanisms, leading to irregular aggregated structures. "Face-face" aggregation mechanisms are enhanced by the GO flakes possessing distinct domains of hydroxylated and graphitic regions, with [Formula: see text]-[Formula: see text] and hydrogen bonding interactions prevalent between these regions on aggregated flakes respectively. These findings furnish explanations for the aggregation characteristics of GO and GOQDs, and provide computational methods to design directed synthesis routes for self-assembled and associated applications.
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7
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Lamprecht AL, Palmblad M, Ison J, Schwämmle V, Al Manir MS, Altintas I, Baker CJO, Ben Hadj Amor A, Capella-Gutierrez S, Charonyktakis P, Crusoe MR, Gil Y, Goble C, Griffin TJ, Groth P, Ienasescu H, Jagtap P, Kalaš M, Kasalica V, Khanteymoori A, Kuhn T, Mei H, Ménager H, Möller S, Richardson RA, Robert V, Soiland-Reyes S, Stevens R, Szaniszlo S, Verberne S, Verhoeven A, Wolstencroft K. Perspectives on automated composition of workflows in the life sciences. F1000Res 2021; 10:897. [PMID: 34804501 PMCID: PMC8573700 DOI: 10.12688/f1000research.54159.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/27/2021] [Indexed: 12/29/2022] Open
Abstract
Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.
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Affiliation(s)
| | - Magnus Palmblad
- Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Jon Ison
- French Institute of Bioinformatics, 91057 Évry, France
| | | | | | - Ilkay Altintas
- University of California San Diego, La Jolla, CA, 92093, USA
| | - Christopher J. O. Baker
- University of New Brunswick, Saint John, E2L 4L5, Canada
- IPSNP Computing Inc., Saint John, E2L 4S6, Canada
| | | | | | | | | | - Yolanda Gil
- University of Southern California, Marina Del Rey, CA, 90292, USA
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Paul Groth
- University of Amsterdam, 1090 GH Amsterdam, The Netherlands
| | - Hans Ienasescu
- Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Pratik Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, 55455, USA
| | | | | | | | - Tobias Kuhn
- VU Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | | | - Steffen Möller
- IBIMA, Rostock University Medical Center, 18057 Rostock, Germany
| | | | | | - Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
- Informatics Institute, University of Amsterdam, 1090 GH Amsterdam, The Netherlands
| | - Robert Stevens
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | | | - Suzan Verberne
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 BE Leiden, The Netherlands
| | - Aswin Verhoeven
- Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, 2333 BE Leiden, The Netherlands
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8
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Gugole F, Coffeng LE, Edeling W, Sanderse B, de Vlas SJ, Crommelin D. Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Comput Biol 2021; 17:e1009355. [PMID: 34534205 PMCID: PMC8480746 DOI: 10.1371/journal.pcbi.1009355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/29/2021] [Accepted: 08/17/2021] [Indexed: 11/18/2022] Open
Abstract
Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.
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Affiliation(s)
| | - Luc E. Coffeng
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wouter Edeling
- Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | | | - Sake J. de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Daan Crommelin
- Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Amsterdam, The Netherlands
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9
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Vassaux M, Wan S, Edeling W, Coveney PV. Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation. J Chem Theory Comput 2021; 17:5187-5197. [PMID: 34280310 PMCID: PMC8389531 DOI: 10.1021/acs.jctc.1c00526] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Indexed: 11/29/2022]
Abstract
Classical molecular dynamics is a computer simulation technique that is in widespread use across many areas of science, from physics and chemistry to materials, biology, and medicine. The method continues to attract criticism due its oft-reported lack of reproducibility which is in part due to a failure to submit it to reliable uncertainty quantification (UQ). Here we show that the uncertainty arises from a combination of (i) the input parameters and (ii) the intrinsic stochasticity of the method controlled by the random seeds. To illustrate the situation, we make a systematic UQ analysis of a widely used molecular dynamics code (NAMD), applied to estimate binding free energy of a ligand-bound to a protein. In particular, we replace the usually fixed input parameters with random variables, systematically distributed about their mean values, and study the resulting distribution of the simulation output. We also perform a sensitivity analysis, which reveals that, out of a total of 175 parameters, just six dominate the variance in the code output. Furthermore, we show that binding energy calculations dampen the input uncertainty, in the sense that the variation around the mean output free energy is less than the variation around the mean of the assumed input distributions, if the output is ensemble-averaged over the random seeds. Without such ensemble averaging, the predicted free energy is five times more uncertain. The distribution of the predicted properties is thus strongly dependent upon the random seed. Owing to this substantial uncertainty, robust statistical measures of uncertainty in molecular dynamics simulation require the use of ensembles in all contexts.
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Affiliation(s)
- Maxime Vassaux
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom
| | - Shunzhou Wan
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom
| | - Wouter Edeling
- Centrum
Wiskunde & Informatica, Scientific Computing Group, Amsterdam 1090 GB, The Netherlands
| | - Peter V. Coveney
- Centre
for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom
- Informatics
Institute, University of Amsterdam, Amsterdam 1012 WX, The Netherlands
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10
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Coveney PV, Groen D, Hoekstra AG. Reliability and reproducibility in computational science: implementing validation, verification and uncertainty quantification in silico. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200409. [PMID: 33775138 DOI: 10.1098/rsta.2020.0409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Affiliation(s)
- Peter V Coveney
- Centre for Computational Science, University College London, Gordon Street, London, UK
| | - Derek Groen
- Department of Computer Science, Brunel University London, London, UK
| | - Alfons G Hoekstra
- Institute for Informatics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
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