VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations

Groen, Derek; Jancauskas, Vytautas; Edeling, Wouter; Jansson, Fredrik; Richardson, Robin A.; Lakhlili, Jalal; Veen, Linda van; Bosak, Bartosz; Kopta, Piotr; Wright, David W.; Karlshoefer, Paul; Suleimenova, Diana; Sinclair, Robert C.; Vassaux, Maxime; Nikishova, Anna; Bieniek, Mariusz; Luk, O. O.; Kulczewski, Michal; Raffin, Erwan; Crommelin, Daan; Hoenen, O.; Coster, D.; Piontek, Tomasz; Coveney, Peter V.

doi:10.1098/rsta.2020.0221

Cited by 21 publications

(16 citation statements)

References 66 publications

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“…These guidelines only refer to medical devices and pharmacokinetics, thus future efforts are needed to define suitable protocols and methods for wider biomedical applications. The criticality of computational model verification, uncertainty quantification, calibration and validation in the biomedical field is also demonstrated by recent publications (Marino et al, 2008;Luraghi et al, 2018;Nikishova et al, 2018Nikishova et al, , 2019Fleeter et al, 2020;Ye et al, 2021a;Curreli et al, 2021;Groen et al, 2021;Luraghi et al, 2021;Rapadamnaba et al, 2021).…”

Section: Challenges and Future Directions Verification Uncertainty Quantification Calibration And Validationmentioning

confidence: 96%

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

Corti

Colombo

Migliavacca

et al. 2021

Front. Bioeng. Biotechnol.

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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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Section: Challenges and Future Directions Verification Uncertainty Quantification Calibration And Validationmentioning

confidence: 96%

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

Corti

Colombo

Migliavacca

et al. 2021

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

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“…In addition, approaches combining ABC and GP methodologies have also been considered (see, e.g., Wilkinson (2014), Meeds and Welling (2014)), although the approach we take here differs in that ABC is used to provide initial evaluations and the GP optimization process provides refining. Moreover, toolkits specifically aimed at model exploration on HPC resources exist (see, e.g., Adams et al (2014b), Groen et al (2021)); however, they are not geared toward integrating external, multilanguage libraries or large-scale mixed ML and simulation workflows.…”

Section: Model Explorationmentioning

confidence: 99%

A population data-driven workflow for COVID-19 modeling and learning

Ozik

Wozniak

Collier

et al. 2021

The International Journal of High Performance Computing Applica

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CityCOVID is a detailed agent-based model that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces, and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of protective behaviors of individuals on viral transmissibility. Throughout the COVID-19 epidemic, CityCOVID model outputs have been provided to city, county, and state stakeholders in response to evolving decision-making priorities, while incorporating emerging information on SARS-CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible, and performant analytical platform for planning and crisis response.

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“…In more formal terms, we may classify these concerns into validation, verification, sensitivity analysis and uncertainty quantification (UQ) of a workflow (or workflow instance), with the canonical use of these terms being found in. 103 These terms, and the distinction between them, are found more often in discussions of computational models used in physics and engineering, 104 , 105 but the concepts are relevant to (and may be adapted to) the general scientific workflows covered in this article.…”

Section: Workflow Assessmentmentioning

confidence: 99%

Perspectives on automated composition of workflows in the life sciences

et al. 2021

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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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VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations

Cited by 21 publications

References 66 publications

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

A population data-driven workflow for COVID-19 modeling and learning

Perspectives on automated composition of workflows in the life sciences

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