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Vermeulen E, Grobbelaar SS, Botha A, Nolte K. Computational modelling in sport: a hybrid simulation of the runner as a complex adaptive system. THEORETICAL ISSUES IN ERGONOMICS SCIENCE 2021. [DOI: 10.1080/1463922x.2021.1983885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- E. Vermeulen
- Department of Industrial Engineering, Stellenbosch University, Stellenbosch, South Africa
| | - S. S. Grobbelaar
- Department of Industrial Engineering, Stellenbosch University, Stellenbosch, South Africa
- DSI-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy (SciSTIP), Stellenbosch University
| | - A. Botha
- Department of Industrial Engineering, Stellenbosch University, Stellenbosch, South Africa
- Next Generation Enterprises and Institutions, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - K. Nolte
- Division of Biokinetics and Sport Science, Department of Physiology, University of Pretoria, Pretoria, South Africa
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Hoekstra AG, Chopard B, Coster D, Portegies Zwart S, Coveney PV. Multiscale computing for science and engineering in the era of exascale performance. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180144. [PMID: 30967040 PMCID: PMC6388008 DOI: 10.1098/rsta.2018.0144] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/09/2018] [Indexed: 05/18/2023]
Abstract
In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing infrastructure, and argue that we could rely on it both on the conceptual modelling level and also when actually executing the multiscale simulation, and maybe should further develop generic frameworks and software tools to facilitate multiscale computing. Next, we focus on simulating multiscale models on high-end computing resources in the face of emerging exascale performance levels. We will argue that although applications could scale to exascale performance relying on weak scaling and maybe even on strong scaling, there are also clear arguments that such scaling may no longer apply for many applications on these emerging exascale machines and that we need to resort to what we would call multi-scaling. 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, The Netherlands
- High Performance Computing Department, ITMO University, St Petersburg, Russia
| | - Bastien Chopard
- Department of Computer Science, University of Geneva, Switzerland
| | | | | | - Peter V. Coveney
- The Centre for Computational Science, Department of Chemistry, University College London, UK
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Hoekstra AG, van Bavel E, Siebes M, Gijsen F, Geris L. Virtual physiological human 2016: translating the virtual physiological human to the clinic. Interface Focus 2017. [DOI: 10.1098/rsfs.2017.0067] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Alfons G. Hoekstra
- Computational Science Lab, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
- High Performance Computing Department, ITMO University, Saint Petersburg, Russia
| | - Ed van Bavel
- Academic Medical Centre, Amsterdam, The Netherlands
| | - Maria Siebes
- Academic Medical Centre, Amsterdam, The Netherlands
| | - Frank Gijsen
- Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Liesbet Geris
- Biomechanics Research Unit, University of Liège, Liège, Belgium
- Biomechanics Section, KU Leuven, Leuven, Belgium
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Hofmann-Apitius M, Ball G, Gebel S, Bagewadi S, de Bono B, Schneider R, Page M, Kodamullil AT, Younesi E, Ebeling C, Tegnér J, Canard L. Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders. Int J Mol Sci 2015; 16:29179-206. [PMID: 26690135 PMCID: PMC4691095 DOI: 10.3390/ijms161226148] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 11/10/2015] [Accepted: 11/12/2015] [Indexed: 12/22/2022] Open
Abstract
Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).
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Affiliation(s)
- Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, Germany.
| | - Gordon Ball
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, Sweden.
- Science for Life Laboratories, Karolinska Institutet, Stockholm SE-171 77, Sweden.
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Shweta Bagewadi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Bernard de Bono
- Institute of Health Informatics, University College London, London NW1 2DA, UK.
- Auckland Bioengineering Institute, University of Auckland, Symmonds Street, Auckland 1142, New Zealand.
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg.
| | - Matt Page
- Translational Bioinformatics, UCB Pharma, 216 Bath Rd, Slough SL1 3WE, UK.
| | - Alpha Tom Kodamullil
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, Germany.
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Christian Ebeling
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany.
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, Sweden.
- Science for Life Laboratories, Karolinska Institutet, Stockholm SE-171 77, Sweden.
| | - Luc Canard
- Translational Science Unit, SANOFI Recherche & Développement, 1 Avenue Pierre Brossolette, Chilly-Mazarin Cedex 91385, France.
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Westerhoff HV, Nakayama S, Mondeel TDGA, Barberis M. Systems Pharmacology: An opinion on how to turn the impossible into grand challenges. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 15:23-31. [PMID: 26464087 DOI: 10.1016/j.ddtec.2015.06.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Revised: 06/23/2015] [Accepted: 06/25/2015] [Indexed: 11/20/2022]
Abstract
A pharmacology that hits single disease-causing molecules with a single drug passively distributing to the target tissue, was almost ready. Such a pharmacology is not (going to be) effective however: a great many diseases are systems biology diseases; complex networks of some hundred thousand types of molecule, determine the functions that constitute human health, through nonlinear interactions. Malfunctions are caused by a variety of molecular failures at the same time; rarely the same variety in different individuals; in complex constellations of OR and AND logics. Few molecules cause disease single-handedly and few drugs will cure the disease all by themselves when dosed for a limited amount of time. We here discuss the implications that this discovery of the network nature of disease should have for pharmacology. We suggest ways in which pharmacokinetics, pharmacodynamics, but also systems biology and genomics may have to change so as better to deal with systems-biology diseases.
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Affiliation(s)
- Hans V Westerhoff
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands; Molecular Cell Physiology, Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam, The Netherlands; Manchester Centre for Integrative Systems Biology, The University of Manchester, UK.
| | - Shintaro Nakayama
- Molecular Cell Physiology, Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam, The Netherlands
| | - Thierry D G A Mondeel
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands
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Papatheodorou I, Oellrich A, Smedley D. Linking gene expression to phenotypes via pathway information. J Biomed Semantics 2015; 6:17. [PMID: 25901272 PMCID: PMC4404592 DOI: 10.1186/s13326-015-0013-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Accepted: 03/19/2015] [Indexed: 11/10/2022] Open
Abstract
Establishing robust links among gene expression, pathways and phenotypes is critical for understanding diseases and developing treatments. In recent years there have been many efforts to develop the computational means to traverse from genes to gene expression, model pathways and classify phenotypes. Numerous ontologies and other controlled vocabularies have been developed, as well as computational methods to combine and mine these data sets and establish connections. Here we discuss these efforts and identify areas of future work that could lead to a better integration of genes, pathways and phenotypes to provide insights into the mechanisms under which gene mutations affect expression and pathways and how these effects are manifested onto the phenotype.
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Affiliation(s)
- Irene Papatheodorou
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Anika Oellrich
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Damian Smedley
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
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