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Kyoda K, Ho KHL, Tohsato Y, Itoga H, Onami S. BD5: An open HDF5-based data format to represent quantitative biological dynamics data. PLoS One 2020; 15:e0237468. [PMID: 32785254 PMCID: PMC7423140 DOI: 10.1371/journal.pone.0237468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/27/2020] [Indexed: 11/18/2022] Open
Abstract
BD5 is a new binary data format based on HDF5 (hierarchical data format version 5). It can be used for representing quantitative biological dynamics data obtained from bioimage informatics techniques and mechanobiological simulations. Biological Dynamics Markup Language (BDML) is an XML (Extensible Markup Language)-based open format that is also used to represent such data; however, it becomes difficult to access quantitative data in BDML files when the file size is large because parsing XML-based files requires large computational resources to first read the whole file sequentially into computer memory. BD5 enables fast random (i.e., direct) access to quantitative data on disk without parsing the entire file. Therefore, it allows practical reuse of data for understanding biological mechanisms underlying the dynamics.
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Affiliation(s)
- Koji Kyoda
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
| | - Kenneth H. L. Ho
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
| | - Yukako Tohsato
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
- Department of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
| | - Hiroya Itoga
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Shuichi Onami
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
- * E-mail: ,
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2
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Erdemir A, Besier TF, Halloran JP, Imhauser CW, Laz PJ, Morrison TM, Shelburne KB. Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Overall Strategy. J Biomech Eng 2020; 141:2730179. [PMID: 31166589 DOI: 10.1115/1.4043346] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Indexed: 12/26/2022]
Abstract
Recent explorations of knee biomechanics have benefited from computational modeling, specifically leveraging advancements in finite element analysis and rigid body dynamics of joint and tissue mechanics. A large number of models have emerged with different levels of fidelity in anatomical and mechanical representation. Adapted modeling and simulation processes vary widely, based on justifiable choices in relation to anticipated use of the model. However, there are situations where modelers' decisions seem to be subjective, arbitrary, and difficult to rationalize. Regardless of the basis, these decisions form the "art" of modeling, which impact the conclusions of simulation-based studies on knee function. These decisions may also hinder the reproducibility of models and simulations, impeding their broader use in areas such as clinical decision making and personalized medicine. This document summarizes an ongoing project that aims to capture the modeling and simulation workflow in its entirety-operation procedures, deviations, models, by-products of modeling, simulation results, and comparative evaluations of case studies and applications. The ultimate goal of the project is to delineate the art of a cohort of knee modeling teams through a publicly accessible, transparent approach and begin to unravel the complex array of factors that may lead to a lack of reproducibility. This manuscript outlines our approach along with progress made so far. Potential implications on reproducibility, on science, engineering, and training of modeling and simulation, on modeling standards, and on regulatory affairs are also noted.
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Affiliation(s)
- Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue (ND20), Cleveland, OH 44195 e-mail:
| | - Thor F Besier
- Department of Engineering Science, Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
| | - Jason P Halloran
- Department of Mechanical Engineering, Center for Human Machine Systems, Cleveland State University, Cleveland, OH 44115
| | - Carl W Imhauser
- Department of Biomechanics, Hospital for Special Surgery, New York, NY 10021
| | - Peter J Laz
- Department of Mechanical and Materials Engineering, Center for Orthopaedic Biomechanics, University of Denver, Denver, CO 80210
| | - Tina M Morrison
- Division of Applied Mechanics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD 20993
| | - Kevin B Shelburne
- Department of Mechanical and Materials Engineering, Center for Orthopaedic Biomechanics, University of Denver, Denver, CO 80210
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3
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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Viceconti M, Cobelli C, Haddad T, Himes A, Kovatchev B, Palmer M. In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies. Proc Inst Mech Eng H 2017; 231:455-466. [PMID: 28427321 DOI: 10.1177/0954411917702931] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
In silico clinical trials, defined as "The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention," have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients' phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.
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Affiliation(s)
- Marco Viceconti
- 1 Department of Mechanical Engineering, INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
| | - Claudio Cobelli
- 2 Department of Information Engineering, University of Padova, Padova, Italy
| | | | | | - Boris Kovatchev
- 4 Center for Diabetes Technology, The University of Virginia, Charlottesville, VA, USA
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5
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Abstract
Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype-phenotype interaction and by a "systemic" nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible-the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done.
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Affiliation(s)
- Marco Viceconti
- Department of Mechanical Engineering and Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield S1 3JD, United Kingdom;
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
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McDougal RA, Bulanova AS, Lytton WW. Reproducibility in Computational Neuroscience Models and Simulations. IEEE Trans Biomed Eng 2016; 63:2021-35. [PMID: 27046845 PMCID: PMC5016202 DOI: 10.1109/tbme.2016.2539602] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. METHODS Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. RESULTS Building on these standard practices, model-sharing sites and tools have been developed that fit into several categories: 1) standardized neural simulators; 2) shared computational resources; 3) declarative model descriptors, ontologies, and standardized annotations; and 4) model-sharing repositories and sharing standards. CONCLUSION A number of complementary innovations have been proposed to enhance sharing, transparency, and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation, and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. SIGNIFICANCE Model management will become increasingly important as multiscale models become larger, more detailed, and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.
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Fernandez J, Zhang J, Heidlauf T, Sartori M, Besier T, Röhrle O, Lloyd D. Multiscale musculoskeletal modelling, data-model fusion and electromyography-informed modelling. Interface Focus 2016; 6:20150084. [PMID: 27051510 DOI: 10.1098/rsfs.2015.0084] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.
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Affiliation(s)
- J Fernandez
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand; Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - J Zhang
- Auckland Bioengineering Institute , University of Auckland , Auckland , New Zealand
| | - T Heidlauf
- Institut für Mechanik (Bau) , University of Stuttgart , Stuttgart , Germany
| | - M Sartori
- Department of Neurorehabilitation Engineering , University Medical Center Göttingen , Göttingen , Germany
| | - T Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand; Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - O Röhrle
- Institut für Mechanik (Bau) , University of Stuttgart , Stuttgart , Germany
| | - D Lloyd
- Centre for Musculoskeletal Research, Menzies Health Institute Queensland, Griffith University, Queensland, Australia; School of Rehabilitation Sciences, Griffith University, Queensland, Australia
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8
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Nickerson D, Atalag K, de Bono B, Geiger J, Goble C, Hollmann S, Lonien J, Müller W, Regierer B, Stanford NJ, Golebiewski M, Hunter P. The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable. Interface Focus 2016; 6:20150103. [PMID: 27051515 PMCID: PMC4759754 DOI: 10.1098/rsfs.2015.0103] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.
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Affiliation(s)
- David Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Koray Atalag
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- National Institute for Health Innovation (NIHI), The University of Auckland, Auckland, New Zealand
| | - Bernard de Bono
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Jörg Geiger
- Interdisciplinary Bank of Biomaterials and Data, University Hospital Würzburg, Würzburg, Germany
| | - Carole Goble
- School of Computer Science, University of Manchester, Manchester, UK
| | - Susanne Hollmann
- Research Center Plant Genomics and Systems Biology, Universitat Potsdam, Potsdam, Germany
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | | | | | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Peter Hunter
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
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Asai Y, Abe T, Li L, Oka H, Nomura T, Kitano H. Databases for multilevel biophysiology research available at Physiome.jp. Front Physiol 2015; 6:251. [PMID: 26441671 PMCID: PMC4563878 DOI: 10.3389/fphys.2015.00251] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 08/24/2015] [Indexed: 11/13/2022] Open
Abstract
Physiome.jp (http://physiome.jp) is a portal site inaugurated in 2007 to support model-based research in physiome and systems biology. At Physiome.jp, several tools and databases are available to support construction of physiological, multi-hierarchical, large-scale models. There are three databases in Physiome.jp, housing mathematical models, morphological data, and time-series data. In late 2013, the site was fully renovated, and in May 2015, new functions were implemented to provide information infrastructure to support collaborative activities for developing models and performing simulations within the database framework. This article describes updates to the databases implemented since 2013, including cooperation among the three databases, interactive model browsing, user management, version management of models, management of parameter sets, and interoperability with applications.
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Affiliation(s)
- Yoshiyuki Asai
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology Graduate University Okinawa, Japan
| | - Takeshi Abe
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology Graduate University Okinawa, Japan
| | - Li Li
- Intasect Communications Inc. Osaka, Japan
| | - Hideki Oka
- Neuroinformatics Japan Center, RIKEN Brain Science Institute Saitama, Japan
| | - Taishin Nomura
- Department of Mechanical Science and Bioengineering, Osaka University Osaka, Japan
| | - Hiroaki Kitano
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology Graduate University Okinawa, Japan ; The Systems Biology Institute Tokyo, Japan
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10
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McKeever S, Johnson D. The role of markup for enabling interoperability in health informatics. Front Physiol 2015; 6:152. [PMID: 26042043 PMCID: PMC4434901 DOI: 10.3389/fphys.2015.00152] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Accepted: 04/27/2015] [Indexed: 11/13/2022] Open
Abstract
Interoperability is the faculty of making information systems work together. In this paper we will distinguish a number of different forms that interoperability can take and show how they are realized on a variety of physiological and health care use cases. The last 15 years has seen the rise of very cheap digital storage both on and off site. With the advent of the Internet of Things people's expectations are for greater interconnectivity and seamless interoperability. The potential impact these technologies have on healthcare are dramatic: from improved diagnoses through immediate access to a patient's electronic health record, to in silico modeling of organs and early stage drug trials, to predictive medicine based on top-down modeling of disease progression and treatment. We will begin by looking at the underlying technology, classify the various kinds of interoperability that exist in the field, and discuss how they are realized. We conclude with a discussion on future possibilities that big data and further standardizations will enable.
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Affiliation(s)
- Steve McKeever
- Department of Informatics and Media, Uppsala UniversityUppsala, Sweden
- Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO)Saint Petersburg, Russia
| | - David Johnson
- Data Science Institute, Imperial College LondonLondon, UK
- Department of Computing, Imperial College LondonLondon, UK
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11
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Hucka M, Nickerson DP, Bader GD, Bergmann FT, Cooper J, Demir E, Garny A, Golebiewski M, Myers CJ, Schreiber F, Waltemath D, Le Novère N. Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative. Front Bioeng Biotechnol 2015; 3:19. [PMID: 25759811 PMCID: PMC4338824 DOI: 10.3389/fbioe.2015.00019] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 02/08/2015] [Indexed: 12/19/2022] Open
Abstract
The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.
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Affiliation(s)
- Michael Hucka
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Gary D. Bader
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Frank T. Bergmann
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- BioQuant/Centre for Organismal Studies (COS), University of Heidelberg, Heidelberg, Germany
| | - Jonathan Cooper
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Emek Demir
- Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Alan Garny
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Martin Golebiewski
- Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
| | - Falk Schreiber
- Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
- Institute of Computer Science, University Halle-Wittenberg, Halle, Germany
| | - Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Nicolas Le Novère
- Babraham Institute, Cambridge, UK
- European Molecular Biology Laboratory-European Bioinformatics Institute, Cambridge, UK
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12
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Abstract
The last four decades have produced a number of significant advances in the developments of computer models to simulate and investigate the electrical activity of cardiac tissue. The tissue descriptions that underlie these simulations have been built from a combination of clever insight and careful comparison with measured data at multiple scales. Tissue models have not only led to greater insights into the mechanisms of life-threatening arrhythmias but have been used to engineer new therapies to treat the consequences of cardiac disease. This paper is a look back at the early years in the cardiac modeling and the challenges facing the field as models move toward the clinic.
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13
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Nickerson DP, Ladd D, Hussan JR, Safaei S, Suresh V, Hunter PJ, Bradley CP. Using CellML with OpenCMISS to Simulate Multi-Scale Physiology. Front Bioeng Biotechnol 2015; 2:79. [PMID: 25601911 PMCID: PMC4283644 DOI: 10.3389/fbioe.2014.00079] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 12/11/2014] [Indexed: 11/13/2022] Open
Abstract
OpenCMISS is an open-source modeling environment aimed, in particular, at the solution of bioengineering problems. OpenCMISS consists of two main parts: a computational library (OpenCMISS-Iron) and a field manipulation and visualization library (OpenCMISS-Zinc). OpenCMISS is designed for the solution of coupled multi-scale, multi-physics problems in a general-purpose parallel environment. CellML is an XML format designed to encode biophysically based systems of ordinary differential equations and both linear and non-linear algebraic equations. A primary design goal of CellML is to allow mathematical models to be encoded in a modular and reusable format to aid reproducibility and interoperability of modeling studies. In OpenCMISS, we make use of CellML models to enable users to configure various aspects of their multi-scale physiological models. This avoids the need for users to be familiar with the OpenCMISS internal code in order to perform customized computational experiments. Examples of this are: cellular electrophysiology models embedded in tissue electrical propagation models; material constitutive relationships for mechanical growth and deformation simulations; time-varying boundary conditions for various problem domains; and fluid constitutive relationships and lumped-parameter models. In this paper, we provide implementation details describing how CellML models are integrated into multi-scale physiological models in OpenCMISS. The external interface OpenCMISS presents to users is also described, including specific examples exemplifying the extensibility and usability these tools provide the physiological modeling and simulation community. We conclude with some thoughts on future extension of OpenCMISS to make use of other community developed information standards, such as FieldML, SED-ML, and BioSignalML. Plans for the integration of accelerator code (graphical processing unit and field programmable gate array) generated from CellML models is also discussed.
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Affiliation(s)
- David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - David Ladd
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Jagir R. Hussan
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Vinod Suresh
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Peter J. Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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14
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Dräger A, Palsson BØ. Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol 2014; 2:61. [PMID: 25538939 PMCID: PMC4259112 DOI: 10.3389/fbioe.2014.00061] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 11/14/2014] [Indexed: 11/17/2022] Open
Abstract
Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.
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Affiliation(s)
- Andreas Dräger
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Cognitive Systems, Center for Bioinformatics Tübingen (ZBIT), Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Bernhard Ø. Palsson
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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D'Alessandro LA, Hoehme S, Henney A, Drasdo D, Klingmüller U. Unraveling liver complexity from molecular to organ level: challenges and perspectives. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 117:78-86. [PMID: 25433231 DOI: 10.1016/j.pbiomolbio.2014.11.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 10/28/2014] [Accepted: 11/19/2014] [Indexed: 12/13/2022]
Abstract
Biological responses are determined by information processing at multiple and highly interconnected scales. Within a tissue the individual cells respond to extracellular stimuli by regulating intracellular signaling pathways that in turn determine cell fate decisions and influence the behavior of neighboring cells. As a consequence the cellular responses critically impact tissue composition and architecture. Understanding the regulation of these mechanisms at different scales is key to unravel the emergent properties of biological systems. In this perspective, a multidisciplinary approach combining experimental data with mathematical modeling is introduced. We report the approach applied within the Virtual Liver Network to analyze processes that regulate liver functions from single cell responses to the organ level using a number of examples. By facilitating interdisciplinary collaborations, the Virtual Liver Network studies liver regeneration and inflammatory processes as well as liver metabolic functions at multiple scales, and thus provides a suitable example to identify challenges and point out potential future application of multi-scale systems biology.
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Affiliation(s)
- L A D'Alessandro
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany
| | - S Hoehme
- Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany
| | - A Henney
- Obsidian Biomedical Consulting Ltd., Macclesfield, UK; The German Virtual Liver Network, University of Heidelberg, 69120 Heidelberg, Germany
| | - D Drasdo
- Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Germany; Institut National de Recherche en Informatique et en Automatique (INRIA), Domaine de Voluceau, 78150 Rocquencourt, France; University Pierre and Marie Curie and CNRS UMR 7598, LJLL, F-75005 Paris, France; CNRS, 7598 Paris, France
| | - U Klingmüller
- Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), INF 280, 69120 Heidelberg, Germany.
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Kyoda K, Tohsato Y, Ho KHL, Onami S. Biological Dynamics Markup Language (BDML): an open format for representing quantitative biological dynamics data. ACTA ACUST UNITED AC 2014; 31:1044-52. [PMID: 25414366 PMCID: PMC4382901 DOI: 10.1093/bioinformatics/btu767] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 11/13/2014] [Indexed: 01/08/2023]
Abstract
Motivation: Recent progress in live-cell imaging and modeling techniques has resulted in generation of a large amount of quantitative data (from experimental measurements and computer simulations) on spatiotemporal dynamics of biological objects such as molecules, cells and organisms. Although many research groups have independently dedicated their efforts to developing software tools for visualizing and analyzing these data, these tools are often not compatible with each other because of different data formats. Results: We developed an open unified format, Biological Dynamics Markup Language (BDML; current version: 0.2), which provides a basic framework for representing quantitative biological dynamics data for objects ranging from molecules to cells to organisms. BDML is based on Extensible Markup Language (XML). Its advantages are machine and human readability and extensibility. BDML will improve the efficiency of development and evaluation of software tools for data visualization and analysis. Availability and implementation: A specification and a schema file for BDML are freely available online at http://ssbd.qbic.riken.jp/bdml/. Contact:sonami@riken.jp Supplementary Information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Koji Kyoda
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan and
| | - Yukako Tohsato
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan and
| | - Kenneth H L Ho
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan and
| | - Shuichi Onami
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan and National Bioscience Database Center, Japan Science and Technology Agency, Tokyo 102-0081, Japan
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Suinesiaputra A, Medrano-Gracia P, Cowan BR, Young AA. Big heart data: advancing health informatics through data sharing in cardiovascular imaging. IEEE J Biomed Health Inform 2014; 19:1283-90. [PMID: 25415993 DOI: 10.1109/jbhi.2014.2370952] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The burden of heart disease is rapidly worsening due to the increasing prevalence of obesity and diabetes. Data sharing and open database resources for heart health informatics are important for advancing our understanding of cardiovascular function, disease progression and therapeutics. Data sharing enables valuable information, often obtained at considerable expense and effort, to be reused beyond the specific objectives of the original study. Many government funding agencies and journal publishers are requiring data reuse, and are providing mechanisms for data curation and archival. Tools and infrastructure are available to archive anonymous data from a wide range of studies, from descriptive epidemiological data to gigabytes of imaging data. Meta-analyses can be performed to combine raw data from disparate studies to obtain unique comparisons or to enhance statistical power. Open benchmark datasets are invaluable for validating data analysis algorithms and objectively comparing results. This review provides a rationale for increased data sharing and surveys recent progress in the cardiovascular domain. We also highlight the potential of recent large cardiovascular epidemiological studies enabling collaborative efforts to facilitate data sharing, algorithms benchmarking, disease modeling and statistical atlases.
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Geris L. Regenerative orthopaedics: in vitro, in vivo...in silico. INTERNATIONAL ORTHOPAEDICS 2014; 38:1771-8. [PMID: 24984594 DOI: 10.1007/s00264-014-2419-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Accepted: 06/05/2014] [Indexed: 11/29/2022]
Abstract
In silico, defined in analogy to in vitro and in vivo as those studies that are performed on a computer, is an essential step in problem-solving and product development in classical engineering fields. The use of in silico models is now slowly easing its way into medicine. In silico models are already used in orthopaedics for the planning of complicated surgeries, personalised implant design and the analysis of gait measurements. However, these in silico models often lack the simulation of the response of the biological system over time. In silico models focusing on the response of the biological systems are in full development. This review starts with an introduction into in silico models of orthopaedic processes. Special attention is paid to the classification of models according to their spatiotemporal scale (gene/protein to population) and the information they were built on (data vs hypotheses). Subsequently, the review focuses on the in silico models used in regenerative orthopaedics research. Contributions of in silico models to an enhanced understanding and optimisation of four key elements-cells, carriers, culture and clinics-are illustrated. Finally, a number of challenges are identified, related to the computational aspects but also to the integration of in silico tools into clinical practice.
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Affiliation(s)
- Liesbet Geris
- Biomechanics Research Unit, University of Liège, Liège, Belgium,
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Lamata P, Sinclair M, Kerfoot E, Lee A, Crozier A, Blazevic B, Land S, Lewandowski AJ, Barber D, Niederer S, Smith N. An automatic service for the personalization of ventricular cardiac meshes. J R Soc Interface 2013; 11:20131023. [PMID: 24335562 PMCID: PMC3869175 DOI: 10.1098/rsif.2013.1023] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Computational cardiac physiology has great potential to improve the management of cardiovascular diseases. One of the main bottlenecks in this field is the customization of the computational model to the anatomical and physiological status of the patient. We present a fully automatic service for the geometrical personalization of cardiac ventricular meshes with high-order interpolation from segmented images. The method is versatile (able to work with different species and disease conditions) and robust (fully automatic results fulfilling accuracy and quality requirements in 87% of 255 cases). Results also illustrate the capability to minimize the impact of segmentation errors, to overcome the sparse resolution of dynamic studies and to remove the sometimes unnecessary anatomical detail of papillary and trabecular structures. The smooth meshes produced can be used to simulate cardiac function, and in particular mechanics, or can be used as diagnostic descriptors of anatomical shape by cardiologists. This fully automatic service is deployed in a cloud infrastructure, and has been made available and accessible to the scientific community.
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Affiliation(s)
- Pablo Lamata
- Department of Biomedical Engineering, King's College of London, St Thomas' Hospital, , London SE1 7EH, UK
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