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Abstract
Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.
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Shahidi N, Pan M, Safaei S, Tran K, Crampin EJ, Nickerson DP. Hierarchical semantic composition of biosimulation models using bond graphs. PLoS Comput Biol 2021; 17:e1008859. [PMID: 33983945 PMCID: PMC8148364 DOI: 10.1371/journal.pcbi.1008859] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/25/2021] [Accepted: 04/27/2021] [Indexed: 11/19/2022] Open
Abstract
Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.
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Affiliation(s)
- Niloofar Shahidi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
| | - Soroush Safaei
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Kenneth Tran
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
| | - David P. Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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3
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Thiele I, Sahoo S, Heinken A, Hertel J, Heirendt L, Aurich MK, Fleming RM. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol Syst Biol 2021; 16:e8982. [PMID: 32463598 PMCID: PMC7285886 DOI: 10.15252/msb.20198982] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 12/18/2019] [Accepted: 12/23/2019] [Indexed: 12/12/2022] Open
Abstract
Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.
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Affiliation(s)
- Ines Thiele
- School of Medicine, National University of Ireland, Galway, Ireland.,Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Ireland.,APC Microbiome, Cork, Ireland.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Swagatika Sahoo
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Almut Heinken
- School of Medicine, National University of Ireland, Galway, Ireland
| | - Johannes Hertel
- School of Medicine, National University of Ireland, Galway, Ireland.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ronan Mt Fleming
- School of Medicine, National University of Ireland, Galway, Ireland.,Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Faculty of Science, University of Leiden, Leiden, The Netherlands
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Neal ML, König M, Nickerson D, Mısırlı G, Kalbasi R, Dräger A, Atalag K, Chelliah V, Cooling MT, Cook DL, Crook S, de Alba M, Friedman SH, Garny A, Gennari JH, Gleeson P, Golebiewski M, Hucka M, Juty N, Myers C, Olivier BG, Sauro HM, Scharm M, Snoep JL, Touré V, Wipat A, Wolkenhauer O, Waltemath D. Harmonizing semantic annotations for computational models in biology. Brief Bioinform 2019; 20:540-550. [PMID: 30462164 PMCID: PMC6433895 DOI: 10.1093/bib/bby087] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/08/2018] [Accepted: 08/17/2018] [Indexed: 02/06/2023] Open
Abstract
Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.
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Affiliation(s)
- Maxwell Lewis Neal
- Seattle Children’s Research Institute, Center for Global Infectious Disease Research, Seattle, USA
| | - Matthias König
- Department of Biology, Humboldt-University Berlin, Institute for Theoretical Biology, Berlin, Germany
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, UK
| | - Reza Kalbasi
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Koray Atalag
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Vijayalakshmi Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael T Cooling
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Daniel L Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA
| | - Miguel de Alba
- German Federal Institute for Risk Assessment, Berlin, Germany
| | | | - Alan Garny
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
| | - Brett G Olivier
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Modelling of Biological Processes, BioQUANT/COS, Heidelberg University, Germany
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Jacky L Snoep
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa
- Department of Molecular Cell Physiology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Manchester Institute for Biotechnology, University of Manchester, Manchester, UK
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa
| | - Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
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6
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Abstract
Resources for rat researchers are extensive, including strain repositories and databases all around the world. The Rat Genome Database (RGD) serves as the primary rat data repository, providing both manual and computationally collected data from other databases.
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Predicting the murine enterocyte metabolic response to diets that differ in lipid and carbohydrate composition. Sci Rep 2017; 7:8784. [PMID: 28821741 PMCID: PMC5562867 DOI: 10.1038/s41598-017-07350-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 06/27/2017] [Indexed: 11/09/2022] Open
Abstract
The small intestine serves as gatekeeper at the interface between body and diet and is thought to play an important role in the etiology of obesity and associated metabolic disorders. A computational modelling approach was used to improve our understanding of the metabolic responses of epithelial cells to different diets. A constraint based, mouse-specific enterocyte metabolic model (named mmu_ENT717) was constructed to describe the impact of four fully characterized semi-purified diets, that differed in lipid and carbohydrate composition, on uptake, metabolism, as well as secretion of carbohydrates and lipids. Our simulation results predicted luminal sodium as a limiting factor for active glucose absorption; necessity of apical localization of glucose transporter GLUT2 for absorption of all glucose in the postprandial state; potential for gluconeogenesis in enterocytes; and the requirement of oxygen for the formation of endogenous cholesterol needed for chylomicron formation under luminal cholesterol-free conditions. In addition, for a number of enzymopathies related to intestinal carbohydrate and lipid metabolism it was found that their effects might be ameliorated through dietary interventions. In conclusion, our improved enterocyte-specific model was shown to be a suitable platform to study effects of dietary interventions on enterocyte metabolism, and provided novel and deeper insights into enterocyte metabolism.
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Marshall-Colon A, Long SP, Allen DK, Allen G, Beard DA, Benes B, von Caemmerer S, Christensen AJ, Cox DJ, Hart JC, Hirst PM, Kannan K, Katz DS, Lynch JP, Millar AJ, Panneerselvam B, Price ND, Prusinkiewicz P, Raila D, Shekar RG, Shrivastava S, Shukla D, Srinivasan V, Stitt M, Turk MJ, Voit EO, Wang Y, Yin X, Zhu XG. Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform. FRONTIERS IN PLANT SCIENCE 2017; 8:786. [PMID: 28555150 PMCID: PMC5430029 DOI: 10.3389/fpls.2017.00786] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 04/26/2017] [Indexed: 05/18/2023]
Abstract
Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.
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Affiliation(s)
- Amy Marshall-Colon
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Stephen P. Long
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
- Department of Crop Sciences, University of Illinois, UrbanaIL, USA
| | - Douglas K. Allen
- United States Department of Agriculture – Agricultural Research Service–Donald Danforth Plant Science Center, St. LouisMO, USA
| | - Gabrielle Allen
- Department of Astronomy–College of Education, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Daniel A. Beard
- Department of Molecular & Integrative Physiology, University of Michigan, Ann ArborMI, USA
| | - Bedrich Benes
- Department of Computer Graphics Technology, Purdue University, West LafayetteIN, USA
| | - Susanne von Caemmerer
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biological Sciences, Australian National University, ActonACT, Australia
| | - A. J. Christensen
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Donna J. Cox
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - John C. Hart
- Department of Computer Science, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Peter M. Hirst
- Department of Horticulture and Landscape Architecture, Purdue University, West LafayetteIN, USA
| | - Kavya Kannan
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Daniel S. Katz
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Jonathan P. Lynch
- Department of Plant Science, Pennsylvania State University, University ParkPA, USA
- Centre for Plant Integrative Biology, University of NottinghamNottingham, UK
| | - Andrew J. Millar
- SynthSys and School of Biological Sciences, Edinburgh UniversityEdinburgh, UK
| | - Balaji Panneerselvam
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | | | | | - David Raila
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Rachel G. Shekar
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Stuti Shrivastava
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Venkatraman Srinivasan
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Mark Stitt
- Max Planck Institute of Molecular Plant PhysiologyGolm, Germany
| | - Matthew J. Turk
- School of Information Science, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Eberhard O. Voit
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, AtlantaGA, USA
| | - Yu Wang
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & ResearchWageningen, Netherlands
| | - Xin-Guang Zhu
- CAS Key Laboratory for Computational Biology–State Key Laboratory for Hybrid Rice, Partner Institute for Computational Biology, Chinese Academy of SciencesShanghai, China
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Grogan JA, Connor AJ, Markelc B, Muschel RJ, Maini PK, Byrne HM, Pitt-Francis JM. Microvessel Chaste: An Open Library for Spatial Modeling of Vascularized Tissues. Biophys J 2017; 112:1767-1772. [PMID: 28494948 PMCID: PMC5425404 DOI: 10.1016/j.bpj.2017.03.036] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 02/22/2017] [Accepted: 03/27/2017] [Indexed: 11/29/2022] Open
Abstract
Spatial models of vascularized tissues are widely used in computational physiology. We introduce a software library for composing multiscale, multiphysics models for applications including tumor growth, angiogenesis, osteogenesis, coronary perfusion, and oxygen delivery. Composition of such models is time consuming, with many researchers writing custom software. Recent advances in imaging have produced detailed three-dimensional (3D) datasets of vascularized tissues at the scale of individual cells. To fully exploit such data there is an increasing need for software that allows user-friendly composition of efficient, 3D models of vascularized tissues, and comparison of predictions with in vivo or in vitro experiments and alternative computational formulations. Microvessel Chaste can be used to build simulations of vessel growth and adaptation in response to mechanical and chemical stimuli; intra- and extravascular transport of nutrients, growth factors and drugs; and cell proliferation in complex 3D geometries. In addition, it can be used to develop custom software for integrating modeling with experimental data processing workflows, facilitated by a comprehensive Python interface to solvers implemented in C++. This article links to two reproducible example problems, showing how the library can be used to build simulations of tumor growth and angiogenesis with realistic vessel networks.
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Affiliation(s)
- James A Grogan
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom.
| | - Anthony J Connor
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom; Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Bostjan Markelc
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United Kingdom
| | - Ruth J Muschel
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United Kingdom
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Helen M Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Joe M Pitt-Francis
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Cooling MT, Nickerson DP, Nielsen PMF, Hunter PJ. Modular modelling with Physiome standards. J Physiol 2016; 594:6817-6831. [PMID: 27353233 PMCID: PMC5134412 DOI: 10.1113/jp272633] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 06/26/2016] [Indexed: 01/27/2023] Open
Abstract
KEY POINTS The complexity of computational models is increasing, supported by research in modelling tools and frameworks. But relatively little thought has gone into design principles for complex models. We propose a set of design principles for complex model construction with the Physiome standard modelling protocol CellML. By following the principles, models are generated that are extensible and are themselves suitable for reuse in larger models of increasing complexity. We illustrate these principles with examples including an architectural prototype linking, for the first time, electrophysiology, thermodynamically compliant metabolism, signal transduction, gene regulation and synthetic biology. The design principles complement other Physiome research projects, facilitating the application of virtual experiment protocols and model analysis techniques to assist the modelling community in creating libraries of composable, characterised and simulatable quantitative descriptions of physiology. ABSTRACT The ability to produce and customise complex computational models has great potential to have a positive impact on human health. As the field develops towards whole-cell models and linking such models in multi-scale frameworks to encompass tissue, organ, or organism levels, reuse of previous modelling efforts will become increasingly necessary. Any modelling group wishing to reuse existing computational models as modules for their own work faces many challenges in the context of construction, storage, retrieval, documentation and analysis of such modules. Physiome standards, frameworks and tools seek to address several of these challenges, especially for models expressed in the modular protocol CellML. Aside from providing a general ability to produce modules, there has been relatively little research work on architectural principles of CellML models that will enable reuse at larger scales. To complement and support the existing tools and frameworks, we develop a set of principles to address this consideration. The principles are illustrated with examples that couple electrophysiology, signalling, metabolism, gene regulation and synthetic biology, together forming an architectural prototype for whole-cell modelling (including human intervention) in CellML. Such models illustrate how testable units of quantitative biophysical simulation can be constructed. Finally, future relationships between modular models so constructed and Physiome frameworks and tools are discussed, with particular reference to how such frameworks and tools can in turn be extended to complement and gain more benefit from the results of applying the principles.
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Affiliation(s)
| | | | - Poul M. F. Nielsen
- Auckland Bioengineering Institutethe University of AucklandNew Zealand
- Department of Engineering Sciencethe University of AucklandNew Zealand
| | - Peter J. Hunter
- Auckland Bioengineering Institutethe University of AucklandNew Zealand
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Neal ML, Gennari JH, Cook DL. Qualitative causal analyses of biosimulation models. CEUR WORKSHOP PROCEEDINGS 2016; 1747:http://ceur-ws.org/Vol-1747/IT604_ICBO2016.pdf. [PMID: 28804276 PMCID: PMC5551042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We describe an approach for performing qualitative, systems-level causal analyses on biosimulation models that leverages semantics-based modeling formats, formal ontology, and automated inference. The approach allows users to quickly investigate how a qualitative perturbation to an element within a model's network (an increment or decrement) propagates throughout the modeled system. To support such analyses, we must interpret and annotate the semantics of the models, including both the physical properties modeled and the dependencies that relate them. We build from prior work understanding the semantics of biological properties, but here, we focus on the semantics for dependencies, which provide the critical knowledge necessary for causal analysis of biosimulation models. We describe augmentations to the Ontology of Physics for Biology, via OWL axioms and SWRL rules, and demonstrate that a reasoner can then infer how an annotated model's physical properties influence each other in a qualitative sense. Our goal is to provide researchers with a tool that helps bring the systems-level network dynamics of biosimulation models into perspective, thus facilitating model development, testing, and application.
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Affiliation(s)
- Maxwell L Neal
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Daniel L Cook
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
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12
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The Creation of Surrogate Models for Fast Estimation of Complex Model Outcomes. PLoS One 2016; 11:e0156574. [PMID: 27258010 PMCID: PMC4892541 DOI: 10.1371/journal.pone.0156574] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 05/17/2016] [Indexed: 11/19/2022] Open
Abstract
A surrogate model is a black box model that reproduces the output of another more complex model at a single time point. This is to be distinguished from the method of surrogate data, used in time series. The purpose of a surrogate is to reduce the time necessary for a computation at the cost of rigor and generality. We describe a method of constructing surrogates in the form of support vector machine (SVM) regressions for the purpose of exploring the parameter space of physiological models. Our focus is on the methodology of surrogate creation and accuracy assessment in comparison to the original model. This is done in the context of a simulation of hemorrhage in one model, “Small”, and renal denervation in another, HumMod. In both cases, the surrogate predicts the drop in mean arterial pressure following the intervention. We asked three questions concerning surrogate models: (1) how many training examples are necessary to obtain an accurate surrogate, (2) is surrogate accuracy homogeneous, and (3) how much can computation time be reduced when using a surrogate. We found the minimum training set size that would guarantee maximal accuracy was widely variable, but could be algorithmically generated. The average error for the pressure response to the protocols was -0.05±2.47 in Small, and -0.3 +/- 3.94 mmHg in HumMod. In the Small model, error grew with actual pressure drop, and in HumMod, larger pressure drops were overestimated by the surrogates. Surrogate use resulted in a 6 order of magnitude decrease in computation time. These results suggest surrogate modeling is a valuable tool for generating predictions of an integrative model’s behavior on densely sampled subsets of its parameter space.
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Pârvu O, Gilbert D. A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking. PLoS One 2016; 11:e0154847. [PMID: 27187178 PMCID: PMC4871515 DOI: 10.1371/journal.pone.0154847] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Accepted: 04/20/2016] [Indexed: 12/15/2022] Open
Abstract
Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.
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Affiliation(s)
- Ovidiu Pârvu
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University London, London, United Kingdom
| | - David Gilbert
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University London, London, United Kingdom
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14
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Zhu XG, Lynch JP, LeBauer DS, Millar AJ, Stitt M, Long SP. Plants in silico: why, why now and what?--an integrative platform for plant systems biology research. PLANT, CELL & ENVIRONMENT 2016; 39:1049-57. [PMID: 26523481 DOI: 10.1111/pce.12673] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Accepted: 10/17/2015] [Indexed: 05/21/2023]
Abstract
A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels.
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Affiliation(s)
- Xin-Guang Zhu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA, 16802, USA
| | - David S LeBauer
- Institute for Genomic Biology and National Center for Supercomputer Applications, University of Illinois, 1206 W Gregory Drive, Urbana, IL, 61801, USA
| | - Andrew J Millar
- SynthSys and School of Biological Sciences, University of Edinburgh, Midlothian, Scotland, UK
| | - Mark Stitt
- Max Planck Institute for Molecular Plant Physiology, D-14476, Potsdam Gölm, Germany
| | - Stephen P Long
- Departments of Crop Sciences and Plant Biology, Institute for Genomic Biology, University of Illinois, Urbana, IL, 61801, USA
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15
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Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases. PLoS One 2015; 10:e0145621. [PMID: 26716837 PMCID: PMC4696653 DOI: 10.1371/journal.pone.0145621] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 11/06/2015] [Indexed: 11/19/2022] Open
Abstract
Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen's semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the "Pandit-Hinch-Niederer" (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.
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16
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Bugenhagen SM, Beard DA. Computational analysis of the regulation of Ca(2+) dynamics in rat ventricular myocytes. Phys Biol 2015; 12:056008. [PMID: 26358004 DOI: 10.1088/1478-3975/12/5/056008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Force-frequency relationships of isolated cardiac myocytes show complex behaviors that are thought to be specific to both the species and the conditions associated with the experimental preparation. Ca(2+) signaling plays an important role in shaping the force-frequency relationship, and understanding the properties of the force-frequency relationship in vivo requires an understanding of Ca(2+) dynamics under physiologically relevant conditions. Ca(2+) signaling is itself a complicated process that is best understood on a quantitative level via biophysically based computational simulation. Although a large number of models are available in the literature, the models are often a conglomeration of components parameterized to data of incompatible species and/or experimental conditions. In addition, few models account for modulation of Ca(2+) dynamics via β-adrenergic and calmodulin-dependent protein kinase II (CaMKII) signaling pathways even though they are hypothesized to play an important regulatory role in vivo. Both protein-kinase-A and CaMKII are known to phosphorylate a variety of targets known to be involved in Ca(2+) signaling, but the effects of these pathways on the frequency- and inotrope-dependence of Ca(2+) dynamics are not currently well understood. In order to better understand Ca(2+) dynamics under physiological conditions relevant to rat, a previous computational model is adapted and re-parameterized to a self-consistent dataset obtained under physiological temperature and pacing frequency and updated to include β-adrenergic and CaMKII regulatory pathways. The necessity of specific effector mechanisms of these pathways in capturing inotrope- and frequency-dependence of the data is tested by attempting to fit the data while including and/or excluding those effector components. We find that: (1) β-adrenergic-mediated phosphorylation of the L-type calcium channel (LCC) (and not of phospholamban (PLB)) is sufficient to explain the inotrope-dependence; and (2) that CaMKII-mediated regulation of neither the LCC nor of PLB is required to explain the frequency-dependence of the data.
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Affiliation(s)
- Scott M Bugenhagen
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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17
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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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18
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Neal ML, Cooling MT, Smith LP, Thompson CT, Sauro HM, Carlson BE, Cook DL, Gennari JH. A reappraisal of how to build modular, reusable models of biological systems. PLoS Comput Biol 2014; 10:e1003849. [PMID: 25275523 PMCID: PMC4183381 DOI: 10.1371/journal.pcbi.1003849] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Maxwell L. Neal
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
- * E-mail:
| | - Michael T. Cooling
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Lucian P. Smith
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Christopher T. Thompson
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Brian E. Carlson
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Daniel L. Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America
| | - John H. Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America
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19
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Arterial stiffening provides sufficient explanation for primary hypertension. PLoS Comput Biol 2014; 10:e1003634. [PMID: 24853828 PMCID: PMC4031054 DOI: 10.1371/journal.pcbi.1003634] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2013] [Accepted: 04/09/2014] [Indexed: 02/07/2023] Open
Abstract
Hypertension is one of the most common age-related chronic disorders, and by predisposing individuals for heart failure, stroke, and kidney disease, it is a major source of morbidity and mortality. Its etiology remains enigmatic despite intense research efforts over many decades. By use of empirically well-constrained computer models describing the coupled function of the baroreceptor reflex and mechanics of the circulatory system, we demonstrate quantitatively that arterial stiffening seems sufficient to explain age-related emergence of hypertension. Specifically, the empirically observed chronic changes in pulse pressure with age and the impaired capacity of hypertensive individuals to regulate short-term changes in blood pressure arise as emergent properties of the integrated system. The results are consistent with available experimental data from chemical and surgical manipulation of the cardio-vascular system. In contrast to widely held opinions, the results suggest that primary hypertension can be attributed to a mechanogenic etiology without challenging current conceptions of renal and sympathetic nervous system function.
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20
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Kirschner DE, Hunt CA, Marino S, Fallahi-Sichani M, Linderman JJ. Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2014; 6:289-309. [PMID: 24810243 PMCID: PMC4102180 DOI: 10.1002/wsbm.1270] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 03/14/2014] [Accepted: 03/19/2014] [Indexed: 01/19/2023]
Abstract
The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work. WIREs Syst Biol Med 2014, 6:225–245. doi:10.1002/wsbm.1270 How to cite this article:WIREs Syst Biol Med 2014, 6:289–309. doi:10.1002/wsbm.1270
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Affiliation(s)
- Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
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21
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Calçada D, Vianello D, Giampieri E, Sala C, Castellani G, de Graaf A, Kremer B, van Ommen B, Feskens E, Santoro A, Franceschi C, Bouwman J. The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach. Mech Ageing Dev 2014; 136-137:138-47. [PMID: 24462698 DOI: 10.1016/j.mad.2014.01.004] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Revised: 01/10/2014] [Accepted: 01/13/2014] [Indexed: 02/07/2023]
Abstract
Aging is a biological process characterized by the progressive functional decline of many interrelated physiological systems. In particular, aging is associated with the development of a systemic state of low-grade chronic inflammation (inflammaging), and with progressive deterioration of metabolic function. Systems biology has helped in identifying the mediators and pathways involved in these phenomena, mainly through the application of high-throughput screening methods, valued for their molecular comprehensiveness. Nevertheless, inflammation and metabolic regulation are dynamical processes whose behavior must be understood at multiple levels of biological organization (molecular, cellular, organ, and system levels) and on multiple time scales. Mathematical modeling of such behavior, with incorporation of mechanistic knowledge on interactions between inflammatory and metabolic mediators, may help in devising nutritional interventions capable of preventing, or ameliorating, the age-associated functional decline of the corresponding systems.
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Affiliation(s)
- Dulce Calçada
- TNO, Microbiology and Systems Biology Group, Utrechtseweg 48, 3704 HE Zeist, The Netherlands; Wageningen University, Department of Human Nutrition, Wageningen, The Netherlands
| | - Dario Vianello
- University of Bologna, Department of Experimental, Diagnostic and Specialty Medicine, Via San Giacomo 12, 40126 Bologna, Italy
| | - Enrico Giampieri
- University of Bologna, Department of Physics and Astronomy, 40127 Bologna, Italy
| | - Claudia Sala
- University of Bologna, Department of Physics and Astronomy, 40127 Bologna, Italy
| | - Gastone Castellani
- University of Bologna, Department of Physics and Astronomy, 40127 Bologna, Italy
| | - Albert de Graaf
- TNO, Microbiology and Systems Biology Group, Utrechtseweg 48, 3704 HE Zeist, The Netherlands
| | - Bas Kremer
- TNO, Microbiology and Systems Biology Group, Utrechtseweg 48, 3704 HE Zeist, The Netherlands
| | - Ben van Ommen
- TNO, Microbiology and Systems Biology Group, Utrechtseweg 48, 3704 HE Zeist, The Netherlands
| | - Edith Feskens
- Wageningen University, Department of Human Nutrition, Wageningen, The Netherlands
| | - Aurelia Santoro
- University of Bologna, Department of Experimental, Diagnostic and Specialty Medicine, Via San Giacomo 12, 40126 Bologna, Italy
| | - Claudio Franceschi
- University of Bologna, Department of Experimental, Diagnostic and Specialty Medicine, Via San Giacomo 12, 40126 Bologna, Italy; University of Bologna, Interdepartmental Centre "L. Galvani" (CIG), 40126 Bologna, Italy
| | - Jildau Bouwman
- TNO, Microbiology and Systems Biology Group, Utrechtseweg 48, 3704 HE Zeist, The Netherlands.
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Extrapolating In Vitro Results to Predict Human Toxicity. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2014. [DOI: 10.1007/978-1-4939-0521-8_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Investigation on cardiovascular risk prediction using physiological parameters. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:272691. [PMID: 24489599 PMCID: PMC3893863 DOI: 10.1155/2013/272691] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2013] [Accepted: 09/23/2013] [Indexed: 12/11/2022]
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI), and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.
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Butterworth E, Jardine BE, Raymond GM, Neal ML, Bassingthwaighte JB. JSim, an open-source modeling system for data analysis. F1000Res 2013; 2:288. [PMID: 24555116 PMCID: PMC3901508 DOI: 10.12688/f1000research.2-288.v1] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/17/2013] [Indexed: 11/28/2022] Open
Abstract
JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms solving ordinary and partial differential equations, run-time multidimensional graphics, parameter optimization (8 methods), sensitivity analysis, and Monte Carlo simulation for defining confidence ranges. JSim uses Mathematical Modeling Language (MML) a declarative syntax specifying algebraic and differential equations. Imperative constructs written in other languages (MATLAB, FORTRAN, C++, etc.) are accessed through procedure calls. MML syntax is simple, basically defining the parameters and variables, then writing the equations in a straightforward, easily read and understood mathematical form. This makes JSim good for teaching modeling as well as for model analysis for research. For high throughput applications, JSim can be run as a batch job. JSim can automatically translate models from the repositories for Systems Biology Markup Language (SBML) and CellML models. Stochastic modeling is supported. MML supports assigning physical units to constants and variables and automates checking dimensional balance as the first step in verification testing. Automatic unit scaling follows, e.g. seconds to minutes, if needed. The JSim Project File sets a standard for reproducible modeling analysis: it includes in one file everything for analyzing a set of experiments: the data, the models, the data fitting, and evaluation of parameter confidence ranges. JSim is open source; it and about 400 human readable open source physiological/biophysical models are available at http://www.physiome.org/jsim/.
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Affiliation(s)
- Erik Butterworth
- Dept. of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | | | - Gary M. Raymond
- Dept. of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Maxwell L. Neal
- Dept. of Bioengineering, University of Washington, Seattle, WA 98195, USA
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25
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Cook DL, Neal ML, Bookstein FL, Gennari JH. Ontology of physics for biology: representing physical dependencies as a basis for biological processes. J Biomed Semantics 2013; 4:41. [PMID: 24295137 PMCID: PMC3904761 DOI: 10.1186/2041-1480-4-41] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Accepted: 11/19/2013] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND In prior work, we presented the Ontology of Physics for Biology (OPB) as a computational ontology for use in the annotation and representations of biophysical knowledge encoded in repositories of physics-based biosimulation models. We introduced OPB:Physical entity and OPB:Physical property classes that extend available spatiotemporal representations of physical entities and processes to explicitly represent the thermodynamics and dynamics of physiological processes. Our utilitarian, long-term aim is to develop computational tools for creating and querying formalized physiological knowledge for use by multiscale "physiome" projects such as the EU's Virtual Physiological Human (VPH) and NIH's Virtual Physiological Rat (VPR). RESULTS Here we describe the OPB:Physical dependency taxonomy of classes that represent of the laws of classical physics that are the "rules" by which physical properties of physical entities change during occurrences of physical processes. For example, the fluid analog of Ohm's law (as for electric currents) is used to describe how a blood flow rate depends on a blood pressure gradient. Hooke's law (as in elastic deformations of springs) is used to describe how an increase in vascular volume increases blood pressure. We classify such dependencies according to the flow, transformation, and storage of thermodynamic energy that occurs during processes governed by the dependencies. CONCLUSIONS We have developed the OPB and annotation methods to represent the meaning-the biophysical semantics-of the mathematical statements of physiological analysis and the biophysical content of models and datasets. Here we describe and discuss our approach to an ontological representation of physical laws (as dependencies) and properties as encoded for the mathematical analysis of biophysical processes.
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Affiliation(s)
- Daniel L Cook
- Department of Physiology & Biophysics, University of Washington, Seattle 98195, USA.
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26
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Beard DA, Pettersen KH, Carlson BE, Omholt SW, Bugenhagen SM. A computational analysis of the long-term regulation of arterial pressure. F1000Res 2013; 2:208. [PMID: 24555102 DOI: 10.12688/f1000research.2-208.v1] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2013] [Indexed: 12/25/2022] Open
Abstract
The asserted dominant role of the kidneys in the chronic regulation of blood pressure and in the etiology of hypertension has been debated since the 1970s. At the center of the theory is the observation that the acute relationships between arterial pressure and urine production-the acute pressure-diuresis and pressure-natriuresis curves-physiologically adapt to perturbations in pressure and/or changes in the rate of salt and volume intake. These adaptations, modulated by various interacting neurohumoral mechanisms, result in chronic relationships between water and salt excretion and pressure that are much steeper than the acute relationships. While the view that renal function is the dominant controller of arterial pressure has been supported by computer models of the cardiovascular system known as the "Guyton-Coleman model", no unambiguous description of a computer model capturing chronic adaptation of acute renal function in blood pressure control has been presented. Here, such a model is developed with the goals of: 1. representing the relevant mechanisms in an identifiable mathematical model; 2. identifying model parameters using appropriate data; 3. validating model predictions in comparison to data; and 4. probing hypotheses regarding the long-term control of arterial pressure and the etiology of primary hypertension. The developed model reveals: long-term control of arterial blood pressure is primarily through the baroreflex arc and the renin-angiotensin system; and arterial stiffening provides a sufficient explanation for the etiology of primary hypertension associated with ageing. Furthermore, the model provides the first consistent explanation of the physiological response to chronic stimulation of the baroreflex.
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Affiliation(s)
- Daniel A Beard
- Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48105, USA
| | - Klas H Pettersen
- Department of Mathematical and Technological Sciences, Norwegian University of Life Science, Oslo, Norway
| | - Brian E Carlson
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Stig W Omholt
- Cardiac Exercise Research Group, Department of Circulation and Medical Imaging, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Scott M Bugenhagen
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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27
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Beard DA, Pettersen KH, Carlson BE, Omholt SW, Bugenhagen SM. A computational analysis of the long-term regulation of arterial pressure. F1000Res 2013; 2:208. [PMID: 24555102 PMCID: PMC3886803 DOI: 10.12688/f1000research.2-208.v2] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/06/2013] [Indexed: 01/08/2023] Open
Abstract
The asserted dominant role of the kidneys in the chronic regulation of blood pressure and in the etiology of hypertension has been debated since the 1970s. At the center of the theory is the observation that the acute relationships between arterial pressure and urine production—the acute pressure-diuresis and pressure-natriuresis curves—physiologically adapt to perturbations in pressure and/or changes in the rate of salt and volume intake. These adaptations, modulated by various interacting neurohumoral mechanisms, result in chronic relationships between water and salt excretion and pressure that are much steeper than the acute relationships. While the view that renal function is the dominant controller of arterial pressure has been supported by computer models of the cardiovascular system known as the “Guyton-Coleman model”, no unambiguous description of a computer model capturing chronic adaptation of acute renal function in blood pressure control has been presented. Here, such a model is developed with the goals of: 1. representing the relevant mechanisms in an identifiable mathematical model; 2. identifying model parameters using appropriate data; 3. validating model predictions in comparison to data; and 4. probing hypotheses regarding the long-term control of arterial pressure and the etiology of primary hypertension. The developed model reveals: long-term control of arterial blood pressure is primarily through the baroreflex arc and the renin-angiotensin system; and arterial stiffening provides a sufficient explanation for the etiology of primary hypertension associated with ageing. Furthermore, the model provides the first consistent explanation of the physiological response to chronic stimulation of the baroreflex.
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Affiliation(s)
- Daniel A Beard
- Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48105, USA
| | - Klas H Pettersen
- Department of Mathematical and Technological Sciences, Norwegian University of Life Science, Oslo, Norway
| | - Brian E Carlson
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Stig W Omholt
- Cardiac Exercise Research Group, Department of Circulation and Medical Imaging, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Scott M Bugenhagen
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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28
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Kretschmer J, Haunsberger T, Drost E, Koch E, Möller K. Simulating physiological interactions in a hybrid system of mathematical models. J Clin Monit Comput 2013; 28:513-23. [PMID: 23990270 DOI: 10.1007/s10877-013-9502-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Accepted: 08/13/2013] [Indexed: 11/29/2022]
Abstract
Mathematical models can be deployed to simulate physiological processes of the human organism. Exploiting these simulations, reactions of a patient to changes in the therapy regime can be predicted. Based on these predictions, medical decision support systems (MDSS) can help in optimizing medical therapy. An MDSS designed to support mechanical ventilation in critically ill patients should not only consider respiratory mechanics but should also consider other systems of the human organism such as gas exchange or blood circulation. A specially designed framework allows combining three model families (respiratory mechanics, cardiovascular dynamics and gas exchange) to predict the outcome of a therapy setting. Elements of the three model families are dynamically combined to form a complex model system with interacting submodels. Tests revealed that complex model combinations are not computationally feasible. In most patients, cardiovascular physiology could be simulated by simplified models decreasing computational costs. Thus, a simplified cardiovascular model that is able to reproduce basic physiological behavior is introduced. This model purely consists of difference equations and does not require special algorithms to be solved numerically. The model is based on a beat-to-beat model which has been extended to react to intrathoracic pressure levels that are present during mechanical ventilation. The introduced reaction to intrathoracic pressure levels as found during mechanical ventilation has been tuned to mimic the behavior of a complex 19-compartment model. Tests revealed that the model is able to represent general system behavior comparable to the 19-compartment model closely. Blood pressures were calculated with a maximum deviation of 1.8 % in systolic pressure and 3.5 % in diastolic pressure, leading to a simulation error of 0.3 % in cardiac output. The gas exchange submodel being reactive to changes in cardiac output showed a resulting deviation of less than 0.1 %. Therefore, the proposed model is usable in combinations where cardiovascular simulation does not have to be detailed. Computing costs have been decreased dramatically by a factor 186 compared to a model combination employing the 19-compartment model.
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Affiliation(s)
- Jörn Kretschmer
- Institute of Technical Medicine, Furtwangen University, Jakob-Kienzle-Str. 17, 78054, Villingen-Schwenningen, Germany,
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29
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Gjuvsland AB, Vik JO, Beard DA, Hunter PJ, Omholt SW. Bridging the genotype-phenotype gap: what does it take? J Physiol 2013; 591:2055-66. [PMID: 23401613 PMCID: PMC3634519 DOI: 10.1113/jphysiol.2012.248864] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
The genotype-phenotype map (GP map) concept applies to any time point in the ontogeny of a living system. It is the outcome of very complex dynamics that include environmental effects, and bridging the genotype-phenotype gap is synonymous with understanding these dynamics. The context for this understanding is physiology, and the disciplinary goals of physiology do indeed demand the physiological community to seek this understanding. We claim that this task is beyond reach without use of mathematical models that bind together genetic and phenotypic data in a causally cohesive way. We provide illustrations of such causally cohesive genotype-phenotype models where the phenotypes span from gene expression profiles to development of whole organs. Bridging the genotype-phenotype gap also demands that large-scale biological ('omics') data and associated bioinformatics resources be more effectively integrated with computational physiology than is currently the case. A third major element is the need for developing a phenomics technology way beyond current state of the art, and we advocate the establishment of a Human Phenome Programme solidly grounded on biophysically based mathematical descriptions of human physiology.
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Affiliation(s)
- Arne B Gjuvsland
- Centre for Integrative Genetics, Department of Mathematical and Technological Sciences, Norwegian University of Life Sciences, Norway
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30
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Zhang YT, Zheng YL, Lin WH, Zhang HY, Zhou XL. Challenges and opportunities in cardiovascular health informatics. IEEE Trans Biomed Eng 2013; 60:633-42. [PMID: 23380853 DOI: 10.1109/tbme.2013.2244892] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cardiovascular health informatics is a rapidly evolving interdisciplinary field concerning the processing, integration/interpretation, storage, transmission, acquisition, and retrieval of information from cardiovascular systems for the early detection, early prediction, early prevention, early diagnosis, and early treatment of cardiovascular diseases (CVDs). Based on the first author's presentation at the first IEEE Life Sciences Grand Challenges Conference, held on October 4-5, 2012, at the National Academy of Sciences, Washington, DC, USA, this paper, focusing on coronary arteriosclerotic disease, will discuss three significant challenges of cardiovascular health informatics, including: 1) to invent unobtrusive and wearable multiparameter sensors with higher sensitivity for the real-time monitoring of physiological states; 2) to develop fast multimodal imaging technologies with higher resolution for the quantification and better understanding of structure, function, metabolism of cardiovascular systems at the different levels; and 3) to develop novel multiscale information fusion models and strategies with higher accuracy for the personalized predication of the CVDs. At the end of this paper, a summary is given to suggest open discussions on these three and more challenges that face the scientific community in this field in the future.
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Affiliation(s)
- Yuan-Ting Zhang
- Joint Research Centre for Biomedical Engineering, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
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