A multiformalism and multiresolution modelling environment: application to the cardiovascular system and its regulation.
Open accessAbstract
The role of modelling and simulation in the systemic analysis of living systems is now clearly established. Emerging disciplines, such as systems biology, and worldwide research actions, such as the Physiome Project or the Virtual Physiological Human, are based on an intensive use of modelling and simulation methodologies and tools. One of the key aspects in this context is to perform an efficient integration of various models representing different biological or physiological functions, at different resolutions, spanning through different scales. This paper presents a multiformalism modelling and simulation environment (M2SL) that has been conceived to ease model integration. A given model is represented as a set of coupled and atomic model components that may be based on different mathematical formalisms with heterogeneous structural and dynamical properties. A co-simulation approach is used to solve these hybrid systems. The pioneering model of the overall regulation of the cardiovascular system proposed by Guyton and co-workers in 1972 has been implemented under M2SL and a pulsatile ventricular model based on a time-varying elastance has been integrated in a multi-resolution approach. Simulations reproducing physiological conditions and using different coupling methods show the benefits of the proposed environment.
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Cites
Textbook of Medical Physiology
Guyton, A. C., & Hall, J. E. (1966) · Annals of Internal Medicine · doi:10.7326/0003-4819-64-6-1363_2 · Cited by 9,760 · see citing works
Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems
Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000) · Cited by 1,143 · see citing works
Circulation: Overall Regulation
Guyton, A. C., Coleman, T. G., & Granger, H. J. (1972) · Annual Review of Physiology · doi:10.1146/annurev.ph.34.030172.000305 · Cited by 870 · see citing works
AToM3: A Tool for Multi-formalism and Meta-modelling
Lara, J. D., & Vangheluwe, H. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45923-5_12 · Cited by 428 · see citing works
The Virtual Cell: a software environment for computational cell biology
Loew, L. M., & Schaff, J. C. (2001) · Trends in biotechnology · doi:10.1016/s0167-7799(01)01740-1 · Cited by 406 · see citing works
Whole-cell simulation: a grand challenge of the 21st century
Tomita, M. (2001) · Trends in biotechnology · doi:10.1016/s0167-7799(01)01636-5 · Cited by 319 · see citing works
Computational modeling of cardiovascular response to orthostatic stress
Heldt, T., Shim, E. B., Kamm, R. D., & Mark, R. G. (2002) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00241.2001 · Cited by 300 · see citing works
Kerckhoffs, R. C. P., Neal, M. L., Gu, Q., Bassingthwaighte, J. B., Omens, J. H., & McCulloch, A. D. (2006) · Annals of Biomedical Engineering · doi:10.1007/s10439-006-9212-7 · Cited by 251 · see citing works
Wendling, F., Hernandez, A. I., Bellanger, J.-J., Chauvel, P., & Bartoloméi, F. (2005) · PubMed · Cited by 236 · see citing works
Watanabe, H., Sugiura, S., Kafuku, H., et al. (2004) · Biophysical Journal · doi:10.1529/biophysj.103.035840 · Cited by 227 · see citing works
An introduction to multi-paradigm modelling and simulation.
Vangheluwe, H., Lara, J. D., & Mosterman, P. J. (2002) · Cited by 164 · see citing works
Modeling wave propagation in realistic heart geometries using the phase-field method
Fenton, F. H., Cherry, E. M., Karma, A., et al. (2005) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.1840311 · Cited by 160 · see citing works
Rompelman, O., Coenen, A. J. R. M., & Kitney, R. I. (1977) · Medical & Biological Engineering & Computing · doi:10.1007/bf02441043 · Cited by 153 · see citing works
Measurement of Heart Rate Variability
Cowan, M. J. (1995) · Western Journal of Nursing Research · doi:10.1177/019394599501700104 · Cited by 141 · see citing works
Discrete Event Simulation of Hybrid Systems
Kofman, E. (2004) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827502418379 · Cited by 136 · see citing works
The EuroPhysiome, STEP and a roadmap for the virtual physiological human
Fenner, J. W., Brook, B. S., Clapworthy, G., Coveney, P. V., Feipel, V., Gregersen, H., Hose, D. R., Kohl, P. K., Lawford, P. V., McCormack, K., et al. (2008) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0089 · Cited by 109 · see citing works
Noble, D. (2004) · Physiology · doi:10.1152/physiol.00004.2004 · Cited by 79 · see citing works
Usyk, T. P., & McCulloch, A. D. (2003) · Journal of Cardiovascular Electrophysiology · doi:10.1046/j.1540.8167.90311.x · Cited by 77 · see citing works
The IUPS Physiome Project: a framework for computational physiology
Hunter, P. J. (2004) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2004.02.006 · Cited by 66 · see citing works
SAPHIR: a physiome core model of body fluid homeostasis and blood pressure regulation
Thomas, S. R., Baconnier, P., Fontecave, J., Françoise, J.-P., Guillaud, F., Hannaert, P., Hernández, A., Le Rolle, V., Mazière, P., Tahi, F., et al. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2008.0079 · Cited by 54 · see citing works
Estimation of cardiac function from computer analysis of the arterial pressure waveform
Guarini, M., Urzúa, J., Cipriano, A. Z., et al. (1998) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.730436 · Cited by 43 · see citing works
Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems
Bassingthwaighte, J. B., Chizeck, H. J., & Atlas, L. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.871775 · Cited by 40 · see citing works
Model-based analysis of myocardial strain data acquired by tissue Doppler imaging
Rolle, V. L., Hernandez, A. I., Richard, P.-Y., et al. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2008.06.001 · Cited by 36 · see citing works
A Bond Graph Model of the Cardiovascular System
Rolle, V. L., Hernandez, A. I., Richard, P.-Y., Buisson, J., & Carrault, G. (2005) · Acta Biotheoretica · doi:10.1007/s10441-005-4881-4 · Cited by 36 · see citing works
Self-citationAddressing the challenges of multiscale model management in systems biology
Hetherington, J., Bogle, I. D. L., Saffrey, P., Margoninski, O., Li, L., Rey, M., Yamaji, S., Baigent, S., Ashmore, J. F., Page, K. M., et al. (2006) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2006.10.004 · Cited by 28 · see citing works
Integrative Biological Modelling In Silico
McCulloch, A. D., & Huber, G. (2002) · Novartis Foundation symposium · doi:10.1002/0470857897.ch2 · Cited by 23 · see citing works
A paradigm for quantifying ventricular contraction.
Palladino, J. L., & Noordergraaf, A. (2002) · PubMed · Cited by 19 · see citing works
Dou, J., Xia, L., Zhang, Y., et al. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/2/012 · Cited by 12 · see citing works
A Computational Model of Cardiac Electromechanics
Nickerson, D. P., Niederer, S., Stevens, C., et al. (2006) · doi:10.1109/iembs.2006.260169 · Cited by 11 · see citing works
Open Source Software Infrastructure for Computational Biology and Visualization
Blackett, S. A., Bullivant, D. P., Stevens, C., et al. (2005) · doi:10.1109/iembs.2005.1615879 · Cited by 8 · see citing works
Multi-formalism Modelling of Cardiac Tissue
Defontaine, A., Hernandez, A. I., & Carrault, G. (2005) · Lecture notes in computer science · doi:10.1007/11494621_39 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Raymond G, Butterworth E, Bassingthwaighte J. Jsim: Free software package for teaching physiological modeling and research. Exper Biol. 2003;280:102. [Google Scholar]
Cited by
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Kutumova, E., Kiselev, I., Sharipov, R., Lifshits, G., & Kolpakov, F. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.746300 · Cited by 16 · see citing works
Modular modeling of biological systems.
Kiselev, I., & Kolpakov, F. (2013) · Virtual Biology · doi:10.12704/vb/e11 · Cited by 4 · see citing works
- Authors
- Hernández, A. I., Le Rolle, V., Defontaine, A., & Carrault, G.
- Affiliations
- INSERM, U642, Rennes 35000, France
- Inserm (FR)
- ORCID
- Carrault, Guy
- Venue
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2009-12-13
- Volume
- 367
- Issue
- 1908
- Pages
- 4923-4940
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- PMC
- PMC3034733
- OpenAlex
- W2118694673
- Cited by
- 47
- References
- 31
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 7 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 5 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 5 · 2012: 4
- FWCI
- 3.90
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche (ANR-06-BYOS-0007)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Modeling and Simulation Systems
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- model integration, co-simulation, hybrid systems, time-varying elastance, virtual physiological human, Physiome Project, model components, physiological modeling
- MeSH terms
- Cardiovascular System, Computer Simulation, Humans, Models, Cardiovascular, Systems Biology
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