The Physiome Model Repository 2

Untitled work

(1994) · Cited by 21,711

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models

Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg015 · Cited by 3,060

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

Li, C., Donizelli, M., Rodríguez, N., Dharuri, H. K., Endler, L., Chelliah, V., Li, L., He, E., Henry, A., Stefan, M. I., et al. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-92 · Cited by 561

Integration from proteins to organs: the Physiome Project

Hunter, P. J., & Borg, T. K. (2003) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1054 · Cited by 486

LibSBML: an API Library for SBML

Bornstein, B. J., Keating, S. M., Jouraku, A., & Hucka, M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn051 · Cited by 373

An Overview of CellML 1.1, a Biological Model Description Language

Cuellar, A. A., Lloyd, C. M., Nielsen, P. F., Bullivant, D. P., Nickerson, D. P., & Hunter, P. J. (2003) · SIMULATION · doi:10.1177/0037549703040939 · Cited by 345

Web-based kinetic modelling using JWS Online

Olivier, B. G., & Snoep, J. L. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth200 · Cited by 326

The CellML Model Repository

Lloyd, C. M., Lawson, J. R., Hunter, P. J., & Nielsen, P. F. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn390 · Cited by 276

FieldML: concepts and implementation

Christie, G. R., Nielsen, P. M. F., Blackett, S. A., Bradley, C. P., & Hunter, P. J. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2009.0025 · Cited by 102

An overview of the CellML API and its implementation

Miller, A. K., Marsh, J., Reeve, A. M., Garny, A., Britten, R. D., Halstead, M. D. B., Cooper, J., Nickerson, D. P., & Nielsen, P. F. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-178 · Cited by 81

BioModels—15 years of sharing computational models in life science

Malik-Sheriff, R. S., Glont, M., Nguyen, T. V. N., Tiwari, K., Roberts, M. G., Xavier, A., Vu, M. T., Men, J., Maire, M., Kananathan, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1055 · Cited by 361

The openCARP simulation environment for cardiac electrophysiology

Plank, G., Loewe, A., Neic, A., Augustin, C. M., Huang, Y.-L., Gsell, M. A. F., Karabelas, E., Nothstein, M., Prassl, A. J., Sánchez, J., et al. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106223 · Cited by 277

Proceedings of the 2017 Winter Simulation Conference

Chan, W.-M., D’Ambrogio, A., Zacharewicz, G., Mustafee, N., Wainer, G., & Page, E. (2017) · HAL · Cited by 251

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

Chase, J. G., Preiser, J., Dickson, J. L., Pironet, A., Chiew, Y. S., Pretty, C. G., Shaw, G. M., Benyó, B. I., Möeller, K., Safaei, S., et al. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0455-y · Cited by 191

Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?

Johnstone, R. H., Chang, E. T. Y., Bardenet, R., de Boer, T. P., Gavaghan, D. J., Pathmanathan, P., Clayton, R. H., & Mirams, G. R. (2015) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2015.11.018 · Cited by 134

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

Collin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., Salehzadeh‐Yazdi, A., Kirschner, M. W., Krobitsch, S., & Kuepfer, L. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020166 · Cited by 128

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative

Hucka, M., Nickerson, D. P., Bader, G. D., Bergmann, F., Cooper, J., Demir, E., Garny, A., Golebiewski, M., Myers, C. J., Schreiber, F., et al. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00019 · Cited by 118

Calibration of ionic and cellular cardiac electrophysiology models

Whittaker, D. J., Clerx, M., Lei, C. L., Christini, D. J., & Mirams, G. R. (2020) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1482 · Cited by 92

The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach

Calçada, D., Vianello, D., Giampieri, E., Sala, C., Castellani, G., de Graaf, A., Kremer, B., van Ommen, B., Feskens, E., Santoro, A., et al. (2014) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2014.01.004 · Cited by 97

Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits

Gleeson, P., Cantarelli, M., Marin, B., Quintana, A., Earnshaw, M., Sadeh, S., Piasini, E., Birgiolas, J., Cannon, R. C., Cayco-Gajic, N. A., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.019 · Cited by 87

The MAP Client: User-Friendly Musculoskeletal Modelling Workflows

Zhang, J., Sorby, H., Clement, J. G., Thomas, C. D. L., Hunter, P. J., Nielsen, P. M. F., Lloyd, D. G., Taylor, M., & Besier, T. F. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12057-7_21 · Cited by 80

Harmonizing semantic annotations for computational models in biology

Neal, M. L., König, M., Nickerson, D., Mısırlı, G., Kalbasi, R., Dräger, A., Atalag, K., Chelliah, V., Cooling, M. T., Cook, D. L., et al. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby087 · Cited by 74

Reproducibility in Computational Neuroscience Models and Simulations

McDougal, R. A., Bulanova, A., & Lytton, W. W. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2539602 · Cited by 73

Computational modelling of atherosclerosis

Parton, A., McGilligan, V., O’Kane, M., Baldrick, F. R., & Watterson, S. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv081 · Cited by 60

Improving Collaboration by Standardization Efforts in Systems Biology

Dräger, A., & Palsson, B. Ã. (2014) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2014.00061 · Cited by 60

Combining computational models, semantic annotations and simulation experiments in a graph database

Henkel, R., Wolkenhauer, O., & Waltemath, D. (2015) · Database · doi:10.1093/database/bau130 · Cited by 50

Kinetic modeling of cell metabolism for microbial production

Costa, R. S., Hartmann, A., & Vinga, S. (2015) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2015.12.023 · Cited by 55

How Modeling Standards, Software, and Initiatives Support Reproducibility in Systems Biology and Systems Medicine

Waltemath, D., & Wolkenhauer, O. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2555481 · Cited by 49

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

Fernandez, J., Ju, Z., Heidlauf, T., Sartori, M., Besier, T. F., Röhrle, O., & Lloyd, D. G. (2016) · Interface Focus · doi:10.1098/rsfs.2015.0084 · Cited by 49

Glycemic control in the intensive care unit: A control systems perspective

Chase, J. G., Benyó, B. I., & Desaive, T. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.03.007 · Cited by 46

Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology

Lei, C. L., Wang, K., Clerx, M., Johnstone, R. H., Hortigon-Vinagre, M. P., Zamora, V., Allan, A., Smith, G. L., Gavaghan, D. J., Mirams, G. R., et al. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00986 · Cited by 40

A call for virtual experiments: Accelerating the scientific process

Cooper, J., Vik, J. O., & Waltemath, D. (2014) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2014.10.001 · Cited by 39

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

Waltemath, D., Golebiewski, M., Blinov, M. L., Gleeson, P., Hermjakob, H., Hucka, M., Inau, E. T., Keating, S., König, M., Krebs, O., et al. (2020) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2020-0005 · Cited by 38

FieldML, a proposed open standard for the Physiome project for mathematical model representation

Britten, R. D., Christie, G. R., Little, C., Miller, A. K., Bradley, C. P., Wu, A., Yu, T., Hunter, P. J., & Nielsen, P. F. (2013) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-013-1097-7 · Cited by 36

The Inflection Point Hypothesis: The Relationship between the Temperature Dependence of Enzyme-Catalyzed Reaction Rates and Microbial Growth Rates

Prentice, E. J., Hicks, J., Ballerstedt, H., Blank, L. M., Liáng, L., Schipper, L. A., & Arcus, V. L. (2020) · Biochemistry · doi:10.1021/acs.biochem.0c00530 · Cited by 34

Ontology of physics for biology: representing physical dependencies as a basis for biological processes

Cook, D. L., Neal, M., Bookstein, F., & Gennari, J. (2013) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-4-41 · Cited by 24

Cellular cardiac electrophysiology modeling with Chaste and CellML

Cooper, J., Spiteri, R. J., & Mirams, G. R. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2014.00511 · Cited by 33

The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable

Nickerson, D. P., Atalağ, K., de Bono, B., Geiger, J., Goble, C., Hollmann, S., Lonien, J., Müller, W., Regierer, B., Stanford, N., et al. (2016) · Interface Focus · doi:10.1098/rsfs.2015.0103 · Cited by 32

Modular assembly of dynamic models in systems biology

Pan, M., Gawthrop, P. J., Cursons, J., & Crampin, E. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009513 · Cited by 34

Provenance in modeling and simulation studies — Bridging gaps

Ruscheinski, A., & Uhrmacher, A. (2017) · 2017 Winter Simulation Conference (WSC) · doi:10.1109/wsc.2017.8247839 · Cited by 31

Mechanics of the foot Part 1: A continuum framework for evaluating soft tissue stiffening in the pathologic foot

Fernandez, J., Haque, M. Z. U., Hunter, P. J., & Mithraratne, K. (2012) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2494 · Cited by 30

Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

Waltemath, D., Bergmann, F., Chaouiya, C., Czauderna, T., Gleeson, P., Goble, C., Golebiewski, M., Hucka, M., Juty, N., Krebs, O., et al. (2014) · Standards in Genomic Sciences · doi:10.4056/sigs.5279417 · Cited by 30

Skin Disease Modeling from a Mathematical Perspective

Tanaka, R. J., & Ono, M. (2013) · Journal of Investigative Dermatology · doi:10.1038/jid.2013.69 · Cited by 29

Digital Twins and Automation of Care in the Intensive Care Unit

Chase, J. G., Zhou, C., Knopp, J. L., Möeller, K., Benyó, B. I., Desaive, T., Wong, J. H. K., Malinen, S. K., Näswall, K., Shaw, G. M., et al. (2023) · Wiley eBooks · doi:10.1002/9781119857433.ch17 · Cited by 25

An algorithm to detect and communicate the differences in computational models describing biological systems

Scharm, M., Wolkenhauer, O., & Waltemath, D. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv484 · Cited by 24

Composing Modeling And Simulation With Machine Learning In Julia

Rackauckas, C., Gwóźdź, M., Jain, A., Ma, Y., Martinuzzi, F., Rajput, U., Saba, E., Shah, V. B., Anantharaman, R., Edelman, A. S., et al. (2022) · Annual Modeling and Simulation Conference (ANNSIM) · doi:10.23919/annsim55834.2022.9859453 · Cited by 24

Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models

Molléro, R., Pennec, X., Delingette, H., Garny, A., Ayache, N., & Maxime Sermesant, B. (2017) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-017-0960-0 · Cited by 24

SemGen: a tool for semantics-based annotation and composition of biosimulation models

Neal, M. L., Thompson, C. T., Kim, K., James, R. C., Cook, D. L., Carlson, B. E., & Gennari, J. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty829 · Cited by 24

Model annotation and discovery with the Physiome Model Repository

Sarwar, D. M., Kalbasi, R., Gennari, J. H., Carlson, B. E., Neal, M. L., de Bono, B., Atalağ, K., Hunter, P. J., & Nickerson, D. P. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2987-y · Cited by 24

Improving the reuse of computational models through version control

Waltemath, D., Henkel, R., Hälke, R., Scharm, M., & Olaf Wolkenhauer, A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt018 · Cited by 23

Ca2+ Release via IP3 Receptors Shapes the Cardiac Ca2+ Transient for Hypertrophic Signaling

Hunt, H., Tilūnaitė, A., Bass, G., Soeller, C., Roderick, H. L., Rajagopal, V., & Crampin, E. J. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.08.001 · Cited by 21

Organizing genome engineering for the gigabase scale

Bartley, B., Beal, J., Karr, J. R., & Strychalski, E. A. (2020) · Nature Communications · doi:10.1038/s41467-020-14314-z · Cited by 23

Myocyte Remodeling Due to Fibro-Fatty Infiltrations Influences Arrhythmogenicity

Coster, T. D., Claus, P., Seemann, G., Willems, R., Sipido, K. R., & Panfilov, A. V. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01381 · Cited by 22

Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation

Sun, Q., Chase, J. G., Zhou, C., Tawhai, M. H., Knopp, J. L., Möller, K., Heines, S. J., Bergmans, D. C. J. J., & Shaw, G. M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103367 · Cited by 21

Relating simulation studies by provenance—Developing a family of Wnt signaling models

Budde, K., Smith, J., Wilsdorf, P., Haack, F., & Uhrmacher, A. M. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009227 · Cited by 23

Self-restoration of cardiac excitation rhythm by anti-arrhythmic ion channel gating

Majumder, R., Coster, T. D., Kudryashova, N., Verkerk, A. O., Kazbanov, I. V., Ördög, B., Harlaar, N. J., Wilders, R., de Vries, A. A. F., Ypey, D. L., et al. (2020) · eLife · doi:10.7554/elife.55921 · Cited by 20

Organizing genome engineering for the gigabase scale

Bartley, B., Beal, J., Karr, J. R., & Strychalski, E. A. (2020) · RePEc: Research Papers in Economics · Cited by 20

Digitization of Medicine

Li, K. C., Marcovici, P. A., Phelps, A. S., Potter, C. A., Tillack, A. A., Tomich, J., & Tridandapani, S. (2013) · Academic Radiology · doi:10.1016/j.acra.2013.09.008 · Cited by 20

Computational Modeling of Glucose Uptake in the Enterocyte

Afshar, N., Safaei, S., Nickerson, D. P., Hunter, P. J., & Suresh, V. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00380 · Cited by 16

Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

Loewe, A., Hunter, P. J., & Kohl, P. K. (2025) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2023.0384 · Cited by 19

Reproducible model development in the cardiac electrophysiology Web Lab

Daly, A. C., Clerx, M., Beattie, K. A., Cooper, J., Gavaghan, D. J., & Mirams, G. R. (2018) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2018.05.011 · Cited by 18

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization

Misirli, G., Cavaliere, M., Waites, W., Pocock, M. R., Madsen, C., Gilfellon, O., Honorato-Zimmer, R., Zuliani, P., Danos, V., & Wipat, A. (2015) · Bioinform. · doi:10.1093/bioinformatics/btv660 · Cited by 21

Notions of similarity for systems biology models

Henkel, R., Hoehndorf, R., Kacprowski, T., Knüpfer, C. R. N., Liebermeister, W., & Waltemath, D. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw090 · Cited by 18

Model-based management of cardiovascular failure: Where medicine and control systems converge

Desaive, T., Horikawa, O., Ortiz, J., & Chase, J. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.05.003 · Cited by 16

Annotation-based feature extraction from sets of SBML models

Alm, R., Waltemath, D., Wolfien, M., Wolkenhauer, O., & Henkel, R. (2015) · Journal of Biomedical Semantics · doi:10.1186/s13326-015-0014-4 · Cited by 22

COMODI: an ontology to characterise differences in versions of computational models in biology

Scharm, M., Waltemath, D., Mendes, P., & Wolkenhauer, O. (2016) · Journal of Biomedical Semantics · doi:10.1186/s13326-016-0080-2 · Cited by 14

Modular modelling with Physiome standards

Cooling, M. T., Nickerson, D. P., Nielsen, P. M. F., & Hunter, P. J. (2016) · The Journal of Physiology · doi:10.1113/jp272633 · Cited by 16

Standards, dissemination, and best practices in systems biology

Shin, J., Porubsky, V., Carothers, J., & Sauro, H. M. (2023) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2023.102922 · Cited by 15

MOSBIE: a tool for comparison and analysis of rule-based biochemical models

Wenskovitch, J. E., Harris, L. A., Tapia, J.-J., Faeder, J. R., & Marai, G. E. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-316 · Cited by 15

Meeting the multiscale challenge: representing physiology processes over ApiNATOMY circuits using bond graphs

de Bono, B., Safaei, S., Grenon, P., & Hunter, P. J. (2017) · Interface Focus · doi:10.1098/rsfs.2017.0026 · Cited by 15

Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective

Schölzel, C., Blesius, V., Ernst, G., & Dominik, A. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00182-w · Cited by 16

Models of the cardiac L‐type calcium current: A quantitative review

Agrawal, A., Wang, K., Polonchuk, L., Cooper, J., Hendrix, M., Gavaghan, D. J., Mirams, G. R., & Clerx, M. (2022) · WIREs Mechanisms of Disease · doi:10.1002/wsbm.1581 · Cited by 14

Network thermodynamics of biological systems: A bond graph approach

Gawthrop, P. J., & Pan, M. (2022) · Mathematical Biosciences · doi:10.1016/j.mbs.2022.108899 · Cited by 15

Linking cortex and contraction—Integrating models along the corticomuscular pathway

Haggie, L., Schmid, L., Röhrle, O., Besier, T. F., McMorland, A. J. C., & Saini, H. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1095260 · Cited by 14

Mathematical modeling of the female reproductive system: from oocyte to delivery

Clark, A. R., & Kruger, J. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1353 · Cited by 15

Musculoskeletal Modelling and the Physiome Project

Fernandez, J., Ju, Z., Shim, V., Munro, J. T., Sartori, M., Besier, T. F., Lloyd, D. G., Nickerson, D. P., & Hunter, P. J. (2017) · Courses and lectures · doi:10.1007/978-3-319-58845-2_3 · Cited by 15

Practical resources for enhancing the reproducibility of mechanistic modeling in systems biology

Blinov, M. L., Gennari, J. H., Karr, J. R., Moraru, I. I., Nickerson, D. P., & Sauro, H. M. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.06.001 · Cited by 11

Identifying frequent patterns in biochemical reaction networks: a workflow

Lambusch, F., Waltemath, D., Wolkenhauer, O., Sandkuhl, K., Rosenke, C., & Henkel, R. (2018) · Database · doi:10.1093/database/bay051 · Cited by 13

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Rajagopal, V., Arumugam, S., Hunter, P. J., Khadangi, A., Chung, J., & Pan, M. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021246 · Cited by 15

openCARP: An Open Sustainable Framework for In-Silico Cardiac Electrophysiology Research

Sánchez, J., Nothstein, M., Neic, A., Huang, Y.-L., Prassl, A. J., Klar, J., Ulrich, R., Bach, F., Zschumme, P., Selzer, M. A., et al. (2020) · Computing in cardiology · doi:10.22489/cinc.2020.111 · Cited by 11

A fully featured COMBINE archive of a simulation study on syncytial mitotic cycles in Drosophila embryos

Scharm, M., & Waltemath, D. (2016) · F1000Research · doi:10.12688/f1000research.9379.1 · Cited by 11

Immediate and Delayed Response of Simulated Human Atrial Myocytes to Clinically-Relevant Hypokalemia

Clerx, M., Mirams, G. R., Rogers, A. J., Narayan, S. M., & Giles, W. R. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.651162 · Cited by 10

Mechanistic modeling confronts the complexity of molecular cell biology

Phair, R. D. (2014) · Molecular Biology of the Cell · doi:10.1091/mbc.e14-08-1333 · Cited by 11

Reproducibility of Model-Based Results in Systems Biology

Waltemath, D., Henkel, R., Winter, F., & Wolkenhauer, O. (2013) · Springer eBooks · doi:10.1007/978-94-007-6803-1_10 · Cited by 9

A brief history of COMBINE

Myers, C. J., Bader, G. D., Gleeson, P., Golebiewski, M., Hucka, M., Novère, N. G. L., Nickerson, D. P., Schreiber, F., & Waltemath, D. (2017) · Winter Simulation Conference · doi:10.1109/wsc.2017.8247840 · Cited by 10

Information Retrieval in Life Sciences: A Programmatic Survey

Lange, M., Henkel, R., Müller, W., Waltemath, D., & Weise, S. F. (2013) · Springer eBooks · doi:10.1007/978-3-642-41281-3_3 · Cited by 8

The Physiome Project, open EHR Archetypes, and the Digital Patient

Nickerson, D. P., Atalağ, K., de Bono, B., & Hunter, P. J. (2016) · Wiley eBooks · doi:10.1002/9781118952788.ch9 · Cited by 9

Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: A software engineering perspectivePreprint

Schölzel, C., Blesius, V., Ernst, G., & Dominik, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.16.875260 · Cited by 9

PathCase-SB: integrating data sources and providing tools for systems biology research

Coskun, S. A., Qi, X., Cakmak, A., Cheng, E., Çiçek, A. E., Yang, L., Jadeja, R., Dash, R. K., Lai, N., Özsoyoğlu, G., et al. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-67 · Cited by 8

A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models

Barral, Y.-S. H. M., Shuttleworth, J. G., Clerx, M., Whittaker, D. J., Wang, K., Polonchuk, L., Gavaghan, D. J., & Mirams, G. R. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.879035 · Cited by 8

A review of the diverse applications of bond graphs in biology and physiology

Ghazani, M. A., Pan, M., Tran, K., Rampadarath, A., & Nickerson, D. P. (2024) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2023.0807 · Cited by 8

The Open Physiology workflow: modeling processes over physiology circuitboards of interoperable tissue units

de Bono, B., Safaei, S., Grenon, P., Nickerson, D. P., Alexander, S., Helvensteijn, M., Kok, J. N., Kokash, N., Wu, A., Yu, T., et al. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2015.00024 · Cited by 9

A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows

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libOmexMeta: enabling semantic annotation of models to support FAIR principles

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Systems approaches in integrative cardiac biology: Illustrations from cardiac heterocellular signalling studies

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OREMPdb: a semantic dictionary of computational pathway models

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Modelling the role of redox-related mechanisms in musculoskeletal ageing

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Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices

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Editorial: Special Issue on Multiscale Modeling and Analysis in Computational Biology and Medicine—Part-1

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Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

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Volunteer Computing Approach for the Collaborative Simulation of Electrophysiological Models

Castro, J., Monasterio, V., & Carro, J. (2016) · IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) · doi:10.1109/wetice.2016.34 · Cited by 6

Qualitative causal analyses of biosimulation models.

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Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

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Pacemaking function of two simplified cell models

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In silico modeling for the hepatic circulation and transport: From the liver organ to lobules

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Excel–SBOL Converter: Creating SBOL from Excel Templates and Vice Versa

Mante, J. V., Abam, J., Samineni, S. P., Pötzsch, I. M., Singh, P., Beal, J., & Myers, C. J. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00521 · Cited by 6

Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning

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Steady-state approximations for Hodgkin-Huxley cell models: Reduction of order for uterine smooth muscle cell model

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A New Model of Docker-based E-learning in Hadoop

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Experiences in teaching of modeling and simulation with emphasize on equation-based and acausal modeling techniques

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CellML Model Repository

Lloyd, C. M., & Yu, T. (2013) · Springer eBooks · doi:10.1007/978-1-4419-9863-7_1523 · Cited by 4

Physiome Model Repository

Lloyd, C. M. (2013) · Springer eBooks · doi:10.1007/978-1-4419-9863-7_1529

Physiome Repository

Yu, T., Britten, R. D., Nickerson, D. P., Hunter, P. J., & Nielsen, P. M. F. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_168

CellML, PMR, and the Physiome journal

Nickerson, D. (2025) · Figshare · doi:10.17608/k6.auckland.30403471

CellML, OpenCOR, and the Physiome Project Repository

Nickerson, D. P. (2014) · Figshare · doi:10.6084/m9.figshare.1170219