The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Virtual Cell modelling and simulation software environment
Open accessAbstract
The Virtual Cell (VCell; http://vcell.org/) is a problem solving environment, built on a central database, for analysis, modelling and simulation of cell biological processes. VCell integrates a growing range of molecular mechanisms, including reaction kinetics, diffusion, flow, membrane transport, lateral membrane diffusion and electrophysiology, and can associate these with geometries derived from experimental microscope images. It has been developed and deployed as a web-based, distributed, client-server system, with more than a thousand world-wide users. VCell provides a separation of layers (core technologies and abstractions) representing biological models, physical mechanisms, geometry, mathematical models and numerical methods. This separation clarifies the impact of modelling decisions, assumptions and approximations. The result is a physically consistent, mathematically rigorous, spatial modelling and simulation framework. Users create biological models and VCell will automatically (i) generate the appropriate mathematical encoding for running a simulation and (ii) generate and compile the appropriate computer code. Both deterministic and stochastic algorithms are supported for describing and running non-spatial simulations; a full partial differential equation solver using the finite volume numerical algorithm is available for reaction-diffusion-advection simulations in complex cell geometries including 3D geometries derived from microscope images. Using the VCell database, models and model components can be reused and updated, as well as privately shared among collaborating groups, or published. Exchange of models with other tools is possible via import/export of SBML, CellML and MatLab formats. Furthermore, curation of models is facilitated by external database binding mechanisms for unique identification of components and by standardised annotations compliant with the MIRIAM standard. VCell is now open source, with its native model encoding language (VCML) being a public specification, which stands as the basis for a new generation of more customised, experiment-centric modelling tools using a new plug-in based platform.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“BioPAX pathway data can also be converted into simulation models, for instance using differential equations51 or rule-based modeling languages52, to predict how a biological system may function after a gene is knocked-out.”
From The BioPAX community standard for pathway data sharing (2010) · cites this paper as [51]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gillespie, D. T. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90041-3 · Cited by 6,029
Computational Methods for Fluid Dynamics
Ferziger, J. H., Perić, M., & Leonard, A. (1997) · Physics Today · doi:10.1063/1.881751 · Cited by 6,023
Computational Methods for Fluid Dynamics
Ferziger, J. H., & Perić, M. (2002) · Springer eBooks · doi:10.1007/978-3-642-56026-2 · Cited by 3,904
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
Self-citationEfficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels
Gibson, M., & Bruck, J. (2000) · The Journal of Physical Chemistry A · doi:10.1021/jp993732q · Cited by 1,610
Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D., & Müller, G. I. (2002) · Nature Biotechnology · doi:10.1038/nbt0402-370 · Cited by 943
Minimum information requested in the annotation of biochemical models (MIRIAM)
Novère, N. L., Finney, A., Hucka, M., Bhalla, U. S., Campagne, F., Collado-Vides, J., Crampin, E. J., Halstead, M., Klipp, E., Mendes, P., et al. (2005) · Nature Biotechnology · doi:10.1038/nbt1156 · Cited by 622
CellML: its future, present and past
Lloyd, C. M., Halstead, M. D. B., & Nielsen, P. F. (2004) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2004.01.004 · Cited by 499
Blinov, M. L., Faeder, J. R., Goldstein, B., & Hlavacek, W. S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth378 · Cited by 432
Self-citationRules for Modeling Signal-Transduction Systems
Hlavacek, W. S., Faeder, J. R., Blinov, M. L., Posner, R. G., Hucka, M., & Fontana, W. (2006) · Science's STKE · doi:10.1126/stke.3442006re6 · Cited by 350
Self-citation
Flagged first, then by citations
Research works printed but not linked: 11
- Moraru, 2005, 20, 169
- Slepchenko, 2003, 13, 570
- Moraru, Proc. 2006 Winter Sim. Conf., IEEE, Inc., 1713
- Hucka, 2003, 19, 524
- Luciano, 2005, 10, 937
- Le Novère, 2005, 23, 1509
- Slepchenko, 2007, 179, 635
- Salis, 2006, 7, 93
- Schoeberl, 2002, 20, 370
- Blinov, 2006, 83, 136
- Blinov, 2004, 20, 3289
Cited by
Sorted by citations
Items 201–238 of 238
Software-unterstützte Erzeugung von mathematischen Modellen zur biotechnologischen Prozessführung
Violet, N. (2016) · doi:10.14279/depositonce-5128 · Cited by 1
Molecular Process Diagram: a precise, scalable and compact visualization of rule-based modelsPreprint
Vasilescu, D. D., Greene, J. B., Schaff, J. C., Moraru, I. I., & Blinov, M. L. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/503359 · Cited by 1
Self-citationFM-Sim: Protocol Definition, Simulation and Rate Inference for Neuroscience Assays
Stewart, D., Gilmore, S. T., & Cousin, M. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12982-2_19
µCell - interdisciplinary research in modelling and simulation of cell spatial behaviour
Orchard, D. A., Gover, J., Herrington, L. L., Lohr, J., Stead, D., Young, C., & Kalvala, S. (2009) · doi:10.82444/warw.33603793
In silico study of mechanical stresses at the cellular level during tissue development
Jeannin‐Girardon, A., Ballet, P., & Rodin, V. (2013) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2013.6701641
Diagrammatic Analysis of Nonhomogeneous Diffusion
Hernández, J. A. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/150826
Computational Design of Biological Systems: From Systems to Synthetic Biology
Mol, M., & Singh, S. (2015) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.1016/b978-1-60805-865-5.50005-8
Techniques for Parallel Simulation of Large Scale Heterogeneous Biophysical Systems
Heien, E. M. (2010) · OUKA (Osaka University Knowledge Archive) (Osaka University)
Barua, D., Hlavacek, W. S., & Lipniacki, T. (2012)
Herajy, M., & Monika Heiner, A. (2012)
Taking Aim at Moving Targets in Computational Cell
Masuzzo, M., Troys, M. V., Ampè, C., & Martens, L. (2015)
Orchard, D. A., Gover, J., Herrington, L. L., Lohr, J., Stead, D., Young, C., & Kalvala, S. (2009)
Proteomic Approaches to Toxicity Assessment
Kim, S., Son, S. J., & Moon, A. (2013) · CRC Press eBooks · doi:10.1201/b15476-6
Lamellipodia in Stationary and Fluctuating States
Holz, D., McMillen, L. M., Ryan, G. L., & Vavylonis, D. (2018) · Modeling and simulation in science, engineering & technology · doi:10.1007/978-3-319-96842-1_8
Dynamics of Scaffold Protein Tethered Signal Transduction
Greenwald, E. C. (2015) · doi:10.18130/v39p00
Waltemath, D. (2011) · Ros Dok Rostocker Dokumentenserver (Universitätsbibliothek Rostock) · doi:10.18453/rosdok_id00000903
Finite Element Models with Smeared Fields Within Tissue – A Review of the Current Developments
Kojić, M., Milošević, M., Simić, V., Geroski, V., Miličević, B., Žiemys, A., & Filipović, N. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-43658-2_3
Johnson, M. E., Chen, A., Faeder, J. R., Henning, P., Moraru, I. I., Meier‐Schellersheim, M., Murphy, R. F., Prüstel, T., Theriot, J. A., & Uhrmacher, A. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.02.185595
Self-citationReal-Time Interactive Modeling and Simulation in Biological Physics and Active Matter with MechanicaPreprint
Somogyi, E., Coulter, J. R., Sun, F., Sauro, H. M., & Glazier, J. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.02476
SED-ML Validator: tool for debugging simulation experimentsPreprint
Shaikh, B., Freiburger, A., König, M., Bergmann, F., Nickerson, D. P., Sauro, H. M., Blinov, M. L., Smith, L. P., Moraru, I. I., & Karr, J. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00844
Self-citationVirtual Cell (VCell) Modeling and Analysis Platform
Moraru, I. I. (2013) · Springer eBooks · doi:10.1007/978-1-4419-9863-7_1462
Self-citationLiberman, A., Kario, D., Mussel, M., Brill, J., Buetow, K. H., Efroni, S., & Nevo, U. (2018) · Default Digital Object Group · doi:10.1063/1.5039473.1
Kraikivski, P. (2021) · Springer eBooks · doi:10.1007/978-3-030-67742-8_18
Ringleb, S. I. (2011) · doi:10.1002/9781118003206.ch4
Computational framework for single-cell spatiotemporal dynamics of optogenetic membrane recruitment
Kuznetsov, I. A., Berlew, E. E., Glantz, S. T., Hannanta‐anan, P., & Chow, B. Y. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100245
Mechanisms underlying divergent relationships between Ca 2+ and YAP/TAZ signalingPreprint
Khalilimeybodi, A., Fraley, S. I., & Rangamani, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.06.511161
Comparative Investigation of the Mechanisms of Calcium Response in Human and Murine Spermatozoa
Korobkina, J. D., Panteleev, M. A., & Sveshnikova, A. N. (2024) · Biochemistry (Moscow) Supplement Series A Membrane and Cell Biology · doi:10.1134/s1990747824700132
Comparative Investigation of the Mechanisms of Calcium Response in Human and Murine Spermatozoa
Korobkina, J. D., Panteleev, M. A., & Sveshnikova, A. N. (2024) · Биологические мембраны Журнал мембранной и клеточной биологии · doi:10.31857/s0233475524030086
Presannan, A., Manoj, G., Nair, P. P., Chavali, L., Melethadathil, N., Zawar, A., Vijayan, A., Dakal, T. C., Kumar, A., & Suravajhala, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00177-9
An open-source software for building and simulating ordinary differential equation models in biology
dos Santos, B. L. M., Castro, D. A. S., da Paz, S. F. C., Pinheiro, D. J. C., Quintela, B. D. M., Lobosco, M., & Pigozzo, A. B. (2025) · PLoS ONE · doi:10.1371/journal.pone.0329148
Relational Learning in Microbial Ecology
Doncescu, A. (2025) · Natural Resources · doi:10.4236/nr.2025.1613038
Transforming macromolecular structures into simulations of self-assembly with ioNERDSSPreprint
Ying, Y. M., Sang, M., Au, G., Chhibber, S., Du, Y., Fischer, J., Foley, S. L., Guo, S., Herzog-Pohl, I., Liu, Z. H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.27.702082
Patel, R. J., & Blinov, M. L. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014121
Self-citationMaignan, S. (2018) · theses.fr (ABES) · doi:10.70675/201aaf72z5527z4569za59bz549421346ead
wang, H., Yang, X., Wang, S., Yang, Z., Yang, X., Yang, Y., Li, Z., Ren, Y., Zhang, Q., Zhao, B., et al. (2026) · iMeta · doi:10.1002/imt2.70146
Danishuddin, Haque, M. A., Madhukar, G., Khan, S., Khan, S. N., & Kim, J.-J. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.109040 · Cited by 1
Making sense of mammalian iron physiology through computational modeling
Osei-Boamah, K., & Mendes, P. (2026) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2026.09.040
FAIRsharing record for: Biological Pathway eXchange
FAIRsharing Team (2015) · FAIRsharing.org · doi:10.25504/fairsharing.sfkmej
Metadata
- Authors
- Moraru, I. I., Schaff, J. C., Slepchenko, B. M., Blinov, M. L., Morgan, F., Lakshminarayana, A., Gao, F., Li, Y., & Loew, L. M.
- Affiliations
- UConn Health (US)
- Venue
- IET Systems Biology
- Published
- 2008-09-15
- Volume
- 2
- Issue
- 5
- Pages
- 352-362
- ISSN
- 1751-8849 (ISSN-L)
- 1751-8857
- PMC
- PMC2711391
- OpenAlex
- W2097319997
- Cited by
- 236
- References
- 20
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 7 · 2023: 5 · 2022: 11 · 2021: 17 · 2020: 12 · 2019: 12 · 2018: 11 · 2017: 8 · 2016: 14 · 2015: 22 · 2014: 16 · 2013: 24 · 2012: 16
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IET Systems Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5u54gm064346-10, 5u54rr022232-05)
- National Institute of General Medical Sciences (U54-GM064346)
- National Center for Research Resources (U54-RR022232, P41RR013186)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Mathematics › Modeling and Simulation › Modeling and Simulation Systems
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- Virtual Cell, computational cell biology, reaction kinetics, advection-diffusion-reaction, membrane transport, electrophysiology, stochastic simulation, partial differential equation solvers, finite volume method, SBML, CellML, model exchange, model reuse, open source software
- MeSH terms
- Computer Simulation, Models, Biological, Programming Languages, Software, User-Computer Interface, Signal Transduction, Databases, Factual, Information Storage and Retrieval, Proteome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review