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Rules for Modeling Signal-Transduction Systems
Abstract
Formalized rules for protein-protein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactions. A set of rules can be as easy to read as a diagrammatic interaction map, but unlike most such maps, rules have precise interpretations. Rules can be processed to automatically generate a mathematical or computational model for a system, which enables explanatory and predictive insights into the system’s behavior. Rules are independent units of a model specification that facilitate model revision. Instead of changing a large number of equations or lines of code, as may be required in the case of a conventional mathematical model, a protein interaction can be introduced or modified simply by adding or changing a single rule that represents the interaction of interest. Rules can be defined and visualized by using graphs, so no specialized training in mathematics or computer science is necessary to create models or to take advantage of the representational precision of rules. Rules can be encoded in a machine-readable format to enable electronic storage and exchange of models, as well as basic knowledge about protein-protein interactions. Here, we review the motivation for rule-based modeling; applications of the approach; and issues that arise in model specification, simulation, and testing. We also discuss rule visualization and exchange and the software available for rule-based modeling.
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“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 [52]
Citations
Cites
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 10,172
Clarke, E. M., Grümberg, O., & Long, D. (1996) · Cited by 6,877
Gillespie, D. T. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90041-3 · Cited by 6,029
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-citationThe Algorithmic Beauty of Plants
Prusinkiewicz, P., & Lindenmayer, A. (1990) · The virtual laboratory · doi:10.1007/978-1-4613-8476-2 · Cited by 2,842
Hunter, T. (2000) · Cell · doi:10.1016/s0092-8674(00)81688-8 · Cited by 2,586
Communicating and Mobile Systems: the Pi-Calculus
Milner, R. (1999) · Cited by 2,257
Approximate accelerated stochastic simulation of chemically reacting systems
Gillespie, D. T. (2001) · The Journal of Chemical Physics · doi:10.1063/1.1378322 · Cited by 1,880
Protein Posttranslational Modifications: The Chemistry of Proteome Diversifications
Walsh, C. T., Garneau‐Tsodikova, S., & Gatto, G. J. J. (2005) · Angewandte Chemie International Edition · doi:10.1002/anie.200501023 · Cited by 1,654
Epidermal growth factor receptor: Mechanisms of activation and signalling
Jorissen, R. (2003) · Experimental Cell Research · doi:10.1016/s0014-4827(02)00098-8 · Cited by 1,565
Research works printed but not linked: 4
- E. O. Voit Computational Analysis of Biochemical Systems (Cambridge Univ. Press Cambridge 2000).
- R. J. Bagley J. D. Farmer W. Fontana Evolution of a metabolism. In Artificial Life II C. G. Langton C. Taylor J. D. Farmer S. Rasmussen Eds. (Addison Wesley Redwood City CA 1991) pp. 141–158.
- C. Talcott D. L. Dill The pathway logic assistant in Third International Workshop on Computational Methods in Systems Biology G. Plotkin Ed. Edinburgh 3 to 5 April 2005 pp. 228–239.
- R. Maimon S. Browning Diagrammatic notation and computational structure of gene networks in Proc. 2nd Int. Conf. Syst. Biol. T.-M. Yi M. Hucka M. Morohashi H. Kitano Eds. (Omnipress Madison WI 2001) pp. 311–317.
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Santibáñez, R., Garrido, D. A., & Martín, A. J. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1040 · Cited by 9
Mack, E. T., Cummings, L. J., & Perez‐Castillejos, R. (2010) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-010-4477-y · Cited by 9
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Metadata
- Authors
- Hlavacek, W. S., Faeder, J. R., Blinov, M. L., Posner, R. G., Hucka, M., & Fontana, W.
- Affiliations
- Los Alamos National Laboratory (US)
- UConn Health (US)
- Translational Genomics Research Institute (US)
- California Institute of Technology (US)
- Harvard University (US)
- ORCID
- Venue
- Science's STKE
- Published
- 2006-07-18
- Volume
- 2006
- Issue
- 344
- Pages
- re6-re6
- ISSN
- 1525-8882 (ISSN-L)
- OpenAlex
- W2069941324
- Cited by
- 350
- References
- 147
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 10 · 2021: 5 · 2020: 8 · 2019: 14 · 2018: 11 · 2017: 11 · 2016: 15 · 2015: 18 · 2014: 29 · 2013: 36 · 2012: 32
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science s STKE (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Institute on Aging (R01AG034994)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- rule-based models, signal transduction, protein-protein interaction, model specification, model exchange, rule visualization, model simulation, model testing, model revision
- MeSH terms
- Computer Simulation, Models, Biological, Proteins, Software, Signal Transduction, Protein Interaction Mapping
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 ›
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