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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]
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Self-citation
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
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