Cited by 350Gene Regulatory Network AnalysisBioinformatics and Genomic NetworksPhilosophy of Biology and Scientific Explanation

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Rules for Modeling Signal-Transduction Systems

Hlavacek, William S., Faeder, James R., Blinov, Michael L., Posner, Richard G., Hucka, Michael, Fontana, Walter·2006·Science's STKE

DOI 10.1126/stke.3442006re6

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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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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: Hlavacek, William S.
Faeder, James R.
Blinov, Michael L.
Posner, Richard G.
Hucka, Michael
Fontana, Walter
Venue: Science's STKE
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2006-07-18
Volume: 2006
Issue: 344
Pages: re6-re6
ISSN: 1525-8882 (ISSN-L)
DOI: 10.1126/stke.3442006re6
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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Rules for Modeling Signal-Transduction Systems

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