The Kappa platform for rule-based modeling
Open accessAbstract
Motivation: We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively exploring rule-based models. The main components of the platform are the Kappa Simulator, the Kappa Static Analyzer and the Kappa Story Extractor. In addition to these components, we describe the Kappa User Interface, which includes a range of interactive visualization tools for rule-based models needed to make sense of the complexity of biological systems. We argue that, in this approach, modeling is akin to programming and can likewise benefit from an integrated development environment. Our platform is a step in this direction. Results: We discuss details about the computation and rendering of static, dynamic, and causal views of a model, which include the contact map (CM), snaphots at different resolutions, the dynamic influence network (DIN) and causal compression. We provide use cases illustrating how these concepts generate insight. Specifically, we show how the CM and snapshots provide information about systems capable of polymerization, such as Wnt signaling. A well-understood model of the KaiABC oscillator, translated into Kappa from the literature, is deployed to demonstrate the DIN and its use in understanding systems dynamics. Finally, we discuss how pathways might be discovered or recovered from a rule-based model by means of causal compression, as exemplified for early events in EGF signaling. Availability and implementation: The Kappa platform is available via the project website at kappalanguage.org. All components of the platform are open source and freely available through the authors’ code repositories.
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
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?
Although the Wnt signaling pathway has also been used to illustrate features of rule-based modeling [95, 96], only few simulation models have been developed based on a rule-based approach.
“Although the Wnt signaling pathway has also been used to illustrate features of rule-based modeling [95, 96], only few simulation models have been developed based on a rule-based approach.”
From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [95]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 30 · Cited by 107
Research works printed but not linked: 26
- Bachman J.A., Sorger P. (2011) New approaches to modeling complex biochemistry. Nat. Methods, 8, 130. 21278724 PMC3100653
- Basso-Blandin A. et al (2016) A knowledge representation meta-model for rule-based modelling of signalling networks In: Muñoz C.A., Pérez J.A. (eds.) Proceedings of the 11th International Workshop on Developments in Computational Models (DCM 2015). EPTCS, Cali, Colombia, pp. 47– 59.
- Boutillier P. et al (2017a) Incremental update for graph rewriting. In: Programming Languages and Systems–26th European Symposium on Programming, ESOP 2017, Springer-Verlag Berlin Heidelberg, Uppsala, Sweden, pp. 201– 228.
- Chylek L.A. et al (2011) Guidelines for visualizing and annotating rule-based models. Mol. BioSyst., 7, 2779– 2795. 21647530 PMC3168731
- Cohen P.R. (2015) DARPA’s Big Mechanism program. Phys. Biol., 12, 045008. 26178259
- Cousot P., Cousot R. (1977) Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proceedings of the 4th Symposium on Principles of Programming Languages, POPL ’77. ACM, New York, NY, pp. 238–252.
- Dang T. et al (2015) ReactionFlow: an interactive visualization tool for causality analysis in biological pathways. BMC Proc., 9, S6. PMC4547159 26361502
- Danos V. et al (2007a) Rule-based modelling of cellular signalling. In: Proceedings of the Eighteenth International Conference on Concurrency Theory, CONCUR 2007, Vol. 4703 of Lecture Notes in Computer Science Lisbon, Portugal, Springer-Verlag Berlin Heidelberg, pp. 17– 41.
- Danos V. et al (2007b) Scalable simulation of cellular signaling networks. In: Proceedings of the Fifth Asian Symposium on Programming Systems, APLAS 2007, Vol. 4807 of Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg, Singapore, pp. 139– 157.
- Danos V. et al (2008) Abstract interpretation of cellular signalling networks. In: Verification, Model Checking, and Abstract Interpretation, VMCAI 2008, Vol. 4905 of LNCS, Springer-Verlag Berlin Heidelberg, San Francisco, USA, pp. 83– 97.
- Danos V. et al (2012) Graphs, rewriting and pathway reconstruction for rule-based models. In: IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2012, Vol. 18, Leibniz International Proceedings in Informatics, Hyderabad, India, pp. 276– 288.
- Faeder J.R. et al (2009) Rule-based modeling of biochemical systems with bionetgen In: Maly I.V. (ed.) Methods in Molecular Biology, Systems Biology, Vol. 500, Springer-Verlag Berlin Heidelberg, Springer, pp. 113– 167. 19399430
- Feret J., Lý K.Q. (2018) Reachability analysis via orthogonal sets of patterns In: Seventh International Workshop on Static Analysis and Systems Biology (SASB’16), Vol. 335, ENTCS. Elsevier, pp. 27– 48.
- Fiedler M. et al (2011) Dishevelled interacts with the DIX domain polymerization interface of Axin to interfere with its function in down-regulating β -catenin. PNAS, 108, 1937– 1942. 21245303 PMC3033301
- Forbes A.G. et al (2018) Dynamic influence networks for rule-based models. IEEE Trans. Visualization Computer Graph., 24, 184– 194. 28866584
- Gillespie D.T. (2007) Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem., 58, 35– 55. 17037977
- Gyori B.M. et al (2017) From word models to executable models of signaling networks using automated assembly. Mol. Syst. Biol., 13, 954. 29175850 PMC5731347
- Harmer R. et al (2017) Bio-curation for cellular signalling: the KAMI project. In: Computational Methods in Systems Biology: 15th International Conference, CMSB 2017, Darmstadt, Germany, Springer, pp. 3– 19. 30908261
- Harris L.A. et al (2016) BioNetGen 2.2: advances in rule-based modeling. Bioinformatics, 32, 3366– 3368. 27402907 PMC5079481
- Kohn K.W. et al (2006) Depicting combinatorial complexity with the molecular interaction map notation. Mol. Syst. Biol., 2, 1– 51. PMC1681518 17016517
- Loew L.M., Schaff J.C. (2001) The Virtual Cell: a software environment for computational cell biology. Trends Biotechnol., 19, 401– 406. 11587765
- Murray P. et al (2017) A taxonomy of visualization tasks for the analysis of biological pathway data. BMC Bioinformatics, 18, 21, 21–1–13. 28251869 PMC5333192
- Paduano F., Forbes A.G. (2015) Extended LineSets: a visualization technique for the interactive inspection of biological pathways. BMC Proc., 9, S4. PMC4547339 26361500
- Sekar J.A.P. et al (2017) Automated visualization of rule-based models. PLoS Comput. Biol., 13, e1005857. 29131816 PMC5703574
- Smith A.M. et al (2012) RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC Bioinformatics, 13, S3. PMC3355338 22607382
- Sneddon M.W. et al (2011) Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat. Methods, 8, 177. 21186362
- Authors
- Boutillier, P., Maasha, M., Li, X., Medina-Abarca, H. F., Krivine, J., Feret, J., Cristescu, I., Forbes, A. G., & Fontana, W.
- Affiliations
- Harvard University (US)
- University of Vermont (US)
- Institut de Recherche en Informatique Fondamentale (FR)
- Département d’informatique de l’ENS (INRIA/ENS/CNRS), PSL Research University, Paris, France
- University of California, Santa Cruz (US)
- Corresponding author
- Boutillier, Pierre
- Forbes, Angus G
- Fontana, Walter
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-06-27
- Volume
- 34
- Issue
- 13
- Pages
- i583-i592
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6022607
- OpenAlex
- W2807744179
- Cited by
- 107
- References
- 30
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 9 · 2023: 8 · 2022: 21 · 2021: 18 · 2020: 24 · 2019: 12 · 2018: 2
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
- Funders
- Defense Advanced Research Projects Agency
- DARPA
- Big Mechanism Program
- US Army Research Office (W911NF-14-1-0367, W911NF-14-1-0395)
- 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 and History of Science
- Keywords
- rule-based models, interactive visualization, mathematical biology, contact maps, Wnt signaling, KaiABC oscillator, EGF signaling, pathway discovery, integrated development environment
- MeSH terms
- Data Visualization, Epidermal Growth Factor, Models, Biological, Software, Signal Transduction, Computational Biology, Wnt Signaling Pathway
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