Kappa Rule-Based Modeling in Synthetic Biology

John Wilson-kanamori, Vincent Danos, Ty Thomson, Ricardo Honorato-zimmer

Research output: Chapter in Book/Report/Conference proceedingChapter


Rule-based modeling, an alternative to traditional reaction-based modeling, allows us to intuitively specify biological interactions while abstracting from the underlying combinatorial complexity. One such rule-based modeling formalism is Kappa, which we introduce to readers in this chapter. We discuss the application of Kappa to three modeling scenarios in synthetic biology: a unidirectional switch based on nitrosylase induction in Saccharomyces cerevisiae, the repressilator in Escherichia coli formed from BioBrick parts, and a light-mediated extension to said repressilator developed by the University of Edinburgh team during iGEM 2010. The second and third scenarios in particular form a case-based introduction to the Kappa BioBrick Framework, allowing us to systematically address the modeling of devices and circuits based on BioBrick parts in Kappa. Through the use of these examples, we highlight the ease with which Kappa can model biological interactions both at the genetic and the protein–protein interaction level, resulting in detailed stochastic models accounting naturally for transcriptional and translational resource usage. We also hope to impart the intuitively modular nature of the modeling processes involved, supported by the introduction of visual representations of Kappa models. Concluding, we explore future endeavors aimed at making modeling of synthetic biology more user-friendly and accessible, taking advantage of the strengths of rule-based modeling in Kappa.
Original languageEnglish
Title of host publicationComputational Methods in Synthetic Biology
Subtitle of host publicationPart II
PublisherSpringer New York
Number of pages31
ISBN (Electronic)978-1-4939-1878-2
ISBN (Print)978-1-4939-1877-5
Publication statusPublished - 13 Nov 2014

Publication series

NameMethods in Molecular Biology
PublisherSpringer New York
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


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