KaSa: A Static Analyzer for Kappa

Boutillier, Pierre; Camporesi, Ferdinanda; Coquet, Jean; Feret, Jérôme; Ly, Kim Quyen; Théret, Nathalie; Vignet, Pierre

doi:10.1007/978-3-319-99429-1_17

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…that no information is lost in the encoding of the reaction network (no approximations are present in the translation). Both sets of results use Kappa's KaSim engine [1] and BNGL's NFsim engine [11] for simulation. Since both use stochastic simulation methods, the results shown from either language will not be identical, but will be statistically indistinguishable.…”

Section: Resultsmentioning

confidence: 99%

“…More subtle translation issues involve language conventions that are not obvious simply by reading a model. One example involves under-the-hood rate modi cation in BNGL-compatible simulation 1 See Appendix B engines in order to maintain consistency with the law of mass action. If symmetries exist in a rule's LHS then the rule's rate is divided by the number of symmetries to account for the resulting combinatorial e ect.…”

Section: Rule-based Modeling Languagesmentioning

confidence: 99%

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TRuML: A Translator for Rule-Based Modeling Languages

Suderman

Hlavacek

2017

Preprint

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Rule-based modeling languages, such as the Kappa and BioNetGen languages (BNGL), are powerful frameworks for modeling the dynamics of complex biochemical reaction networks. Each language is distributed with a distinct software suite and modelers may wish to take advantage of both toolsets. This paper introduces a practical application called TRuML that translates models written in either Kappa or BNGL into the other language. While similar in many respects, key di erences between the two languages makes translation su ciently complex that automation becomes a useful tool. TRuML accommodates the languages' complexities and produces a semantically equivalent model in the alternate language of the input model when possible and an approximate model in certain other cases. Here, we discuss a number of these complexities and provide examples of equivalent models in both Kappa and BNGL.

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Section: Resultsmentioning

confidence: 99%

Section: Rule-based Modeling Languagesmentioning

confidence: 99%

TRuML: A Translator for Rule-Based Modeling Languages

Suderman

Hlavacek

2017

Preprint

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“…Static analysis enhances the confidence in models. The static analyzer KaSa [31,46,11] computes and proves some structural properties about the complexes that may be formed in a given model, given an initial configuration. This tool uses the abstract interpretation framework [25] to approximate the computation of a least fix point over sets of patterns of interest.…”

Section: Static Analysismentioning

confidence: 99%

The Rule‐Based Model Approach:A Kappa Model for Hepatic Stellate Cells Activation by TGFB1

Bouguéon

Boutillier²,

Feret

et al. 2022

Systems Biology Modelling and Analysis

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The rule-based model approach.A Kappa model for hepatic stellate cells activation by TGFB1In this chapter, we introduce Kappa, a site graph rewriting language. As a realistic case study, we model a population of hepatic stellate cells under the effect of the TGFB1 protein. Kappa offers a rule-centric approach, inspired from chemistry, where interaction rules locally modify the state of a system that is defined as a graph of components, connected or not. In this case study, the components will be occurrences of hepatic stellate cells in different states, and occurrences of the protein TGFB1. The protein TGFB1 induces different behaviors of hepatic stellate cells thereby contributing either to tissue repair or to fibrosis. Better understanding the overall behavior of the mechanisms that are involved in these processes is a key issue to identify markers and therapeutic targets likely to promote the resolution of fibrosis at the expense of its progression.

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“…Kappa ecosystem [Boutillier et al, 2018b] offers several tools to assist the modeler during her task. Static analysis [Danos et al, 2008, Boutillier et al, 2018a] may be used to curate models. Canonical and secondary pathways may be extracted thanks to causality analysis [Danos et al, 2007a, Danos et al, 2012].…”

Section: Modeling Approaches For Tgf-β Signalingmentioning

confidence: 99%

Integrative Models for TGF-β Signaling and Extracellular Matrix

Théret

Feret

Hodgkinson

et al. 2020

Biology of Extracellular Matrix

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The extracellular matrix (ECM) is the most important regulator of cellcell communication within tissues. ECM is a complex structure, made up of a wide variety of molecules including proteins, proteglycans and glycoaminoglycans. It contributes to cell signaling through the action of both its constituents and their proteolytic cleaved fragments called matricryptins [Hynes and Naba, 2012, Ricard-Blum andVallet, 2019]. In addition, ECM acts as a "reservoir" of growth factors and cytokines and regulates their bioavailability at the cell surface. By controlling cell signaling inputs, ECM plays a key role in regulating cell phenotype (differentiation, proliferation, migration, etc.). In this context, signaling networks associated with the polypeptide transforming growth factor TGF-β are unique since their activation are controlled by ECM and TGF-β is a major regulator of ECM remodeling in return.

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KaSa: A Static Analyzer for Kappa

Cited by 7 publications

References 9 publications

TRuML: A Translator for Rule-Based Modeling Languages

TRuML: A Translator for Rule-Based Modeling Languages

The Rule‐Based Model Approach:A Kappa Model for Hepatic Stellate Cells Activation by TGFB1

Integrative Models for TGF-β Signaling and Extracellular Matrix

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