A Stochastic Pi Calculus for Concurrent Objects

Kuttler, Celine; Lhoussaine, Cédric; Niehren, Joachim

doi:10.1007/978-3-540-73433-8_17

Cited by 22 publications

(32 citation statements)

References 23 publications

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“…The transitions of S are derived from its possible communications, transition weights are obtained by summing up the rate constants of all possible communications leading to the same successor state. As shown in, e.g., [KLN07], stochastic simulators can be derived directly from such stochastic π-calculus semantics.…”

Section: Operational Semanticsmentioning

confidence: 99%

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Constructing and visualizing chemical reaction networks from pi-calculus models

et al. 2013

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Abstract. The π-calculus, in particular its stochastic version the stochastic π-calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π-calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequences of reaction sets, which are obtained from state trajectories, in terms of reaction networks. Our translation from model states to reaction sets is formally defined and shown to be correct, in the sense that it reflects the states and transitions as they are derived from the continuous time Markov chain-semantics of the stochastic π-calculus. Our visualization concept combines high level measures of network complexity with interactive, table-based network visualizations. It directly reflects the structures introduced in the first step and allows modelers to explore the resulting simulation traces by providing both: an overview of a network's evolution and a detail inspection on demand.

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Section: Operational Semanticsmentioning

confidence: 99%

“…spico [KLN07] associates sets of functions to channels. In [Kut06] an encoding from spico to the stochastic π-calculus is presented that only increases the amount of used channel names, thus yielding larger reaction sets.…”

Section: Applicability To Extensions Of the π-Calculusmentioning

confidence: 99%

Constructing and visualizing chemical reaction networks from pi-calculus models

et al. 2013

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“…We write dom(ρ) = fn(e) for the Fig. 4, is equal to that of the attributed π-calculus [11], except that we now permit imperative assignments in L. It extends on the usual syntax of the stochastic π-calculus [17,16,13], by permitting expressions to describe channel values, adding conditions to receivers and senders, and generalizing stochastic rate constants of channels to arbitrary values.…”

Section: Imperative π-Calculusmentioning

confidence: 99%

Dynamic Compartments in the Imperative π-Calculus

John

Lhoussaine

Niehren

2009

Computational Methods in Systems Biology

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Abstract. Dynamic compartments with mutable configurations and variable volumes are of basic interest for the stochastic modeling of biochemistry in cells. We propose a new language to express dynamic compartments that we call the imperative π-calculus. It is obtained from the attributed π-calculus by adding imperative assignment operations to a global store. Previous approaches to dynamic compartments are improved in flexibility or efficiency. This is illustrated by an appropriate model of osmosis and a correct encoding of BioAmbients.

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“…Certain authors as Cardelli and Zavattaro (2008) and Pradalier et al (2007) support the idea that binary reactions are sufficient to represent chemical knowledge. They do so to advocate formal object-centric representations that are confined to binary interactions, namely recent languages based on the stochastic π-calculus (Regev 2002, Phillips and Cardelli 2007, Kuttler et al 2007, Dematté et al 2008. However, rewriting n-ary to binary reactions is tedious and requires sufficient expressiveness of formal languages.…”

Section: Kuttler Lhoussaine and Nebutmentioning

confidence: 99%

“…The core ingredient for our model are rule schemas and n-ary chemical reactions. The need for n-ary reactions renders representations in object-centered languages such as the stochastic pi-calculus (Regev 2002, Phillips and Cardelli 2007, Kuttler et al 2007) inappropriate, in practice. We used the Kappa factory for stochastic simulation , which provides convenient analysis tools.…”

Section: Simulationmentioning

confidence: 99%

Rule-based modeling of transcriptional attenuation at the tryptophan operon

Kuttler

Lhoussaine

Nebut

2009

Proceedings of the 2009 Winter Simulation Conference (WSC)

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To cite this version:Celine Kuttler, Cédric Lhoussaine, Mirabelle Nebut. Rule-based modeling of transcriptional attenuation at the tryptophan operon. ABSTRACTTranscriptional attenuation at E.coli's tryptophan operon is a prime example of RNA-mediated gene regulation. In this paper, we present a discrete stochastic model of the fine-grained control of attenuation, based on chemical reactions. Stochastic simulation of our model confirms results that were previously obtained by master or differential equations. Our approach is easier to understand than master equations, although mathematically well founded. It is compact due to rule schemas that define finite sets of chemical reactions. Moreover, our model makes intense use of reaction rules with more than two reactants. As we show, such n-ary rules are essential to concisely capture the control of attenuation. Our model could not adequately be represented in object-centered modeling languages based on the pi-calculus, because these are limited to binary interactions.

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A Stochastic Pi Calculus for Concurrent Objects

Cited by 22 publications

References 23 publications

Constructing and visualizing chemical reaction networks from pi-calculus models

Constructing and visualizing chemical reaction networks from pi-calculus models

Dynamic Compartments in the Imperative π-Calculus

Rule-based modeling of transcriptional attenuation at the tryptophan operon

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