We provide translations between process algebra and systems of chemical reactions. We show that the translations preserve discrete-state (stochastic) and continuous-state (concentration) semantics, and in particular that the continuous-state semantics of processes corresponds to the differential equations of chemistry based on the law of mass action. The novel semantics of processes so obtained equates processes that have the same state occupation dynamics, but that may have different interaction interfaces. 1

Web services help in achieving increased automation across organizational boundaries. In this paper, we present an approach for annotating WSDL documents with semantically rich descriptions. We also present an algorithm that considers such annotations in addition to just the types of input and output parameters. Our matchmaking algorithm not only returns match/no-match answers but in case of a match a set of conditions under which a web service offers the desired functionality. 1.

We investigate the collective behavior of processes in terms of differential equations, using chemistry as a stepping stone. Chemical reactions can be converted to ordinary differential equations, and also to processes in a stochastic process algebra. Conversely, certain stochastic processes (in Chemical Parametric Form, or CPF) can be converted to chemical reactions. CPF is a subset of π-calculus, but is already more powerful that what is strictly needed to represent chemistry: it supports also parameterization and compositional reuse of models. The mapping of CPF to chemistry thus induces a parametric and compositional mapping of CPF to differential equations; the indirect mapping through chemistry is easier to define and understand than a direct mapping. As an example, we derive a quantitative interleaving law from the differential equations. 1

In life science, deeper understanding of biomolecular systems is acquired by computational modeling and analysis. For the modeling of several kinds of reaction networks, e.g. signaling pathways, information on intracellular space, like the locations and motions of molecules, has to be taken into account. In this paper, we introduce Labeled SpacePi, an extension of the π-calculus, in order to model spatio-temporal processes in cells. The formalism is tailored to the available data and knowledge about biomolecular systems. For the analysis, we employ model checking techniques known from the field of safety-critical systems. To this end, we develop a translation of Labeled SpacePi models into hybrid automata. Two use cases- one considering the activation of a signaling pathway and the other one concerning active transport in cells- demonstrate our concept by making use of the established analysis tools HyTech and HySat.

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