Modelling and Analysis of the NF-κB Pathway in Bio-PEPA

Ciocchetta, Federica; Degasperi, Andrea; Heath, John K.; Hillston, Jane

doi:10.1007/978-3-642-11712-1_7

Cited by 9 publications

(3 citation statements)

References 40 publications

(135 reference statements)

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“…Ordinary differential equations (ODEs) are often used to model the dynamics of biochemical processes, and the availability of supporting tools [2]- [4] makes their power accessible to end users. Process calculi are also classified as executable approaches, and tools based on such formalisms have been successfully used in modeling complex biological events [5], [6]. However, most of the existing modeling tools require theoretical foundations or training in mathematics, and this may hamper the widespread creation of in silico models within the biological community.…”

Section: Modeling Biological Pathway Dynamics Withmentioning

confidence: 99%

Modeling Biological Pathway Dynamics With Timed Automata

Schivo

Scholma²,

Wanders

et al. 2014

IEEE J. Biomed. Health Inform.

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Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires capturing their dynamics through computational support and models. ANIMO (analysis of Networks with Interactive Modeling) is a tool that enables the construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analyzed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signaling networks. This enforces precision and uniformity in the definition of signaling pathways, contributing to the integration of isolated signaling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behavior of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behavior of the network components is displayed graphically, allowing for an intuitive and interactive modeling experience.

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Section: Modeling Biological Pathway Dynamics Withmentioning

confidence: 99%

Modeling Biological Pathway Dynamics With Timed Automata

Schivo

Scholma²,

Wanders

et al. 2014

IEEE J. Biomed. Health Inform.

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“…Bio-PEPA has also been applied to the modelling of many different biological systems including Goldbeter's model of cyclin oscillation [29,25], the Repressilator [28], the NF-κB signalling pathway [27], the MAPK model [35], circadian clocks [1,2], the gp130/JAK/STAT pathway [44] and trafficking of Src in the mammalian cell [38].…”

Section: Semanticsmentioning

confidence: 99%

Hybrid semantics for Bio-PEPA

Galpin

2014

Information and Computation

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This paper investigates the stochastic, continuous and instantaneous (hybrid) modelling of systems defined in Bio-PEPA, a quantitative process algebra for biological modelling. This is achieved by mapping a Bio-PEPA model to a model in stochastic HYPE, a process algebra that models these three behaviour types in a compositional and structured manner. The novel mapping between process algebras provides another method of analysis for Bio-PEPA models and presents the modeller with a well-structured stochastic HYPE model which can itself be easily modified and is only a small constant larger in size than the Bio-PEPA model. The structure of the stochastic HYPE model generated has desirable properties and also gives a general framework for modelling biochemical systems where the advantages of both stochastic and deterministic simulation are required. Thresholds are introduced for each reaction, and when all values are above these thresholds, the reaction is treated deterministically. However, if a relevant value is below a threshold, the reaction is treated stochastically (as are the changes in species quantities as a result of that reaction). It is proved that in the purely deterministic case and in the purely stochastic case, the stochastic HYPE model has the same behaviour as the Bio-PEPA model when considered purely deterministically and purely stochastically, respectively. Furthermore, addition of instantaneous events in the style of Bio-PEPA with events is illustrated, and a proposal for mapping Bio-PEPA with delays (Bio-PEPAd) to stochastic HYPE is presented.

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“…Apart from probabilistic (or stochastic) model checking, other formal techniques have been used for studying biological systems in recent years as well. For instance, the process algebra bio-PEPA is used to model and analyze the NF-kappaB pathway [7]; Petri nets are used for quantitative predictive modeling of C.elegans vulval development [6]; and the rule-based modeling language Kappa is used to model epigenetic information maintenance [23]. More related work, especially case studies of applying formal methods in systems biology, can be found in [31,11].…”

Section: Introductionmentioning

confidence: 99%