Simulating Signalling Pathways With BioWayS

Chiarugi, Davide; Falaschi, Moreno; Hermith, Diana; Guzmán, Michell; Olarte, Carlos

doi:10.1016/j.entcs.2013.02.016

Cited by 4 publications

(1 citation statement)

References 29 publications

(44 reference statements)

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“…Encouraged by these findings, we are planning to use our technique for performing case-specific discrete-state continuoustime non-Markovian algorithms for sampling waiting times from experimentally inferred PDFs. Moreover, we are currently embedding the algorithm proposed here in BioWayS [ 37 , 38 ], our piece of software designed for modelling and simulating biochemical processes. Another interesting issue for future work would be to study through our method how competition for enzymes by different reactions may affect the overall dynamics of the investigated system.…”

Section: Discussionmentioning

confidence: 99%

Modelling non-Markovian dynamics in biochemical reactions

et al. 2015

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BackgroundBiochemical reactions are often modelled as discrete-state continuous-time stochastic processes evolving as memoryless Markov processes. However, in some cases, biochemical systems exhibit non-Markovian dynamics. We propose here a methodology for building stochastic simulation algorithms which model more precisely non-Markovian processes in some specific situations. Our methodology is based on Constraint Programming and is implemented by using Gecode, a state-of-the-art framework for constraint solving.ResultsOur technique allows us to randomly sample waiting times from probability density functions that not necessarily are distributed according to a negative exponential function. In this context, we discuss an important case-study in which the probability density function is inferred from single-molecule experiments that describe the distribution of the time intervals between two consecutive enzymatically catalysed reactions. Noticeably, this feature allows some types of enzyme reactions to be modelled as non-Markovian processes.ConclusionsWe show that our methodology makes it possible to obtain accurate models of enzymatic reactions that, in specific cases, fit experimental data better than the corresponding Markovian models.

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Section: Discussionmentioning

confidence: 99%

Modelling non-Markovian dynamics in biochemical reactions

et al. 2015

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A Declarative View of Signaling Pathways

Chiarugi

Falaschi

Olarte

et al. 2015

Programming Languages With Applications to Biology and Security

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Due to the inherent limitations of wet-lab techniques, the experimental data regarding cellular signaling pathways often consider single pathways or a small subset of them. We propose a methodology for composing signaling pathways data in a coherent framework. Our method consists in specifying the signaling pathway as a computationally executable model. We rely on the timed concurrent constraint language ntcc to represent the system in hand as a set of stoichiometric-like equations resembling the essential features of molecular interactions. The main advantages of our approach stem from the use of constraints (formulas in logic) and from modeling of discrete time clocks in ntcc. We can deal with partial information, representing the fact that several features of the biological system may be undetermined. We can explicitly represent the time needed for a reaction to occur. We model and simulate some well known cross-talking networks, such as the TNFα, the EGF and the insulin signaling pathways as well as their interactions

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