Compositional modelling of signalling pathways in timed concurrent constraint programming

International audienceThe biological data regarding the signalling pathways often consider single pathways or a small number of them. We propose a methodology for composing this kind of data in a coherent framework, in order to be able to investigate a bigger number of signalling pathways. We specify a biological system by means of a set of stoichiometric-like equations resembling the essential features of molecular interactions. We represent these equations by a timed concurrent constraint (ntcc) language, which can deal with partial information and the time for a reaction to occur. We describe a freely available prototypical implementation of our framework

Modeling Cellular Signaling Systems: An Abstraction-Refinement Approach

International audienceThe molecular mechanisms of cell communication with the environment involve many concurrent processes governing dynamically the cell function. This concurrent behavior makes traditional methods, such as differential equations, unsatisfactory as a modeling strategy since they do not scale well when a more detailed view of the system is required. Concurrent Constraint Programming (CCP) is a declarative model of concurrency closely related to logic for specifying reactive systems, i.e., systems that continuously react with the environment. Agents in CCP interact by telling and asking information represented as constraints (e.g., x > 42). In this paper we describe a modeling strategy for cellular signaling systems based on a temporal and probabilistic extension of CCP. Starting from an abstract model, we build refinements adding further details coming from experimentation or abstract assumptions. The advantages of our approach are: due to the notion of partial information as constraints in CCP, the model can be straightforwardly extended when more information is available; qualitative and quantitative information can be represented by means of probabilistic constructs of the language; finally, the model is a runnable specification and can be executed, thus allowing for the simulation of the system. We outline the use of this methodology to model the interaction of G-protein-coupled receptors with their respective G-proteins that activates signaling pathways inside the cell. We also present simulation results obtained from an implementation of the framewor

Verification of Spatial and Temporal Modalities in Biochemical Systems

AbstractBiochemical systems such as metabolic and signaling pathways tend to be arranged in a physical space: the product of one reaction must be in the right place to become the reactant for the subsequent reaction in the pathway. Moreover, in some cases, the behavior of the systems can depend on both, the location of the reactants as well as on the time needed for the reaction to occur. We address the problem of specifying and verifying properties of biochemical systems that exhibit both temporal and spatial modalities at the same time. For that, we use as specification language a fragment of intuitionistic linear logic with subexponentials (SELL). The subexponential signature allows us to capture the spatial relations among the different components of the system and the timed constraints for reactions to occur. We show that our framework is general enough to give a declarative semantics to P-Systems and we show that such logical characterization has a strong level of adequacy. Hence, derivations in SELL follow exactly the behavior of the modeled system

Modelling non-Markovian dynamics in biochemical reactions

Biochemical 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