Modeling a spatially distributed signaling pathway in MGS


The initial state of a signal transduction pathway.

This image represents the initial state of a signaling pathway. The model is proposed by A. Bugrim ( A Logic-based Approach for Computational Analysis of Spatially Distributed Biochemical Networks, Andrej Bugrim, ISMB 2000; see also this article) and takes into account the spatial localisation of the molecules. The reaction, diffusion and transport processes described in the Bugrim model are modeled as multiset transformations taking place in a nest of multisets. This is reminiscent of the P system approach. This figure is automatically generated by the MGS simulation program. Each box corresponds to a multiset: the external one represents the universe and contains three elements: the agonist molecule pictured as a thin cone;  the calcium (the thick cone); and the plasma membrane which is represented as a multiset and figured by a translucid box. The various molecules anchored in the plasma membrane are drawn as various solid volumes. The ellipsoidal container represents the cytosol and the solid sphere in the middle of it, the nucleus. Such a figure can be generated at each simulation step to visualize the trajectory of the dynamical system. See below for the first three steps.

Three steps in the simulation

The corresponding MGS program

The organization of the cell is implemented using a nest of multisets and there is a MGS rule for each chemical reaction and diffusion or transport process. A membrane corresponds also to a multiset: the multiset of molecules anchored in this membrane. The diagram below gives a schematic view of the reaction and of the spatial organization involved.

A signal transduction pathway

Clément Boin and Nicolas Thibault have developped this program as part of a student project. Thanks for their work !

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Pages started: May 2002. Last revision: 24 jully 2003.

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English keywords for indexation: computer science, programming language, topological collections, transformation, declarative programming language, functional languages, simulation of biological processes, cell model, biological pathway, interaction network, gene regulation, signal transduction, morphogenesis, developmental biology, integrative simulation, biological organization, dynamical systems, dynamical structure, Gamma, CHAM, P system, L system, Paun, Lindenmayer, cellular automata, membrane computing, aqueous computing, artificial chemistry, GBF, Cayley graph, data fields, nested collections, rewriting, rule based programming, pattern-matching, intentional programming, compilation, interpretation, type, type inference, nested type, polytypism, catamorphism, static analysis, sequence, multiset, combinatorial algebraic topology, chain complex, chain group.