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Open Access Research article

Applications of a formal approach to decipher discrete genetic networks

Fabien Corblin12*, Eric Fanchon1* and Laurent Trilling1

Author Affiliations

1 Laboratoire TIMC-IMAG, UMR CNRS/UJF 5525, Domaine de la Merci, 38710 La Tronche, France

2 Laboratoire IRISA-INRIA centre de Rennes, Campus de Beaulieu, 35042 Rennes, France

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BMC Bioinformatics 2010, 11:385  doi:10.1186/1471-2105-11-385

Published: 20 July 2010

Abstract

Background

A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language.

Results

In this article our formal and declarative approach is applied to three biological applications. The software environment that we developed allows to specifically address each application through a new class of biologically relevant queries. We show that we can describe easily and in a formal manner the partial knowledge about a genetic network. Moreover we show that this environment, based on a constraint algorithmic approach, offers a wide variety of functionalities, going beyond simple simulations, such as proof of consistency, model revision, prediction of properties, search for minimal models relatively to specified criteria.

Conclusions

The formal approach proposed here deeply changes the way to proceed in the exploration of genetic and biochemical networks, first by avoiding the usual trial-and-error procedure, and second by placing the emphasis on sets of solutions, rather than a single solution arbitrarily chosen among many others. Last, the constraint approach promotes an integration of model and experimental data in a single framework.