GNA (Genetic Network Analyzer) is a computer tool for the modeling, simulation and analysis of genetic regulatory networks. The aim of GNA is to assist biologists and bioinformaticians in constructing a model of a regulatory network using knowledge about regulatory interactions in combination with gene expression data.
GNA consists of a simulator of in the form of piecewise-linear differential equations, a class of models originally introduced by Leon Glass and Stuart Kauffman. The simulator has been implemented in Java 1.5 and has been applied to the analysis of various regulatory systems, including the networks controlling the initiation of sporulation in B. subtilis and the carbon starvation in E. coli. The simulator can be used in combination with standard model-checking tools in order to analyze important model properties.
GNA has been developped at INRIA Grenoble-Rhône-Alpes, initially by Hidde de Jong and Michel Page. The following persons have contributed to the current and previous versions of the software: Grégory Batt (now at INRIA Rocquencourt), Bruno Besson, Estelle Dumas, Céline Hernandez (now at the Swiss Institute for Bioinformatics, Geneva) and Pedro Monteiro. Jean-Luc Gouzé (INRIA Sophia Antipolis) and Tewfik Sari (Université de Haute-Alsace, Mulhouse) have made contributions to the qualitative simulation method underlying the tool, while Johannes Geiselmann (Université Joseph Fourier, Grenoble) and Delphine Ropers have worked on applications to actual regulatory networks. GNA has recently been transferred to Genostar, but remains freely available for non profit academic research purposes.
General information on the qualitative modeling and simulation of genetic regulatory networks can be found in:
H. de Jong, D. Ropers (2005), Qualitative approaches towards the analysis of genetic regulatory networks, Z. Szallasi, V. Periwal, J. Stelling (eds), System Modeling in Cellular Biology: From Concepts to Nuts and Bolts, MIT Press, Cambridge, MA. 125-148.
More information on the qualitative simulation method and its extensions can be found in:
G. Batt, H. de Jong, M. Page, J. Geiselmann (2008), Symbolic reachability analysis of genetic regulatory networks using qualitative abstractions, Automatica. 44(4):982-989.
G. Batt, D. Ropers, H. de Jong, J. Geiselmann, R. Mateescu, M. Page, D. Schneider (2005), Validation of qualitative models of genetic regulatory networks by model checking: Analysis of the nutritional stress response in Escherichia coli, Bioinformatics, 21(Suppl 1):i19-i28.
R. Casey, H. de Jong, J.-L. Gouzé (2006), Piecewise-linear models of genetic regulatory networks: Equilibria and their stability, Journal of Mathematical Biology. 52(1):124-152.
H. de Jong, M. Page (2008), Search for steady states of piecewise-linear differential equation models of genetic regulatory networks, ACM/IEEE Transactions on Computational Biology and Bioinformatics. 5(2): 208-222.
P.T. Monteiro, D. Ropers, R. Mateescu, A.T. Freitas, H. de Jong (2008), Temporal logic patterns for querying dynamic models of cellular interaction networks, Bioinformatics, 24(16) :i227-i233. Special issue ECCB-2008
R. Mateescu, P.T. Monteiro, E. Dumas, H. de Jong (2008), Computation tree regular logic for genetic regulatory networks. In S. Cha, J.-Y. Choi, M. Kim, I. Lee, M. Viswanathan (eds.), Proceedings of the 6th International Symposium on Automated Technology for Verification and Analysis (ATVA’08), Lecture Notes in Computer Science 5311, Springer, Berlin, 48-63, 2008
A previous version of GNA is described in:
The application of GNA is illustrated in:
D. Ropers, H. de Jong, M. Page, D. Schneider, J. Geiselmann (2006), Qualitative simulation of the carbon starvation response in Escherichia coli, Biosystems, 84(2):124-152.
J-A. Sepulchre, S. Reverchon, W. Nasser (2007), Modeling the onset of virulence in a pectinolytic bacterium, Journal of Theoretical Biology, 44(2):239-257.
A. Usseglio Viretta, M. Fussenegger (2004), Modeling the quorum sensing regulatory network of human-pathogenic Pseudomonas aeruginosa, Biotechnology Progress, 20(3):670-678.