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Background  The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as  reverse engineering  of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering  qualitative  causal relationships between genes with high accuracy from microarray data exist, but large scale  quantitative  analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.    Results  This paper performs an analysis of several existing evolutionary algorithms for  quantitative  gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.    Conclusions  Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

Comparison of evolutionary algorithms in gene regulatory network model inference