%0 Journal Article
%T Inferring Drosophila gap gene regulatory network: a parameter sensitivity and perturbation analysis
%A Yves Fomekong-Nanfack
%A Marten Postma
%A Jaap A Kaandorp
%J BMC Systems Biology
%D 2009
%I BioMed Central
%R 10.1186/1752-0509-3-94
%X Here we investigate the sensitivity and robustness of circuits obtained from reverse engineering a model capable of simulating measured gene expression patterns. As a case study we use the early gap gene segmentation mechanism in Drosophila melanogaster. We consider the limitations of the connectionist model used to describe GRN Inferred from spatio-temporal gene expression. We address the problem of circuit discrimination, where the selection criterion within the optimization technique is based of the least square minimization on the error between data and simulated results.Parameter sensitivity analysis allows one to discriminate between circuits having significant parameter and qualitative differences but exhibiting the same quantitative pattern. Furthermore, we show that using a stochastic model derived from a deterministic solution, one can introduce fluctuations within the model to analyze the circuits' robustness. Ultimately, we show that there is a close relation between circuit sensitivity and robustness to fluctuation, and that circuit robustness is rather modular than global. The current study shows that reverse engineering of GRNs should not only focus on estimating parameters by minimizing the difference between observation and simulation but also on other model properties. Our study suggests that multi-objective optimization based on robustness and sensitivity analysis has to be considered.Gene regulatory networks (GRNs) play a fundamental role in body plan formation and development [1]. In most cases the published gene regulatory networks are based on experimental studies. Furthermore, various studies reveal that network dynamics depend on qualitative aspects (network structure) as well as the quantitative properties [2,3]. An additional option is to study gene regulatory networks in mathematical models and simulation studies. Qualitative and quantitative models may provide insights in the causal relationships between components of a network as well a
%U http://www.biomedcentral.com/1752-0509/3/94