Large-scale inference of the transcriptional regulation of Bacillus subtilis

This paper addresses the inference of the transcriptional regulatory network of Bacillus subtilis. Two inference approaches, a linear, additive model and a non-linear power-law model, are used to analyze the expression of 747 genes from B. subtilis obtained using Affymetrix GeneChip® arrays under three different experimental conditions. A robustness analysis is introduced for identifying confidence levels for all inferred regulatory connections. Both the linear and non-linear methods produce candidate networks that share a scale-free or a “huband-spoke” topology with a small number of global regulator genes influencing the expression of a large number of target genes. The two c t s r ©