Description: Background: Network inference of gene expression data is an important challenge in systems biology. Novelalgorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases such as rheumatoid arthritis (RA), in which activated synovial fibroblasts(SFBs) play a major role. Since the detailed mechanisms underlying this activation are still unclear,simultaneous investigation of multi-stimuli activation of SFBs offers the possibility to elucidate theregulatory effects of multiple mediators and to gain new insights into disease pathogenesis. Methods: A GRN was therefore inferred from RA-SFBs treated with 4 different stimuli (IL-1ß, TNF-ß, TGF-ß,and PDGF-D). Data from time series microarray experiments (0, 1, 2, 4, 12 h; Affymetrix HG-U133Plus 2.0) were batch-corrected applying 'ComBat', analyzed for differentially expressed genes overtime with 'Limma', and used for the inference of a robust GRN with NetGenerator V2.0, a heuristicordinary differential equation-based method with soft integration of prior knowledge. Results: Using all genes differentially expressed over time in RA-SFBs for any stimulus, and selecting thegenes belonging to the most significant gene ontology (GO) term, i.e., 'cartilage development', adynamic, robust, moderately complex multi-stimuli GRN was generated with 24 genes and 57 edgesin total, 31 of which were gene-to-gene edges. Prior literature-based knowledge derived from PathwayStudio or manual searches was reflected in the final network by 25/57 confirmed edges (44%). Themodel contained known network motifs crucial for dynamic cellular behavior, e.g., cross-talk amongpathways, positive feed-back loops, and positive feed-forward motifs (including suppression of thetranscriptional repressor OSR2 by all 4 stimuli. Conclusion: A multi-stimuli GRN highly concordant with literature data was successfully generated by networkinference from the gene expression of stimulated RA-SFBs. The GRN showed high reliability, since10 predicted edges were independently validated by literature findings post network inference. Theselected GO term 'cartilage development' contained a number of differentiation markers, growthfactors, and transcription factors with potential relevance for RA. Finally, the model provided newinsight into the response of RA-SFBs to multiple stimuli implicated in the pathogenesis of RA, inparticular to the 'novel' potent growth factor PDGF-D.