Fig. 2

Overview of our strategy and work-flow of our computational pipeline with a plain example. Our strategy uses a computational pipeline based on a reverse-engineering technique. The pipeline takes as inputs the results of transcription (gene expression data ɛ and connectivity information \( \mathbf{T} \) and outputs the sources of transcription (strengths \( \mathbf{\mathcal{W}} \) and concentrations \( \mathbf{\mathcal{C}} \)). The pipeline is composed of five parts: Construction: RMA normalization of gene expression profiling data ɛ and a binary matrix containing connection topology \( \mathbf{T} \) is constructed using by forward-engineering strategy. Computation: The gene expression profiling data and connectivity data are utilized to infer TF-gene interaction strengths \( \mathbf{W} \) and TF concentration levels \( \mathbf{C} \). Investigation: Once the strengths and concentrations are inferred, the actual TF activities are estimated by normalizing the strengths on the concentrations. The statistically significant changes in the TF-gene interactions strength, TF concentration levels, and TF activities are calculated. Illustration: The changes are illustrated in round limpet-like plot or in the scattered plots that shows the changes between individual TF and genes. Identification: The candidate TFs are identified, and Gene Ontology (GO) analysis are performed on the genes that are regulated by the candidate TFs. The literature is reviewed to find the supporting evidence, and the individual links between the candidate TFs and their potential biological functions are identified and summarized in a table. Based on the table, we finally construct the comprehensive TF network for p38α