BRNI: Modular analysis of transcriptional regulatory programs

Abstract

Background: Transcriptional responses often consist of regulatory modules -sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program.

A parallel algorithm for reverse engineering of biological networks

“…Methods that can capture different possible solutions enhance the robustness of the predicted interactions and produce better approximations to the global solution. 26,27 Our proposed method decomposes the problem of inferring a network of size N into N different subnetworks, where the goal is to identify the regulators of one of the genes in the network at a time. We then combine the results and get the globally optimal solution.…”

“…Furthermore, attempts using optimization algorithms tend to result in suboptimal solutions due to the large, non-convex solution space. Methods that can capture different possible solutions enhance the robustness of the predicted interactions and produce better approximations to the global solution 26,27 . Our proposed method decomposes the problem of inferring a network of size N into N different subnetworks, where the goal is to identify the regulators of one of the genes in the network at a time.…”

Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle

“…Indeed in our NCA, fkh2 displays strong late G2 activity (Figure 1 ). Recently, Nachman and Regev [ 10 ], using a Biochemical Regulatory Network Inference (BRNI) approach, have shown that cell-division specific genes ace2 and fkh2 act together in a combinatorial regulation way and that fkh2 and sep1 are involved in a negative feedback loop that may control regulatory activity at the G2/M phase of the fission yeast cell cycle. Interestingly, fkh2 also shows high coordination with SPBC19G7.04 (Figure 1 and Additional file 5 Figure S4), a HMG box TF that is periodically expressed ( P < 10 -33 ) at the onset of M phase [ 9 , 32 ].…”

“…A subsequent meta-analysis [ 9 ] of ten experiments from the three studies was a first step towards aligning the data sets but no major attempt to reconstruct a S. pombe regulatory network has been made. Using one of the gene expression time course studies of the S. pombe cell cycle, Nachman and Regev [ 10 ] used a Biochemical Regulatory Network Inference (BRNI) method to identify transcriptional- and motif-based modules comprising some of the known and novel regulatory networks that control the fission yeast cell cycle. However, the limited gene expression data set representing a single form of cell cycle synchronization (elutriation), the noise inherent in the small data set and phase-specific differences between genes and transcription factors (TFs) all pose real challenges to the discovery of a comprehensive and reliable regulatory network.…”