The TAO-Gen Algorithm for Identifying Gene Interaction Networks with Application to SOS Repair in E. coli

Abstract

One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts.

Gradient Descent Optimization in Gene Regulatory Pathways

“…S3 , S4 , S5 [27] and Fig. S6 [28] . The results have been compared with that of the existing extreme pathway analysis [12] , [13] .…”

“…There is a single path from ssb leading to the target gene rpoD through , which is the desired optimal regulatory pathway. The importance of the starting gene lexA, the intermediate gene ssb and the target gene rpoD has been observed in [28] , [61] , [62] .…”

A fast and efficient gene-network reconstruction method from multiple over-expression experiments

“…There are basically two types of reverse engineering approaches depending on the experimental setup, inferring the gene network from steady-state [ 8 , 9 ] or from time-series [ 14 , 15 ] experiments. By using steady-state experiments, one can not draw any conclusion about the dynamics of gene regulation.…”

“…Further, due to costs full time-series data on gene expression are in general not available. As described in [ 1 ], one can further divide the reverse engineering methods into four categories: differential equation models [ 5 , 6 ], boolean network models [ 7 ], Bayesian network models [ 8 ] and association networks [ 16 ]. The reverse engineering methods based on differential equations further may rely on linear [ 9 ] or nonlinear differential equations [ 17 ].…”

“…Yet, its predictions do not better or completely equal the ones made by NIR. Another algorithm has been proposed recently [ 8 ], which correctly identifies the recA-hub in the E. coli SOS response network and reports a higher statistical significance of the reconstructed SOS network. Yet, the algorithm is restricted to a-cyclic networks.…”