Gene Interaction Network Suggests Dioxin Induces a Significant Linkage between Aryl Hydrocarbon Receptor and Retinoic Acid Receptor Beta

Abstract

Gene expression arrays (gene chips) have enabled researchers to roughly quantify the level of mRNA expression for a large number of genes in a single sample. Several methods have been developed for the analysis of gene array data including clustering, outlier detection, and correlation studies. Most of these analyses are aimed at a qualitative identification of what is different between two samples and/or the relationship between two genes. We propose a quantitative, statistically sound methodology for the analysis of gene regulatory networks using gene expression data sets. The method is based on Bayesian networks for direct quantification of gene expression networks. Using the gene expression changes in HPL1A lung airway epithelial cells after exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin at levels of 0.1, 1.0, and 10.0 nM for 24 hr, a gene expression network was hypothesized and analyzed. The method clearly demonstrates support for the assumed network and the hypothesis linking the usual dioxin expression changes to the retinoic acid receptor system. Simulation studies demonstrated the method works well, even for small samples.

Characteristics of Transcriptional Activity in Nonlinear Dynamics of Genetic Regulatory Networks

“…This means that any gene-specific mRNA concentration is in a sense a product of the teamwork of cooperating genes, and in which the gene responsible for the mRNA in question may not even play a dominant role. In the literature, one may find numerous examples in which mRNA measurements are directly used for fitting the models of genetic regulatory networks 12–19. A good review of these efforts is given in 20.…”

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

“…There are four remaining significant linkages: ssb and rpoS repress and activate rpo D, respectively, and recF activates umuDC and rpoH activates ssb . Table 1 provides summary information on the parameter estimates estimated by treating the identified network (Figure 2) as known and quantifying the linkages between genes by the method of Toyoshiba et al (2004). With the exception of the plasmid-induced change in recF , all linkages in Figure 2 are statistically significant ( p < 0.05).…”

“…To directly compare the Gardner et al network to the one shown in Figure 2, the Gardner et al network was made acyclic by removing the linkages for genes as their own parents and by removing the linkage between dinI and lexA . When the Bayesian estimation algorithm was applied (Toyoshiba et al 2004), the posterior log-likelihood for this model had a mean value of 329.2 compared with 354.7 from the model identified by the TAO-Gen algorithm, suggesting a considerably better fit of the model in Figure 2 to the data. Using the “known model” suggested by Gardner et al (2003), the resulting mean of the posterior log-likelihood was 311.0, also suggesting a serious lack of fit.…”

“…Also, the use of acyclic models to develop gene interaction networks is somewhat limited. A fully dynamic model using time-dependent differential equations could be used with the TAO-Gen algorithm provided multitime point data were available; the method would simply need to link models across time as suggested elsewhere (Toyoshiba et al 2004) or use dynamic Bayesian networks. Here we assume samples are independent; in time-course data, that would not necessarily be the case and the error structure between samples would need to be altered (in Equation 4 and subsequent derivations) to account for the longitudinal nature of such data.…”

Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

“…Other less-frequently used supervised-learning methods include nonlinear regression, for example spline regression, to detect nonlinear connections between genes [51][52][53]. Tree-based methods, for example random forest, can be used to detect more-complex relationships between genes than can regression methods [54,55].…”