Mathematical modeling shows that Gln3 and Zap1 affect the dynamics of the gene regulatory network controlling the cold shock response in Saccharomyces cerevisiae

Abstract

A gene regulatory network (GRN) consists of a set of transcription factors that regulate the level of expression of genes encoding other transcription factors. The dynamics of a GRN show how gene expression in the network changes over time. While the transcriptional regulation of the response to the environmental stress of heat shock in budding yeast, Saccharomyces cerevisiae, is well‐understood, which transcription factors regulate the early response to cold shock is not fully understood. Thus, the focus of this study was to determine the GRN that controls the cold shock response in yeast and to model its dynamics. Microarray experiments were performed in the Dahlquist lab at various timepoints after cold shock (t=15, 30, and 60 minutes) to examine the gene expression patterns of both the wild type and mutant strains of yeast that had been deleted for the transcription factors Gln3 and Zap1. The microarray data were normalized using the limma package in R and a modified ANOVA was used to determine which genes had a log2 fold change significantly different than zero at any of the timepoints studied. The genes that met the significance criterion of an adjusted Benjamini and Hochberg p < 0.05 were submitted to the YEASTRACT database to determine which transcription factors potentially regulated those genes. Two GRNs, one derived from the Gln3 deletion strain data and one from the Zap1 deletion strain data were created. The GRNs and the corresponding microarray data were then input into GRNmap, a MATLAB software package that uses ordinary differential equations to model the dynamics of medium‐scale GRNs. The program estimated the production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on the microarray data, and then performed a forward simulation of the dynamics of the network. The results of the modeling for both networks were similar. The best fit of model to data was for genes directly connected to the deleted transcription factor (either Gln3 or Zap1). However, the deletion of either transcription factor from their respective networks had a large effect on the dynamics of several genes in the network that were not accounted for by the network connections. This suggests that the network structure did not fully model the actual cellular conditions of cold shock. This may be because the regulatory networks in the YEASTRACT database are based on measurements taken during other types of growth conditions, not cold shock. Our future directions include expanding the number of GRNs that we model to determine which transcription factors are missing from the current model. Our working code is available on the GRNmap page (http://kdahlquist.github.io/GRNmap/), and visualization of the network is available on GRNsight (http://dondi.github.io/GRNsight/).Support or Funding InformationThis work was partially supported by NSF award 0921038 (B.G.F., K.D.D.), a Kadner‐Pitts Research Grant (K.D.D.) and the Loyola Marymount University Rains Research Assistant Program (T.A.M.).