|SONG, MINGZHOU - NEW MEXICO STATE UNIV
|OUYANG, ZHENGYU - NEW MEXICO STATE UNIV
Submitted to: IET Systems Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/21/2008
Publication Date: 12/29/2008
Citation: Song, M., Ouyang, Z., Liu, Z. 2008. Discrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfurfural tolerance for ethanologenic yeast. IET Systems Biology. 3:203-218.
Interpretive Summary: Yeast tolerance to fermentation inhibitors such as furfural and 5-hydroxymethylfurfural (HMF) are important to a sustainable low cost biomass-to-ethanol industry. However, development of tolerant strains is hampered due to a lack of understanding of genetic mechanisms underlying stress tolerance for ethanologenic yeast. We investigated global profiling of yeast under challenge of HMF and identified more than 300 genes significantly involved in regulation of HMF tolerance and detoxification. In this study, we describe a data-driven discrete dynamic system modeling methodology to detect gene regulatory interactions and to predict system dynamic behavior based on large-scale microarray data sets. Using our model system, we identified 12 potentially significant regulatory interactions, among which, were two significant regulatory elements for HMF tolerance in yeast. The use of a discrete dynamic system model to detect interactions in a network is novel. The method itself has potential for broad applications in biology. Our application in yeast tolerance and results obtained from this study contribute understanding of mechanisms of stress tolerance; and benefit research and development of more tolerant strains of yeast for conversion of renewable agricultural residues to ethanol.
Technical Abstract: Composed of linear difference equations, a discrete dynamic system model was designed to reconstruct transcriptional regulations in gene regulatory networks in response to 5-hydroxymethylfurfural, a bioethanol conversion inhibitor for ethanologenic yeast Saccharomyces cerevisiae. The modeling aims at identification of a system of linear difference equations to represent temporal interactions among significantly expressed genes. Power-stability is imposed on a system model when exposed to the normal condition in the absence of the inhibitor. Non-uniform sampling, typical in a time course experimental design, is addressed by a log-time domain interpolation. A statistically significant discrete dynamic system model of the yeast gene regulatory network derived from time course gene expression measurements by exposure to 5-hydroxymethylfurfural, revealed several verified transcriptional regulation events. These events implicate Yap1 and Pdr3, transcription factors consistently known for their regulatory roles by other studies or predicted by independent sequence motif analysis, suggesting their involvement in yeast tolerance and detoxification of the inhibitor.