Submitted to: Lecture Notes in Bioinformatics
Publication Type: Peer reviewed journal
Publication Acceptance Date: 11/3/2006
Publication Date: 5/1/2007
Citation: Song, M., Liu, Z. 2007. A linear discrete dynamic system model for temporal gene interaction and regulatory network influence in response to bioethanol conversion inhibitor HMF for ethanologenic yeast. Lecture Notes in Bioinformatics. 4532:77-95. Interpretive Summary: One major barrier to the economic conversion of biomass to ethanol is inhibitory compounds generated during biomass pretreatment using dilute acid hydrolysis such as furfural and 5-hydroxymethylfurfural (HMF). Development of stress tolerant yeast strains is a key factor for cost-efficient biomass conversion to ethanol. However, development of such strains is hampered due to a lack of understanding of genetic mechanisms underlying stress tolerance for ethanologenic yeast. Genomic expression profiling analysis provides insight into dynamic living response of the yeast to inhibitors to understand mechanisms of stress tolerance. The gene regulatory network is a complex interactive dynamic system, especially when the yeast is under the challenges of the inhibitors. Computational modeling of the gene regulatory network presented in this study reveals statistically significant known transcriptional regulators as well as potential significant genes involved in detoxification of HMF. The model system enables complete snapshots of molecular processes during bioethanol conversion and provides a more accurate account of the genomic mechanism of detoxification and tolerance for ethanologenic yeast. This research provides insight into genomic mechanisms of stress tolerance and will benefit microbiologists and biotechnologists for tolerant strain development for cost-efficient bioethanol production.
Technical Abstract: A linear discrete dynamic system model is constructed to represent the temporal interactions among significantly expressed genes in response to bioethanol conversion inhibitor 5-hydroxymethylfurfural for ethanologenic yeast Saccharomyces cerevisiae. This study identifies the most significant linear difference equations for each gene in a network. A log-time domain interpolation addresses the non-uniform sampling issue typically observed in a time course experimental design. This system model also insures its power stability under the normal condition in the absence of the inhibitor. The statistically significant system model, estimated from time course gene expression measurements during the earlier exposure to 5-hydroxymethylfurfural, reveals known transcriptional regulations as well as potential significant genes involved in detoxification for bioethanol conversion by yeast.