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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT), January 2006 Last Date of Visit: (September 12, 2006), http://www.yeastract.com .
Akutsu, T., Kuhara, S., Maruyama, O., Miyano, S.: Identification of genetic networks by strategic gene disruptions and gene overexpressions under a Boolean model. Theoretical Computer Science 298(1), 235–251 (2003)
Bonneau, R., Reiss, D.J, Shannon, P., Facciotti, M., Hood, L., Baliga, N.S, Thorsson, V.: The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biology 7(5), R36 (2006)
Bothast, R., Saha, B.: Ethanol production from agricultural biomass substrate. Adv. App. Microbiol. 44, 261–286 (1997)
Devaux, F., Carvajal, E., Moye-Rowley, S., Jacq, C.: Genome-wide studies on the nuclear PDR3-controlled response to mitochondrial dysfunction in yeast. FEBS Letters 515(1-3), 25–28 (2002)
D’haeseleer, P., Wen, X., Fuhrman, S., Somogyi, R.: Linear modeling of mRNA expression levels during CNS development and injury. In: Pacific Symposium on Biocomputing, pp. 41–52. World Scientific Publishing Co, Singapore (1999)
Edelstein-Keshet, L.: Mathematical Models in Biology. SIAM (2004)
Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004)
Golub, G.H., van Loan, C.F.: Matrix Computations, 3rd edn. The Johns Hopkins University Press, Baltimore, MD (1996)
Haugen, A.C., Kelley, R., Collins, J.B., Tucker, C.J., Deng, C., Afshari, C.A., Brown, J.M., Ideker, T., Van Houten, B.: Integrating phenotypic and expression profiles to map arsenic-response networks. Genome Biology 5(12), R95 (2004)
Hegde, P., Qi, R., Abernathy, K., Gay, C., Dharap, S., Gaspard, R., Earle-Hughes, J., Snesrud, E., Lee, N., Quackenbush, J.: A concise guide to cdna microarray analysis. BioTechniques 29, 548–562 (2000)
Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., Miyano, S.: Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Journal of Bioinformatics and Computational Biology 1(2), 231–252 (2003)
Lee, J., Godon, C., Lagniel, G., Spector, D., Garin, J., Labarre, J., Toledano, M.B.: Yap1 and Skn7 control two specialized oxidative stress response regulons in yeast. J. Biol. Chem. 274(23), 16040–16046 (1999)
Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., Young, R.A.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298(5594), 763–764 (2002)
Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pacific Symposium on Biocomputing 3, 18–29 (1998)
Liu, Z.L.: Genomic adaptation of ethanologenic yeast to biomass conversion inhibitors. Appl. Microbiol. Biotech. 73, 27–36 (2006)
Liu, Z.L., Slininger, P.J.: Development of genetically engineered stress tolerant ethanologenic yeasts using integrated functional genomics for effective biomass conversion to ethanol, CAB International, Wallingford, UK, pp. 283–294 (2005)
Liu, Z.L., Slininger, P.J.: Transcriptome dynamics of ethanologenic yeast in response to 5-hydroxymethylfurfural stress related to biomass conversion to ethanol. In: Recent Research Developments in Multidisciplinary Applied Microbiology: Understanding and Exploiting Microbes and Their Interactions-Biological, Physical, Chemical and Engineering Aspects, pp. 679–684. Wiley-VCH, Chichester (2006a)
Liu, Z.L., Slininger, P.J.: Universal external RNA controls for microbial gene expression analysis using microarray and qRT-PCR. J. Microbiol. Methods, doi:10.1016/j.mimet.2006.10.014 (2006b)
Liu, Z.L., Slininger, P.J., Dien, B.S., Berhow, M.A., Kurtzman, C.P., Gorsich, S.W.: Adaptive response of yeasts to furfural and 5-hydroxymethylfurfural and new chemical evidence for HMF conversion to 2,5-bis-hydroxymethylfuran. J. Ind. Microbiol Biotechnol. 31, 345–352 (2004)
Liu, Z.L., Slininger, P.J., Gorsich, S.W.: Enhanced biotransformation of furfural and 5-hydroxy methylfurfural by newly developed ethanologenic yeast strains. Appl. Biochem. Biotechnol. 121-124, 451–460 (2005)
Lucau-Danila, A., Lelandais, G., Kozovska, Z., Tanty, V., Delaveau, T., Devaux, F., Jacq, C.: Early expression of yeast genes affected by chemical stress. Mol. Cell Biol. 25(5), 1860–1868 (2005)
Luo, C., Brink, D., Blanch, H.: Identification of potential fermentation inhibitors in conversion of hybrid poplar hydrolyzate to ethanol. Biomass Bioenergy 22, 125–138 (2002)
Martin, C., Jonsson, L.: Comparison of the resistance of industrial and laboratory strains of Saccharomyces and Zygosaccharomyces to lignocellulose-derived fermentation inhibitors. Enzy. Micro. Technol. 32, 386–395 (2003)
Meir, E., Munro, E.M., Odell, G.M., von Dassow, G.: Ingeneue: A versatile tool for reconstituting genetic networks, with examples from the segment polarity network. Journal of Experimental Zoology 294, 216–251 (2002)
Ong, I.M., Glasner, J.D., Page, D.: Modelling regulatory pathways in E. coli from time series expression profiles. Bioinformatics 18, S241–S248 (July 2002)
Pal, R., Ivanov, I., Datta, A., Bittner, M.L., Dougherty, E.R.: Generating Boolean networks with a prescribed attractor structure. Bioinformatics 21, 4021–4025 (November 2005)
Palmqvist, E., Almeida, J., Hahn-Hägerdal, B.: Influence of furfural on anaerobic glycolytic kinetics of Saccharomyces cerevisiae in batch culture. Biotechnol. Bioeng. 62, 447–454 (1999)
R Development Core Team.: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2006), http://www.R-project.org ISBN 3-900051-07-0
Saha, B.: Hemicellulose bioconversion. Journal of Industrial Microbiology and Biotechnology 30, 279–291 (2003)
Schlitt, T., Brazma, A.: Modelling in molecular biology: describing transcription regulatory networks at different scales. Philosophical Transactions of the Royal Society B: Biological Sciences 361(1467), 483–494 (2006)
Schmitt, M.E., Brown, T.A., Trumpower, B.L.: A rapid and simple method for preparation of RNA from Saccharomyces cerevisiae. Nucl. Acid Res. 18, 3091–3092 (1990)
Shmulevich, I., Dougherty, E.R., Kim, S., Zhang, W.: Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18, 261–274 (2002)
Taherzadeh, M., Gustafsson, L., Niklasson, C.: Physiological effects of 5-Hydroxymethylfurfural on Saccharomyces cerevisiae. App. Microbiol. Biotechnol. 53, 701–708 (2000)
Takahashi, K.: Multi-algorithm and multi-timescale cell biology simulation. PhD thesis, Keio University, Fujisawa, Japan (2004)
Takahashi, K., Arjunan, S.N.V., Tomita, M.: Space in systems biology of signaling pathways – towards intracellular molecular crowding in silico. FEBS Letters 579, 1783–1788 (2005)
Teixeira, M.C., Monteiro, P., Jain, P., Tenreiro, S., Fernandes, A.R., Mira, N.P., Alenquer, M., Freitas, A.T., Oliveira, A.L., Sá-Correia, I.: The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Nucl. Acids Res. 34, D446–451 (2006)
Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T.S., Matsuzaki, Y., Miyoshi, F., Saito, K., Tanida, S., Yugi, K., Venter, J.C., Hutchison III, C.A.: E-CELL: software environment for whole-cell simulation. Bioinformatics 15(1), 72–84 (1999)
van Kampen, N.: Stochastic Processes in Physics and Chemistry. Elsevier, Amsterdam (1997)
Wahbom, C.F., Hahn-Hägerdal, B.: Furfural, 5-hydroxymethylfurfrual, and acetone act as external electron acceptors during anaerobic fermentation of xylose in recombinant Saccharomyces cerevisiae. Biotechnol. Bioeng. 78, 172–178 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, M.(., Liu, Z.L. (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. In: Ideker, T., Bafna, V. (eds) Systems Biology and Computational Proteomics. RSB RCP 2006 2006. Lecture Notes in Computer Science(), vol 4532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73060-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-73060-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73059-0
Online ISBN: 978-3-540-73060-6
eBook Packages: Computer ScienceComputer Science (R0)