本論文利用基因剔除策略(OptKnock)研究釀酒酵母之基因尺寸新陳代謝網路(Sc_iMM904)，當基因被剔除後會影響釀酒酵母的代謝能力。發現當剔除基因YBR196C和YEL047C後，會使酒精產率增加12.51%。

利用模擬退火 ( Simulated Annealing )方法當作程序的最佳化操作條件搜尋工具。在發酵程序中，以葡萄糖生產速率為目標函數，有氧的狀況下酒精生產速率優於無氧狀況。由於剔除基因YBR196C和YEL047C，使釀酒酵母生長能力減低。因此，在有氧發酵程序中，它的酒精生產速率比野生菌株低0.0434 g/L/hr。在挑選新陳代謝工程策略時，應選擇使酒精產率增加且同時具有高比生長速率的策略，比挑選增加最多酒精產率的策略較佳。

In this research, use OptKnock for studying the response of genome-scale of the Saccharomyces cerevisiae metabolic network (Sc_iMM904) after gene knockouts. When genes YBR196C and YEL047C are knocked out, ethanol yield will enhance 12.5% than wild type.

The optimization tool used in the research is Simulated Annealing method. When the objective function is ethanol productivity, aerobic culture has higher productivity than anaerobic culture in the fermentation process. Genes YBR196C and YEL047C are knocked out, so S. cerevisiae loses ability of growth. Therefore, ethanol productivity is lower than wild type strain with 0.0434 g/L/hr in aerobic culture. When we choose strategy of metabolic engineering, we should choose the strategy that can be enhancing of ethanol yield and high ability of growth. It is better than the strategy that can be enhancing the maximum ethanol yield.

[中文]

[1] 柯喬鐙，「纖維素生質酒精程序之操作策略」，碩士論文，國立台灣科技大學，台北 (2008)。

[英文]

[1] Becker, S. A., Feist, A. M., Mo, M. L., Hannum, G., Palsson, B. Ø., Herrgard, M. J., “Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox”, Nature Protocols, Vol. 2, Pp. 727-738 (2007)

[2] Bro, C., Regenberg, B., Förster, J.,“In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production”, Metabolic Engineering, Vol. 8, Pp. 102-111 (2006)

[3] Burgard, A. P., Pharkya, P., Maranas, C. D.,“OptKnock: A Bilevel Programming Framework for Identifying Gene Knockout Strategies for Microbial Strain Optimization”, Biotechnology and Bioengineering, Vol. 84, Pp. 647-657 (2003)

[4] Duarte, N. C., Herrgard, M. J., Palsson, B. Ø.,“ Reconstruction and Validation of Saccharomyces cerevisiae iND750, a Fully Compartmentalized Genome-Scale Metabolic Model”, Genome Research, Vol. 14, Pp. 1298-1309 (2004)

[5] Fong, S. S., Burgard, A. P., Herring, C. D., Knight, E. M., Blattner, F. R., Maranas, C. D., Palsson, B. Ø.,“In Silico Design and Adaptive Evolution of Escherichia coli for Production of Lactic Acid”, Biotechnology and Bioengineering, Vol. 91, Pp. 643-648 (2005)

[6] Förster, J., Famili, I., Fu, P.,“Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network”, Genome Research, Vol. 13, Pp. 244-253 (2003)

[7] Hjersted, J. L., Henson, M. A.,“Optimization of Fed-Batch Saccharomyces cerewisiae Fermentation Using Dynamic Flux Balance Models”, Biotechnology Progress, Vol. 22, Pp. 1239-1248 (2006)

[8] Hjersted, J. L., Henson, M. A., Mahadevan, R., “Genome-Scale Analysis of Saccharomyces cerevisiae Metabolism and Ethanol Production in Fed-Batch Culture”, Biotechnology and Bioengineering, Vol. 97, Pp. 1190-1204 (2007)

[9] Kabir, M. M., Ho, P. Y., Shimizu, K.,“ Effect of ldhA gene deletion on the metabolism of Escherichia coli based on gene expression, enzyme activities, intracellular metabolite concentrations, and metabolic flux distribution.”, Biochemical Engineering Journal, Vol. 26, Pp. 1-11 (2005)

[10] Manisha, S., Venkateshb, K.V., Banerjeea, R.,“Metabolic flux analysis of biological hydrogen production by Escherichia coli”, International Journal of Hydrogen Energy, Vol. 32, Pp. 3820-3830 (2007)

[11] Mo, M. L., Palsson, B. Ø., Herrgård, M. J.,“Connecting extracellular metabolomic measurements to intracellular flux states in yeast”, BMC Systems Biology, 3:37 (2009)

[12] Nissen, T. L., Schulze, U., Nielsen, J., Villadsen, J., “Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae”, Microbiology, Vol. 143, Pp. 203-218 (1997)

[13] Noorman, H. J., Heijnen, J. J., Luyben, K. Ch. A. M. ,“Linear Relations in Microbial Reaction Systems: A General Overview of Their Origin, Form, and Use”, Biotechnology and Bioengineering, Vol. 38, Pp. 603-618 (1991)

[14] Pharkya, P, Burgard, A. P., Maranas, C.D., “Exploring the Overproduction of Amino Acids Using the Bilevel Optimization Framework OptKnock”, Biotechnology and Bioengineering, Vol. 84, Pp. 887-899 (2003)
[15] Schilling, C., H., Letscher, D., Palsson, B. Ø., “Theory for the Systemic De5nition of Metabolic Pathways and their use in Interpreting Metabolic Function from a Pathway-Oriented Perspective”, Journal of Theoretical Biology, Vol. 203, Pp. 229-248 (2000)

[16] van Gulik, W. M., Heijnen, J. J.,“A Metabolic Network Stoichiometry Analysis of Microbial Growth and Product Formatio”, Biotechnology and Bioengineering, Vol. 48, Pp. 681-698 (1995)

[17] Vanrolleghem, P. A., de Jong-Gubbels, P., van Gulik, W. M., Pronk, J. T., van Dijken, J. P., Heijnen, S., “Validation of a Metabolic Network for Saccharomyces cerevisiae Using Mixed Substrate Studies”, Biotechnology Progress, Vol. 12, Pp. 434-448 (1996)

[18] Varma, A., Palsson, B. Ø.,“Stoichiometric Flux Balance Models Quantitatively Predict Growth and Metabolic By-Product Secretion in Wild-Type Escherichia coli W3110”, Applied and Environmental Microbiology, Vol. 60, Pp. 3724-3731 (1994)