重组枯草芽孢杆菌生产核黄素发酵优化及代谢组学研究
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摘要
本文在重组B. subtilis RH44产核黄素发酵优化的基础上,基于酶活测定、代谢通量分析等方法对具有显著影响的培养基成分在核黄素生产中的作用机理进行探讨,并利用LC-MS分析手段对产核黄素B. subtilis RH44的代谢物组学进行研究。
     首先通过一系列统计实验设计方法对核黄素发酵培养基组成进行优化。根据Plackett-Burman实验确定影响核黄素生成的5个重要成分分别为葡萄糖、NaNO_3、K_2HPO_4、ZnSO_4和MnCl_2;然后,通过中心组成(CCD)设计和响应面方法(RSM)获得核黄素生产的优化培养基。该优化培养基在摇瓶培养中可获得核黄素6.65 g/L。采用RSM和神经网络(ANN)耦合遗传算法(GA)两种方法共同优化B. subtilis RH44在5 L罐中间歇发酵核黄素的培养参数(pH、接种量、通气量、搅拌速率和温度)。ANN-GA方法可获得核黄素最大产量为7.53 g/L,比RSM法高出8.6%。
     对比研究了间隔补料、恒速补料和葡萄糖限制补料三种间歇补料方式对B. subtilis RH44发酵生产核黄素的影响。结果表明葡萄糖限制间歇补料模式(葡萄糖浓度维持在5~10 g/L)为最适合的补料方式。研究pH对B. subtilis RH44间歇补料发酵生产核黄素的影响表明,菌体生长的最适pH为6.8,但在pH7.2时,核黄素浓度和核黄素合成酶活性达到最大。使用pH转换策略可明显改善核黄素的生产,最大的核黄素浓度比常数pH操作的最好结果提高了13.3%。pH对副产物浓度及相关酶比活的影响表明优化的pH转换策略能够在一定程度上抑制副产物的形成和积累。用NH_4OH代替NaOH调节pH,pH转换策略可使核黄素浓度进一步提高,48 h达到17.4 g/L。
     初步探讨了对B. subtilis RH44产核黄素具有显著促进作用的三种培养基成分(NaNO_3、ZnSO_4和MnCl_2)的作用机理。对比B. subtilis 168和B. subtilis RH44在NH_4~+存在时利用NO_3~–的情况说明B. subtilis RH44已经解除了NH_4~+对NO_3~–利用的抑制作用。进一步代谢通量分析表明,优化氮源使发酵中后期的葡萄糖代谢流由EMP途径向PP途径迁移,使得更多的通量流向核黄素合成的途径。通过酶学研究表明Mn~(2+)激活了PP途径的关键酶葡萄糖-6-磷酸脱氢酶(G6PDH)的比活性,并抑制形成乙酸的相关酶磷酸转乙酰酶(PTA)的比活性。根据预测的B. subtilis GTP环水解酶II(GCHII)的立体结构,推断Zn~(2+)对核黄素生产的促进作用可能与GCHII有密切关系。建立产核黄素B. subtilis代谢网络,利用代谢通量分析进一步研究Mn~(2+)和Zn~(2+)对核黄素生产过程中代谢流的影响。
     建立利用LC-MS分析手段进行产核黄素B. subtilis代谢物组学研究的方法。对不同发酵阶段的胞内代谢物负离子ESI-MS数据进行主成分分析,结果表明代谢物组学方法可以区分不同发酵阶段的代谢产物。通过标样和二级质谱对代谢物进行定性分析,可初步确定16种代谢物。根据代谢物轮廓分析分别对比研究不同发酵时间、不同氮源和不同菌种的代谢物差异,并推测出B. subtilis RH44合成核黄素的可能限制因素。
Riboflavin fermentation process by recombinant Bacillus subtilis RH44 was optimized, and then the mechanisms of the significant factors enhancing riboflavin production were discussed according to enzyme activity assay and metabolic flux analysis. The metabolomics of B. subtilis RH44 was investigated primarily using LC-MS.
     A sequential optimization strategy, based on statistical experimental designs, was used to enhance the production of riboflavin. Plackett-Burman design was implemented to screen medium components that significantly influence riboflavin production. Among the fifteen variables tested, glucose, NaNO_3, K_2HPO_4, ZnSO_4, and MnCl_2 were identified as the most significant factors for riboflavin production. The optimal values of these five variables were determined by response surface methodology (RSM) based on the central composite design (CCD). This optimum medium led to a maximum riboflavin concentration of 6.65 g/L in shake flasks. The optimal culture parameters (i.e. pH, inoculum level, stirring speed, temperature and airflow rate) for maximum riboflavin production by batch cultivations in a 5 L fermentor were determined by using artificial neural networks (ANN) incorporating genetic algorithm (GA) and RSM. ANN-GA led to the maximum riboflavin production of 7.53 g/L, which was 8.6% higher than that by RSM.
     Three fed-batch modes, intermittent, constant rate and glucose-limited, were carried out to reduce overflow in this work. The maximum riboflavin titer was obtained in a glucose-limited fed-batch process by maintaining constant (5~10 g/L) glucose concentration in the culture broth. The effects of pH on riboflavin production were investigated using a series of glucose-limited fed-batch fermentations. Although constant pH 6.8 was favorable for the cell formation, constant pH 7.2 resulted in the highest riboflavin production and also maximum specific activity of riboflavin synthase of B. subtilis RH44. Hence, a pH-shift strategy was developed to improve riboflavin production. The results showed that the maximum riboflavin concentration was increased by 13.3% when compared with the best results of constant pH. The results of pH influence on both by-product levels and by-product forming enzyme activities indicated that the optimum pH-shift strategy had the capability of inhibiting the accumulation of by-products to a certain extent. In addition, when pH was adjusted with NH4OH instead of NaOH, in the pH-shift strategy, further improvement (17.4 g/L in 48 h) was achieved in riboflavin production.
     The mechamisms of three significant factors enhancing riboflavin production were discussed further. The comparison of the consumption courses of NH_4~+ and NO_3~–of B. subtilis 168 and B. subtilis RH44 showed that the utilization of NO_3~– in B. subtilis RH44 did not appear to be inhibited by NH_4~+. The metabolic flux shifted from EMP to PP during the mid- and later phases when the optimum nitrogen sources were used. Mn~(2+) has an activation function to glucose-6-phosphate dehydrogenase (G6PDH), while an inhibition to the activity of phosphortransacetylase (PTA). Possibly, the effect of Zn~(2+) on riboflavin biosynthesis had a close relationship with GCHII. The functions of Mn~(2+) and Zn~(2+) to riboflavin production were further investigated using metabolic flux analysis.
     A scheme of metabolomics analysis by LC-MS on B. subtilis was developed. The metabolomic analysis was valid to distinguish various culture phases by principal components analysis of negative ions ESI-MS data. In addition, 16 metabolites were determined by qualification analysis based on m/z of metabolites as well as standard and MS-MS. The limited factors of riboflavin production by B. subtilis RH44 were discussed according to the comparison of metabolites of different nitrogen sources and different strains by metabolic profiling analysis.
引文
[1] Vitreschak AG, Rodionov DA, Mironov AA, et al. Regulation of riboflavin biosynthesis and transport genes in bacteria by transcriptional and translational attenuation. Nucleic Acids Research. 2002, 30: 3141-3151.
    [2] 尹光琳,战立克,赵根楠.发酵工业全书.北京:中国医药科技出社,1992, p 237-248.
    [3] Stahmann KP, Revuelta JL, Seulberger H. Three biotechnical processes using Ashybya gossypii, Candida famata, or Bacillus subtilis compete with chemical riboflavin production. Appl Microbiol Biotechnol. 2000, 53: 509-516.
    [4] Koizumi S, Yonetani Y, Maruyama A, et al. Production of riboflavin by metabolically engineered Corynebacterium ammoniagenes, Appl Microbiol Biotechnol. 2000, 53: 674-679.
    [5] Van Loon APGM, Hohmann HP, Bretzel W, et al. Development of a Fermentation Process fort he Manufacture of Riboflavin. Chimia. 1996, 50: 410-412.
    [6] Bettina Knorr. Scale-down and parallel operation of a riboflavin production process with Bacillus subtilis. PhD-thesis Technical University of Munich, 2005.
    [7] Bretzel W, Schurter W, Ludwig B, et al. Commercial riboflavin production by recombinant Bacillus subtilis: downstream processing and comparison of the composition of riboflavin produced by fermentation or chemical synthesis. J Indus Microbiol Biotechnol. 1999, 22: 19-26.
    [8] Karos M, Vilarino C, Bollschweiler C, et al. A genome-wide transcription analysis of a fungal riboflavin overproducer. J Biotechnol. 2004, 113: 69-76.
    [9] Heefner D,Weaver CA, Yarus MJ, et al. Riboflavin producing strains of microorganisms, method for selecting, and method for fermentation. Patent WO 09822.1988-06.
    [10] Lee KH, Park YH, Han JK, et al. Microorganism for producing riboflavin and method for producing riboflavin using the same. US patent, 7166456. 2007.
    [11] Mironov AS, Korolkova NV, Errais LL, et al. Method for producing riboflavin. WO2004/046347 A1.
    [12] Kunst F, Ogasawara N, Moszer I, et al. The complete genome sequence of the gram-positive bacterium Bacillus subtilis. Nature. 1997, 390: 249-256.
    [13] Dauner M, Sauer U. Stoichiometric Growth Model for Riboflavin- Producing Bacillus subtilis. Biotechnol Bioeng. 2001, 76: 132-143.
    [14] Sauer U, Bailey JE. Estimation of P-to-O ratio in Bacillus subtilis and itsinfluence on maximum riboflavin yield. Biotechnol Bioeng. 1999, 64: 750-754.
    [15] Zamboni N, Mouncey N, Hohmann HP, et al. Reducing maintenance metabolism by metabolic engineering of respiration improves riboflavin production by Bacillus subtilis. Metabol Eng. 2003, 5: 49-55.
    [16] Nyberg PMA. Production of α-amylase by Bacillus subtilis containing a cloned α-amylase gene expression in the laboratory and semi industrial scale. Eur Congr Biotechnol. 1984, 3: 409-413.
    [17] Kuhad RC, Singh A, Eriksson KEL. Microorganisms and enzymes involved in the degradation of plant fiber cell walls. Adv. Biochem. Eng. Biothechnol. 1997, 57: 45-125.
    [18] Haima P, Van Sideren D, Schotting H, et al. Development of a beta- galactosidase alpha-complementation system for molecular cloning in Bacillus subtilis. Gnen. 1990, 86: 63-69.
    [19] Peypoux F, Bonmatin JM, Wallach J. Recent trends in the biotechemistry of surfactin. Appl. Microbiol. Biotechnol. 1999, 51: 553-563.
    [20] Perkins JB, Sloma A, Hermann T, et al. Genetic engineering of Bacillus subtilis for the commercial production of riboflavin. J Ind Microbiol Biotechnol. 1999, 22: 8-18.
    [21] Ogawa Y, Yamaguch F, Yuasa K, et al. Efficient production of poly-(γ-glutamic acid) by Bacillus subtilis (natto) in jar fermenters. Biosci Biotechnol Biochem. 1997, 61: 1684-1687.
    [22] De Wulf P, Vandamme EJ. Production of D-ribose by fermentation. Appl Microbiol Biotechnol. 1997, 48: 141-148.
    [23] Foor F, Brown GM. Purification and properties of guanosine triphosphate cyclohydrolase II. J Biol Chem. 1975, 250: 3545-3551.
    [24] Richter G, Fischer M, Krieger C, et al. Biosynthesis of riboflavin: characterization of the bifunctional deaminase-reductase of E. coli and Bacillus Subtilis. J Bacterial. 1997, 179: 2022-2028.
    [25] Shavlovskii G M. Enzyme activity in the second step of flavingensis of reductase in the yeast P. guilliermondii. Mikrobiologiya. 1981, 50: 1008-1011.
    [26] Volk R, Bacher A. Studies on the 4-Carbon precursor in the biosynthesis of riboflavin. J Biol Chem. 1990, 265: 19479-19485.
    [27] Nakajima K. Possibility of diacetyl and related compounds at 4-carbon compound necessary for the foemation of riboflavin in A. gossyii. Acta Vitaminal Enzymal. 1984, 6: 271-282.
    [28] Schott K, Ladenstein R, Konig A. The lumazine synthase-riboflavin synthase complex of Bacillus subtilis. J Bio Chem. 1990, 265: 12686-12689.
    [29] Plaut GW. Biosynthesis of water-soluble vitamins. Annu Rev Biochem. 1974, 43:899-922.
    [30] 张克旭.代谢控制发酵.北京: 中国轻工业出版社,1998,p 237-259.
    [31] H?mbelin M, Griesser V, Keller T, et al. GTP cyclohydrolase II and 3,4- dihydroxy-2-butanone 4-phosphate synthase are rate-limiting enzymes in riboflavin synthesis of an industrial Bacillus subtilis strain used for riboflavin production. J Ind Microbiol Biotechnol. 1999, 22: 1-7.
    [32] Burrows RB. Presence in E. coli of deaminase and reductase involved in biosynthesis of riboflavin. J Bacteriol. 1978, 136: 657-667.
    [33] Bacher A and Mailander B. Biosynthesis of riboflavin in Bacillus subtilis: Function and genetic control of the riboflavin synthase complex. J Bacteriol 1978, 134: 476-482.
    [34] Perkins JB, Alan S, Janiee G, et al. Bacterial strains which overproduced riboflavin. US patent, 5925538. 1999-07.
    [35] Winkler WC, Nahvi A., Sudarsan N, et al. An mRNA structure that controls gene expression by binding FMN. Proc Natl Acad Sci. 2002, 99: 15908-15913.
    [36] Stepanov G. Production of riboflavin by bacteria. French patent, 2546907. 1984-12.
    [37] Perkins J B, Sloma A, Hermann T, et al. Genetic engineering of Bacillus subtilis for the commercial production of riboflavin. J Ind Microbiol Biotechnol. 1999, 22: 8-18.
    [38] Brent Erickson, Christopher J. Hessler, New Biotech Tool for Cleaner Environment Industrial Biotechnology for Pollution Prevention Resource Conversation and Cost Reduction,2004, Biotechnology Industry Organization, www.bio.org.
    [39] 张星元,王琴.代谢调节理论指导下的核黄素发酵条件.无锡轻工业大学学学报,1997,16:1-6.
    [40] Zhu YB, Chen X, Chen T, et al. Enhancement of riboflavin production by overexpression of acetolactate synthase in a pta mutant of Bacillus subtilis. FEMS Microbiol Lett. 2007, 266: 224-130.
    [41] Li XJ, Chen T, Chen X, et al. Redirection electron flow to high coupling efficiency of terminal oxidase to enhance riboflavin biosynthesis. Appl. Microbiol Biotechnol. 2006, 73:374-383.
    [42] Chen T, Chen X, Wang JY, et al. Effect of riboflavin operon dosage on riboflavin productivity in Bacillus subtilis. Trans Tianjin Univ. 2005, 11: 1-5.
    [43] 陈涛,董文明,李晓静,等.产核黄素枯草芽孢杆菌工程菌的构建及其发酵的初步研究.高校化学工程学报,2007,21: 356-360.
    [44] 赵学明,陈涛,陈洵,等.产核黄素的工程菌株及其构建方法.CN 1891813A,2006.
    [45] 赵学明,陈涛,陈洵,等.产核黄素的工程菌株及其生产核黄素的方法.CN 1891814 A,2006.
    [46] 陈涛.应用代谢工程方法改进枯草芽孢杆菌的核黄素合成.天津大学: 博士后研究工作报告.2006.
    [47] Plackett RL, Burman JP. The design of optimum multifactorial experiments. Biometrika. 1946, 33: 305–325.
    [48] Krishnan S, Prapulla SG, Rajalakshmi D. et al. Screening and selection of media components for lactic acid production using Plackett-Burman design. Bioprocess Engineering. 1998, 19: 61-65.
    [49] Myers RH, Khuri AI, Carter WH. Response surface methodology: 1966-1988. Technometrics. 1989, 31: 137–157.
    [50] Myers RH. Response Surface Methodology-Current Status and Future Direction. J Quality Technol. 1999, 31: 30-44.
    [51] Adinarayana K, Ellaiah P, Srinivasulu B, et al., Response surface methodological approach to optimize the nutritional parameters for neomycin production by Streptomyces marinensis under solid-state fermentation, Process Biochem. 2003, 38: 1565-1572.
    [52] Hounsa CG, Aubry JM, Dubourguier HC. Application of factorial and doehlert designs for optimization of pectate lyase production by a recombinant Escherichia coli. Appl Microbiol Biotechnol. 1996, 45: 764-770.
    [53] Rao KJ, Kim CH, Rhee SK, Statistical optimization of medimun for the production of recombinant hirudin from Saccharomyces cerevisiae using response surface methodology. Process Biochemistry. 2000, 35: 639-647.
    [54] Rajiv V, Pranav V, Chhatpar HS. Statistical optimization of medium components for the production of chitinase by Alcaligenes xylosoxydans. Enzy Microbiol Technol. 2003, 33: 92-96.
    [55] Thibault J, Van Breusegem VC, Cheruy A. On-line prediction of fermentation variables using neural networks. Biotechnol Bioeng. 1990, 43: 1041-1048.
    [56] Najjar YM, Basheer LA, Hajmeer MH. Computational neural networks for predictive microbiology: I. Methodology. Int J Food Microbiol. 1997, 34: 27-49.
    [57] Hajmeer MH, Basheer IA, A hybrid Bayesian-neural network approach for probabilistic modeling of bacterial growth/no growth interface. Int J Food Microbiol. 2003, 82: 233-243.
    [58] Geeraerd AH, Herremans CH, Cennens C, et al. Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products. Int J Food Microbiol. 1998, 44: 49-68.
    [59] Kennedy M, Krouse D. Strategies for improving fermentation medium performance: a review. J Ind Microbiol Biotechnol. 1999, 23: 456-475.
    [60] Weuster-Botz D. Experimental design for fermentation media development: statistical design or global random search. J Biosci Bioeng. 2000, 90: 473-483.
    [61] Liu YQ, Xie XS. Robust stability of uncertain singular systems with time delay. J South China University Technol. 1996, 24: 44-50.
    [62] Xie S L, Xie LH, Robust dissipative control for linear systems with dissipative uncertainty and nonlinear perturbation. Systems & Control Letters. 1997, 29: 255-268.
    [63] 周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社,1999.
    [64] 阎平凡,张长水.人工神经网络与模拟进化计算.北京:清华大学出版社, 1999.
    [65] De Souza C E, Li X. Delay-dependent robust H control of uncertain linear state-delayed systems. Automatica. 1999, 35: 1313-1321.
    [66] 陈霁威,乐慧丰,黄道,等.基于神经网络和遗传算法的在线优化软件设计与实现.华东理工大学学报,2000,28:419-422.
    [67] Patil SV, Jayaraman VK, Kulkarni BD. Optimization of media by evolutionary algorithms for production of polyols. Appl Biochem Biotechnol. 2002, 102: 119-128.
    [68] Baishan F, Hongwen C, Xiaolan X, et al. Using genetic algorithms coupling neural networks in a study of xylitol production: medium optimization. Proc Biochem. 2003, 38: 979-985.
    [69] Marteijn RCL, Jurrius O, Dhont J, et al. Optimization of a feed culture medium for fed-batch culture of insect cells using a genetic algorithm. Biotechnol Bioeng. 2003, 81: 269-278.
    [70] Kovárová-Kovar K, Gehlen S, Kunze A, et al. Application of model-predictive control based on artificial neural networks to optimize the fed-batch process for riboflavin production. J Biotechnol. 2000, 79: 39-52.
    [71] Yamane T, Shimizu S. Fed-batch techniques in microbial processes. Adv Biochem Eng. 1984, 30: 147-194.
    [72] O’Connor, Sanchez-Riera F, Cooney CL. Design and evaluation of control strategies for high cell density fermentation. Biotechnol Bioeng. 1992, 39: 293-304.
    [73] Yee L, Blanch HW. Recombinant protein expression in high cell density fed-batch cultures of Escherichia coli. Biotechnol.1992, 10: 1550-1556.
    [74] Lee AY. High cell-density culture of Escherichia coli. Trends Biotechnol. 1996, 14: 98-105.
    [75] Park YS, Kai K, Iijima S, Kobayashi T. Enhanced β-galactosidase production by high cell-density culture of recombinant Bacillus subtilis with glucose concentration control. Biotechnol Bioeng. 1992, 40: 686-696.
    [76] Cayuela C, Kai K, Park YS, et al. Insectiside production by recombinant Bacillus subtilis 1A96 in fedbatch culture with control of glucose concentration. J Ferment Bioeng. 1993, 75: 383-386.
    [77] Vuolanto A, Weymarn NV, Kerovuo J, et al. Phytase production by high cell density culture of recombinant Bacillus subtilis. Biotechnol Lett. 2001, 23: 761-766.
    [78] Park YS, Shi ZP, Shiba S, et al. Application of fuzzy reasoning to control of glucose and ethanol concentrations in baker's yeast culture. Appl Microbiol Biotechnol. 1993, 38: 649-655.
    [79] Kitsuta Y, Kishimoto M. Fuzzy supervisory control of glutamic acid production. Biotechnol Bioeng. 1994, 44: 87-94.
    [80] Konstantinov KB, Yoshida T. Physiological state control of fermentation processes. Biotechnol Bioeng.1989, 33: 1145-1156.
    [81] Horiuchi J, Hiraga K. Industrial application of fuzzy control to large-scale recombinant vitamin B2 production. J Biosci Bioeng. 1999, 87: 365-371.
    [82] Bailey JE. Towards a science of metabolic engineering. Science. 1991, 252: 1668-1674.
    [83] Stephanopoulos G, Vallino JJ. Network rigidity and metabolic engineering in metabolite overproduction. Science. 1991, 252: 1675-1681.
    [84] Nielsen J. Metabolic Engineering. App1 Microbio1 Biotechno1. 200l, 55: 263-283.
    [85] Lee SY, Hong SH, Moon SY. In silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example. Genome inform. 2002, 13: 214-223.
    [86] Zamboni N, Fischer E, Muffler A, et al. Transient expression and flux changes during a shift from high to low riboflavin production in continuous cultures of Bacillus subtilis. Biotechnol Bioeng. 2005, 89: 219-232.
    [87] Franzén CJ. Metabolic flux analysis of RQ-controlled microaerobic ethanol production by Saccharomyces cerevisiae. Yeast. 2003, 20: 117-132.
    [88] 白冬梅,付卫明,赵学明,等.代谢通量分析优化米根霉R1021发酵生产 L(+)-乳酸过程.无锡轻工大学学报,2002,21:554-558.
    [89] Bai DM, Zhao XM, Li XG, et al. Strain improvement and metabolic flux analysis in the wild-type and a mutant Lactobacillus lactis atrain for L(+)-lactic acid production. Biotechnol Bioeng. 2004, 88: 681-689.
    [90] Sauer U, Hatzimanikatis V, Bailey JE, et al. Metabolic fluxes in riboflavin -producing Bacillus subtilis. Nat Biotechnol. 1997, 15: 448-452.
    [91] Sauer U, Bailey JE. Estimation of P-to-O ration in bacillus subtilis and its influence on maximum riboflavin yield. Biotechnol Bioeng. 1999, 64: 750-754.
    [92] Sauer U, Hatzimanikatis V, Hohmann HP, et al. Physiology and metabolic fluxes of wild-type and riboflavin-producing Bacillus subtilis. Appl Environ Microbiol. 1996, 62: 3687-3696.
    [93] Sauer U, Cameron DC, Baily JE. Metabolic capacity of Bacillus subtilis for the production of purine nucleosides, riboflavin, and folic acid. Biotechnol Bioeng. 1998, 59:227-238.
    [94] Dauner M, Sonderegger M, Hochuli M, et al. Intracellular carbon fluxes in riboflavin-producing Bacillus subtilis during growth on two-carbon substrate mixtures. Appl. Environ. Microbiol. 2002, 68: 1760-1771.
    [95] 马红武.由发酵实验数据和基因组信息基于计量关系分析代谢网络.[博士学位论文],天津:天津大学,2001.
    [96] 陈涛,王靖宇,周士奇,等.基于基因组重排的代谢工程用于改进产核黄素Bacillus subtilis的性能.化工学报,2004,55:1842-1848.
    [97] Doelle HW, Ewings KN, Hollywood NW. Regulation of glucose metabolism in bacterial systems. Adv Biochem Eng. 1982, 23: 1-35.
    [98] Shuler ML, Kargi F. Bioprocess engineering: basic concepts. Prentice-Hall Inc., New Jersey.1992.
    [99] Jense KF, Pederson S. Metabolic growth rate control in Escherchia coli may be a consequence of subsaturation of macromolecular apparatus with substrates and catalytic components. Microbiol Rev. 1990, 54: 89-100.
    [100] Snay J, Jeong JW, Ataai MM. Effects of growth conditions on carbon utilization and organic by-product formation in B. subtilis. Biotechnol Prog. 1989, 5: 63-69.
    [101] Amanullah A, McFarlane CM, Emery AN, et al. Scale-down model to simulate spatial pH variations in large-scale bioreactors. Biotechnol Bioeng. 2001, 73: 390-399.
    [102] Taylor J, King RD, Altmann T, et al. Application of metabolomics to plant genotype discrimination using statistics and machine learning. Bioinformatics. 2002, l8: 241-248.
    [103] Goodacre R. Metabolomics – the way forward. Metabolomics. 2005, 1: 1-2.
    [104] Fiehn O. Metabolomics-the link between genotypes and phenotypes. Plant Mol Biol. 2002, 48: 155-171.
    [105] Zywicki B, Catchpole G, Draper J, et al. Comparison of rapid liquid chromatography-electrospray ionization-tandem mass spectrometry methods for determination of glycoalkaloids in transgenic field-grown potatoes. Anal Biochem. 2005, 336: 178-186
    [106] Bolling C, Fiehn O. Metabolite profiling of Chlamydomonas reinhardtii under nutrient deprivation. Plant Physiol. 2005, 139: 1995-2005.
    [107] Fiehn O. Metabolite profiling in Arabidopsis. Methods Mol Biol. 2006, 323:439-447
    [108] Nicholson LK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to patho physiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999, 29: 1181-1189.
    [109] Nicholson JK, Bollard ME. Lindon JC, et al. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov, 2002, 1: l53-l62.
    [110] Beckwith-Hall BM, Brindle JT. Barton RH, et al. Application of orthogonal signal correction to minimise the effects of physical and biological variation in high resolution 1H NMR spectra of biofluids. Analyst, 2002, 127: l283-128.
    [111] Oliver SG. Guilt-by-association goes global. Nature. 2000, 403: 601-603.
    [112] Oliver SG, Winson MK, Kell DB, et al. Systematic functional analysis of the yeast genome. Trends Biotechnol. 1998, 16: 373-378.
    [113] Fiehn O. Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol. 2002, 48: 155-171.
    [114] Capote FP, Jimenez JR, Granados JM, et al. Identification and determination of fat-soluble vitamins and metabolites in human serum by liquid chromatography/triple quadrupole mass spectrometry with multiple reaction monitoring. Rapid Commun Mass Spectrom. 2007, 21: 1745-1754.
    [115] Horning EC, Horning MG. Human metabolic profiles obtained by GC and GC/MS. J Chromatogr Sci. 1971, 9: 129-140.
    [116] Messerli G, Partovi Nia V, Trevisan M, et al. Rapid Classification of Phenotypic Mutants of Arabidopsis via Metabolite Fingerprinting. Plant Physiol. 2007, 143: 1484-1492.
    [117] Dettmer K, Aronov PA, Hammock BD, et al. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007, 26: 51-78.
    [118] Kell DB, Brown M, Davey HM, et al. Metabolic footprinting and systems biology: the medium is the message. Nat Rev Microbiol. 2005, 3: 557-65.
    [119] Allen J, Davey HM, Broadhurst D, et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nature Biotechnol. 2003, 21: 692-696.
    [120] Jewett MC, Hofmann G, Nielsen J. Fungal metabolite analysis in genomics and phenomics. Curr Opin Biotechnol. 2006, 17: 191-197.
    [121] Bino RJ, Hall RD, Fiehn O, et al. Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 2004, 9: 418-425.
    [122] Mashego MR, Rumbold K, De Mey M, et al. Microbial metabolomics: past, present and future methodologies. Biotechnol Lett. 2007, 29: 1-16.
    [123] Wang QZ, Wu CY, Chen T, et al. Integrating metabolomics into a systemsbiology framework to exploit metabolic complexity: strategies and applications in microorganisms. Appl Microbiol Biotechnol. 2006, 70: 151-61.
    [124] 杨军,宋硕林,Jose CP,等.代谢组学及其应用.生物工程学报,2005,21:1-5.
    [125] 尹恒,李曙光,白雪芳,等.植物代谢组学的研究方法及其应用.植物学通报,2005,22:532-534.
    [126] Chassagnole C, Noisommit-Rizzi N, Schmid JW, et al. Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol Bioeng. 2002, 79: 53-73.
    [127] Mashego MR, van Gulik WM, Vinke JL, et al. Critical evaluation of sampling techniques for residual glucose determination in carbon-limited chemostat culture of Saccharomyces cerevisiae. Biotechnol Bioeng. 2003, 83: 395-399.
    [128] Weibel KE, Mor JR, Fiechter A. Rapid sampling of yeast cells and automated assays of adenylate, citrate, pyruvate and glucose-6-phosphate pools. Anal Biochem. 1974, 58: 208-216.
    [129] Visser D, van Zuylen GA, van Dam JC, et al. Rapid sampling for analysis of in vivo kinetics using the BioScope: a system for continuous pulse experiments. Biotechnol Bioeng. 2002, 79: 674-681.
    [130] Buziol S, Bashir I, Baumeister A, et al. New bioreactor-coupled rapid stopped-flow sampling technique for measurements of metabolite dynamics on a subsecond time scale. Biotechnol Bioeng. 2002, 80: 632-636.
    [131] Larsson G, Tornkvist M. Rapid sampling, cell inactivation and evaluation of low extracellular glucose concentrations during fed-batch cultivation. J Biotechnol. 1996, 49: 69-82.
    [132] Hiller J, Franco-Lara E, Papaioannou V, et al. Fast sampling and quenching procedures for microbial metabolic profiling. Biotechnol Lett. DOI:10.1007 /s10529-007-9383-9.
    [133] Bundy JG, Spurgeon DJ, Svendsen C, et al. Earthworm species of the genus Eisenia can be phenotypically differentiated by metabolic profiling. FEBS Let 2002, 521: 115-120.
    [134] Villas-Boas SG, Mas S, Akesson M, et al. Mass spectrometry in metabolome analysis. Mass Spectrom Rev. 2005, 24: 613-646.
    [135] Jensen NB, Jokumsen KV, Villadsen J. Determination of the phosphorylated sugars of the Embden-Meyerhoff-Parnas pathway in Lactococcus lactis using a fast sampling technique and solid phase extraction. Biotechnol Bioeng. 1999, 63: 356-362.
    [136] Mashego MR, Wu L, Van Dam JC, et al. MIRACLE: mass isotopomer ratio analysis of U–13C-labeled extracts. A new method for accurate quantification ofchanges in concentrations of intracellular metabolites. Biotechnol Bioeng. 2004, 85: 620-628.
    [137] Al Zaid Siddiquee K, Arauzo-Bravo MJ, Shimizu K. Metabolic flux analysis of pykF gene knockout Escherichia coli based on 13C-labeling experiments together with measurements of enzyme activities and intracellular metabolite concentrations. Appl Microbiol Biotechnol. 2004, 63: 407-417.
    [138] Wittmann C, Kro¨mer JO, Kiefer P, et al. Impact of the cold shock phenomenon on quantification of intracellular metabolites in bacteria. Anal Biochem. 2004, 327: 135-139.
    [139] Ruijter GJG, Visser J. Determination of intermediary metabolites in Aspergillus niger. J Microbiol Methods. 1996, 25: 295-302.
    [140] de Koning W, van Dam K. A method for the determination of changes of glycolytic metabolites in yeast on a subsecond time scale using extraction at neutral pH. Anal Biochem. 1992, 204: 118-123.
    [141] Zaldivar J, Borges A, Johansson B, et al. Fermentation performance and intracellular metabolite patterns in laboratory and industrial xylose-fermenting Saccharomyces cerevisiae. Appl Microbiol Biotechnol. 2002, 59: 436-442.
    [142] Bochner BR, Ames BN. Complete analysis of cellular nucleotides by two-dimensional thin layer chromatography. J Biol Chem. 1982, 257: 759–769.
    [143] Mengin-Lecreulx D, Flouret B, van HJ. Pool levels of UDP N-acetyl glucosamine and UDP N-acetylglucosamine-enolpyruvate in Escherichia coli and correlation with peptidoglycan synthesis. J. Bacteriol. 1983, 154: 1284-1290.
    [144] Tempest DW, Meers JL, Brown CM. Influence of environment on the content and composition of microbial free amino acid pools. J Gen Microbiol. 1970, 64: 171-185.
    [145] Gonzalez B, Francois J, Renaud M. A rapid and reliable method for metabolite extraction in yeast using boiling buffered ethanol. Yeast. 1997, 13: 1347-1355.
    [146] Maharjan RP, Ferenci T. Global metabolite analysis: The influence of extraction methodology on metabolome profiles of Escherichia coli. Anal Biochem. 2003, 313: 145-154.
    [147] 王庆昭,杨育谛,陈洵,等.大肠杆菌代谢物组测定中抽提方法的比较分析.分析化学,2006,34:1295-1298.
    [148] Hiller J, Franco-Lara E, Weuster-Botz D. Metabolic profiling of Escherichia coli cultivations: evaluation of extraction and metabolite analysis procedures. Biotechnol Lett. DOI:10.1007/s10529-007-9384-8.
    [149] Duran AL, Yang J, Wang L, et al. Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics. 2003,19: 2283-2293.
    [150] Tikunov Y, Lommen A, de Vos CH, et al. A novel approach for nontargeted dataanalysis for metabolomics. Large-scale profiling of tomato fruit volatiles. Plant Physiol. 2005, 139: 1125-1137.
    [151] Katajamaa M, Oresic M. Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics. 2005, 6: 179-191.
    [152] Smith CA, Want EJ, O'Maille G, et al. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006, 78: 779-787.
    [153] Broeckling CD, Reddy IR, Duran AL, et al. MET-IDEA: Data. extraction tool for mass spectrometry-based metabolomics. Anal Chem. 2006, 78: 4334-4341.
    [154] Goodacre R, Vaidyanathan S, Dunn WB, et al. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends in Biotechnology. 2004, 22: 245-252.
    [155] Sumnera LW, Mendesb P, Dixona AR. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry. 2003, 62: 817-836.
    [156] Lindon JC,Holmes E,Nicholson JK. Pattern recognition methods and applications in biomedical magnetic resonance. Progress in Nuclear Magnetic Resonance Spectroscopy. 2001, 39: 1-40.
    [157] Brindle JT, Antti H, Holmes E, et al. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomies. Nat Med, 2002, 8: l439–l444.
    [158] Lutz U, Lutz RW, Lutz WK. Metabolic profiling of glucuronides in human urine by LC-MS/MS and partial least-squares discriminant analysis for classification and prediction of gender. Anal Chem. 2006, 78: 4564-4571.
    [159] Lindon JC, Nicholson JK, Holmes E, et al. Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol Appl Pharmacol. 2003, 187: 137-146.
    [160] Roessner U, Wagner C, Kopka J, et al. Simultaneous analysis of metabolites in potato tuber by gas chromatography–mass spectrometry. Plant J. 2000, 23: 131-142.
    [161] Fiehn O, Kopka J, Dormann P, et al. Metabolite profiling for plant functional genomics. Nat Biotechnol. 2000, 18:1157-1161.
    [162] 董良广,何桂珍.代谢组学在临床营养研究中的应用.中国临床营养杂志,2006,4:238-242.
    [163] 许国旺,杨军.代谢组学及其研究进展.色谱,2003,21:316-320.
    [164] Grivet JP, Delort AM, Portals JC. NMR and microbiology: from physiology to metabolomies. Biochimie. 2003, 85: 823-840.
    [165] Raamsdonk LM, Teusink B, Broadhurst D, et al. A functional genomics strategythat uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol. 2001, 19: 45-50.
    [166] Boersma MG, Solyanikova IP, Van Berkel WJ, et al. 19F NMR metabolomics for the elucidation of microbial degradation pathways of fluorophenols. J Ind Microbiol Biotechnol. 2001, 26: 22-34.
    [167] Bundy JG, Papp B, Harmston R, et al. Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling. Genome Res. 2007, 17: 510-519.
    [168] 朱航,唐惠儒,张许,等.基于 NMR 的代谢组学研究.化学通报,2006,69:1-9.
    [169] Ward JL, Harris C, Lewis J, Beale MH. Assessment of 1H NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arbidopsis thaliana. Phaytochemistry. 2003, 62: 949-957.
    [170] Castrillo JI, Hayes A, Mohammed S, et al. An optimized protocol for metabolome analysis in yeast using direct infusion electrospray mass spectrometry. Phytochemistry. 2003, 62: 929-937.
    [171] Smedsgaard J, Nielsen J. Metabolite profiling of fungi and yeast: from phenotype to metabolome by MS and informatics. J Exp Bot. 2005, 56: 273-286.
    [172] Villas-B?as SG, Delicado DG, Akesson M, et al. Simultaneous analysis of amino and nonamino organic acids as methyl chloroformate derivatives using gas chromatography–mass spectrometry. Anal Biochem. 2003, 322: 134-138.
    [173] Villas-B?as SG, Moxley JF, Akesson M, , et al. High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. Biochem J. 2005, 388: 669-677.
    [174] Koek MM, Muilwijk B, van der Werf MJ, et al. Microbial metabolomics with gas chromatography/mass spectrometry. Anal Chem. 2006, 78: 1272-1281.
    [175] Buchholz A, Takors R, Wandrey C. Quantification of intracellular metabolites in Escherichia coli K12 using liquid chromatographicelectrospray ionization tandem mass spectrometric techniques. Anal Biochem. 2001, 295: 129-137.
    [176] Buchholz A, Hurlebaus J, Wandrey C, et al. Metabolomics: Quantification of intracellular metabolite dynamics. Biomol Eng. 2002, 19: 5-15.
    [177] Mashego MR, Jansen ML, Vinke JL, et al. Changes in the metabolome of Saccharomyces cerevisiae associated with evolution in aerobic glucose-limited chemostats. FEMS Yeast Res. 2005, 5: 419-430.
    [178] Coulier L, Bas R, Jespersen S, et al. Simultaneous quantitative analysis of metabolites using ion-pair liquid chromatography-electrospray ionization mass spectrometry. Anal Chem. 2006, 78:6573-6582.
    [179] Dalluge JJ, Smith S, Sanchez-Riera F, et al. Potential of fermentation profilingvia rapid measurement of amino acid metabolism by liquid chromatography–tandem mass spectrometry. J Chromatogr. 2004, 1043: 3-7.
    [180] Shi G. Application of co-eluting structural analog internal standards for expanded linear dynamic range in liquid chromatography/electrospray mass spectrometry. Rapid Commun Mass Spectrom. 2002, 17: 202-206.
    [181] Wu L, Mashego MR, van Dam JC, et al. Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards. Anal Biochem. 2005, 336: 164-171.
    [182] Tolstikov RN, Fiehn O. Analysis of highly polar compounds of plant origin: combination of hydrophi1ic interaction chromatography and electrospray ion trap mass spectroscopy. Annals of Biochemistry. 2002, 301: 298-307.
    [183] Dunn WB, Ellis DI. Metabolomics: Current analytical platforms and methodologies. Trends in Analytical Chemistry. 2005, 24: 285-294.
    [184] Wilson ID, Plumb R, Granger J, et al. HPLC-MS-based methods for the study of metabonomics. J Chromatogr B. 2005, 817: 67-76.
    [185] Plumb RS, Granger JH, Stumpf CL, et al. A rapid screening approach to metabonomics using UPLC and oa-TOF mass spectrometry: application to age, gender and diurnal variation in normal/Zucker obese rats and black, white and nude mice. Analyst. 2005, 130: 844-849.
    [186] Wilson ID, Nicholson JK, Castro-Perez J, et al. High resolution "Ultra performance" liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteom Res. 2005, 4: 591-598.
    [187] Nordstrom A, O'Maille G, Qin C, et al. Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: Quantitative analysis of endogenous and exogenous metabolites in human serum. Anal Chem. 2006, 78: 3289-3295.
    [188] O'Connor D, Mortishire-Smith R. High-throughput bioanalysis with simultaneous acquisition of metabolic route data using ultra performance liquid chromatography coupled with time-of-flight mass spectrometry. Anal Bioanal Chem. 2006, 385:114-121.
    [189] Ramautar R, Demirci A, de Jong GJ. Capillary electrophoresis in metabolomics. Trends Analyt Chem. 2006, 25: 455-466.
    [190] Soga T, Ueno Y, Naraoka H, et al. Simultaneous determination of anionic intermediates for Bacillus subtilis metabolic pathways by capillary electrophoresis electrospray ionization mass spectrometry. Anal Chem. 2002, 74: 2233-2239.
    [191] Soga T, Ohashi Y, Ueno Y, et al. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res. 2003, 2: 488-494.
    [192] Kaderbhai NN, Broadhurst DI, Ellis DI, et al. Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp Funct Genomics. 2003, 4: 376-391.
    [193] Villas-B?as SG, Noel S, Lane GA, et al. Extracellular metabolomics: A metabolic footprinting approach to assess fiber degradation in complex media. Anal Biochem. 2006, 349: 297-305.
    [194] Mas S, Villas-Boas SG, Hansen ME, et al. A comparison of direct infusion MS and GC-MS for metabolic footprinting of yeast mutants. Biotechnol Bioeng. 2007, 96: 1014-1022.
    [195] Weeks ME, Sinclair J, Butt A, et al. A parallel proteomic and metabolomic analysis of the hydrogen peroxide- and Sty1p-dependent stress response in Schizosaccharomyces pombe. Proteomics. 2006, 6: 2772-2796.
    [196] Hirai MY, Klein M, Fujikawa Y, et al. Elucidation of gene-to-gene and metabolite-to-gene networks in arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem. 2005, 280: 25590-25595.
    [197] Hirai MY, Yano M, Goodenowe DB, et al. Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2004, 101: 9949-9950.
    [198] Ezeji TC, Qureshi N, Blaschek HP. Acetone butanol ethanol (ABE) production from concentrated substrate: reduction in substrate inhibition by fed-batch technique and product inhibition by gas stripping. Appl Microbiol Biotechnol. 2004, 63: 653-658.
    [199] Kalingan AK, Liao CM. Influence of type and concentration of flavinogenic factors on production of riboflavin by Eremothecium ashbyii NRRL 1363, Bioresour Technol. 2002, 82: 219-224.
    [200] Ertrk E, Erkmen O, Oner MD. Effects of various supplements on riboflavin production by Ashbya gossypii in Whey. Turk J Eng Environ Sci. 1998, 22: 371-376.
    [201] Plackett RL, Burman JP. The design of optimum multi-factorial experiments, Biometrika. 1946, 33: 305-325.
    [202] Abdel-Fattsh YR, Saeed HM, Gohar YM. Improved production of Pseudomonas aeruginosa uricase by optimization of process parameters through statistical experimental designs. Process Biochem. 2005, 40: 1707-1714.
    [203] Rathi P, Goswami VK, Sahai V, et al. Statistical medium optimization and production of hyperthermostable lipase from Burkholderia Cepacra in bioreactor. J Appl Microbiol. 2002, 93: 930-936.
    [204] Coninck JD, Bouquelet S, Dumortier V, et al. Industrial media and fermentationprocess for improved growth and protease production by Tetrahymena thermophila BIII. J Ind Microbiol. Biotechnol. 2000, 24: 285-290.
    [205] Wu QL, Chen T, Gan Y, et al. Optimization of riboflavin production by recombinant Bacillus subtilis RH44 using statistical designs. Appl Microbiol Biotechnol. DOI 10.1007/s00253-007-1049-y.
    [206] Montgomery DC. Design and analysis of experiments, 4th ed. New York: John Wiley and Sons. 1997.
    [207] Marks E. Profile analysis in a two-way classification problem. Multivar Behav Res. 1968, 3: 95-106.
    [208] Goel A, Lee J, Domach MM, et al. Suppressed acid formation by cofeeding glucose and citrate in Bacillus cultures: emergence of pyruvate kinase as a potential metabolic engineering site. Biotechnol Prog. 1995, 11: 380-386.
    [209] Lee J, Goel A, Ataai MM, et al. Supply-side analysis of growth of Bacillus subtilis on glucose-citrate medium: feasible network alternatives and yield optimality. Appl Environ Microbiol. 1997, 63: 710-718.
    [210] Hajmeer M, Basheer I, Najjar Y. Computational neural networks for predictive microbiology: II. Application to microbe growth. Int J Food Microbiol. 1997, 34: 51-66.
    [211] Lou W, Nakai S, Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH and water activity. Food Res Int. 2001, 34: 573-579.
    [212] Takayama K, Fujikawa M, Obata Y, et al. Neural network based optimization of drug formulations. Adv Drug Deliver Rev. 2003, 55: 1217-1231.
    [213] Suna Y, Penga Y, Chenb Y, et al. Application of artificial neural networks in the design of controlled release drug delivery systems. Adv Drug Deliver Rev. 2003, 55: 1201-1215.
    [214] Dutta JR, Dutta PK, Banerjee R. Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models. Process Biochem. 2004, 39: 2193-2198.
    [215] Lopes JA, Menezes JC. Multivariate monitoring of fermentation processes with non-linear modelling methods. Analytica Chimica Acta. 2004, 515: 101-108.
    [216] David G. Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company Inc. 1989.
    [217] Fang BS, Chen HW, Xie XL, et al. Using genetic algorithms coupling neural networks in a study of xylitol production: medium optimization. Process Biochem. 2003, 38: 979-985.
    [218] Chen LZ, Nguang SK, Chen XD, et al. Modelling and optimization of fed-batchfermentation processes using dynamic neural networks and genetic algorithms. Biochem Eng J. 2004, 22:51-61.
    [219] Soria MA, Gonzalez Funes JL, Garcia AF. A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening. J Ind Microbiol Biotechnol. 2004, 31: 469-474.
    [220] Zhang Q, Reid JF, Litchfield JB, et al. A prototype neural network supervised control system for Bacillus thuringiensis fermentations. Biotechnol Bioeng. 1994, 43: 483-489.
    [221] Esnoz A, Periago PM, Conesa R, et al. Application of artificial neural networks to describe the combined effect of pH and NaCl on the heat resistance of Bacillus stearothermophilus. Int J Food Microbiol. 2006, 106: 153-158.
    [222] Foresee FD, Hagan MT. Gauss–Newton approximation to Bayesian regularization. Proceedings of the 1997 International Joint Conference on Neural Networks. 1997, p 1930–1935.
    [223] Hagan MT, Menhaj M. Training feedforward networks with the marquardt algorithm, IEEE Trans. Neural Netw. 1994, 5: 989-993.
    [224] MacKay D. Bayesian interpolation. Neural Comput. 1992, 4: 415-447.
    [225] Periago PM, Fernández PS, Salmerón MC, et al. Predictive model to describe the combined effect of pH and NaCl on apparent heat resistance of Bacillus stearothermophilus. Int J Food Microbiol. 1998, 44: 21-30.
    [226] Danaher S, Datta S, Waddle I, et al. Erosion modelling using Bayesian regulated artificial neural networks. Wear. 2004, 256: 879-888.
    [227] Ozcelik B, Erzurumlu T. Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. J Mater Proc Technol. 2006, 171: 437-445.
    [228] ?alik P, Bilir E, ?alika G, et al. Bioreactor operation parameters as tools for metabolic regulations in fermentation processes: influence of pH conditions. Chem Eng Sci. 2003, 58: 759-766.
    [229] Abadias M, Teixidó N, Usall J, et al. Optimization of growth conditions of the postharvest biocontrol agent Candida sake CPA-1 in a lab-scale fermenter. J Appl Microbiol. 2003, 95: 301-309.
    [230] Bretz K, Ilijevic S, Gruneberg M, et al. Biomass recycling from a riboflavin cultivation with B. subtilis: lysis, extract production and testing as substrate in riboflavin cultivation. Biotechnol Bioeng. 2006, 95: 1023-1031.
    [231] Hervás C, Zurera G, García RM, et al. Optimization of computational neural network for its application to the prediction of microbial growth in foods. Food Sci and Technol Int. 2001, 7: 159-163.
    [232] 陆文清,章克昌.核黄素产生菌 T30 的补料发酵.无锡轻工大学学报:食品与生物技术,1999,18:1-5.
    [233] 陆文清,章克昌.核黄素产生菌的补料发酵.无锡轻工大学学报:食品与生物技术,2000,19:240-243.
    [234] Zheng MY, Du GC, Chen J. pH control strategy of batch microbial transglutaminase production with Streptoverticillium mobaraense. Enzyme Microb Technol. 2002, 31: 477-481.
    [235] Sánchez S, Bravo V, Castro E, et al. The influence of pH and aeration rate on the fermentation of D-xylose by Candida shehatae. Enzyme Microb Technol. 1997, 21: 355-360.
    [236] Zhu Y, Yang ST. Effect of pH on metabolic pathway shift in fermentation of xylose by Clostridium tyrobutyricum. J Biotechnol. 2004, 110: 143-157.
    [237] ?alik P, Bilir E, ?alika G, et al. Influence of pH conditions on metabolic regulations in serine alkaline protease production by Bacillus licheniformis. Enzyme Microb Technol. 2002, 31: 685-697.
    [238] ?alik P, Bilir E, ?alika G, et al. Bioreactor operation parameters as tools for metabolic regulations in fermentation processes: influence of pH conditions. Chem Eng Sci. 2003, 58: 759-766.
    [239] Knorr B. Scale-down and parallel operation of a riboflavin production process with Bacillus subtilis. PhD-thesis. Technical University of Munich, 2005.
    [240] Riscaldati E, Moresi M, Federici F, et al. Effect of pH and stirring rate on itaconate production by Aspergillus terreus. J Bacteriol. 2000, 83: 219-230.
    [241] Hu ZC, Zheng YG, Wang Z, et al. pH control strategy in astaxanthin fermentation bioprocess by Xanthophyllomyces dendrorhous. Enzyme Microb Technol. 2006, 39: 586-590.
    [242] Liu SC, Chun Li, Fang XC, et al. Optimal pH control strategy for high-level production of long-chain α,ω-dicarboxylic acid by Candida tropicalis. Enzyme Microb Technol. 2004, 34:73-77.
    [243] Lee, KH, Park YH, Han JK, et al. Microorganism for producing riboflavin and method for producing riboflavin using the same. International application published under the patent cooperation treaty (PCT). WO 2004/050862 A1.
    [244] Bacher A, Baur R, Eggers U, et al. Riboflavin synthases of Bacillus subtilis. Purification and properties. J Biol Chem. 1980, 255:632-637.
    [245] Zhu Y, Yang ST. Adaptation of Clostridium tyrobutyricum for enhanced tolerance to butyric acid in a fibrous-bed bioreactor. Biotechnol Prog. 2003, 19: 365-372.
    [246] Stearn EW, Stearn AE. The effect of the reaction of the medium on the characteristics of bacteria II. behavior of Bacillus subtilis. J Bacteriol. 1933, 26:37-55.
    [247] ?al?k P, ?al?k G, ?zdamar TH. Bioprocess development for serine alkaline protease production: a review. Rev Chem Eng. 2001;17(Suppl ):1–62.
    [248] Taber HW. Respiratory chains. In: Sonenshein AL, Hoch JA, Lorich R, editors. Bacillus subtilis and other gram-positive bacteria: biochemistry, physiology, and molecular genetics. Washington, DC: American Society for Microbiology, 1993. p 199–212.
    [249] Nielsen J, Villadsen J. Bioreaction engineering principles. New York: Plenum Press, 1994. p 55–83.
    [250] Harzer G, Rokos H, Otto MK, et al. Biosynthesis of riboflavin. 6,7-Dimethyl -8-ribityllumazine 5'-phosphate is not a substrate for riboflavin synthase. Biochim Biophys Acta. 1978, 540:48-54.
    [251] Kis K, Bacher A. Substrate channeling in the lumazine synthase/riboflavin synthase complex of Bacillus subtilis. J Biol Chem. 1995, 14, 270:16788-16795.
    [252] Harrison DEF, Pirt SJ. The influence of dissolved oxygen concentration on the respiration and glucose metabolism of Klebsiella aerogenes during growth. J Gen Microbiol. 1967, 46: 193–198.
    [253] Tang IC, Okos MR, Yang ST. Effects of pH and acetic acid on homoacetic fermentation of lactate by Clostridium formicoaceticum. Biotechnol Bioeng. 1989, 34:1063-1074.
    [254] Aristidou AA, Bennett GN, San KY. Modification of the central metabolic pathways Escherichia coli to reduce the acetate accumulation by the heterologous expression of the bacillus subtilis acetolactate synthase gene. Biotechnol Bioeng. 1994, 44: 944–951.
    [255] Maria CR, Najimudin N, Leslie RW, et al. Regulation of the Bacillus sibtilis alsD and alsR genes involved in post-exponential-phase production of acetoin. J Bacteriol. 1993, 175: 3863–3874.
    [256] Platteeuw C, Hugenholtz J, Starrenburg M, et al. Metabolic engineering of Lactococcus lactis: influence of the overproduction of α-cetolactate synthase in strains deficient in lactate dehydrogenase as a function of culture conditions. Appl Environ Microbiol. 1995, 61:3967–3971.
    [257] L?ken JP, St?rmer FC. Acetolactate decarboxylase from Aerobacter aerogenes: purification and properties. Eur J Biochem. 1970,14:133–137.
    [258] Larsen SH, Stormer FC. Diacetyl (acetoin) reductase from Aerobacter aerogenes: kinetic mechanism and regulation by acetate of the reversible reduction of acetoin to 2,3-butanediol. Eur J Biochem. 1973,34: 100–106.
    [259] Speck EL, Freese E. Metabolite secretion in Bacillus subtilis. J Gen Microbiol. 1973, 78: 261– 275.
    [260] Lee KH, Park YH, Han JK, et al. Microorganism for producing riboflavin and method for producing riboflavin using the same. US patent, 7166456. 2007.
    [261] Edwards JS, Palsson BO. How will bioinformatics influence metabolic engineering. Biotechno1 Bioeng. l998, 58:l62- l69.
    [262] Varma A, Palsson BO. Biochemical production capabilities of Echerichia coli. Biotechnol Bioeng. 1993,42:59-73.
    [263] Sahm H, Eggeling L, Eikmanns B, et al. Metabolic design in amino acid producing bacterium Corynebacterium glutamicum. FEMS Microbiol Rev. 1995, 16:243-252.
    [264] Goel A, Ferrance J, Jeong J, et al. Analysis of metabolic fluxes in batch and continuous cultures of Bacillus subtilis. Biotechnol Bioeng. 1993, 42:686-696.
    [265] Dauner M, Bailey JE, Sauer U. Metabolic flux analysis with a comprehensive isotopomer model in Bacillus subtilis. Biotechnol Bioeng. 2001, 76:144-156.
    [266] 陈涛.基于基因组重排的产核黄素枯草芽孢杆菌的代谢工程.[博士学位论文],天津:天津大学,2004.
    [267] 李晓静.枯草芽孢杆菌核黄素操纵子及呼吸链的代谢工程改造.[博士学位论文],天津:天津大学,2006.
    [268] Desvaux M, Guedon E, Petitdemange H. Metabolic flux in cellulose batch and cellulose-fed continuous cultures of Clostridium cellulolyticum in response to acidic environment. Microbiol. 2001, 147:1461-1471.
    [269] Jurgen B, Tobisch S, Wumpelmann M, et al. Global expression profiling of Bacillus subtilis cells during industrial-close fed-batch fermentations with different nitrogen sources. Biotechnol. Bioeng. 2005, 92: 277-298.
    [270] Glaser P, Danchin A, Kunst F, et al. Identification and isolation of a gene required for nitrate assimilation and anaerobic growth of Bacillus subtilis. J Bacteriol. 1995, 177:1112-1115.
    [271] Ogawa K, Akagawa E, YamaneK, et al. The nasB operon and nasA gene are required for nitrate/nitrite assimilation in Bacillus subtilis, J Bacteriol. 1995, 177: 1409-1413.
    [272] Hoffmann T, Troup B, Szabo A, et al. The anaerobic life of Bacillus subtilis: cloning of the genes encoding the respiratory nitrate reductase system. FEMS Microbiol Lett. 1995, 131:219-225.
    [273] Sun G, Sharkova E, Chesnut R, et al. Regulators of aerobic and anaerobic respiration in Bacillus subtilis. J Bacteriol. 1996, 178:1374-1385.
    [274] Clements LD, Streips UN, Miller BS. Differential proteomic analysis of Bacillus subtilis nitrate respiration and fermentation in defined medium, Proteomics. 2002, 2: 1724-1734.
    [275] Espinosa-de-los-Monteros J, Martinez A, Valle F. Metabolic profiles and aprEexpression in anaerobic cultures of Bacillus subtilis using nitrate as terminal electron acceptor, Appl. Microbiol. Biotechnol. 2001, 57: 379-384.
    [276] Nakano MM, Hoffmann T, Zhu Y, et al. Nitrogen and oxygen regulation of Bacillus subtilis nasDEF encoding NADH-dependent nitrite reductase by TnrA and ResDE. J Bacteriol. 1998, 180: 5344-5350.
    [277] Nakano MM, Yang F, Hardin P, et al. Nitrogen regulation of nasA and the nasB operon, which encode genes required for nitrate assimilation in Bacillus subtilis. J Bacteriol. 1995, 177: 573-579.
    [278] Wray LV Jr, Ferson AE, Rohrer K, et al. TnrA, a transcription factor required for global nitrogen regulation in Bacillus subtilis. Proc. Natl. Acad. Sci. USA. 1996, 93: 8841-8845.
    [279] Rado TA, Hoch JA. Phosphotransacetylase from Bacillus subtilis: purification and physiological studies. Biochim Biophys Acta. 1973, 321: 114-25.
    [280] Majewski RA, Domaeh MM. Simple constrained-optimization view of acetate overflow in E.coli. Biotechnol Bioeng. 1990, 35: 732-738.
    [281] Kaiser J, Schramek N, EberhardtS, et al. Biosynthesis of vitamin B2 An essential zinc ion at the catalytic site of GTP cyclohydrolase II, Eur J Biochem. 2002, 269:5264-5270.
    [282] Ren J, Kotaka M, Lockyer M, et al. GTP cyclohydrolase II structure and mechanism. J Biol Chem. 2005, 280: 36912-36919.
    [283] H?mbelin M, Griesser V, Keller T, et al. GTP cyclohydrolase II and 3,4- dihydroxy-2-butanone 4-phosphate synthase are rate-limiting enzymes in riboflavin synthesis of an industrial Bacillus subtilis strain used for riboflavin production. J Ind Microbiol Biotechnol. 1999, 22: 1-7.
    [284] Lambert C, Leonard N, De Bolle X, et al. ESyPred3D: Prediction of proteins 3D structures. Bioinformatics. 2002, 18:1250-1256.
    [285] 宋勇波.基于酶学分析的鸟苷和肌苷发酵代谢调控研究.华东理工大学,硕士论文,2002.
    [286] Ujita S, Kimura K. Glucose-6-phosphate dehydrogenase, vegetative and spore Bacillus subtilis. Methods Enzymol. 1982, 89:258-261.
    [287] Dalluge JJ, Smith S, Sanchez-Riera F, et al. Potential of fermentation profiling via rapid measurement of. amino acid metabolism by liquid chromatography–tandem. mass spectrometry. J Chromatogr A. 2004, 1043: 3-7.
    [288] Kaderbhai NN, Broadhurst DI, Ellis DI, et al. Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp Funct Genomics. 2003, 4:376-391.
    [289] Even S, Lindley ND, Cocaign-Bousquet M. Transcriptional, translational andmetabolic regulation of glycolysis in Lactococcus lactis subsp. cremoris MG1363 grown in continuous acidic cultures. Microbiology. 2003, 149: 1935- 1944.
    [290] Mandal M, Boese B, Barrick JE, et al. Riboswitches control fundamental biochemical pathways in Bacillus subtilis and other bacteria. Cell. 2003, 113, 577-586.

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