Design of Superior Cell Factories Based on Systems Wide Omics Analysis

  • Katsunori Yoshikawa
  • Chikara Furusawa
  • Takashi Hirasawa
  • Hiroshi Shimizu
Chapter

Abstract

The bioproduction industry is expanding towards sustainable production of energy, chemicals and materials requesting for superior, high-productivity cell factories. Recent advances in measurement technologies enable comprehensive analysis of cellular components, so-called “omics” analysis, which is expected to accelerate the construction of superior cell factories. As example, transcriptome analysis is widely used for genome-wide screening of candidate genes that may be manipulated to improve productivity. However, the massive amounts of data produced by this method, requests for smart approaches to narrow the selection of promising candidate genes as targets for higher productivity. In this chapter, we review several studies that demonstrate successful breeding based on omics data, and discuss how we can design experiments and screen for target genes to be manipulated for the development of superior cell factories.

Keywords

Systems wide omics analysis Cell factory Genome Transcriptome Metabolome Proteome Fluxome Phenome Multi-omics analyses Fossil resources Biofuel Building block of chemicals Breeding Stress tolerance Metabolic engineering Systems metabolic engineering DNA microarray In silico simulation Genome-scale metabolic model Flux balance analysis Metabolic flux analysis Evolutionary engineering Adaptive evolution Next-generation sequencing technologies R programming language 

References

  1. 1.
    Alwine JC, Kemp DJ, Stark GR (1977) Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc Natl Acad Sci USA 74(12):5350–5354PubMedGoogle Scholar
  2. 2.
    Asadollahi MA, Maury J, Patil KR, Schalk M, Clark A, Nielsen J (2009) Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab Eng 11(6):328–334. doi: 10.1016/j.ymben.2009.07.001 PubMedGoogle Scholar
  3. 3.
    Askenazi M, Driggers EM, Holtzman DA, Norman TC, Iverson S, Zimmer DP, Boers ME, Blomquist PR, Martinez EJ, Monreal AW, Feibelman TP, Mayorga ME, Maxon ME, Sykes K, Tobin JV, Cordero E, Salama SR, Trueheart J, Royer JC, Madden KT (2003) Integrating transcriptional and metabolite profiles to direct the engineering of lovastatin-producing fungal strains. Nat Biotechnol 21(2):150–156. doi: 10.1038/nbt781 PubMedGoogle Scholar
  4. 4.
    Atsumi S, Hanai T, Liao JC (2008) Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451(7174):86–89. doi: 10.1038/nature06450 PubMedGoogle Scholar
  5. 5.
    Atsumi S, Wu TY, Machado IM, Huang WC, Chen PY, Pellegrini M, Liao JC (2010) Evolution, genomic analysis, and reconstruction of isobutanol tolerance in Escherichia coli. Mol Syst Biol 6:449. doi: 10.1038/msb.2010.98 PubMedGoogle Scholar
  6. 6.
    Attfield PV (1997) Stress tolerance: the key to effective strains of industrial baker’s yeast. Nat Biotechnol 15(13):1351–1357. doi: 10.1038/nbt1297-1351 PubMedGoogle Scholar
  7. 7.
    Auerbach D, Thaminy S, Hottiger MO, Stagljar I (2002) The post-genomic era of interactive proteomics: facts and perspectives. Proteomics 2(6):611–623. doi:10.1002/1615-9861(200206)2:6<611::AID-PROT611>3.0.CO;2-YPubMedGoogle Scholar
  8. 8.
    Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, Datsenko KA, Tomita M, Wanner BL, Mori H (2006) Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol Syst Biol 2:2006.0008. doi: 10.1038/msb4100050 PubMedGoogle Scholar
  9. 9.
    Becker J, Klopprogge C, Herold A, Zelder O, Bolten CJ, Wittmann C (2007) Metabolic flux engineering of L-lysine production in Corynebacterium glutamicum–over expression and modification of G6P dehydrogenase. J Biotechnol 132(2):99–109. doi: 10.1016/j.jbiotec.2007.05.026 PubMedGoogle Scholar
  10. 10.
    Becker J, Klopprogge C, Wittmann C (2008) Metabolic responses to pyruvate kinase deletion in lysine producing Corynebacterium glutamicum. Microb Cell Fact 7:8. doi: 10.1186/1475-2859-7-8 PubMedGoogle Scholar
  11. 11.
    Bengtsson O, Jeppsson M, Sonderegger M, Parachin NS, Sauer U, Hahn-Hagerdal B, Gorwa-Grauslund MF (2008) Identification of common traits in improved xylose-growing Saccharomyces cerevisiae for inverse metabolic engineering. Yeast 25(11):835–847. doi: 10.1002/yea.1638 PubMedGoogle Scholar
  12. 12.
    Bhan A, Galas DJ, Dewey TG (2002) A duplication growth model of gene expression networks. Bioinformatics 18(11):1486–1493PubMedGoogle Scholar
  13. 13.
    Bilban M, Buehler LK, Head S, Desoye G, Quaranta V (2002) Normalizing DNA microarray data. Curr Issues Mol Biol 4(2):57–64PubMedGoogle Scholar
  14. 14.
    Blazeck J, Alper H (2010) Systems metabolic engineering: genome-scale models and beyond. Biotechnol J 5(7):647–659. doi: 10.1002/biot.200900247 PubMedGoogle Scholar
  15. 15.
    Boer VM, Crutchfield CA, Bradley PH, Botstein D, Rabinowitz JD (2010) Growth-limiting intracellular metabolites in yeast growing under diverse nutrient limitations. Mol Biol Cell 21(1):198–211. doi: 10.1091/mbc.E09-07-0597 PubMedGoogle Scholar
  16. 16.
    Bro C, Knudsen S, Regenberg B, Olsson L, Nielsen J (2005) Improvement of galactose uptake in Saccharomyces cerevisiae through overexpression of phosphoglucomutase: example of transcript analysis as a tool in inverse metabolic engineering. Appl Environ Microbiol 71(11):6465–6472. doi: 10.1128/AEM.71.11.6465-6472.2005 PubMedGoogle Scholar
  17. 17.
    Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647–657. doi: 10.1002/bit.10803 PubMedGoogle Scholar
  18. 18.
    Cheng JS, Qiao B, Yuan YJ (2008) Comparative proteome analysis of robust Saccharomyces cerevisiae insights into industrial continuous and batch fermentation. Appl Microbiol Biotechnol 81(2):327–338. doi: 10.1007/s00253-008-1733-6 PubMedGoogle Scholar
  19. 19.
    Cho A, Yun H, Park JH, Lee SY, Park S (2010) Prediction of novel synthetic pathways for the production of desired chemicals. BMC Syst Biol 4:35. doi: 10.1186/1752-0509-4-35 PubMedGoogle Scholar
  20. 20.
    Choi HS, Lee SY, Kim TY, Woo HM (2010) In silico identification of gene amplification targets for improvement of lycopene production. Appl Environ Microbiol 76(10):3097–3105. doi: 10.1128/AEM.00115-10 PubMedGoogle Scholar
  21. 21.
    Cullum AJ, Bennett AF, Lenski RE (2001) Evolutionary adaptation to temperature. IX. Preadaptation to novel stressful environments of Escherichia coli adapted to high temperature. Evolution 55(11):2194–2202PubMedGoogle Scholar
  22. 22.
    DeRisi JL, Iyer VR, Brown PO (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278(5338):680–686PubMedGoogle Scholar
  23. 23.
    Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(25):14863–14868PubMedGoogle Scholar
  24. 24.
    Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3:121. doi: 10.1038/msb4100155 PubMedGoogle Scholar
  25. 25.
    Feist AM, Herrgard MJ, Thiele I, Reed JL, Palsson BO (2009) Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 7(2):129–143. doi: 10.1038/nrmicro1949 PubMedGoogle Scholar
  26. 26.
    Fodor SP, Read JL, Pirrung MC, Stryer L, Lu AT, Solas D (1991) Light-directed, spatially addressable parallel chemical synthesis. Science 251(4995):767–773PubMedGoogle Scholar
  27. 27.
    Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO (2005) In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng 91(5):643–648. doi: 10.1002/bit.20542 PubMedGoogle Scholar
  28. 28.
    Fong SS, Joyce AR, Palsson BO (2005) Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res 15(10):1365–1372. doi: 10.1101/gr.3832305 PubMedGoogle Scholar
  29. 29.
    Furusawa C, Kaneko K (2003) Zipf’s law in gene expression. Phys Rev Lett 90(8):088102PubMedGoogle Scholar
  30. 30.
    Furusawa C, Ono N, Suzuki S, Agata T, Shimizu H, Yomo T (2009) Model-based analysis of non-specific binding for background correction of high-density oligonucleotide microarrays. Bioinformatics 25(1):36–41. doi: 10.1093/bioinformatics/btn570 PubMedGoogle Scholar
  31. 31.
    Gelperin DM, White MA, Wilkinson ML, Kon Y, Kung LA, Wise KJ, Lopez-Hoyo N, Jiang L, Piccirillo S, Yu H, Gerstein M, Dumont ME, Phizicky EM, Snyder M, Grayhack EJ (2005) Biochemical and genetic analysis of the yeast proteome with a movable ORF collection. Genes Dev 19(23):2816–2826. doi: 10.1101/gad.1362105 PubMedGoogle Scholar
  32. 32.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80. doi: 10.1186/gb-2004-5-10-r80 PubMedGoogle Scholar
  33. 33.
    Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, Lucau-Danila A, Anderson K, Andre B, Arkin AP, Astromoff A, El-Bakkoury M, Bangham R, Benito R, Brachat S, Campanaro S, Curtiss M, Davis K, Deutschbauer A, Entian KD, Flaherty P, Foury F, Garfinkel DJ, Gerstein M, Gotte D, Guldener U, Hegemann JH, Hempel S, Herman Z, Jaramillo DF, Kelly DE, Kelly SL, Kotter P, LaBonte D, Lamb DC, Lan N, Liang H, Liao H, Liu L, Luo C, Lussier M, Mao R, Menard P, Ooi SL, Revuelta JL, Roberts CJ, Rose M, Ross-Macdonald P, Scherens B, Schimmack G, Shafer B, Shoemaker DD, Sookhai-Mahadeo S, Storms RK, Strathern JN, Valle G, Voet M, Volckaert G, Wang CY, Ward TR, Wilhelmy J, Winzeler EA, Yang Y, Yen G, Youngman E, Yu K, Bussey H, Boeke JD, Snyder M, Philippsen P, Davis RW, Johnston M (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418(6896):387–391. doi: 10.1038/nature00935 PubMedGoogle Scholar
  34. 34.
    Gibson BR, Lawrence SJ, Leclaire JP, Powell CD, Smart KA (2007) Yeast responses to stresses associated with industrial brewery handling. FEMS Microbiol Rev 31(5):535–569. doi: 10.1111/j.1574-6976.2007.00076.x PubMedGoogle Scholar
  35. 35.
    Gonzalez R, Tao H, Purvis JE, York SW, Shanmugam KT, Ingram LO (2003) Gene array-based identification of changes that contribute to ethanol tolerance in ethanologenic Escherichia coli: comparison of KO11 (parent) to LY01 (resistant mutant). Biotechnol Prog 19(2):612–623. doi: 10.1021/bp025658q PubMedGoogle Scholar
  36. 36.
    Goodarzi H, Bennett BD, Amini S, Reaves ML, Hottes AK, Rabinowitz JD, Tavazoie S (2010) Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli. Mol Syst Biol 6:378. doi: 10.1038/msb.2010.33 PubMedGoogle Scholar
  37. 37.
    Guimaraes PM, Francois J, Parrou JL, Teixeira JA, Domingues L (2008) Adaptive evolution of a lactose-consuming Saccharomyces cerevisiae recombinant. Appl Environ Microbiol 74(6):1748–1756. doi: 10.1128/AEM.00186-08 PubMedGoogle Scholar
  38. 38.
    Hanai T, Atsumi S, Liao JC (2007) Engineered synthetic pathway for isopropanol production in Escherichia coli. Appl Environ Microbiol 73(24):7814–7818. doi: 10.1128/AEM.01140-07 PubMedGoogle Scholar
  39. 39.
    Hasunuma T, Sanda T, Yamada R, Yoshimura K, Ishii J, Kondo A (2011) Metabolic pathway engineering based on metabolomics confers acetic and formic acid tolerance to a recombinant xylose-fermenting strain of Saccharomyces cerevisiae. Microb Cell Fact 10(1):2. doi: 10.1186/1475-2859-10-2 PubMedGoogle Scholar
  40. 40.
    Hatti-Kaul R, Tornvall U, Gustafsson L, Borjesson P (2007) Industrial biotechnology for the production of bio-based chemicals—a cradle-to-grave perspective. Trends Biotechnol 25(3):119–124. doi: 10.1016/j.tibtech.2007.01.001 PubMedGoogle Scholar
  41. 41.
    Haverkorn van Rijsewijk BR, Nanchen A, Nallet S, Kleijn RJ, Sauer U (2011) Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli. Mol Syst Biol 7:477. doi: 10.1038/msb.2011.9 PubMedGoogle Scholar
  42. 42.
    Hawkins RD, Hon GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev Genet 11(7):476–486. doi: 10.1038/nrg2795 PubMedGoogle Scholar
  43. 43.
    Heinemann M, Kummel A, Ruinatscha R, Panke S (2005) In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network. Biotechnol Bioeng 92(7):850–864. doi: 10.1002/bit.20663 PubMedGoogle Scholar
  44. 44.
    Henry CS, Zinner JF, Cohoon MP, Stevens RL (2009) iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations. Genome Biol 10(6):R69. doi: 10.1186/gb-2009-10-6-r69 PubMedGoogle Scholar
  45. 45.
    Henry CS, Broadbelt LJ, Hatzimanikatis V (2010) Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3-hydroxypropanoate. Biotechnol Bioeng 106(3):462–473. doi: 10.1002/bit.22673 PubMedGoogle Scholar
  46. 46.
    Hirasawa T, Nakakura Y, Yoshikawa K, Ashitani K, Nagahisa K, Furusawa C, Katakura Y, Shimizu H, Shioya S (2006) Comparative analysis of transcriptional responses to saline stress in the laboratory and brewing strains of Saccharomyces cerevisiae with DNA microarray. Appl Microbiol Biotechnol 70(3):346–357. doi: 10.1007/s00253-005-0192-6 PubMedGoogle Scholar
  47. 47.
    Hirasawa T, Yoshikawa K, Nakakura Y, Nagahisa K, Furusawa C, Katakura Y, Shimizu H, Shioya S (2007) Identification of target genes conferring ethanol stress tolerance to Saccharomyces cerevisiae based on DNA microarray data analysis. J Biotechnol 131(1):34–44. doi: 10.1016/j.jbiotec.2007.05.010 PubMedGoogle Scholar
  48. 48.
    Hirasawa T, Ookubo A, Yoshikawa K, Nagahisa K, Furusawa C, Sawai H, Shimizu H (2009) Investigating the effectiveness of DNA microarray analysis for identifying the genes involved in l-lactate production by Saccharomyces cerevisiae. Appl Microbiol Biotechnol 84(6):1149–1159. doi: 10.1007/s00253-009-2209-z PubMedGoogle Scholar
  49. 49.
    Holter NS, Maritan A, Cieplak M, Fedoroff NV, Banavar JR (2001) Dynamic modeling of gene expression data. Proc Natl Acad Sci USA 98(4):1693–1698. doi: 10.1073/pnas.98.4.1693 PubMedGoogle Scholar
  50. 50.
    Hong KK, Vongsangnak W, Vemuri GN, Nielsen J (2011) Unravelling evolutionary strategies of yeast for improving galactose utilization through integrated systems level analysis. Proc Natl Acad Sci USA 108(29):12179–12184. doi: 10.1073/pnas.1103219108 PubMedGoogle Scholar
  51. 51.
    Horinouchi T, Tamaoka K, Furusawa C, Ono N, Suzuki S, Hirasawa T, Yomo T, Shimizu H (2010) Transcriptome analysis of parallel-evolved Escherichia coli strains under ethanol stress. BMC Genomics 11:579. doi: 10.1186/1471-2164-11-579 PubMedGoogle Scholar
  52. 52.
    Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18(Suppl 1):S96–S104PubMedGoogle Scholar
  53. 53.
    Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292(5518):929–934. doi: 10.1126/science.292.5518.929 PubMedGoogle Scholar
  54. 54.
    Ingram LO, Aldrich HC, Borges AC, Causey TB, Martinez A, Morales F, Saleh A, Underwood SA, Yomano LP, York SW, Zaldivar J, Zhou S (1999) Enteric bacterial catalysts for fuel ethanol production. Biotechnol Prog 15(5):855–866. doi: 10.1021/bp9901062 PubMedGoogle Scholar
  55. 55.
    Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A, Hirasawa T, Naba M, Hirai K, Hoque A, Ho PY, Kakazu Y, Sugawara K, Igarashi S, Harada S, Masuda T, Sugiyama N, Togashi T, Hasegawa M, Takai Y, Yugi K, Arakawa K, Iwata N, Toya Y, Nakayama Y, Nishioka T, Shimizu K, Mori H, Tomita M (2007) Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316(5824):593–597. doi: 10.1126/science.1132067 PubMedGoogle Scholar
  56. 56.
    Jami MS, Barreiro C, Garcia-Estrada C, Martin JF (2010) Proteome analysis of the penicillin producer Penicillium chrysogenum: characterization of protein changes during the industrial strain improvement. Mol Cell Proteomics 9(6):1182–1198. doi: 10.1074/mcp.M900327-MCP200 PubMedGoogle Scholar
  57. 57.
    John RP, Nampoothiri KM, Pandey A (2007) Fermentative production of lactic acid from biomass: an overview on process developments and future perspectives. Appl Microbiol Biotechnol 74(3):524–534. doi: 10.1007/s00253-006-0779-6 PubMedGoogle Scholar
  58. 58.
    Jozefczuk S, Klie S, Catchpole G, Szymanski J, Cuadros-Inostroza A, Steinhauser D, Selbig J, Willmitzer L (2010) Metabolomic and transcriptomic stress response of Escherichia coli. Mol Syst Biol 6:364. doi: 10.1038/msb.2010.18 PubMedGoogle Scholar
  59. 59.
    Khatri P, Draghici S (2005) Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21(18):3587–3595. doi: 10.1093/bioinformatics/bti565 PubMedGoogle Scholar
  60. 60.
    Kim J, Fukuda H, Hirasawa T, Nagahisa K, Nagai K, Wachi M, Shimizu H (2010) Requirement of de novo synthesis of the OdhI protein in penicillin-induced glutamate production by Corynebacterium glutamicum. Appl Microbiol Biotechnol 86(3):911–920. doi: 10.1007/s00253-009-2360-6 PubMedGoogle Scholar
  61. 61.
    Kim TY, Kim HU, Lee SY (2010) Data integration and analysis of biological networks. Curr Opin Biotechnol 21(1):78–84. doi: 10.1016/j.copbio.2010.01.003 PubMedGoogle Scholar
  62. 62.
    Kind S, Wittmann C (2011) Bio-based production of the platform chemical 1,5-diaminopentane. Appl Microbiol Biotechnol 91(5):1287–1296. doi: 10.1007/s00253-011-3457-2 PubMedGoogle Scholar
  63. 63.
    Kind S, Kreye S, Wittmann C (2011) Metabolic engineering of cellular transport for overproduction of the platform chemical 1,5-diaminopentane in Corynebacterium glutamicum. Metab Eng 13(5):617–627. doi: 10.1016/j.ymben.2011.07.006 PubMedGoogle Scholar
  64. 64.
    Kitagawa M, Ara T, Arifuzzaman M, Ioka-Nakamichi T, Inamoto E, Toyonaga H, Mori H (2005) Complete set of ORF clones of Escherichia coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biological research. DNA Res 12(5):291–299. doi: 10.1093/dnares/dsi012 PubMedGoogle Scholar
  65. 65.
    Kjeldsen KR, Nielsen J (2009) In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network. Biotechnol Bioeng 102(2):583–597. doi: 10.1002/bit.22067 PubMedGoogle Scholar
  66. 66.
    Klimacek M, Krahulec S, Sauer U, Nidetzky B (2010) Limitations in xylose-fermenting Saccharomyces cerevisiae, made evident through comprehensive metabolite profiling and thermodynamic analysis. Appl Environ Microbiol 76(22):7566–7574. doi: 10.1128/AEM.01787-10 PubMedGoogle Scholar
  67. 67.
    Knoop H, Zilliges Y, Lockau W, Steuer R (2010) The metabolic network of Synechocystis sp. PCC 6803: systemic properties of autotrophic growth. Plant Physiol 154(1):410–422. doi: 10.1104/pp.110.157198 PubMedGoogle Scholar
  68. 68.
    Kohlstedt M, Becker J, Wittmann C (2010) Metabolic fluxes and beyond-systems biology understanding and engineering of microbial metabolism. Appl Microbiol Biotechnol 88(5):1065–1075. doi: 10.1007/s00253-010-2854-2 PubMedGoogle Scholar
  69. 69.
    Kono N, Arakawa K, Ogawa R, Kido N, Oshita K, Ikegami K, Tamaki S, Tomita M (2009) Pathway projector: web-based zoomable pathway browser using KEGG atlas and Google Maps API. PLoS One 4(11):e7710. doi: 10.1371/journal.pone.0007710 PubMedGoogle Scholar
  70. 70.
    Krömer JO, Heinzle E, Schröder H, Wittmann C (2006) Accumulation of homolanthionine and activation of a novel pathway for isoleucine biosynthesis in Corynebacterium glutamicum McbR deletion strains. J Bacteriol 188(2):609–618. doi: 10.1128/JB.188.2.609-618.2006 PubMedGoogle Scholar
  71. 71.
    Kwon YD, Kim S, Lee SY, Kim P (2011) Long-term continuous adaptation of Escherichia coli to high succinate stress and transcriptome analysis of the tolerant strain. J Biosci Bioeng 111(1):26–30. doi: 10.1016/j.jbiosc.2010.08.007 PubMedGoogle Scholar
  72. 72.
    Laub MT, McAdams HH, Feldblyum T, Fraser CM, Shapiro L (2000) Global analysis of the genetic network controlling a bacterial cell cycle. Science 290(5499):2144–2148PubMedGoogle Scholar
  73. 73.
    Lee SY, Papoutsakis ET (1999) Metabolic engineering. Marcel Dekker, New YorkGoogle Scholar
  74. 74.
    Lee SJ, Lee DY, Kim TY, Kim BH, Lee J, Lee SY (2005) Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 71(12):7880–7887. doi: 10.1128/AEM.71.12.7880-7887.2005 PubMedGoogle Scholar
  75. 75.
    Lee JW, Lee SY, Song H, Yoo JS (2006) The proteome of Mannheimia succiniciproducens, a capnophilic rumen bacterium. Proteomics 6(12):3550–3566. doi: 10.1002/pmic.200500837 PubMedGoogle Scholar
  76. 76.
    Lee KH, Park JH, Kim TY, Kim HU, Lee SY (2007) Systems metabolic engineering of Escherichia coli for L-threonine production. Mol Syst Biol 3:149. doi: 10.1038/msb4100196 PubMedGoogle Scholar
  77. 77.
    Lee JW, Kim HU, Choi S, Yi J, Lee SY (2011) Microbial production of building block chemicals and polymers. Curr Opin Biotechnol. doi: 10.1016/j.copbio.2011.02.011
  78. 78.
    Li C, Hung Wong W (2001) Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol 2(8):RESEARCH0032PubMedGoogle Scholar
  79. 79.
    Li GZ, Bu HL, Yang MQ, Zeng XQ, Yang JY (2008) Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis. BMC Genomics 9(Suppl 2):S24. doi: 10.1186/1471-2164-9-S2-S24 PubMedGoogle Scholar
  80. 80.
    Lin Y, Tanaka S (2006) Ethanol fermentation from biomass resources: current state and prospects. Appl Microbiol Biotechnol 69(6):627–642. doi: 10.1007/s00253-005-0229-x PubMedGoogle Scholar
  81. 81.
    Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 14(13):1675–1680. doi: 10.1038/nbt1296-1675 PubMedGoogle Scholar
  82. 82.
    Matsumura H, Yoshida K, Luo S, Kimura E, Fujibe T, Albertyn Z, Barrero RA, Kruger DH, Kahl G, Schroth GP, Terauchi R (2010) High-throughput SuperSAGE for digital gene expression analysis of multiple samples using next generation sequencing. PLoS One 5(8):e12010. doi: 10.1371/journal.pone.0012010 PubMedGoogle Scholar
  83. 83.
    Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev Genet 11(1):31–46. doi: 10.1038/nrg2626 PubMedGoogle Scholar
  84. 84.
    Mlecnik B, Scheideler M, Hackl H, Hartler J, Sanchez-Cabo F, Trajanoski Z (2005) PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Res 33(Web Server issue):W633–W637. doi: 10.1093/nar/gki391 PubMedGoogle Scholar
  85. 85.
    Mo ML, Palsson BO, Herrgard MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3:37. doi: 10.1186/1752-0509-3-37 PubMedGoogle Scholar
  86. 86.
    Montagud A, Navarro E, Fernandez de Cordoba P, Urchueguia JF, Patil KR (2010) Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium. BMC Syst Biol 4:156. doi: 10.1186/1752-0509-4-156 PubMedGoogle Scholar
  87. 87.
    Morozova O, Marra MA (2008) Applications of next-generation sequencing technologies in functional genomics. Genomics 92(5):255–264. doi: 10.1016/j.ygeno.2008.07.001 PubMedGoogle Scholar
  88. 88.
    Moxley JF, Jewett MC, Antoniewicz MR, Villas-Boas SG, Alper H, Wheeler RT, Tong L, Hinnebusch AG, Ideker T, Nielsen J, Stephanopoulos G (2009) Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc Natl Acad Sci USA 106(16):6477–6482. doi: 10.1073/pnas.0811091106 PubMedGoogle Scholar
  89. 89.
    Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320(5881):1344–1349. doi: 10.1126/science.1158441 PubMedGoogle Scholar
  90. 90.
    Nakahigashi K, Toya Y, Ishii N, Soga T, Hasegawa M, Watanabe H, Takai Y, Honma M, Mori H, Tomita M (2009) Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism. Mol Syst Biol 5:306PubMedGoogle Scholar
  91. 91.
    Noh K, Gronke K, Luo B, Takors R, Oldiges M, Wiechert W (2007) Metabolic flux analysis at ultra short time scale: isotopically non-stationary 13C labeling experiments. J Biotechnol 129(2):249–267. doi: 10.1016/j.jbiotec.2006.11.015 PubMedGoogle Scholar
  92. 92.
    Nuwaysir EF, Huang W, Albert TJ, Singh J, Nuwaysir K, Pitas A, Richmond T, Gorski T, Berg JP, Ballin J, McCormick M, Norton J, Pollock T, Sumwalt T, Butcher L, Porter D, Molla M, Hall C, Blattner F, Sussman MR, Wallace RL, Cerrina F, Green RD (2002) Gene expression analysis using oligonucleotide arrays produced by maskless photolithography. Genome Res 12(11):1749–1755. doi: 10.1101/gr.362402 PubMedGoogle Scholar
  93. 93.
    Oh YK, Palsson BO, Park SM, Schilling CH, Mahadevan R (2007) Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem 282(39):28791–28799. doi: 10.1074/jbc.M703759200 PubMedGoogle Scholar
  94. 94.
    Okino S, Noburyu R, Suda M, Jojima T, Inui M, Yukawa H (2008) An efficient succinic acid production process in a metabolically engineered Corynebacterium glutamicum strain. Appl Microbiol Biotechnol 81(3):459–464. doi: 10.1007/s00253-008-1668-y PubMedGoogle Scholar
  95. 95.
    Ookubo A, Hirasawa T, Yoshikawa K, Nagahisa K, Furusawa C, Shimizu H (2008) Improvement of L-lactate production by CYB2 gene disruption in a recombinant Saccharomyces cerevisiae strain under low pH condition. Biosci Biotechnol Biochem 72(11):3063–3066PubMedGoogle Scholar
  96. 96.
    Pandey G, Yoshikawa K, Hirasawa T, Nagahisa K, Katakura Y, Furusawa C, Shimizu H, Shioya S (2007) Extracting the hidden features in saline osmotic tolerance in Saccharomyces cerevisiae from DNA microarray data using the self-organizing map: biosynthesis of amino acids. Appl Microbiol Biotechnol 75(2):415–426. doi: 10.1007/s00253-007-0837-8 PubMedGoogle Scholar
  97. 97.
    Park JH, Lee SY (2008) Towards systems metabolic engineering of microorganisms for amino acid production. Curr Opin Biotechnol 19(5):454–460. doi: 10.1016/j.copbio.2008.08.007 PubMedGoogle Scholar
  98. 98.
    Park JH, Lee KH, Kim TY, Lee SY (2007) Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc Natl Acad Sci USA 104(19):7797–7802. doi: 10.1073/pnas.0702609104 PubMedGoogle Scholar
  99. 99.
    Patil KR, Nielsen J (2005) Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci USA 102(8):2685–2689. doi: 10.1073/pnas.0406811102 PubMedGoogle Scholar
  100. 100.
    Piddocke MP, Fazio A, Vongsangnak W, Wong ML, Heldt-Hansen HP, Workman C, Nielsen J, Olsson L (2011) Revealing the beneficial effect of protease supplementation to high gravity beer fermentations using “-omics” techniques. Microb Cell Fact 10:27. doi: 10.1186/1475-2859-10-27 PubMedGoogle Scholar
  101. 101.
    Pluskal T, Nakamura T, Villar-Briones A, Yanagida M (2010) Metabolic profiling of the fission yeast S. pombe: quantification of compounds under different temperatures and genetic perturbation. Mol Biosyst 6(1):182–198. doi: 10.1039/b908784b PubMedGoogle Scholar
  102. 102.
    Rabilloud T, Vaezzadeh AR, Potier N, Lelong C, Leize-Wagner E, Chevallet M (2009) Power and limitations of electrophoretic separations in proteomics strategies. Mass Spectrom Rev 28(5):816–843. doi: 10.1002/mas.20204 PubMedGoogle Scholar
  103. 103.
    Rabilloud T, Chevallet M, Luche S, Lelong C (2010) Two-dimensional gel electrophoresis in proteomics: past, present and future. J Proteomics 73(11):2064–2077. doi: 10.1016/j.jprot.2010.05.016 PubMedGoogle Scholar
  104. 104.
    Rivals I, Personnaz L, Taing L, Potier MC (2007) Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics 23(4):401–407. doi: 10.1093/bioinformatics/btl633 PubMedGoogle Scholar
  105. 105.
    Sangurdekar DP, Srienc F, Khodursky AB (2006) A classification based framework for quantitative description of large-scale microarray data. Genome Biol 7(4):R32. doi: 10.1186/gb-2006-7-4-r32 PubMedGoogle Scholar
  106. 106.
    Sauer U (2001) Evolutionary engineering of industrially important microbial phenotypes. Adv Biochem Eng Biotechnol 73:129–169PubMedGoogle Scholar
  107. 107.
    Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470PubMedGoogle Scholar
  108. 108.
    Schilling CH, Schuster S, Palsson BO, Heinrich R (1999) Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog 15(3):296–303. doi: 10.1021/bp990048k PubMedGoogle Scholar
  109. 109.
    Schilling O, Frick O, Herzberg C, Ehrenreich A, Heinzle E, Wittmann C, Stülke J (2007) Transcriptional and metabolic responses of Bacillus subtilis to the availability of organic acids: transcription regulation is important but not sufficient to account for metabolic adaptation. Appl Environ Microbiol 73(2):499–507. doi: 10.1128/AEM.02084-06 PubMedGoogle Scholar
  110. 110.
    Schmitt AP, McEntee K (1996) Msn2p, a zinc finger DNA-binding protein, is the transcriptional activator of the multistress response in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 93(12):5777–5782PubMedGoogle Scholar
  111. 111.
    Shalon D, Smith SJ, Brown PO (1996) A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res 6(7):639–645PubMedGoogle Scholar
  112. 112.
    Shendure J (2008) The beginning of the end for microarrays? Nat Methods 5(7):585–587. doi: 10.1038/nmeth0708-585 PubMedGoogle Scholar
  113. 113.
    Shi S, Chen T, Zhang Z, Chen X, Zhao X (2009) Transcriptome analysis guided metabolic engineering of Bacillus subtilis for riboflavin production. Metab Eng 11(4–5):243–252. doi: 10.1016/j.ymben.2009.05.002 PubMedGoogle Scholar
  114. 114.
    Shinfuku Y, Sorpitiporn N, Sono M, Furusawa C, Hirasawa T, Shimizu H (2009) Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum. Microb Cell Fact 8:43. doi: 10.1186/1475-2859-8-43 PubMedGoogle Scholar
  115. 115.
    Shirai T, Fujimura K, Furusawa C, Nagahisa K, Shioya S, Shimizu H (2007) Study on roles of anaplerotic pathways in glutamate overproduction of Corynebacterium glutamicum by metabolic flux analysis. Microb Cell Fact 6:19. doi: 10.1186/1475-2859-6-19 PubMedGoogle Scholar
  116. 116.
    Shiraki T, Kondo S, Katayama S, Waki K, Kasukawa T, Kawaji H, Kodzius R, Watahiki A, Nakamura M, Arakawa T, Fukuda S, Sasaki D, Podhajska A, Harbers M, Kawai J, Carninci P, Hayashizaki Y (2003) Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc Natl Acad Sci USA 100(26):15776–15781. doi: 10.1073/pnas.2136655100 PubMedGoogle Scholar
  117. 117.
    Sindelar G, Wendisch VF (2007) Improving lysine production by Corynebacterium glutamicum through DNA microarray-based identification of novel target genes. Appl Microbiol Biotechnol 76(3):677–689. doi: 10.1007/s00253-007-0916-x PubMedGoogle Scholar
  118. 118.
    Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T (2003) Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res 2(5):488–494PubMedGoogle Scholar
  119. 119.
    Sopko R, Huang D, Preston N, Chua G, Papp B, Kafadar K, Snyder M, Oliver SG, Cyert M, Hughes TR, Boone C, Andrews B (2006) Mapping pathways and phenotypes by systematic gene overexpression. Mol Cell 21(3):319–330. doi: 10.1016/j.molcel.2005.12.011 PubMedGoogle Scholar
  120. 120.
    Stephanopoulos G, Aristidou A, Nielsen J (1998) Metabolic engineering. Academic, San DiegoGoogle Scholar
  121. 121.
    Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96(6):2907–2912PubMedGoogle Scholar
  122. 122.
    Thomas R, Paredes CJ, Mehrotra S, Hatzimanikatis V, Papoutsakis ET (2007) A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data. BMC Bioinformatics 8:228. doi: 10.1186/1471-2105-8-228 PubMedGoogle Scholar
  123. 123.
    Tokuhiro K, Ishida N, Nagamori E, Saitoh S, Onishi T, Kondo A, Takahashi H (2009) Double mutation of the PDC1 and ADH1 genes improves lactate production in the yeast Saccharomyces cerevisiae expressing the bovine lactate dehydrogenase gene. Appl Microbiol Biotechnol 82(5):883–890. doi: 10.1007/s00253-008-1831-5 PubMedGoogle Scholar
  124. 124.
    van Maris AJ, Geertman JM, Vermeulen A, Groothuizen MK, Winkler AA, Piper MD, van Dijken JP, Pronk JT (2004) Directed evolution of pyruvate decarboxylase-negative Saccharomyces cerevisiae, yielding a C2-independent, glucose-tolerant, and pyruvate-hyperproducing yeast. Appl Environ Microbiol 70(1):159–166PubMedGoogle Scholar
  125. 125.
    Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270(5235):484–487PubMedGoogle Scholar
  126. 126.
    Vemuri GN, Eiteman MA, Altman E (2002) Succinate production in dual-phase Escherichia coli fermentations depends on the time of transition from aerobic to anaerobic conditions. J Ind Microbiol Biotechnol 28(6):325–332. doi: 10.1038/sj/jim/7000250 PubMedGoogle Scholar
  127. 127.
    Vongsangnak W, Hansen K, Nielsen J (2011) Integrated analysis of the global transcriptional response to alpha-amylase over-production by Aspergillus oryzae. Biotechnol Bioeng 108(5):1130–1139. doi: 10.1002/bit.23033 PubMedGoogle Scholar
  128. 128.
    Wang Y, Joshi T, Zhang XS, Xu D, Chen L (2006) Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 22(19):2413–2420. doi: 10.1093/bioinformatics/btl396 PubMedGoogle Scholar
  129. 129.
    Warringer J, Ericson E, Fernandez L, Nerman O, Blomberg A (2003) High-resolution yeast phenomics resolves different physiological features in the saline response. Proc Natl Acad Sci USA 100(26):15724–15729. doi: 10.1073/pnas.2435976100 PubMedGoogle Scholar
  130. 130.
    Whited GM, Feher FJ, Benko DA, Cervin MA, Chotani GK, McAuliffe JC, LaDuca RJ, Ben-Shoshan EA, Sanford KJ (2010) Development of a gas-phase bioprocess for isoprene-monomer production using metabolic pathway engineering. Ind Biotechnol 6(3):152–163Google Scholar
  131. 131.
    Wiechert W, Noh K (2005) From stationary to instationary metabolic flux analysis. Adv Biochem Eng Biotechnol 92:145–172PubMedGoogle Scholar
  132. 132.
    Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, Andre B, Bangham R, Benito R, Boeke JD, Bussey H, Chu AM, Connelly C, Davis K, Dietrich F, Dow SW, El Bakkoury M, Foury F, Friend SH, Gentalen E, Giaever G, Hegemann JH, Jones T, Laub M, Liao H, Liebundguth N, Lockhart DJ, Lucau-Danila A, Lussier M, M’Rabet N, Menard P, Mittmann M, Pai C, Rebischung C, Revuelta JL, Riles L, Roberts CJ, Ross-MacDonald P, Scherens B, Snyder M, Sookhai-Mahadeo S, Storms RK, Veronneau S, Voet M, Volckaert G, Ward TR, Wysocki R, Yen GS, Yu K, Zimmermann K, Philippsen P, Johnston M, Davis RW (1999) Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285(5429):901–906. doi: 10.1126/science.285.5429.901 PubMedGoogle Scholar
  133. 133.
    Wisselink HW, Toirkens MJ, del Rosario Franco Berriel M, Winkler AA, van Dijken JP, Pronk JT, van Maris AJ (2007) Engineering of Saccharomyces cerevisiae for efficient anaerobic alcoholic fermentation of L-arabinose. Appl Environ Microbiol 73(15):4881–4891. doi: 10.1128/AEM.00177-07 PubMedGoogle Scholar
  134. 134.
    Wisselink HW, Cipollina C, Oud B, Crimi B, Heijnen JJ, Pronk JT, van Maris AJ (2010) Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae. Metab Eng 12(6):537–551. doi: 10.1016/j.ymben.2010.08.003 PubMedGoogle Scholar
  135. 135.
    Wittmann C (2007) Fluxome analysis using GC-MS. Microb Cell Fact 6:6. doi: 10.1186/1475-2859-6-6 PubMedGoogle Scholar
  136. 136.
    Wu X, Dewey TG (2006) From microarray to biological networks: analysis of gene expression profiles. Methods Mol Biol 316:35–48PubMedGoogle Scholar
  137. 137.
    Yang L, Cluett WR, Mahadevan R (2011) EMILiO: a fast algorithm for genome-scale strain design. Metab Eng 13(3):272–281. doi: 10.1016/j.ymben.2011.03.002 PubMedGoogle Scholar
  138. 138.
    Yim H, Haselbeck R, Niu W, Pujol-Baxley C, Burgard A, Boldt J, Khandurina J, Trawick JD, Osterhout RE, Stephen R, Estadilla J, Teisan S, Schreyer HB, Andrae S, Yang TH, Lee SY, Burk MJ, Van Dien S (2011) Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat Chem Biol 7(7):445–452. doi: 10.1038/nchembio.580 PubMedGoogle Scholar
  139. 139.
    Yoshida S, Hashimoto K, Shimada E, Ishiguro T, Minato T, Mizutani S, Yoshimoto H, Tashiro K, Kuhara S, Kobayashi O (2007) Identification of bottom-fermenting yeast genes expressed during lager beer fermentation. Yeast 24(7):599–606. doi: 10.1002/yea.1494 PubMedGoogle Scholar
  140. 140.
    Yoshida S, Imoto J, Minato T, Oouchi R, Sugihara M, Imai T, Ishiguro T, Mizutani S, Tomita M, Soga T, Yoshimoto H (2008) Development of bottom-fermenting saccharomyces strains that produce high SO2 levels, using integrated metabolome and transcriptome analysis. Appl Environ Microbiol 74(9):2787–2796. doi: 10.1128/AEM.01781-07 Google Scholar
  141. 141.
    Yoshikawa K, Tanaka T, Furusawa C, Nagahisa K, Hirasawa T, Shimizu H (2009) Comprehensive phenotypic analysis for identification of genes affecting growth under ethanol stress in Saccharomyces cerevisiae. FEMS Yeast Res 9(1):32–44. doi: 10.1111/j.1567-1364.2008.00456.x PubMedGoogle Scholar
  142. 142.
    Yoshikawa K, Kojima Y, Nakajima T, Furusawa C, Hirasawa T, Shimizu H (2011) Reconstruction and verification of a genome-scale metabolic model for Synechocystis sp. PCC6803. Appl Microbiol Biotechnol 92(2):347–358. doi: 10.1007/s00253-011-3559-x PubMedGoogle Scholar
  143. 143.
    Yousofshahi M, Lee K, Hassoun S (2011) Probabilistic pathway construction. Metab Eng 13(4):435–444. doi: 10.1016/j.ymben.2011.01.006 PubMedGoogle Scholar
  144. 144.
    Zamboni N, Fendt SM, Rühl M, Sauer U (2009) 13C-based metabolic flux analysis. Nat Protoc 4(6):878–892. doi: 10.1038/nprot.2009.58 PubMedGoogle Scholar
  145. 145.
    Zhang Y, Szustakowski J, Schinke M (2009) Bioinformatics analysis of microarray data. Methods Mol Biol 573:259–284. doi: 10.1007/978-1-60761-247-6_15 PubMedGoogle Scholar
  146. 146.
    Zhang W, Li F, Nie L (2010) Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology 156(Pt 2):287–301. doi: 10.1099/mic.0.034793-0 PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Katsunori Yoshikawa
    • 1
  • Chikara Furusawa
    • 1
  • Takashi Hirasawa
    • 1
  • Hiroshi Shimizu
    • 1
  1. 1.Department of Bioinformatic Engineering, Graduate School of Information Science and TechnologyOsaka UniversitySuitaJapan

Personalised recommendations