Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014, Article ID 649453, 12 pages
http://dx.doi.org/10.1155/2014/649453
Review Article

Computational Approaches for Microalgal Biofuel Optimization: A Review

Division of Science and Math and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi, UAE

Received 6 June 2014; Revised 28 August 2014; Accepted 1 September 2014; Published 21 September 2014

Academic Editor: Meisam Tabatabaei

Copyright © 2014 Joseph Koussa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. E. Koskimaki, A. S. Blazier, A. F. Clarens, and J. A. Papin, “Computational models of algae metabolism for industrial applications,” Industrial Biotechnology, vol. 9, no. 4, pp. 185–195, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. R. Usaite, K. R. Patil, T. Grotkjær, J. Nielsen, and B. Regenberg, “Global transcriptional and physiological responses of Saccharomyces cerevisiae to ammonium, L-alanine, or L-glutamine limitation,” Applied and Environmental Microbiology, vol. 72, no. 9, pp. 6194–6203, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. R. M. Zelle, E. de Hulster, W. A. van Winden et al., “Malic acid production by Saccharomyces cerevisiae: engineering of pyruvate carboxylation, oxaloacetate reduction, and malate export,” Applied and Environmental Microbiology, vol. 74, no. 9, pp. 2766–2777, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Izallalen, R. Mahadevan, A. Burgard et al., “Geobacter sulfurreducens strain engineered for increased rates of respiration,” Metabolic Engineering, vol. 10, no. 5, pp. 267–275, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. M. A. Oberhardt, B. Ø. Palsson, and J. A. Papin, “Applications of genome-scale metabolic reconstructions,” Molecular Systems Biology, vol. 5, no. 1, article 320, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. P. D. Karp, S. M. Paley, M. Krummenacker et al., “Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology,” Briefings in Bioinformatics, vol. 11, no. 1, Article ID bbp043, pp. 40–79, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. I. Schomburg, A. Chang, S. Placzek et al., “BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA,” Nucleic Acids Research, vol. 41, no. 1, pp. D764–D772, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Artimo, M. Jonnalagedda, K. Arnold et al., “ExPASy: SIB bioinformatics resource portal,” Nucleic Acids Research, vol. 40, no. 1, pp. W597–W603, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. T. U. Consortium, “Ongoing and future developments at the Universal Protein Resource,” Nucleic Acids Research, vol. 39, supplement 1, pp. D214–D219, 2011. View at Google Scholar
  10. R. Caspi, T. Altman, R. Billington et al., “The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome databases,” Nucleic Acids Research, vol. 42, no. 1, pp. D459–D471, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M. Tanabe, “KEGG for integration and interpretation of large-scale molecular data sets,” Nucleic Acids Research, vol. 40, no. 1, pp. D109–D114, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Croft, G. O'Kelly, G. Wu et al., “Reactome: a database of reactions, pathways and biological processes,” Nucleic Acids Research, vol. 39, supplement 1, pp. D691–D697, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Schellenberger, J. O. Park, T. M. Conrad, and B. T. Palsson, “BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions,” BMC Bioinformatics, vol. 11, article 213, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. S. A. Becker, A. M. Feist, M. L. Mo, G. Hannum, B. Ø. Palsson, and M. J. Herrgard, “Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox,” Nature Protocols, vol. 2, no. 3, pp. 727–738, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Schellenberger, R. Que, R. M. T. Fleming et al., “Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0,” Nature Protocols, vol. 6, no. 9, pp. 1290–1307, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. S. G. Thorleifsson and I. Thiele, “rBioNet: a COBRA toolbox extension for reconstructing high-quality biochemical networks,” Bioinformatics, vol. 27, no. 14, Article ID btr308, pp. 2009–2010, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Devoid, R. Overbeek, M. DeJongh et al., “Automated genome annotation and metabolic model reconstruction in the SEED and model SEED,” in Systems Metabolic Engineering, pp. 17–45, 2013. View at Publisher · View at Google Scholar
  18. P. D. Karp, S. Paley, and P. Romero, “The pathway tools software,” Bioinformatics, vol. 18, supplement 1, pp. S225–S232, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Caspi, T. Altman, J. M. Dale et al., “The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases,” Nucleic Acids Research, vol. 38, supplement 1, Article ID gkp875, pp. D473–D479, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Caspi, T. Altman, K. Dreher et al., “The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases,” Nucleic Acids Research, vol. 40, no. 1, pp. D742–D753, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Caspi, H. Foerster, C. A. Fulcher et al., “The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome databases,” Nucleic Acids Research, vol. 36, supplement 1, pp. D623–D631, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Kasprzyk, “BioMart: driving a paradigm change in biological data management,” Database, vol. 2011, Article ID bar049, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Y. Kim, S. B. Sohn, Y. B. Kim, W. J. Kim, and S. Y. Lee, “Recent advances in reconstruction and applications of genome-scale metabolic models,” Current Opinion in Biotechnology, vol. 23, no. 4, pp. 617–623, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. M. A. Oberhardt, J. Puchałka, V. A. P. M. dos Santos, and J. A. Papin, “Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis,” PLoS Computational Biology, vol. 7, no. 3, Article ID e1001116, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. I. Thiele and B. Ø. Palsson, “A protocol for generating a high-quality genome-scale metabolic reconstruction,” Nature Protocols, vol. 5, no. 1, pp. 93–121, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. R. A. Notebaart, F. H. J. van Enckevort, C. Francke, R. J. Siezen, and B. Teusink, “Accelerating the reconstruction of genome-scale metabolic networks,” BMC Bioinformatics, vol. 7, article 296, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. Y.-C. Liao, M.-H. Tsai, F.-C. Chen, and C. A. Hsiung, “GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization,” Bioinformatics, vol. 28, no. 13, Article ID bts267, pp. 1752–1758, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. X. Feng, Y. Xu, Y. Chen, and Y. J. Tang, “MicrobesFlux: a web platform for drafting metabolic models from the KEGG database,” BMC Systems Biology, vol. 6, article 94, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Kumar, P. F. Suthers, and C. D. Maranas, “MetRxn: A knowledgebase of metabolites and reactions spanning metabolic models and databases,” BMC Bioinformatics, vol. 13, no. 1, article 6, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. C. S. Henry, M. Dejongh, A. A. Best, P. M. Frybarger, B. Linsay, and R. L. Stevens, “High-throughput generation, optimization and analysis of genome-scale metabolic models,” Nature Biotechnology, vol. 28, no. 9, pp. 977–982, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. N. Swainston, K. Smallbone, P. Mendes, D. Kell, and N. Paton, “The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks,” Journal of Integrative Bioinformatics, vol. 8, article 186, no. 2, 2011. View at Google Scholar · View at Scopus
  32. J. Boele, B. G. Olivier, and B. Teusink, “FAME, the flux analysis and modeling environment,” BMC Systems Biology, vol. 6, no. 1, p. 8, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. R. Agren, L. Liu, S. Shoaie, W. Vongsangnak, I. Nookaew, and J. Nielsen, “The RAVEN toolbox and its use for generating a genome-scale metabolic model for penicillium chrysogenum,” PLoS Computational Biology, vol. 9, no. 3, Article ID e1002980, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. J. J. Hamilton and J. L. Reed, “Software platforms to facilitate reconstructing genome-scale metabolic networks,” Environmental Microbiology, vol. 16, no. 1, pp. 49–59, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. T. Altman, M. Travers, A. Kothari, R. Caspi, and P. D. Karp, “A systematic comparison of the MetaCyc and KEGG pathway databases,” BMC Bioinformatics, vol. 14, article 112, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. M. E. Smoot, K. Ono, J. Ruscheinski, P.-L. Wang, and T. Ideker, “Cytoscape 2.8: new features for data integration and network visualization,” Bioinformatics, vol. 27, no. 3, pp. 431–432, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. R. Saito, M. E. Smoot, K. Ono et al., “A travel guide to Cytoscape plugins,” Nature Methods, vol. 9, no. 11, pp. 1069–1076, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. M. DeJongh, B. Bockstege, P. Frybarger, N. Hazekamp, J. Kammeraad, and T. McGeehan, “CytoSEED: a Cytoscape plugin for viewing, manipulating and analyzing metabolic models created by the model SEED,” Bioinformatics, vol. 28, no. 6, pp. 891–892, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. M. König and H. Holzhütter, “Fluxviz-cytoscape plug-in for visualization of flux distributions in networks,” in Proceedings of the International Conference on Genome Informatics, 2010.
  40. H. Rohn, A. Junker, A. Hartmann et al., “VANTED v2: a framework for systems biology applications,” BMC Systems Biology, vol. 6, article 139, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. B. H. Junker, C. Klukas, and F. Schreiber, “Vanted: a system for advanced data analysis and visualization in the context of biological networks,” BMC Bioinformatics, vol. 7, article 109, 13 pages, 2006. View at Publisher · View at Google Scholar · View at Scopus
  42. H. Rohn, A. Hartmann, A. Junker, B. H. Junker, and F. Schreiber, “FluxMap: A VANTED add-on for the visual exploration of flux distributions in biological networks,” BMC Systems Biology, vol. 6, article 33, 2012. View at Publisher · View at Google Scholar · View at Scopus
  43. E. Grafahrend-Belau, C. Klukas, B. H. Junker, and F. Schreiber, “FBA-SimVis: interactive visualization of constraint-based metabolic models,” Bioinformatics, vol. 25, no. 20, pp. 2755–2757, 2009. View at Publisher · View at Google Scholar · View at Scopus
  44. A. Kostromins and E. Stalidzans, “Paint4Net: COBRA Toolbox extension for visualization of stoichiometric models of metabolism,” BioSystems, vol. 109, no. 2, pp. 233–239, 2012. View at Publisher · View at Google Scholar · View at Scopus
  45. P. A. Jenseny and J. A. Papin, “MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data,” Bioinformatics, vol. 30, no. 9, pp. 1327–1328, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. V. Satish Kumar, M. S. Dasika, and C. D. Maranas, “Optimization based automated curation of metabolic reconstructions,” BMC Bioinformatics, vol. 8, article 212, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. P. Kharchenko, D. Vitkup, and G. M. Church, “Filling gaps in a metabolic network using expression information,” Bioinformatics, vol. 20, supplement 1, pp. i178–i185, 2004. View at Publisher · View at Google Scholar · View at Scopus
  48. M. L. Green and P. D. Karp, “A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases,” BMC Bioinformatics, vol. 5, article 76, 2004. View at Publisher · View at Google Scholar · View at Scopus
  49. V. S. Kumar and C. D. Maranas, “GrowMatch: an automated method for reconciling In Silico/In Vivo growth predictions,” PLoS Computational Biology, vol. 5, no. 3, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. V. Hatzimanikatis, C. Li, J. A. Ionita, C. S. Henry, M. D. Jankowski, and L. J. Broadbelt, “Exploring the diversity of complex metabolic networks,” Bioinformatics, vol. 21, no. 8, pp. 1603–1609, 2005. View at Publisher · View at Google Scholar · View at Scopus
  51. M. Lakshmanan, G. Koh, B. K. S. Chung, and D.-Y. Lee, “Software applications for flux balance analysis,” Briefings in Bioinformatics, vol. 15, no. 1, pp. 108–122, 2014. View at Publisher · View at Google Scholar · View at Scopus
  52. S. A. Becker and B. O. Palsson, “Context-specific metabolic networks are consistent with experiments,” PLoS Computational Biology, vol. 4, no. 5, Article ID e1000082, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  53. A. S. Blazier and J. A. Papin, “Integration of expression data in genome-scale metabolic network reconstructions,” Frontiers in Physiology, vol. 3, article 299, 2012. View at Publisher · View at Google Scholar · View at Scopus
  54. D. Machado and M. Herrgård, “Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism,” PLoS Computational Biology, vol. 10, no. 4, Article ID e1003580, 2014. View at Publisher · View at Google Scholar · View at Scopus
  55. H. Zur, E. Ruppin, and T. Shlomi, “iMAT: an integrative metabolic analysis tool,” Bioinformatics, vol. 26, no. 24, pp. 3140–3142, 2010. View at Publisher · View at Google Scholar · View at Scopus
  56. P. A. Jensen and J. A. Papin, “Functional integration of a metabolic network model and expression data without arbitrary thresholding,” Bioinformatics, vol. 27, no. 4, pp. 541–547, 2011. View at Publisher · View at Google Scholar · View at Scopus
  57. C. Colijn, A. Brandes, J. Zucker et al., “Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production,” PLoS Computational Biology, vol. 5, no. 8, Article ID e1000489, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  58. A. P. Burgard, P. Pharkya, and C. D. Maranas, “Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization,” Biotechnology and Bioengineering, vol. 84, no. 6, pp. 647–657, 2003. View at Publisher · View at Google Scholar · View at Scopus
  59. P. Pharkya, A. P. Burgard, and C. D. Maranas, “OptStrain: a computational framework for redesign of microbial production systems,” Genome Research, vol. 14, no. 11, pp. 2367–2376, 2004. View at Publisher · View at Google Scholar · View at Scopus
  60. I. Rocha, P. Maia, P. Evangelista et al., “OptFlux: an open-source software platform for in silico metabolic engineering,” BMC Systems Biology, vol. 4, no. 1, article 45, 2010. View at Publisher · View at Google Scholar · View at Scopus
  61. M. Cvijovic, R. Olivares-Hernandez, R. Agren et al., “BioMet Toolbox: genome-wide analysis of metabolism,” Nucleic Acids Research, vol. 38, supplement 2, Article ID gkq404, pp. W144–W149, 2010. View at Publisher · View at Google Scholar · View at Scopus
  62. K. Yizhak, O. Gabay, H. Cohen, and E. Ruppin, “Model-based identification of drug targets that revert disrupted metabolism and its application to ageing,” Nature Communications, vol. 4, 2013. View at Publisher · View at Google Scholar · View at Scopus
  63. P. A. Jensen, K. A. Lutz, and J. A. Papin, “TIGER: toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks,” BMC Systems Biology, vol. 5, no. 1, article 147, 2011. View at Publisher · View at Google Scholar · View at Scopus
  64. P. Gawand, P. Hyland, A. Ekins, V. J. J. Martin, and R. Mahadevan, “Novel approach to engineer strains for simultaneous sugar utilization,” Metabolic Engineering, vol. 20, pp. 63–72, 2013. View at Publisher · View at Google Scholar · View at Scopus
  65. J.-H. Kim, D. E. Block, and D. A. Mills, “Simultaneous consumption of pentose and hexose sugars: an optimal microbial phenotype for efficient fermentation of lignocellulosic biomass,” Applied Microbiology and Biotechnology, vol. 88, no. 5, pp. 1077–1085, 2010. View at Publisher · View at Google Scholar · View at Scopus
  66. S. K. Lee, H. Chou, T. S. Ham, T. S. Lee, and J. D. Keasling, “Metabolic engineering of microorganisms for biofuels production: from bugs to synthetic biology to fuels,” Current Opinion in Biotechnology, vol. 19, no. 6, pp. 556–563, 2008. View at Publisher · View at Google Scholar · View at Scopus
  67. N. M. D. Courchesne, A. Parisien, B. Wang, and C. Q. Lan, “Enhancement of lipid production using biochemical, genetic and transcription factor engineering approaches,” Journal of Biotechnology, vol. 141, no. 1-2, pp. 31–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  68. K. Yizhak, T. Benyamini, W. Liebermeister, E. Ruppin, and T. Shlomi, “Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model,” Bioinformatics, vol. 26, no. 12, Article ID btq183, pp. i255–i260, 2010. View at Publisher · View at Google Scholar · View at Scopus
  69. N. Jamshidi and B. Ø. Palsson, “Mass action stoichiometric simulation models: incorporating kinetics and regulation into stoichiometric models,” Biophysical Journal, vol. 98, no. 2, pp. 175–185, 2010. View at Publisher · View at Google Scholar · View at Scopus
  70. L. Jerby, T. Shlomi, and E. Ruppin, “Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism,” Molecular Systems Biology, vol. 6, article 401, 2010. View at Publisher · View at Google Scholar · View at Scopus
  71. B. R. Bochner, “Global phenotypic characterization of bacteria,” FEMS Microbiology Reviews, vol. 33, no. 1, pp. 191–205, 2009. View at Publisher · View at Google Scholar · View at Scopus
  72. B. R. Bochner, “New technologies to assess genotype-phenotype relationships,” Nature Reviews Genetics, vol. 4, no. 4, pp. 309–314, 2003. View at Publisher · View at Google Scholar · View at Scopus
  73. L. A. I. Vaas, J. Sikorski, V. Michael, M. Göker, and H.-P. Klenk, “Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics,” PLoS ONE, vol. 7, no. 4, Article ID e34846, 2012. View at Publisher · View at Google Scholar · View at Scopus
  74. J. L. Reed, T. R. Patel, K. H. Chen et al., “Systems approach to refining genome annotation,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 46, pp. 17480–17484, 2006. View at Publisher · View at Google Scholar · View at Scopus
  75. M. H. Medema, R. van Raaphorst, E. Takano, and R. Breitling, “Computational tools for the synthetic design of biochemical pathways,” Nature Reviews Microbiology, vol. 10, no. 3, pp. 191–202, 2012. View at Publisher · View at Google Scholar · View at Scopus
  76. A. F. Talebi, M. Tohidfar, A. Bagheri et al., “Manipulation of carbon flux into fatty acid biosynthesis pathway in Dunaliella salina using AccD and ME genes to enhance lipid content and to improve produced biodiesel quality,” Biofuel Research Journal, vol. 1, no. 3, pp. 91–97, 2014. View at Google Scholar
  77. E. Andrianantoandro, S. Basu, D. K. Karig, and R. Weiss, “Synthetic biology: new engineering rules for an emerging discipline,” Molecular Systems Biology, vol. 2, no. 1, Article ID msb4100073, 2006. View at Publisher · View at Google Scholar · View at Scopus
  78. S. A. Benner and A. M. Sismour, “Synthetic biology,” Nature Reviews Genetics, vol. 6, no. 7, pp. 533–543, 2005. View at Publisher · View at Google Scholar · View at Scopus
  79. D. A. Drubin, J. C. Way, and P. A. Silver, “Designing biological systems,” Genes and Development, vol. 21, no. 3, pp. 242–254, 2007. View at Publisher · View at Google Scholar · View at Scopus
  80. M. A. Marchisio and J. Stelling, “Computational design of synthetic gene circuits with composable parts,” Bioinformatics, vol. 24, no. 17, pp. 1903–1910, 2008. View at Publisher · View at Google Scholar · View at Scopus
  81. D. Lopez, D. Casero, S. J. Cokus, S. S. Merchant, and M. Pellegrini, “Algal functional annotation tool: a web-based analysis suite to functionally interpret large gene lists using integrated annotation and expression data,” BMC Bioinformatics, vol. 12, no. 1, article 282, 2011. View at Publisher · View at Google Scholar · View at Scopus
  82. R. L. Chang, L. Ghamsari, A. Manichaikul et al., “Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism,” Molecular Systems Biology, vol. 7, article 518, 2011. View at Publisher · View at Google Scholar · View at Scopus
  83. N. R. Boyle and J. A. Morgan, “Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii,” BMC Systems Biology, vol. 3, article 4, 2009. View at Publisher · View at Google Scholar · View at Scopus
  84. A. Manichaikul, L. Ghamsari, E. F. Y. Hom et al., “Metabolic network analysis integrated with transcript verification for sequenced genomes,” Nature Methods, vol. 6, no. 8, pp. 589–592, 2009. View at Publisher · View at Google Scholar · View at Scopus
  85. C. G. de Oliveira Dal'Molin, L.-E. Quek, R. W. Palfreyman, and L. K. Nielsen, “AlgaGEM—a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome,” BMC Genomics, vol. 12, no. 4, article S5, 2011. View at Publisher · View at Google Scholar · View at Scopus
  86. P. May, J. O. Christian, S. Kempa, and D. Walther, “ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii,” BMC Genomics, vol. 10, article 209, 2009. View at Publisher · View at Google Scholar · View at Scopus
  87. A. J. M. Walhout, G. F. Temple, M. A. Brasch et al., “GATEWAY recombinational cloning: application to the cloning of large numbers of open reading frames or ORFeomes,” Methods in Enzymology, vol. 328, pp. 575–592, 2000. View at Publisher · View at Google Scholar · View at Scopus