Advances in metabolic modeling of oleaginous microalgae Mike Himmel

Biotechnology for Biofuels

Volumn 11, Issue 1, 2018, Pages

  Tibocha Bonilla, Juan D ^a   Zuñiga, Cristal ^b   Godoy Silva, Rubén D ^a   Zengler, Karsten ^b,c,d  

^a UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^c UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^d UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

Central carbon metabolism; Constraint based metabolic modeling; Lipid production; Oleaginous phototrophs

Indexed keywords

ALGAE; BIOFUELS; MICROORGANISMS; PHYSIOLOGY;

CENTRAL CARBON METABOLISMS; CONSTRAINT-BASED MODELING; LIPID PRODUCTIONS; METABOLIC MODELING; NON-RENEWABLE RESOURCE; PHOTOSYNTHETIC MICROORGANISMS; PHOTOTROPHIC MICROORGANISMS; PHOTOTROPHS;

METABOLISM;

ACCURACY ASSESSMENT; BIOENERGY; BIOFUEL; CELL ORGANELLE; GENOME; LIPID; METABOLISM; MICROALGA; MICROORGANISM; MODELING; PHOTOAUTOTROPHY;

CYANOBACTERIA;

EID: 85053135706     PISSN: 17546834     EISSN: None     Source Type: Journal    
DOI: 10.1186/s13068-018-1244-3     Document Type: Review

References (96)

1. Falkowski PG, Raven JA. An introduction to photosynthesis in aquatic systems. In: Elworthy S, editor. Aquatic photosynthesis. Princeton: Princeton University Press; 2013. p. 1-43.
2. Gavrilescu M, Chisti Y. Biotechnology - a sustainable alternative for chemical industry. Biotechnol Adv. 2005;23:471-99.
3. Ghasemi Y, Rasoul-Amini S, Fotooh-Abadi E. The biotransformation, biodegradation, and bioremediation of organic compounds by microalgae. J Phycol. 2011;47:969-80.
4. EPA. Atmospheric concentrations of greenhouse gases. Environmental panel agency (EPA). https://www.epa.gov/climate-indicators/climate-change-indicators-atmospheric-concentrations-greenhouse-gases. Accessed 2 Apr 2018.
5. 2 records from the Law Dome DE08, DE08-2, and DSS ice cores. In trends: a compendium of data on global change. http://cdiac.ess-dive.lbl.gov/trends/co2/lawdome.html. Accessed 6 Dec 2017.
6. NASA. Climate change: vital signs of the planet: carbon dioxide. 2017. https://climate.nasa.gov/vital-signs/carbon-dioxide/. Accessed 6 Dec 2017.
7. 2 emissions. A compendium of data on global change. http://cdiac.ess-dive.lbl.gov/trends/emis/overview.html. Accessed 6 Dec 2017.
8. Brennan L, Owende P. Biofuels from microalgae - a review of technologies for production, processing, and extractions of biofuels and co-products. Renew Sustain Energy Rev. 2010;14:557-77.
9. EPA. The sources and solutions: fossil fuels. 2015. https://www.epa.gov/nutrientpollution/sources-and-solutions-fossil-fuels. Accessed 15 Nov 2017.
10. European Academies Science Advisory Council. The current status of biofuels in the European Union, their environmental impacts and future prospects. http://www.easac.eu/fileadmin/PDF-s/reports
11. Rulli MC, Bellomi D, Cazzoli A, De Carolis G, D'Odorico P. The water-land-food nexus of first-generation biofuels. Sci Rep. 2016;6:22521.
12. Chisti Y. Biodiesel from microalgae. Trends Biotechnol. 2008;26:126-31.
13. Sandeep K. Sub- and supercritical water technology for biofuels. In: Lee JW, editor. Advanced biofuels and bioproducts. New York: Springer; 2013. p. 147-83.
14. Greenwell HC, Laurens LML, Shields RJ, Lovitt RW, Flynn KJ. Placing microalgae on the biofuels priority list: a review of the technological challenges. J R Soc Interface. 2009;7:703-26.
15. Sheehan J, Dunahay T, Benemann J, Roessler P. Look back at the U.S. Department of energy's aquatic species program: biodiesel from algae. https://www.nrel.gov/docs/legosti/fy98/24190.pdf. Accessed 30 Nov 2017.
16. Muylaert K, Bastiaens L, Vandamme D, Gouveia L. Harvesting of microalgae: overview of process options and their strengths and drawbacks. Microalgae-based biofuels and bioproducts. Lisbon: Woodhead Publishing; 2017. p. 113-32.
17. Lee RE. Basic characteristics of the algae. In: Lee RE, editor. Phycology. New York: Cambridge University Press; 2008. p. 3-30.
18. Serrano-Bermúdez LM, Serrano Bermúdez LM. Estudio de cuatro cepas nativas de microalgas para evaluar su potencial uso en la producción de biodiesel. 2012. http://www.bdigital.unal.edu.co/7825/1/299883.2012.pdf. Accessed 10 June 2017.
19. May P, Wienkoop S, Kempa S, Usadel B, Christian N, Rupprecht J, et al. Metabolomics- and proteomics-assisted genome annotation and analysis of the draft metabolic network of Chlamydomonas reinhardtii. Genetics. 2008;179:157-66.
20. Christian N, May P, Kempa S, Handorf T, Ebenhöh O. An integrative approach towards completing genome-scale metabolic networks. Mol BioSyst. 2009;5:1889.
21. Imam S, Schäuble S, Valenzuela J, De Lomana ALG, Carter W, Price ND, et al. A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses. Plant J. 2015;84:1239-56.
22. Boyle NR, Sengupta N, Morgan JA. Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii. PLoS ONE. 2017;12:e0177292.
23. Boyle NR, Morgan JA. Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii. BMC Syst Biol. 2009;3:4.
24. Manichaikul A, Ghamsari L, Hom EFY, Lin C, Murray RR, Chang RL, et al. Metabolic network analysis integrated with transcript verification for sequenced genomes. Natl Inst Health. 2009;6:589-92.
25. De Dal'Molin CG, Quek L-E, Palfreyman RW, Nielsen LK. AlgaGEM - a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome. BMC Genomics. 2011;12(Suppl 4):5.
26. Chang RL, Ghamsari L, Manichaikul A, Hom EFY, Balaji S, Fu W, et al. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol Syst Biol. 2011;7:518.
27. Cogne G, Rügen M, Bockmayr A, Titica M, Dussap CG, Cornet JF, et al. A model-based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: application to the green algae Chlamydomonas reinhardtii. Biotechnol Prog. 2011;27:631-40.
28. Kliphuis AMJ, Klok AJ, Martens DE, Lamers PP, Janssen M, Wijffels RH. Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance. J Appl Phycol. 2012;24:253-66.
29. Rügen M, Bockmayr A, Legrand J, Cogne G. Network reduction in metabolic pathway analysis: elucidation of the key pathways involved in the photoautotrophic growth of the green alga Chlamydomonas reinhardtii. Metab Eng. 2012;14:458-67.
30. Chaiboonchoe A, Dohai BS, Cai H, Nelson DR, Jijakli K, Salehi-Ashtiani K. Microalgal metabolic network model refinement through high-throughput functional metabolic profiling. Front Bioeng Biotechnol. 2014;2:1-12.
31. Wu C, Xiong W, Dai J, Wu Q. Genome-based metabolic mapping and 13C flux analysis reveal systematic properties of anoleaginous microalga Chlorella protothecoides. Plant Physiol. 2015;167:586-99.
32. Zuñiga C, Li C-T, Huelsman T, Levering J, Zielinski DC, McConnell BO, et al. Genome-scale metabolic model for the green alga Chlorella vulgaris UTEX 395 accurately predicts phenotypes under autotrophic, heterotrophic, and mixotrophic growth Conditions. Plant Physiol. 2016;172:589-602.
33. Zuñiga C, Levering J, Antoniewicz MR, Guarnieri MT, Betenbaugh MJ, Zengler K, et al. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris. Plant Physiol. 2017;176:450-62.
34. 13C-tracer and gas chromatography-mass spectrometry analyses reveal metabolic flux distribution in the oleaginous microalga Chlorella protothecoides. Plant Physiol. 2010;154:1001-11.
35. Yang C, Hua Q, Shimizu K. Energetics and carbon metabolism during growth of microalgal cells under photoautotrophic, mixotrophic and cyclic light-autotrophic/dark-heterotrophic conditions. Biochem Eng J. 2000;6:87-102.
36. Pham N. Genome-scale constraint-based metabolic modeling and analysis of Nannochloropsis sp. 2016. https://brage.bibsys.no/xmlui/bitstream/handle/11250/2399905/12511-FULLTEXT.pdf?sequence=1&isAllowed=y. Accessed 4 Dec 2017.
37. Loira N, Mendoza S, Paz Cortés M, Rojas N, Travisany D, Di Genova A, et al. Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production. BMC Syst Biol. 2017;11:66.
38. Shah AR, Ahmad A, Srivastava S, Jaffar Ali BM. Reconstruction and analysis of a genome-scale metabolic model of Nannochloropsis gaditana. Algal Res. 2017;26:354-64.
39. Shastri A, Morgan J. Flux balance analysis of photoautotrophic metabolism. Biotechnol Prog. 2005;21:1617-26.
40. Navarro E, Montagud A, Fernández de Córdoba P, Urchueguía JF. Metabolic flux analysis of the hydrogen production potential in Synechocystis sp. PCC6803. Int J Hydrogen Energy. 2009;34:8828-38.
41. Knoop H, Zilliges Y, Lockau W, Steuer R. The metabolic network of Synechocystis sp. PCC 6803: systemic properties of autotrophic growth. Plant Physiol. 2010;154:410-22.
42. Yoshikawa K, Kojima Y, Nakajima T, Furusawa C, Hirasawa T, Shimizu H. Reconstruction and verification of a genome-scale metabolic model for Synechocystis sp. PCC6803. Appl Microbiol Biotechnol. 2011;92:347-58.
43. Montagud A, Zelezniak A, Navarro E, de Córdoba PF, Urchueguía JF, Patil KR. Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803. Biotechnol J. 2011;6:330-42.
44. Montagud A, Navarro E, Fernández de Córdoba P, Urchueguía JF, Patil K. Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium. BMC Syst Biol. 2010;4:156.
45. Knoop H, Gründel M, Zilliges Y, Lehmann R, Hoffmann S, Lockau W, et al. Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803. PLoS Comput Biol. 2013;9:e1003081.
46. Nogales J, Gudmundsson S, Knight EM, Palsson BO, Thiele I. Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. Proc Natl Acad Sci. 2012;109:2678-83.
47. Lim DKY, Schuhmann H, Thomas-Hall SR, Chan KCK, Wass TJ, Aguilera F, et al. RNA-Seq and metabolic flux analysis of Tetraselmis sp. M8 during nitrogen starvation reveals a two-stage lipid accumulation mechanism. Bioresour Technol. 2017;244:1281-93.
48. Bogen C, Al-Dilaimi A, Albersmeier A, Wichmann J, Grundmann M, Rupp O, et al. Reconstruction of the lipid metabolism for the microalga Monoraphidium neglectum from its genome sequence reveals characteristics suitable for biofuel production. BMC Genomics. 2013;14:926.
49. Krumholz EW, Yang H, Weisenhorn P, Henry CS, Libourel IGL. Genome-wide metabolic network reconstruction of the picoalga Ostreococcus. J Exp Bot. 2012;63:2353-62.
50. Baroukh C, Muñoz-Tamayo R, Steyer JP, Bernard O. A new framework for metabolic modeling under non-balanced growth. Application to carbon metabolism of unicellular microalgae. PLoS ONE. 2013;12:107-12.
51. Kroth PG, Chiovitti A, Gruber A, Martin-Jezequel V, Mock T, Parker MS, et al. A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis. PLoS ONE. 2008;3:e1426.
52. Singh D, Carlson R, Fell D, Poolman M. Modelling metabolism of the diatom Phaeodactylum tricornutum. Biochem Soc Trans. 2015;43:1182-6.
53. Kim J, Fabris M, Baart G, Kim MK, Goossens A, Vyverman W, et al. Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum. Plant J. 2016;85:161-76.
54. Levering J, Broddrick J, Dupont CL, Peers G, Beeri K, Mayers J, et al. Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom. PLoS ONE. 2016;11:1-22.
55. Banerjee A, Banerjee C, Negi S, Chang J-S, Shukla P. Improvements in algal lipid production: a systems biology and gene editing approach. Crit Rev Biotechnol. 2017;38:369-85.
56. Čuperlović-Culf M. Dynamic metabolic profiling and metabolite network and pathways modeling. In: Nakamura K, editor. NMR metabolomics in cancer research. Oxford: Woodhead Publishing; 2013. p. 365-83.
57. Tan J, Zuñiga C, Zengler K. Unraveling interactions in microbial communities - from co-cultures to microbiomes. J Microbiol. 2015;53:295-305.
58. Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010;28:245-8.
59. Thiele I, Palsson BØ. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5:93-121.
60. Feist AM, Palsson BØ. The growing scope of application of genome-scale metabolic reconstructions: the case of E. coli. Nat Biotechnol. 2008;26:659-67.
61. Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 2005;33:5691-702.
62. Snyder EE, Kampanya N, Lu J, Nordberg EK, Karur HR, Shukla M, et al. PATRIC: the VBI PathoSystems resource integration center. Nucleic Acids Res. 2007;35:D401-6.
63. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. The RAST server: rapid annotations using subsystems technology. BMC Genomics. 2008;9:75.
64. Fritzemeier CJ, Hartleb D, Szappanos B, Papp B, Lercher MJ. Erroneous energy-generating cycles in published genome scale metabolic networks: identification and removal. PLoS Comput Biol. 2017;13:1-14.
65. Terzer M, Maynard ND, Covert MW, Stelling J. Genome-scale metabolic networks. Wiley Interdiscip Rev Biol Med. 2009;1:285-97.
66. Villadsen J, Nielsen J, Lidén G. Biochemical reaction networks. In: Nielsen J, Villadsen J, editors. Bioreaction engineering principles. 3rd ed. Berlin: Springer; 2011. p. 151-214.
67. Price ND, Papin JA, Schilling CH, Palsson BO. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol. 2003;21:162-9.
68. Chen K, Gao Y, Mih N, O'Brien EJ, Yang L, Palsson BO. Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proc Natl Acad Sci. 2017;114:11548-53.
69. 2 levels. mSystems. 2017;2:e00142-216.
70. Takahashi O, Park Y-I, Nakamura Y. Biotechnology of microalgae, based on molecular biology and biochemistry of eukaryotic algae and cyanobacteria. FEBS Lett. 2009;583:3882-90.
71. Wan MX, Wang RM, Xia JL, Rosenberg JN, Nie ZY, Kobayashi N, et al. Physiological evaluation of a new Chlorella sorokiniana isolate for its biomass production and lipid accumulation in photoautotrophic and heterotrophic cultures. Biotechnol Bioeng. 2012;109:1958-64.
72. Toledo-Cervantes A, Garduño Solórzano G, Campos JE, Martínez-García M, Morales M. Characterization of Scenedesmus obtusiusculus AT-UAM for high-energy molecules accumulation: deeper insight into biotechnological potential of strains of the same species. Biotechnol Rep. 2018;17:16-23.
73. Cabello J, Morales M, Revah S. Dynamic photosynthetic response of the microalga Scenedesmus obtusiusculus to light intensity perturbations. Chem Eng J. 2014;252:104-11.
74. Dikicioglu D, Klrdar B, Oliver SG. Biomass composition: the "elephant in the room" of metabolic modelling. Metabolomics. 2015;11:1690-701.
75. García Sánchez CE, Torres Sáez RG. Comparison and analysis of objective functions in flux balance analysis. Biotechnol Prog. 2014;30:985-91.
76. Serrano-Bermúdez LM, González Barrios AF, Maranas CD, Montoya D. Clostridium butyricum maximizes growth while minimizing enzyme usage and ATP production: metabolic flux distribution of a strain cultured in glycerol. BMC Syst Biol. 2017;11:1-13.
77. Feist AM, Palsson BO. The biomass objective function. Curr Opin Microbiol. 2010;13:344-9.
78. Broddrick JT, Rubin BE, Welkie DG, Du N, Mih N, Diamond S, et al. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis. Proc Natl Acad Sci. 2016;113:E8344-53.
79. Muthuraj M, Palabhanvi B, Misra S, Kumar V, Sivalingavasu K, Das D. Flux balance analysis of Chlorella sp. FC2 IITG under photoautotrophic and heterotrophic growth conditions. Photosynth Res. 2013;118:167-79.
80. Mahadevan R, Edwards JS, Doyle FJ. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J. 2002;83:1331-40.
81. Cuthrell JE, Biegler LT. On the optimization of differential-algebraic process systems. AIChE J. 1987;33:1257-70.
82. Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE, Palsson BO. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep. 2017;7:1-12.
83. Wiechert W. 13C metabolic flux analysis. Metab Eng. 2001;3:195-206.
84. Trinh CT, Wlaschin A, Srienc F. Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl Microbiol Biotechnol. 2009;81:813-26.
85. Baroukh C, Muñoz-Tamayo R, Bernard O, Steyer JP. Mathematical modeling of unicellular microalgae and cyanobacteria metabolism for biofuel production. Curr Opin Biotechnol. 2015;33:198-205.
86. De Bhowmick G, Koduru L, Sen R. Metabolic pathway engineering towards enhancing microalgal lipid biosynthesis for biofuel application - a review. Renew Sustain Energy Rev. 2015;50:1239-53.
87. Nikolaev EV, Burgard AP, Maranas CD. Elucidation and structural analysis of conserved pools for genome-scale metabolic reconstructions. Biophys J. 2005;88:37-49.
88. Nelson DL, Cox MM. Glycolysis, gluconeogenesis, and the pentose phosphate pathway. In: Freeman WH, editor. Lehninger principles of biochemistry. 4th ed. New York: Cox Publisher; 2008. p. 521-60.
89. Coronil T, Lara C, Guerrero MG. Shift in carbon flow and stimulation of amino-acid turnover induced by nitrate and ammonium assimilation in Anacystis nidulans. Planta. 1993;189:461-7.
90. Rai AK. Symbiotic systems with cyanobacteria-cyanobioses. In: Rai AK, editor. Cyanobacterial nitrogen metabolism and environmental biotechnology. Chennai: Narosa Pub House; 1997. p. 299.
91. Juneja A, Chaplen FWR, Murthy GS. Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels. Bioresour Technol. 2015;213:103-10.
92. Hamilton JJ, Reed JL. Identification of functional differences in metabolic networks using comparative genomics and constraint-based models. PLoS ONE. 2012;7:e34670.
93. Vu TT, Hill EA, Kucek LA, Konopka AE, Beliaev AS, Reed JL. Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production. Biotechnol J. 2013;8:619-30.
94. Qian X, Kim MK, Kumaraswamy GK, Agarwal A, Lun DS, Dismukes GC. Flux balance analysis of photoautotrophic metabolism: uncovering new biological details of subsystems involved in cyanobacterial photosynthesis. Biochim Biophys Acta Bioenerg. 2016;1858:276-87.
95. Hendry JI, Prasannan CB, Joshi A, Dasgupta S, Wangikar PP. Metabolic model of Synechococcus sp. PCC 7002: prediction of flux distribution and network modification for enhanced biofuel production. Bioresour Technol. 2016;213:190-7.
96. Yang C. Metabolic flux analysis in Synechocystis using isotope distribution from 13C-labeled glucose. Metab Eng. 2002;4:202-16.