Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization

Metabolic Engineering

Volumn 25, Issue , 2014, Pages 159-173

  Sánchez, Benjamín J ^a   Pérez Correa, José R ^a   Agosin, Eduardo ^a  

^a PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

Author keywords

Dynamic flux balance analysis; Genome scale metabolic models; Metaheuristic optimization; Parameter estimation; Sensitivity analysis; Yeast

Indexed keywords

GENE EXPRESSION; ITERATIVE METHODS; METABOLISM; MODEL STRUCTURES; PARAMETER ESTIMATION; SENSITIVITY ANALYSIS;

ENVIRONMENTAL CONDITIONS; FLUX BALANCE ANALYSIS; GENETIC MODIFICATIONS; GENOME-SCALE METABOLIC MODELING; GENOME-SCALE METABOLIC MODELS; META-HEURISTIC OPTIMIZATIONS; MODEL CALIBRATION; REPARAMETERIZATION;

YEAST;

AEROBIC FERMENTATION; ANAEROBIC FERMENTATION; ANATOMIC MODEL; ARTICLE; CALIBRATION; CONTROLLED STUDY; FED BATCH CULTURE; FUNGAL METABOLISM; FUNGAL REPRODUCTION; FUNGUS GROWTH; GENOME SCALE METABOLIC MODEL; MATHEMATICAL MODEL; METABOLIC ENGINEERING; METABOLIC PARAMETERS; NONHUMAN; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SACCHAROMYCES CEREVISIAE; SENSITIVITY ANALYSIS; SPECIES CULTIVATION; ALGORITHM; BIOLOGICAL MODEL; COMPUTER SIMULATION; METABOLIC FLUX ANALYSIS; METABOLISM; PHYSIOLOGY; PROCEDURES; SIGNAL TRANSDUCTION;

BIOLOGICAL PRODUCT; PROTEOME; SACCHAROMYCES CEREVISIAE PROTEIN;

ALGORITHMS; BIOLOGICAL PRODUCTS; COMPUTER SIMULATION; METABOLIC FLUX ANALYSIS; MODELS, BIOLOGICAL; PROTEOME; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SIGNAL TRANSDUCTION;

EID: 84907316784     PISSN: 10967176     EISSN: 10967184     Source Type: Journal    
DOI: 10.1016/j.ymben.2014.07.004     Document Type: Article

References (90)

1. Aceituno F.F., Orellana M., Torres J., Mendoza S., Slater A.W., Melo F., Agosin E. Oxygen response of the wine yeast Saccharomyces cerevisiae EC1118 grown under carbon-sufficient, nitrogen-limited enological conditions. Appl. Environ. Microbiol. 2012, 78:8340-8352.
2. Åkesson M., Förster J., Nielsen J. Integration of gene expression data into genome-scale metabolic models. Metab. Eng. 2004, 6:285-293.
3. Alberton K.P., Alberton A.L., Di Maggio J.A., Díaz M.S., Secchi A.R. Accelerating the parameters identifiability procedure: set by set selection. Comput. Chem. Eng. 2013, 55:181-197.
4. Alper H., Jin Y.-S., Moxley J.F., Stephanopoulos G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 2005, 7:155-164.
5. Balsa-Canto E., Alonso A., Banga J. An iterative identification procedure for dynamic modeling of biochemical networks. BMC Syst. Biol. 2010, 4. (Article no. 11).
6. Balsa-Canto E., Rodriguez-Fernandez M., Banga J. Optimal design of dynamic experiments for improved estimation of kinetic parameters of thermal degradation. J. Food Eng. 2007, 82:178-188.
7. Becker S.A., Feist A.M., Mo M.L., Hannum G., Palsson B.Ø., Herrgard M.J. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat. Protoc. 2007, 2:727-738.
8. Benedict S.R. A reagent for the detection of reducing sugars. J. Biol. Chem. 1909, 5:485-487.
9. Bornstein B.J., Keating S.M., Jouraku A., Hucka M. LibSBML: an API library for SBML. Bioinformatics 2008, 24:880-881.
10. Bro C., Regenberg B., Förster J., Nielsen J. In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab. Eng. 2006, 8:102-111.
11. Cárcamo M., Saa P.A., Torres J., Torres S., Mandujano P., Correa J.R.P., Agosin E. Effective dissolved oxygen control strategy for high cell-density cultures. IEEE Lat. Am. Trans. 2014, 12:389-394.
12. Cherry J.M., Hong E.L., Amundsen C., Balakrishnan R., Binkley G., Chan E.T., Christie K.R., Costanzo M.C., Dwight S.S., Engel S.R., Fisk D.G., Hirschman J.E., Hitz B.C., Karra K., Krieger C.J., Miyasato S.R., Nash R.S., Park J., Skrzypek M.S., Simison M., Weng S., Wong E.D. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 2012, 40:D700-D705.
13. Chu Y., Hahn J. Integrating parameter selection with experimental design under uncertainty for nonlinear dynamic systems. AIChE J. 2008, 54:2310-2320.
14. Copeland W.B., Bartley B.A., Chandran D., Galdzicki M., Kim K.H., Sleight S.C., Maranas C.D., Sauro H.M. Computational tools for metabolic engineering. Metab. Eng. 2012, 14:270-280.
15. Covert M.W., Knight E.M., Reed J.L., Herrgård M.J., Palsson B.Ø. Integrating high-throughput and computational data elucidates bacterial networks. Nature 2004, 429:92-96.
16. Covert M.W., Schilling C.H., Palsson B.Ø. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 2001, 213:73-88.
17. Covert M.W., Xiao N., Chen T.J., Karr J.R. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008, 24:2044-2050.
18. Curran K.A., Crook N.C., Alper H.S. Using flux balance analysis to guide microbial metabolic engineering. Methods Mol. Biol. 2012, 834:197-216.
19. Dennis J.E., Gay D.M., Walsh R.E. An adaptive nonlinear least-squares algorithm. ACM Trans. Math. Softw. 1981, 7:348-368.
20. Di Maggio J., Diaz Ricci J.C., Diaz M.S. Global sensitivity analysis in dynamic metabolic networks. Comput. Chem. Eng. 2010, 34:770-781.
21. Diderich J.A., Schepper M., Van Hoek P., Luttik M.A.H., Van Dijken J.P., Pronk J.T., Klaasen P., Boelens H.F.M., De Mattos M.J.T., Van Dam K., Kruckeberg A.L. Glucose uptake kinetics and transcription of HXT genes in chemostat cultures of Saccharomyces cerevisiae. J. Biol. Chem. 1999, 274:15350-15359.
22. Dobson P.D., Smallbone K., Jameson D., Simeonidis E., Lanthaler K., Pir P., Lu C., Swainston N., Dunn W.B., Fisher P., Hull D., Brown M., Oshota O., Stanford N.J., Kell D.B., King R.D., Oliver S.G., Stevens R.D., Mendes P. Further developments towards a genome-scale metabolic model of yeast. BMC Syst. Biol. 2010, 4. (Article no. 145).
23. Edwards J.S., Palsson B.Ø. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl. Acad. Sci. U. S. A. 2000, 97:5528-5533.
24. Egea J.A., Balsa-Canto E. Dynamic optimization of nonlinear processes with an enhanced scatter search method. Ind. Eng. Chem. Res. 2009, 48:4388-4401.
25. Feng X., Xu Y., Chen Y., Tang Y.J. Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1. PLoS Comput. Biol. 2012, 8. (Article no. 2).
26. Förster J., Famili I., Fu P., Palsson B.Ø., Nielsen J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 2003, 13:244-253.
27. Gao J., Gorenflo V.M., Scharer J.M., Budman H.M. Dynamic metabolic modeling for a MAB bioprocess. Biotechnol. Prog. 2008, 23:168-181.
28. Gianchandani E.P., Chavali A.K., Papin J.A. The application of flux balance analysis in systems biology. Wiley Interdiscip. Rev.: Syst. Biol. Med 2010, 2:372-382.
29. Gurobi, 2013. version 5.5.0. Gurobi Optimization, Inc., Houston, Texas.
30. Hanly T.J., Henson M.A. Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures. Biotechnol. Bioeng. 2011, 108:376-385.
31. Hanly T.J., Henson M.A. Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures. Biotechnol. Biofuels 2013, 6. (Article no. 44).
32. Hanly T.J., Urello M., Henson M.A. Dynamic flux balance modeling of S. cerevisiae and E. coli co-cultures for efficient consumption of glucose/xylose mixtures. Appl. Microbiol. Biotechnol. 2012, 93:2529-2541.
33. Hao H., Zak D., Sauter T., Schwaber J., Ogunnaike B. Modeling the VPAC2-activated cAMP/PKA signaling pathway: from receptor to circadian clock gene induction. Biophys. J. 2006, 90:1560-1571.
34. Heavner B.D., Smallbone K., Barker B., Mendes P., Walker L.P. Yeast 5-an expanded reconstruction of the Saccharomyces cerevisiae metabolic network. BMC Syst. Biol. 2012, 6. (Article no. 55).
35. Heavner B.D., Smallbone K., Price N.D., Walker L.P. Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance. Database 2013, 2013. (Article ID bat059).
36. Henry C.S., DeJongh M., Best A.a., Frybarger P.M., Linsay B., Stevens R.L. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 2010, 28:977-982.
37. Herrgård M.J., Swainston N., Dobson P., Dunn W.B., Arga K.Y., Arvas M., Blüthgen N., Borger S., Costenoble R., Heinemann M., Hucka M., Le Novère N., Li P., Liebermeister W., Mo M.L., Oliveira A.P., Petranovic D., Pettifer S., Simeonidis E., Smallbone K., Spasić I., Weichart D., Brent R., Broomhead D.S., Westerhoff H.V., Kirdar B., Penttilä M., Klipp E., Palsson B.Ø., Sauer U., Oliver S.G., Mendes P., Nielsen J., Kell D.B. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat. Biotechnol. 2008, 26:1155-1160.
38. Hibbs M.A., Hess D.C., Myers C.L., Huttenhower C., Li K., Troyanskaya O.G. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 2007, 23:2692-2699.
39. Hjersted J.L., Henson M.A. Optimization of fed-batch Saccharomyces cerevisiae fermentation using dynamic flux balance models. Biotechnol. Prog. 2006, 22:1239-1248.
40. Hjersted J.L., Henson M.A. Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae. IET Syst. Biol. 2009, 3:167-179.
41. Hjersted J.L., Henson M.A., Mahadevan R. Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture. Biotechnol. Bioeng. 2007, 97:1190-1204.
42. Höffner K., Harwood S.M., Barton P.I. A reliable simulator for dynamic flux balance analysis. Biotechnol. Bioeng. 2013, 110:792-802.
43. Holzhütter H. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem. 2004, 271:2905-2922.
44. Jacquez J.A., Greif P. Numerical parameter identifiability and estimability: integrating identifiability, estimability, and optimal sampling design. Math. Biosci. 1985, 77:201-227.
45. Jaqaman K., Danuser G. Linking data to models: data regression. Nat. Rev. Mol. Cell Biol. 2006, 7:813-819.
46. Jouhten P., Wiebe M., Penttilä M. Dynamic flux balance analysis of the metabolism of Saccharomyces cerevisiae during the shift from fully respirative or respirofermentative metabolic states to anaerobiosis. FEBS J. 2012, 279:3338-3354.
47. Karr J.R., Sanghvi J.C., Macklin D.N., Gutschow M.V., Jacobs J.M., Bolival B., Assad-Garcia N., Glass J.I., Covert M.W. A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150:389-401.
48. Keating S.M., Bornstein B.J., Finney A., Hucka M. SBMLToolbox: an SBML toolbox for MATLAB users. Bioinformatics 2006, 22:1275-1277.
49. Kitano H. Systems biology: a brief overview. Science 2002, 295:1662-1664.
50. Kravaris C., Hahn J., Chu Y. Advances and selected recent developments in state and parameter estimation. Comput. Chem. Eng. 2013, 51:111-123.
51. Kruger N., Ratcliffe G., Steuer R. Computational approaches to the topology, stability and dynamics of metabolic networks. Phytochemistry 2007, 68:2139-2151.
52. Lai L.-C., Kosorukoff A.L., Burke P.V., Kwast K.E. Metabolic-state-dependent remodeling of the transcriptome in response to anoxia and subsequent reoxygenation in Saccharomyces cerevisiae. Eukaryot. Cell 2006, 5:1468-1489.
53. Landaw E.M., DiStefano J.J. Multiexponential, multicompartmental, and noncompartmental modeling. II. Data analysis and statistical considerations. Am. J. Physiol. 1984, 246:R665-R677.
54. Li P., Vu Q.D. Identification of parameter correlations for parameter estimation in dynamic biological models. BMC Syst. Biol. 2013, 7. (Article no. 91).
55. Lisha K.P., Sarkar D. Dynamic flux balance analysis of batch fermentation: effect of genetic manipulations on ethanol production. Bioprocess Biosyst. Eng. 2014, 37:617-627.
56. Mahadevan R., Edwards J.S., Doyle F.J. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys. J. 2002, 83:1331-1340.
57. MATLAB, 2013. version 8.1 (R2013a). The MathWorks Inc., Natick, Massachusetts.
58. Meadows A.L., Karnik R., Lam H., Forestell S., Snedecor B. Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation. Metab. Eng. 2010, 12:150-160.
59. Mehra A., Lee K.H., Hatzimanikatis V. Insights into the relation between mRNA and protein expression patterns: I. Theoretical considerations. Biotechnol. Bioeng. 2003, 84:822-833.
60. Møller K., Sharif M.Z., Olsson L. Production of fungal α-amylase by Saccharomyces kluyveri in glucose-limited cultivations. J. Biotechnol. 2004, 111:311-318.
61. Nolan R.P., Lee K. Dynamic model of CHO cell metabolism. Metab. Eng. 2011, 13:108-124.
62. Nookaew I., Jewett M.C., Meechai A., Thammarongtham C., Laoteng K., Cheevadhanarak S., Nielsen J., Bhumiratana S. The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. BMC Syst. Biol. 2008, 2. (Article no. 71).
63. Oberhardt M.A., Palsson B.Ø., Papin J.A. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 2009, 5. (Article no. 320).
64. Oddone G.M., Mills D.A., Block D.E. A dynamic, genome-scale flux model of Lactococcus lactis to increase specific recombinant protein expression. Metab. Eng. 2009, 11:367-381.
65. Orellana M., Aceituno F.F., Slater A.W., Almonacid L.I., Melo F., Agosin E. Metabolic and transcriptomic response of the wine yeast Saccharomyces cerevisiae strain EC1118 after an oxygen impulse under carbon-sufficient, nitrogen-limited fermentative conditions. FEMS Yeast Res. 2014, 14:412-424.
66. Orth J.D., Thiele I., Palsson B.Ø. What is flux balance analysis?. Nat. Biotechnol. 2010, 28:245-248.
67. Osterlund T., Nookaew I., Nielsen J. Fifteen years of large scale metabolic modeling of yeast: developments and impacts. Biotechnol. Adv. 2012, 30:979-988.
68. Park J.M., Kim T.Y., Lee S.Y. Constraints-based genome-scale metabolic simulation for systems metabolic engineering. Biotechnol. Adv. 2009, 27:979-988.
69. Petersen B., Gernaey K., Vanrolleghem P.A. Practical identifiability of model parameters by combined respirometric-titrimetric measurements. Water Sci. Technol. 2001, 43:347-355.
70. Pizarro F., Varela C., Martabit C., Bruno C., Pérez-Correa J.R., Agosin E. Coupling kinetic expressions and metabolic networks for predicting wine fermentations. Biotechnol. Bioeng. 2007, 98:986-998.
71. Provost A., Bastin G. Dynamic metabolic modelling under the balanced growth condition. J. Process Control 2004, 14:717-728.
72. Provost A., Bastin G., Agathos S.N., Schneider Y.-J. Metabolic design of macroscopic bioreaction models: application to Chinese hamster ovary cells. Bioprocess Biosyst. Eng. 2006, 29:349-366.
73. Saa P.A., Moenne M.I., Pérez-Correa J.R., Agosin E. Modeling oxygen dissolution and biological uptake during pulse oxygen additions in oenological fermentations. Bioprocess Biosyst. Eng. 2012, 35:1167-1178.
74. Sacher J., Saa P., Cárcamo M., López J., Gelmi C., Pérez-Correa J.R. Improved calibration of a solid substrate fermentation model. Electron. J. Biotechnol. 2011, 14. (Article no. 5).
75. Sainz J., Pizarro F., Pérez-Correa J.R., Agosin E. Modeling of yeast metabolism and process dynamics in batch fermentation. Biotechnol. Bioeng. 2003, 81:818-828.
76. Schellenberger J., Que R., Fleming R.M.T., Thiele I., Orth J.D., Feist A.M., Zielinski D.C., Bordbar A., Lewis N.E., Rahmanian S., Kang J., Hyduke D.R., Palsson B.Ø. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 2011, 6:1290-1307.
77. Schuetz R., Zamboni N., Zampieri M., Heinemann M., Sauer U. Multidimensional optimality of microbial metabolism. Science 2012, 336:601-604.
78. Smith W.R., Missen R.W. Sensitivity analysis in chemical education: part 1. Introduction and application to explicit models. Chem. Eng. Educ. 2003, 2003:222-227.
79. Sriram K., Rodriguez-Fernandez M., Doyle F.J. Modeling cortisol dynamics in the neuro-endocrine axis distinguishes normal, depression, and post-traumatic stress disorder (PTSD) in humans. PLoS Comput. Biol. 2012, 8. (Article no. 2).
80. Stephanopoulos G., Aristidou A.A., Nielsen J. Metabolic Engineering: Principles and Methodologies 1998, Academic Press, San Diego, USA.
81. Tepeli A., Hortaçsu A. A fuzzy logic approach for regulation in flux balance analysis. Biochem. Eng. J. 2008, 39:137-148.
82. Thiele I., Palsson B.Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 2010, 5:93-121.
83. Vargas F.A., Pizarro F., Pérez-Correa J.R., Agosin E. Expanding a dynamic flux balance model of yeast fermentation to genome-scale. BMC Syst. Biol. 2011, 5. (Article no. 75).
84. Varma A., Palsson B.Ø. Metabolic capabilities of Escherichia coli II. Optimal growth patterns. J. Theor. Biol. 1993, 165:503-522.
85. Varma A., Palsson B.Ø. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 1994, 60:3724-3731.
86. Villadsen J., Patil K.R. Optimal fed-batch cultivation when mass transfer becomes limiting. Biotechnol. Bioeng. 2007, 98:706-710.
87. Waldherr, S., Oyarzún, D.A., Bockmayr, A., 2013. Dynamic optimization of metabolic networks coupled with gene expression [WWW Document]. Cornell Univ. Libr. URL (accessed 1.2.14.). http://arxiv.org/abs/1309.4936.
88. Yin, W., 2011. Gurobi Mex: A MATLAB interface for Gurobi.
89. Zhuang K., Izallalen M., Mouser P., Richter H., Risso C., Mahadevan R., Lovley D.R. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 2011, 5:305-316.
90. Zomorrodi A.R., Suthers P.F., Ranganathan S., Maranas C.D. Mathematical optimization applications in metabolic networks. Metab. Eng. 2012, 14:672-686.