Optimizing ethanol production selectivity

Mathematical and Computer Modelling

Volumn 53, Issue 7-8, 2011, Pages 1363-1373

  Lall, Raman ^a   Donohue, Timothy J ^a   Marino, Simeone ^b   Mitchell, Julie C ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

Ethanol production; Glycolysis pathway; Michaelis Menten; S systems; Selectivity maximization

Indexed keywords

ETHANOL PRODUCTION; GLYCOLYSIS PATHWAY; MICHAELIS-MENTEN; S-SYSTEMS; SELECTIVITY MAXIMIZATION;

GLUCOSE; MATHEMATICAL MODELS; METABOLISM; NUCLEAR MAGNETIC RESONANCE; OPTIMIZATION; PATHOLOGY; RESONANCE; VOLATILE FATTY ACIDS;

ETHANOL;

EID: 79951579832     PISSN: 08957177     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mcm.2010.01.016     Document Type: Article

References (36)

1. Sjoberg A., Persson I., Quednau M., Hahn-Hagerdal B. The influence of limiting and non-limiting growth conditions on glucose and maltose metabolism in Lactococcus lactis ssp. lactis strain. Appl. Microbiol. Biotechnol. 1995, 42:931-938.
2. Thomas T.D., Turner K.W., Crow V.L. Galactose fermentation by Streptococcus lactis and Streptococcus cremoris: Pathways, products and regulation. J. Bacteriol. 1980, 144:672-682.
3. A.R. Neves, Metabolic strategies to reroute carbon fluxes in Lactococcus lactis: Kinetics of intracellular metabolite pools by in vivo nuclear magnetic resonance, Doctoral Dissertation, Universidade Nova de Lisboa, Instituto de Tecnologia Quimica e Biologica, 2001.
4. Takahashi S., Abbe K., Yamada T. Purification of pyruvate formate-Lyase from Streptococcus mutants and its regulatory properties. J. Bacteriol. 1982, 149:1034-1040.
5. Hoefnagel H.N.M., Starrenburg C.J.M., Martens E.D., Hugenholtz J., Kleerebezem M., Van Swam, Bongers R., Westerhoff V.H., Snoep L.J. Metabolic engineering of lactic acid bacteria, the combined approach: Kinetic modeling, metabolic control and experimental analysis. Microbiology 2002, 148:1003-1013.
6. Savageau M.A. Biochemical systems theory: Operational differences among variant representations and their significance. J. Theoret. Biol. 1991, 151:509-530.
7. Kacser H., Acerenza L. A universal method for achieving increases in metabolite production. Eur. J. Biochem. 1993, 216:361-367.
8. Kholodenko B.N., Cascante M., Hoek J.B., Westerhoff H.V., Schwaber J. Metabolic design: How to engineer a living cell to desired metabolite concentrations and fluxes. Biotechnol. Bioeng. 1998, 59:239-247.
9. Savageau M.A. Biochemical systems analysis. 1. Some mathematical properties of the rate law for the component enzymatic reactions. J. Theoret. Biol. 1969, 25:365-369.
10. Savageau M.A. Biochemical systems analysis. 2. The steady-state solutions for an n-pool system using a power law approximation. J. Theoret. Biol. 1969, 25:370-379.
11. Voit E.O., Savageau M.A. Accuracy of alternative representations for integrated biochemical systems. Biochemistry 1987, 26:6869-6880.
12. Regan L., Bogle I.D.L., Dunnill P. Simulation and optimization of metabolic pathways. Comput. Chem. Eng. 1993, 17(5-6):627-637.
13. Torres N.V., Voit E.O., Gonzalez-Alcon C. Optimization of nonlinear biotechnological processes with linear programming: Application to citric acid production by Aspergillus niger. Biotechnol. Bioeng. 1996, 49:247-258.
14. Voit E.O. Canonical Nonlinear Modeling: S-System Approach to Understanding Complexity 1991, Van Nostrand Reinhold, New York, p. xii-365.
15. Torres N.V., Voit E.O., Gonzalez-Alcon C., Rodriguez-Acosta F. An integrated optimization method for biochemical systems. Description of method and application to ethanol, glycerol and carbohydrate production in Saccharomyces cervisiae. Biotechnol. Bioeng. 1997, 55(5):758-772.
16. Shuster S., Heinrich R. Minimization of intermediates concentrations as a suggested optimality principle for biochemical networks. I. Theoretical analysis. J. Math. Biol. 1991, 29:425-442.
17. Shuster S., Schuster R., Heinrich R. Minimization of intermediates concentrations as a suggested optimality principle for biochemical networks, II. Time hierarchy, enzymatic rate laws, and erytrocyte metabolism. J. Math. Biol. 1991, 29:443-455.
18. Pettersson G. Evolutionary optimization of the catalytic efficiency of enzymes. Eur. J. Biochem. 1992, 206:289-295.
19. Rodriguez-Acosta F., Carlos R.M., Nestor T.V. Non-linear optimization of biotechnological processes by stochastic algorithms: Application to the maximization of the production rate of ethanol, glycerol and carbohydrates by Saccharomyces cerevisiae. J. Biotechnol. 1999, 68:15-28.
20. Nocedal J., Wright S.J. Numerical Optimization 1999, Springer, New York.
21. Fletcher R. Practical Methods of Optimization 1987, Wiley, New York.
22. Bijork A. Numerical Methods for Least Squares Problems 1996, SIAM, Philadelphia, PA.
23. Reeves C.R., Rowe J.E. Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory 2003, Kluwer Academic Publishers, Boston.
24. Marino S., Voit E.O. An automated procedure for the extraction of metabolic network information from time series data. J. Bioinform. Comput. Biol. 2006, 4(3):665-691.
25. Voit E.O. Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists 2000, Cambridge University Press, New York.
26. Savageau M.A. Biochemical systems analysis: A study of function and design in molecular biology. Advanced Book Program 1976, Addison Wesley Pub. Co., Reading, MA, USA, p. xvii-379.
27. Fogler S.H. Elements of Chemical Reaction Engineering 1992, Prentice-Hall, New Jersey.
28. Voit E.O., Almeida J. Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 2004, 11:1670-1681.
29. Maki Y., Ueda T., Okamoto M., Uematsu N., Inamura Y., Eguchi Y. Inference of genetic network using the expression profile time course data of mouse P19 cells. Genome Informatics 2002, 13:382-383.
30. Voit E.O., Almeida J., Marino S., Lall R., Goel G., Neves A.R., Santos H. Regulation of glycolysis in Lactococcus lactis: An unfinished systems biological case study. IEEE Proc. Syst. Biol. 2006, 153(4).
31. Neves A.R., Ramos A., Nunes M.C., Kleerebezem M., Hugenholtz J de Vos W.M., Almeida J., Santos H. in vivo Nuclear Magnetic Resonance studies of glycolytic kinetics in Lactococcus lactis. Biotechnol. Bioeng. 1999, 64(2):200-212.
32. Neves A.R., Ramos A., Shearman C., Gasson M.J., Almeida J.S., Santos H. Metabolic characterization of Lactococcus lactis deficient in lactate dehydrogenase using in vivo 13C NMR. Eur. J. Biochem. 2000, 267(12):3859-3868.
33. Neves A.R., Ventura R., Mansour N., Shearman C., Gasson M.J., Maycock C., Ramos A., Santos H. Is the glycolytic flux in Lactococcus lactis primarily controlled by the redox charge? Kinetics of NAD+ and NADH pools determined in vivo by 13C NMR. J. Biol. Chem. 2002, 277(31):28088-28098.
34. Vos D.M.W., Hugenholtz J. Engineering metabolic highways in Lactococci and other lactic acid bacteria. Trends Biotechnol. 2004, 22(2):72-79.
35. Olivier B.G., Snoep J.L. Web-based kinetic modelling using JWS online. Bioinformatics 2004, 20:2143-2144.
36. Lall R., Voit E.O. Parameter estimation in modulated, unbranched reaction chains within biochemical systems. Comput. Biol. Chem. 2005, 29:309-318.