Optimization and evolution in metabolic pathways: Global optimization techniques in Generalized Mass Action models

Journal of Biotechnology

Volumn 149, Issue 3, 2010, Pages 141-153

  Sorribas, Albert ^a   Pozo, Carlos ^b   Vilaprinyo, Ester ^c   Guillén Gosálbez, Gonzalo ^b   Jiménez, Laureano ^b   Alves, Rui ^a  

^a UNIVERSITY OF LLEIDA   (Spain)

^b UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^c HOSPITAL VERGE DE LA CINTA   (Spain)

Author keywords

Design principles; Evolution; Operation principles; Optimization; Power law formalism

Indexed keywords

GLOBAL OPTIMIZATION; METABOLIC ENGINEERING; METABOLISM; OPTIMIZATION; PHYSIOLOGY;

DESIGN PRINCIPLES; ENGINEERING APPLICATIONS; EVOLUTION; EVOLUTIONARY OPTIMIZATIONS; GLOBAL OPTIMIZATION TECHNIQUES; NONLINEAR GLOBAL OPTIMIZATION; OPERATION PRINCIPLES; POWER-LAW;

PHYSIOLOGICAL MODELS;

ADAPTATION; ALCOHOL PRODUCTION; ARTICLE; BIOTECHNOLOGY; CELL METABOLISM; ENZYME ACTIVITY; EVOLUTION; HEAT SHOCK; MASS ACTION; METHODOLOGY; NONHUMAN; PRIORITY JOURNAL; STEADY STATE; YEAST;

EID: 77956011197     PISSN: 01681656     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbiotec.2010.01.026     Document Type: Article

References (89)

1. Aldana M., Balleza E., Kauffman S., Resendiz O. Robustness and evolvability in genetic regulatory networks. J. Theor. Biol. 2007, 245:433-448.
2. Alvarez-Vasquez F., Sims K.J., Hannun Y.A., Voit E.O. Integration of kinetic information on yeast sphingolipid metabolism in dynamical pathway models. J. Theor. Biol. 2004, 226:265-291.
3. Alves R., Savageau M.A. Extending the method of mathematically controlled comparison to include numerical comparisons. Bioinformatics 2000, 16:786-798.
4. Bailey J.E. Reflections on the scope and the future of metabolic engineering and its connections to functional genomics and drug discovery. Metab. Eng. 2001, 3:111-114.
5. Bailey J.E. Lessons from metabolic engineering for functional genomics and drug discovery. Nat. Biotechnol. 1999, 17:616-618.
6. Bailey J.E., Sburlati A., Hatzimanikatis V., Lee K., Renner W.A., Tsai P.S. Inverse metabolic engineering: a strategy for directed genetic engineering of useful phenotypes. Biotechnol. Bioeng. 1996, 52:109-121.
7. Bailey J.E. Toward a science of metabolic engineering. Science 1991, 252:1668-1675.
8. Bailey J.E., Birnbaum S., Galazzo J.L., Khosla C., Shanks J.V. Strategies and challenges in metabolic engineering. Ann. N. Y. Acad. Sci. 1990, 589:1-15.
9. Banga J.R. Optimization in computational systems biology. BMC Syst. Biol. 2008, 2:47.
10. Bedford T., Hartl D.L. Optimization of gene expression by natural selection. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:1133-1138.
11. Braunstein A., Mulet R., Pagnani A. Estimating the size of the solution space of metabolic networks. BMC Bioinformatics 2008, 9:240.
12. Cascante M., Curto R., Sorribas A. Comparative characterization of the fermentation pathway of Saccharomyces cerevisiae using biochemical systems theory and metabolic control analysis: steady-state analysis. Math. Biosci. 1995, 130:51-69.
13. Causton H.C., Ren B., Koh S.S., Harbison C.T., Kanin E., Jennings E.G., Lee T.I., True H.L., Lander E.S., Young R.A. Remodeling of yeast genome expression in response to environmental changes. Mol. Biol. Cell 2001, 12:323-337.
14. Chachuat B., Singer A.B., Barton P.I. Global methods for dynamic optimization and mixed-integer dynamic optimization. Ind. Eng. Chem. Res. 2006, 45:8373-8392.
15. Chang Y.J., Sahinidis N.V. Optimization of metabolic pathways under stability considerations. Comput. Chem. Eng. 2005, 29:467-479.
16. Chou I.C., Voit E.O. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 2009, 219:57-83.
17. Coelho P.M., Salvador A., Savageau M.A. Quantifying global tolerance of biochemical systems: Design implications for moiety-transfer cycles. PLoS Comput. Biol. 2009, 5:e1000319.
18. Curto R., Voit E.O., Sorribas A., Cascante M. Validation and steady-state analysis of a power-law model of purine metabolism in man. Biochem. J. 1997, 324(Pt 3):761-775.
19. Curto R., Sorribas A., Cascante M. Comparative characterization of the fermentation pathway of Saccharomyces cerevisiae using biochemical systems theory and metabolic control analysis: model definition and nomenclature. Math. Biosci. 1995, 130:25-50.
20. Dayarian A., Chaves M., Sontag E.D., Sengupta A.M. Shape, size, and robustness: feasible regions in the parameter space of biochemical networks. PLoS Comput. Biol. 2009, 5:e1000256.
21. de Atauri P., Sorribas A., Cascante M. Analysis and prediction of the effect of uncertain boundary values in modeling a metabolic pathway. Biotechnol. Bioeng. 2000, 68:18-30.
22. Eisen M.B., Spellman P.T., Brown P.O., Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U.S.A. 1998, 95:14863-14868.
23. El-Samad H., Kurata H., Doyle J.C., Gross C.A., Khammash M. Surviving heat shock: control strategies for robustness and performance. Proc. Natl. Acad. Sci. U.S.A. 2005, 102:2736-2741.
24. Esposito W.R., Floudas C.A. Deterministic global optimization in nonlinear optimal control problems. J. Global Optim. 2000, 17:97-126.
25. Garcia-Martinez J., Gonzalez-Candelas F., Perez-Ortin J.E. Common gene expression strategies revealed by genome-wide analysis in yeast. Genome Biol. 2007, 8:R222.
26. Gasch A.P., Spellman P.T., Kao C.M., Carmel-Harel O., Eisen M.B., Storz G., Botstein D., Brown P.O. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 2000, 11:4241-4257.
27. Gianchandani E.P., Oberhardt M.A., Burgard A.P., Maranas C.D., Papin J.A. Predicting biological system objectives de novo from internal state measurements. BMC Bioinformatics 2008, 9:43.
28. Goodman C. Engineering ingenuity at iGEM. Nat. Chem. Biol. 2008, 4:13.
29. Grossman I.E., Biegler L.T. Part II. Future perspective on optimization. Comput. Chem. Eng. 2004, 28:1193-1218.
30. Guillén-Gosálbez G., Sorribas A. Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses. BMC Bioinformatics 2009, 10:386.
31. Han J.D. Understanding biological functions through molecular networks. Cell Res. 2008, 18:224-237.
32. Hansen P., Jaumard B., Lu S. Global optimization of univariate lipschitz functions. I. Surrey and properties. Math. Program. 1992, 55:251-272.
33. Hatzimanikatis V., Liao J.C. A memorial review of jay bailey's contribution in prokaryotic metabolic engineering. Biotechnol. Bioeng. 2002, 79:504-508.
34. Hatzimanikatis V., Emmerling M., Sauer U., Bailey J.E. Application of mathematical tools for metabolic design of microbial ethanol production. Biotechnol. Bioeng. 1998, 58:154-161.
35. Hatzimanikatis V., Floudas C.A., Bailey J.E. Optimization of regulatory architectures in metabolic reaction networks. Biotechnol. Bioeng. 1996, 52:485-500.
36. Hlavacek W.S., Savageau M.A. Method for determining natural design principles of biological control circuits. J. Intell. Fuzzy Syst. 1998, 6:87.
37. Jenkins G.M. The emerging role for sphingolipids in the eukaryotic heat shock response. Cell Mol. Life Sci. 2003, 60:701-710.
38. Kashiwagi A., Urabe I., Kaneko K., Yomo T. Adaptive response of a gene network to environmental changes by fitness-induced attractor selection. PLoS One 2006, 1:e49.
39. Kitano H. Towards a theory of biological robustness. Mol. Syst. Biol. 2007, 3:137.
40. Kitano H. Biological robustness. Nat. Rev. Genet. 2004, 5:826-837.
41. Klipp E., Nordlander B., Kruger R., Gennemark P., Hohmann S. Integrative model of the response of yeast to osmotic shock. Nat. Biotechnol. 2005, 23:975-982.
42. Koonin E.V. Darwinian evolution in the light of genomics. Nucleic Acids Res. 2009, 37:1011-1034.
43. Kurata H., El-Samad H., Iwasaki R., Ohtake H., Doyle J.C., Grigorova I., Gross C.A., Khammash M. Module-based analysis of robustness tradeoffs in the heat shock response system. PLoS Comput. Biol. 2006, 2:e59.
44. Lee J.M., Gianchandani E.P., Papin J.A. Flux balance analysis in the era of metabolomics. Brief Bioinform. 2006, 7:140-150.
45. Marin-Sanguino A., Voit E.O., Gonzalez-Alcon C., Torres N.V. Optimization of biotechnological systems through geometric programming. Theor. Biol. Med. Model. 2007, 4:38.
46. Martens H., Veflingstad S.R., Plahte E., Martens M., Bertrand D., Omholt S.W. The genotype-phenotype relationship in multicellular pattern-generating models-the neglected role of pattern descriptors. BMC Syst. Biol. 2009, 3:87.
47. Mitchell A., Romano G.H., Groisman B., Yona A., Dekel E., Kupiec M., Dahan O., Pilpel Y. Adaptive prediction of environmental changes by microorganisms. Nature 2009, 460:220-224.
48. Molina-Navarro M.M., Castells-Roca L., Belli G., Garcia-Martinez J., Marin-Navarro J., Moreno J., Perez-Ortin J.E., Herrero E. Comprehensive transcriptional analysis of the oxidative response in yeast. J. Biol. Chem. 2008, 283:17908-17918.
49. Morohashi M., Winn A.E., Borisuk M.T., Bolouri H., Doyle J., Kitano H. Robustness as a measure of plausibility in models of biochemical networks. J. Theor. Biol. 2002, 216:19-30.
50. Nielsen J. Principles of optimal metabolic network operation. Mol. Syst. Biol. 2007, 3:126.
51. Nikolaev E.V. The elucidation of metabolic pathways and their improvements using stable optimization of large-scale kinetic models of cellular systems. Metab. Eng. 2009.
52. O'Connor T.D., Mundy N.I. Genotype-phenotype associations: Substitution models to detect evolutionary associations between phenotypic variables and genotypic evolutionary rate. Bioinformatics 2009, 25:i94-i100.
53. Papamichail I., Adjiman C.S. Global optimization of dynamic systems. Comput. Chem. Eng. 2004, 28:403-415.
54. Papamichail I., Adjiman C.S. A rigorous global optimization algorithm for problems with ordinary differential equations. J. Global Optim. 2002, 24:1-33.
55. Polisetty P.K., Gatzke E.P., Voit E.O. Yield optimization of regulated metabolic systems using deterministic branch-and-reduce methods. Biotechnol. Bioeng. 2008, 99:1154-1169.
56. Porn R., Bjork K.M., Westerkund T. Global solution of optimization problems with signomial parts. Discrete Optim. 2008, 5:108-120.
57. Pozo, C., Sorribas, A., Jiménez, L., Guillén-Gosálbez, G., submitted for publication. Outer approximation-based algorithm for biotechnology studies in systems biology. Comput. Chem. Eng.
58. Quesada I., Grossman I.E. A global optimization algorithm for linear fractional and bilinear programs. J. Global Optim. 1995, 6:39-76.
59. Raiford D.W., Heizer E.M., Miller R.V., Akashi H., Raymer M.L., Krane D.E. Do amino acid biosynthetic costs constrain protein evolution in Saccharomyces cerevisiae?. J. Mol. Evol. 2008, 67:621-630.
60. Romero-Santacreu, L., Moreno, J., Perez-Ortin, J.E., Alepuz, P., 2009. Specific and global regulation of mRNA stability during osmotic stress in Saccharomyces cerevisiae. RNA.
61. Salvador A., Savageau M.A. Evolution of enzymes in a series is driven by dissimilar functional demands. Proc. Natl. Acad. Sci. U.S.A. 2006, 103:2226-2231.
62. Salvador A., Savageau M.A. Quantitative evolutionary design of glucose 6-phosphate dehydrogenase expression in human erythrocytes. Proc. Natl. Acad. Sci. U.S.A. 2003, 100:14463-14468.
63. Savageau M.A., Coelho P.M., Fasani R.A., Tolla D.A., Salvador A. Phenotypes and tolerances in the design space of biochemical systems. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:6435-6440.
64. Savageau M.A. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics 1998, 149:1665-1676.
65. Savageau M.A. Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology 1976, Addison-Wesley, Reading, MA.
66. Savageau M.A. Optimal design of feedback control by inhibition: dynamic considerations. J. Mol. Evol. 1975, 5:199-222.
67. Savageau M.A. Optimal design of feedback control by inhibition. J. Mol. Evol. 1974, 4:139-156.
68. Savageau M.A. Genetic regulatory mechanisms and the ecological niche of Escherichia coli. Proc. Natl. Acad. Sci. U.S.A. 1974, 71:2453-2455.
69. Savageau M.A. Parameter sensitivity as a criterion for evaluating and comparing the performance of biochemical systems. Nature 1971, 229:542-544.
70. Savageau M.A. Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. J. Theor. Biol. 1969, 25:370-379.
71. Savageau M.A. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J. Theor. Biol. 1969, 25:365-369.
72. Schuetz R., Kuepfer L., Sauer U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 2007, 3:119.
73. Sims K.J., Spassieva S.D., Voit E.O., Obeid L.M. Yeast sphingolipid metabolism: clues and connections. Biochem. Cell Biol. 2004, 82:45-61.
74. Singer A.B., Barton P.I. Global optimization with nonlinear ordinary differential equations. J. Global Optim. 2006, 34:159-190.
75. Tawarmalani M., Sahinidis N.V. Convexification and global optimization. Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications (Nonconvex Optimization and its Applications) 2002, Springer. Anonymous (Ed.).
76. Teusink B., Wiersma A., Jacobs L., Notebaart R.A., Smid E.J. Understanding the adaptive growth strategy of lactobacillus plantarum by in silico optimisation. PLoS Comput. Biol. 2009, 5:e1000410.
77. Torres N.V., Voit E.O., Glez-Alcon C., Rodriguez F. An indirect optimization method for biochemical systems: description of method and application to the maximization of the rate of ethanol, glycerol, and carbohydrate production in Saccharomyces cerevisiae. Biotechnol. Bioeng. 1997, 55:758-772.
78. 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.
79. Trotter E.W., Berenfeld L., Krause S.A., Petsko G.A., Gray J.V. Protein misfolding and temperature up-shift cause G1 arrest via a common mechanism dependent on heat shock factor in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U.S.A. 2001, 98:7313-7318.
80. Vilaprinyo E., Alves R., Sorribas A. Use of physiological constraints to identify quantitative design principles for gene expression in yeast adaptation to heat shock. BMC Bioinformatics 2006, 7:184.
81. Vital-Lopez F.G., Armaou A., Nikolaev E.V., Maranas C.D. A computational procedure for optimal engineering interventions using kinetic models of metabolism. Biotechnol. Prog. 2006, 22:1507-1517.
82. Voit E.O. Design principles and operating principles: the yin and yang of optimal functioning. Math. Biosci. 2003, 182:81-92.
83. Voit E.O. Computational Analysis of Biochemical Systems. A Practical Guide for Biochemists and Molecular Biologists 2000, Cambridge University Press, Cambridge, U.K.
84. Voit E.O., Radivoyevitch T. Biochemical systems analysis of genome-wide expression data. Bioinformatics 2000, 16:1023-1037.
85. Voit E.O. Optimization in integrated biochemical systems. Biotechnol. Bioeng. 1992, 40:572-582.
86. Wagner A. Energy constraints on the evolution of gene expression. Mol. Biol. Evol. 2005, 22:1365-1374.
87. Watson T.G. Effects of sodium chloride on steady-state growth and metabolism of Saccharomyces cerevisiae. J. Gen. Microbiol. 1970, 64:91-99.
88. Wiebe M.G., Rintala E., Tamminen A., Simolin H., Salusjarvi L., Toivari M., Kokkonen J.T., Kiuru J., Ketola R.A., Jouhten P., Huuskonen A., Maaheimo H., Ruohonen L., Penttila M. Central carbon metabolism of Saccharomyces cerevisiae in anaerobic, oxygen-limited and fully aerobic steady-state conditions and following a shift to anaerobic conditions. FEMS Yeast Res. 2008, 8:140-154.
89. Wilkins A.S. Between " design" and " bricolage": genetic networks, levels of selection, and adaptive evolution. Proc. Natl. Acad. Sci. U.S.A. 2007, 104(Suppl. 1):8590-8596.