Co-evolution of strain design methods based on flux balance and elementary mode analysis

Metabolic Engineering Communications

Volumn 2, Issue , 2015, Pages 85-92

  Machado, Daniel ^a   Herrgård, Markus J ^b  

^a UNIVERSITY OF MINHO   (Portugal)

^b Chalmers University of Technology   (Denmark)

Author keywords

Computational methods; Constraint based modeling; Metabolic engineering; Rational strain design

Indexed keywords

1, 4 BUTANEDIOL; 2, 3 BUTANEDIOL; ACETIC ACID; ACTINORHODINE; ALCOHOL; FLAVONOID; FUMARIC ACID; GLUCOSE; GLYCEROL; ISOBUTANOL; LACTIC ACID; LYCOPENE; MALONYL COENZYME A; PUTRESCINE; SUCCINIC ACID; THREONINE; UNCLASSIFIED DRUG; VALINE; VANILLIN; XYLOSE;

BIOMASS PRODUCTION; CELL GROWTH; ELEMENTARY MODE ANALYSIS; ESCHERICHIA COLI; FLUX ASSAY; GENE DELETION; GENE OVEREXPRESSION; GENETIC MANIPULATION; GENETIC TRANSCRIPTION; METABOLITE; MINIMAL CUT SET; MINIMAL METABOLIC FUNCTIONALITY; NONHUMAN; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REVIEW; SIGNAL TRANSDUCTION; SIMULATION; STRAIN IMPROVEMENT; SYSTEM ANALYSIS; UPREGULATION;

ESCHERICHIA COLI; SACCHAROMYCES CEREVISIAE;

EID: 84938074954     PISSN: None     EISSN: 22140301     Source Type: Journal    
DOI: 10.1016/j.meteno.2015.04.001     Document Type: Review

References (89)

1. Alper H., Jin Y.-S., Moxley J., Stephanopoulos G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in escherichia coli. Metabol. Eng. 2005, 7(3):155-164.
2. Alper H., Miyaoku K., Stephanopoulos G. Construction of lycopene-overproducing e. coli strains by combining systematic and combinatorial gene knockout targets. Nat. Biotechnol. 2005, 23(5):612-616.
3. Asadollahi M.A., Maury J., Patil K.R., Schalk M., Clark A., Nielsen J. Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metabol. Eng. 2009, 11(6):328-334.
4. Becker J., Zelder O., Häfner S., Schröder H., Wittmann C. From zero to herodesign-based systems metabolic engineering of Corynebacterium glutamicum for l-lysine production. Metabol. Eng. 2011, 13(2):159-168.
5. Bohl, K., de Figueiredo, L.F., Hädicke, O., Klamt, S., Kost, C., Schuster, S., Kaleta, C., 2010. Casop gs: computing intervention strategies targeted at production improvement in genome-scale metabolic networks. In: Lecture Notes in Informatics, pp. 71-80.
6. Bordbar A., Monk J.M., King Z.A., Palsson B.O. Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 2014, 15(2):107-120.
7. Brochado A.R., Matos C., Møller B.L., Hansen J., Mortensen U.H., Patil K.R. Improved vanillin production in Baker[U+05F3]s yeast through in silico design. Microb. Cell Fact. 2010, 9(1):84.
8. Burgard A.P., Pharkya P., Maranas C.D. Optknock. a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 2003, 84(6):647-657.
9. Chemler J.A., Fowler Z.L., McHugh K.P., Koffas M.A. Improving nadph availability for natural product biosynthesis in Escherichia coli by metabolic engineering. Metabol. Eng. 2010, 12(2):96-104.
10. Choi H.S., Lee S.Y., Kim T.Y., Woo H.M. In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol. 2010, 76(10):3097-3105.
11. Choon Y.W., Mohamad M.S., Deris S., Chong C.K., Chai L.E., Ibrahim Z., Omatu S. Identifying gene knockout strategies using a hybrid of bees algorithm and flux balance analysis for in silico optimization of microbial strains. Distributed Computing and Artificial Intelligence 2012, 371-378. Springer, Berlin, Heidelberg.
12. Choon Y.W., Mohamad M.S., Deris S., Illias R.M., Chong C.K., Chai L.E., Omatu S., Corchado J.M. Differential bees flux balance analysis with optknock for in silico microbial strains optimization. PLoS One 2014, 9(7):e102744.
13. Chowdhury A., Zomorrodi A.R., Maranas C.D. k-optforce. integrating kinetics with flux balance analysis for strain design. PLoS Comput. Biol. 2014, 10(2):e1003487.
14. Costanza J., Carapezza G., Angione C., Lió P., Nicosia G. Robust design of microbial strains. Bioinformatics 2012, 28(23):3097-3104.
15. Cotten C., Reed J. Constraint-based strain design using continuous modifications (cosmos) of flux bounds finds new strategies for metabolic engineering. Biotechnol. J. 2013, 8(5):595-604.
16. de Figueiredo L.F., Podhorski A., Rubio A., Kaleta C., Beasley J.E., Schuster S., Planes F.J. Computing the shortest elementary flux modes in genome-scale metabolic networks. Bioinformatics 2009, 25(23):3158-3165.
17. Edwards, J., Palsson, B., 2000. The escherichia coli mg1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl. Acad. Sci. 97 (10), 5528-5533.
18. Egen D., Lun D.S. Truncated branch and bound achieves efficient constraint-based genetic design. Bioinformatics 2012, 28(12):1619-1623.
19. Erickson K.E., Gill R.T., Chatterjee A. Constrictor. constraint modification provides insight into design of biochemical networks. PloS One 2014, 9(11):e113820.
20. Feist A.M., Henry C.S., Reed J.L., Krummenacker M., Joyce A.R., Karp P.D., Broadbelt L.J., Hatzimanikatis V., Palsson B.Ø. A genome-scale metabolic reconstruction for Escherichia coli k-12 mg1655 that accounts for 1260 orfs and thermodynamic information. Mol. Syst. Biol. 2007, 3(1).
21. Feist A.M., Zielinski D.C., Orth J.D., Schellenberger J., Herrgard M.J., Palsson B.Ø. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metabol. Eng. 2010, 12(3):173-186.
22. Flowers D., Thompson R.A., Birdwell D., Wang T., Trinh C.T. Smet. systematic multiple enzyme targeting-a method to rationally design optimal strains for target chemical overproduction. Biotechnol. J. 2013, 8(5):605-618.
23. Fong S.S., Burgard A.P., Herring C.D., Knight E.M., Blattner F.R., Maranas C.D., Palsson B.O. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 2005, 91(5):643-648.
24. Förster J., Famili I., Fu P., Palsson B.Ø, Nielsen J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 2003, 13(2):244-253.
25. Fowler Z.L., Gikandi W.W., Koffas M.A. Increased malonyl coenzyme a biosynthesis by tuning the Escherichia coli metabolic network and its application to flavanone production. Appl. Environ. Microbiol. 2009, 75(18):5831-5839.
26. Hädicke O., Klamt S. Casop. a computational approach for strain optimization aiming at high productivity. J. Biotechnol. 2010, 147(2):88-101.
27. Hädicke O., Klamt S. Computing complex metabolic intervention strategies using constrained minimal cut sets. Metabol. Eng. 2011, 13(2):204-213.
28. Imielinski M., Belta C. Exploiting the pathway structure of metabolism to reveal high-order epistasis. BMC Syst. Biol. 2008, 2(1):40.
29. Izallalen M., Mahadevan R., Burgard A., Postier B., Didonato R., Sun J., Schilling C.H., Lovley D.R. Geobacter sulfurreducens strain engineered for increased rates of respiration. Metabol. Eng. 2008, 10(5):267-275.
30. Jantama K., Haupt M., Svoronos S.A., Zhang X., Moore J., Shanmugam K., Ingram L. Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli c that produce succinate and malate. Biotechnol. Bioeng. 2008, 99(5):1140-1153.
31. Jungreuthmayer C., Zanghellini J. Designing optimal cell factories. integer programming couples elementary mode analysis with regulation. BMC Syst. Biol. 2012, 6(1):103.
32. Kaleta, C., de Figueiredo, L., Behre, J., Schuster, S., 2009. EFMEvolver: computing elementary flux modes in genome-scale metabolic networks. In: Lecture Notes in Informatics, vol. 157, pp. 179-189.
33. Kim J., Reed J. Optorf. optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Syst. Biol. 2010, 4(1):53.
34. Kim J., Reed J.L., Maravelias C.T. Large-scale bi-level strain design approaches and mixed-integer programming solution techniques. PLoS One 2011, 6(9):e24162.
35. Kim M., Sang Yi, J., Kim J., Kim J.-N., Kim M.W., Kim B.-G. Reconstruction of a high-quality metabolic model enables the identification of gene overexpression targets for enhanced antibiotic production in streptomyces coelicolor a3. Biotechnol. J. 2014, (2).
36. King Z.A., Feist A.M. Optimizing cofactor specificity of oxidoreductase enzymes for the generation of microbial production strainsoptswap. Ind. Biotechnol. 2013, 9(4):236-246.
37. Klamt S., Gilles E.D. Minimal cut sets in biochemical reaction networks. Bioinformatics 2004, 20(2):226-234.
38. Lee K.H., Park J.H., Kim T.Y., Kim H.U., Lee S.Y. Systems metabolic engineering of escherichia coli for l-threonine production. Mol. Syst. Biol. 2007, 3:1.
39. Lee S.J., Lee D.-Y., Kim T.Y., Kim B.H., Lee J., Lee S.Y. Metabolic engineering of escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl. Environ. Microbiol. 2005, 71(12):7880-7887.
40. Lewis N.E., Hixson K.K., Conrad T.M., Lerman J.A., Charusanti P., Polpitiya A.D., Adkins J.N., Schramm G., Purvine S.O., Lopez-Ferrer D., et al. Omic data from evolved e. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 2010, 6(1).
41. Lewis N.E., Nagarajan H., Palsson B.O. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 2012, 10(4):291-305.
42. Li S., Huang D., Li Y., Wen J., Jia X. Rational improvement of the engineered isobutanol-producing bacillus subtilis by elementary mode analysis. Microb. Cell Fact. 2012, 11:101.
43. Lun D.S., Rockwell G., Guido N.J., Baym M., Kelner J.A., Berger B., Galagan J.E., Church G.M. Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 2009, 5(1).
44. Machado D., Costa R.S., Rocha M., Ferreira E.C., Tidor B., Rocha I. Modeling formalisms in systems biology. AMB Express 2011, 1(1):1-14.
45. Machado D., Soons Z., Patil K.R., Ferreira E.C., Rocha I. Random sampling of elementary flux modes in large-scale metabolic networks. Bioinformatics 2012, 28(18):i515-i521.
46. McCloskey D., Palsson B.Ø, Feist A.M. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol. Syst. Biol. 2013, 9:1.
47. Melzer G., Esfandabadi M.E., Franco-Lara E., Wittmann C. Flux design. in silico design of cell factories based on correlation of pathway fluxes to desired properties. BMC Syst. Biol. 2009, 3(1):120.
48. Neuner A., Heinzle E. Mixed glucose and lactate uptake by Corynebacterium glutamicum through metabolic engineering. Biotechnol. J. 2011, 6(3):318-329.
49. Ng C.Y., Jung M.-y., Lee J., Oh M.-K. Production of 2,3-butanediol in Saccharomyces cerevisiae by in silico aided metabolic engineering. Microb. Cell Fact. 2012, 11(1):68.
50. Oh Y.-G., Lee D.-Y., Lee S.Y., Park S. Multiobjective flux balancing using the nise method for metabolic network analysis. Biotechnol. Prog. 2009, 25(4):999-1008.
51. Ohno, S., Shimizu, H., Furusawa, C., 2013. Fastpros: screening of reaction knockout strategies for metabolic engineering. Bioinformatics, btt672,. http://dx.doi.org/10.1093/bioinformatics/btt672.
52. Orth J., Fleming R., Palsson B. Reconstruction and use of microbial metabolic networks. the core Escherichia coli metabolic model as an educational guide. EcoSal-Escherichia coli and Salmonella: Cellular and Molecular Biology 2009, 56-99. ASM Press, Washington, DC. A. Bock, I.R. Curtiss, J. Kaper, P. Karp, F. Neidhardt, T. Nystrom, J. Slauch, C. Squires, D. Ussery (Eds.).
53. Orth J.D., Thiele I., Palsson B.Ø. What is flux balance analysis?. Nat. Biotechnol. 2010, 28(3):245-248.
54. Otero J.M., Cimini D., Patil K.R., Poulsen S.G., Olsson L., Nielsen J. Industrial systems biology of Saccharomyces cerevisiae enables novel succinic acid cell factory. PloS One 2013, 8(1):e54144.
55. Park J.H., Kim T.Y., Lee K.H., Lee S.Y. Fed-batch culture of Escherichia coli for l-valine production based on in silico flux response analysis. Biotechnol. Bioeng. 2011, 108(4):934-946.
56. Park, J.H., Lee, K.H., Kim, T.Y., Lee, S.Y., 2007. Metabolic engineering of escherichia coli for the production of l-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. 104 (19), 7797-7802.
57. Park J.M., Park H.M., Kim W.J., Kim H.U., Kim T.Y., Lee S.Y. Flux variability scanning based on enforced objective flux for identifying gene amplification targets. BMC Syst. Biol. 2012, 6(1):106.
58. Patil K., Rocha I., Forster J., Nielsen J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinf. 2005, 6(1):308.
59. Pharkya P., Burgard A.P., Maranas C.D. Optstrain. a computational framework for redesign of microbial production systems. Genome Res. 2004, 14(11):2367-2376.
60. Pharkya P., Maranas C.D. An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metabol. Eng. 2006, 8(1):1-13.
61. Poblete-Castro I., Binger D., Rodrigues A., Becker J., Martins dos Santos V.A., Wittmann C. In-silico-driven metabolic engineering of pseudomonas putida for enhanced production of poly-hydroxyalkanoates. Metabol. Eng. 2013, 15:113-123.
62. Ranganathan S., Suthers P.F., Maranas C.D. Optforce. an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol. 2010, 6(4):e1000744.
63. Ranganathan S., Tee T.W., Chowdhury A., Zomorrodi A.R., Yoon J.M., Fu Y., Shanks J.V., Maranas C.D. An integrated computational and experimental study for overproducing fatty acids in Escherichia coli. Metabol. Eng. 2012, 14(6):687-704.
64. Ren S., Zeng B., Qian X. Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints. BMC Bioinf. 2013, 14(Suppl 2):S17.
65. Rocha M., Maia P., Mendes R., Pinto J.P., Ferreira E.C., Nielsen J., Patil K.R., Rocha I. Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinf. 2008, 9(1):499.
66. Rockwell G., Guido N.J., Church G.M. Redirector. designing cell factories by reconstructing the metabolic objective. PLoS Comput. Biol. 2013, 9(1):e1002882.
67. Sánchez A.M., Bennett G.N., San K.-Y. Batch culture characterization and metabolic flux analysis of succinate-producing Escherichia coli strains. Metabol. Eng. 2006, 8(3):209-226.
68. Sander J.D., Joung J.K. Crispr-cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 2014, 32(4):347-355.
69. Schuster S., Hilgetag C. On elementary flux modes in biochemical reaction systems at steady state. J. Biol. Syst. 1994, 2(02):165-182.
70. Segrè, D., Vitkup, D., Church, G.M., 2002. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. 99 (23), 15112-15117.
71. Shlomi, T., Berkman, O., Ruppin, E., 2005. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc. Natl. Acad. Sci. 102 (21), 7695-7700.
72. Soons Z.I., Ferreira E.C., Patil K.R., Rocha I. Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes. PloS One 2013, 8(4):e61648.
73. Tepper N., Shlomi T. Predicting metabolic engineering knockout strategies for chemical production. accounting for competing pathways. Bioinformatics 2010, 26(4):536-543.
74. Tokuyama K., Ohno S., Yoshikawa K., Hirasawa T., Tanaka S., Furusawa C., Shimizu H. Increased 3-hydroxypropionic acid production from glycerol, by modification of central metabolism in Escherichia coli. Microb. Cell Fact. 2014, 13(1):1-11.
75. Toya, Y., Shiraki, T., Shimizu, H., 2014. Ssdesign: Computational metabolic pathway design based on flux variability using elementary flux modes. Biotechnol. Bioeng,. http://dx.doi.org/10.1002/bit.25498.
76. Trinh C.T., Carlson R., Wlaschin A., Srienc F. Design, construction and performance of the most efficient biomass producing E. coli bacterium. Metabol. Eng. 2006, 8(6):628-638.
77. Trinh C.T., Li J., Blanch H.W., Clark D.S. Redesigning escherichia coli metabolism for anaerobic production of isobutanol. Appl. Environ. Microbiol. 2011, 77(14):4894-4904.
78. Trinh C.T., Srienc F. Metabolic engineering of Escherichia coli for efficient conversion of glycerol to ethanol. Appl. Environ. Microbiol. 2009, 75(21):6696-6705.
79. Trinh C.T., Unrean P., Srienc F. Minimal escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses. Appl. Environ. Microbiol. 2008, 74(12):3634-3643.
80. Trinh C.T., Wlaschin A., Srienc F. Elementary mode analysis. a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl. Microbiol. Biotechnol. 2009, 81(5):813-826.
81. Unrean P., Trinh C.T., Srienc F. Rational design and construction of an efficient E. coli for production of diapolycopendioic acid. Metabol. Eng. 2010, 12(2):112-122.
82. von Kamp A., Klamt S. Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput. Biol. 2014, 10(1):e1003378.
83. Xu C., Liu L., Zhang Z., Jin D., Qiu J., Chen M. Genome-scale metabolic model in guiding metabolic engineering of microbial improvement. Appl. Microbiol. Biotechnol. 2013, 97(2):519-539.
84. Xu P., Ranganathan S., Fowler Z.L., Maranas C.D., Koffas M.A. Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-coa. Metabol. Eng. 2011, 13(5):578-587.
85. Xu Z., Zheng P., Sun J., Ma Y. Reacknock. identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network. PloS One 2013, 8(12):e72150.
86. Yang L., Cluett W.R., Mahadevan R. Emilio. a fast algorithm for genome-scale strain design. Metabol. Eng. 2011, 13(3):272-281.
87. Yim H., Haselbeck R., Niu W., Pujol-Baxley C., Burgard A., Boldt J., Khandurina J., Trawick J.D., Osterhout R.E., Stephen R., et al. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat. Chem. Biol. 2011, 7(7):445-452.
88. Yousofshahi M., Orshansky M., Lee K., Hassoun S., et al. Probabilistic strain optimization under constraint uncertainty. BMC Syst. Biol. 2013, 7(1):29.
89. Zhuang K., Yang L., Cluett W.R., Mahadevan R. Dynamic strain scanning optimization. an efficient strain design strategy for balanced yield, titer, and productivity. Dyssco strategy for strain design. BMC Biotechnol. 2013, 13(1):1-15.