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Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies

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Abstract

A review of the use of elementary flux modes (EFMs) and their applications in metabolic engineering covered with yield space analysis (YSA) is presented. EFMs are an invaluable tool in mathematical modeling of biochemical processes. They are described from their inception in 1994, followed by various improvements of their computation in later years. YSA constitutes another precious tool for metabolic network modeling, and is presented in details along with EFMs in this article. The application of these techniques is discussed for several case studies of metabolic network modeling provided in respective original articles. The article is concluded by some case studies in which the application of EFMs and YSA turned out to be most useful, such as the analysis of intracellular polyhydroxyalkanoate (PHA) formation and consumption in Cupriavidus necator, including the constraint-based description of the steady-state flux cone of the strain’s metabolic network, the profound analysis of a continuous five-stage bioreactor cascade for PHA production by C. necator using EFMs and, finally, the study of metabolic fluxes in the metabolic network of C. necator cultivated on glycerol.

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References

  • Anonymus 1 (2015a) http://sbrg.ucsd.edu/InSilicoOrganisms/OtherOrganisms. Accessed 16 Mar 2015

  • Anonymus 2 (2015b) http://2011.igem.org/Team:Tokyo_Tech/Projects/Urea-cooler/index.htm. Accessed 17 Mar 2015

  • Atlić A, Koller M, Scherzer D, Kutschera C, Grillo-Fernandes E, Horvat P, Chiellini E, Braunegg G (2011) Continuous production of poly ([R]-3-hydroxybutyrate) by Cupriavidus necator in a multistage bioreactor cascade. Appl Microbiol Biot 91:295–304. doi:10.1007/s00253-011-3260-0

    Google Scholar 

  • Averesch NJH, Krömer JO (2014) Tailoring strain construction strategies for muconic acid production in S. cerevisiae and E. coli. Metab Eng Commun 1:19–28. doi:10.1016/j.meteno.2014.09.001

    Google Scholar 

  • Bell SL, Palsson BØ (2005) Expa: a program for calculating extreme pathways in biochemical reaction networks. Bioinformatics 21:1739–1740. doi:10.1093/bioinformatics/bti228

    CAS  Google Scholar 

  • Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2008) GenBank. Nucleic Acids Res 36:D25–D30. doi:10.1093/nar/gkm929

    CAS  Google Scholar 

  • Boghigian BA, Shi H, Lee K, Pfeifer BA (2010) Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design. BMC Syst Biol 2010:4–49. doi:10.1186/1752-0509-4-49

    Google Scholar 

  • Borrero-de Acuña JM, Bielecka A, Häussler S, Schobert M, Jahn M, Wittmann C, Poblete-Castro I (2014) Production of medium chain length polyhydroxyalkanoate in metabolic flux optimized Pseudomonas putida. Microb Cell Fact 13:88–102. doi:10.1186/1475-2859-13-88

    Google Scholar 

  • Burgard AP, Nikolaev EV, Schilling CH, Maranas CD (2004) Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 14:301–312. doi:10.1101/gr.1926504

    CAS  Google Scholar 

  • Bushell ME, Sequeira SIP, Khannapho C, Zhao H, Chater KF, Butler MJ, Kierzek AM, Avignone-Rossa CA (2006) The use of genome scale metabolic flux variability analysis for process feed formulation based on an investigation of the effects of the zwf mutation on antibiotic production in Streptomyces coelicolor. Enzyme Microb Technol 39:1347–1353. doi:10.1016/j.enzmictec.2006.06.011

    CAS  Google Scholar 

  • Cakir T, Kirdar B, Onsan ZI, Ulgen KO, Nielsen J (2007) Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae. BMC Syst Biol 1:18–27. doi:10.1186/1752-0509-1-18

    Google Scholar 

  • Carlson R, Srienc F (2004a) Fundamental Escherichia coli biochemical pathways for biomass and energy production: identification of reactions. Biotech Bioeng 85:1–18. doi:10.1002/bit.10812

    CAS  Google Scholar 

  • Carlson R, Srienc F (2004b) Fundamental Escherichia coli biochemical pathways for biomass and energy production: creation of overall flux states. Biotechnol Bioeng 86:149–162. doi:10.1002/bit.20044

    CAS  Google Scholar 

  • Carlson R, Fell D, Srienc F (2002) Metabolic pathway analysis of a recombinant yeast for rational strain development. Biotechnol Bioeng 79:121–134. doi:10.1002/bit.10305

    CAS  Google Scholar 

  • Carlson R, Wlaschin A, Srienc F (2005) Kinetic studies and biochemical pathway analysis of anaerobic poly-(R)-3-hydroxybutyric acid synthesis in Escherichia coli. Appl Environ Microbiol 71:713–720. doi:10.1128/AEM.71.2.713-720.2005

    CAS  Google Scholar 

  • Caspi R, Foerster H, Fulcher CA, Hopkinson R, Ingraham J, Kaipa P, Krummenacker M, Paley S, Pick J, Rhee SY, Tissier C, Zhang P, Karp PD (2006) MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucl Acids Res 34:D511–D516. doi:10.1093/nar/gkj128

    CAS  Google Scholar 

  • Chen Z, Liu H, Zhang J, Liu D (2010) Elementary mode analysis for the rational design of efficient succinate conversion from glycerol by Escherichia coli. J Biomed Biotechnol Article ID 518743. doi:10.1155/2010/518743

  • Clarke BL (1981) Complete set of steady states for the general stoichiometric dynamical system. J Chem Phys 75:4970–4979. doi:10.1063/1.441885

    CAS  Google Scholar 

  • Clarke BL (1988) Stoichiometric network analysis. Cell Biophys 12:237. doi:10.1007/BF02918360

    CAS  Google Scholar 

  • De Figueiredo LF, Podhorski A, Rubio A, Kaleta C, Beasley JE, Schuster S, Planes FJ (2009) Computing the shortest elementary flux modes in genome-scale metabolic networks. Bioinformatics 25:3158–3165. doi:10.1093/bioinformatics/btp564

    Google Scholar 

  • Diniz SC, Voss IV, Steinbuchel A (2006) Optimization of cyanophycin production in recombinant strains of Pseudomonas putida and Ralstonia eutropha employing elementary mode analysis and statistical experimental design. Biotechnol Bioeng 93:698–717. doi:10.1002/bit.20760

    CAS  Google Scholar 

  • Dräger A, Kronfeld M, Ziller MJ, Supper J, Planatscher H, Magnus JB, Oldiges M, Kohlbacher O, Zell A (2009) Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies. BMC Syst Biol 3:5–28. doi:10.1186/1752-0509-3-5

    Google Scholar 

  • Edwards JS, Ramakrishna R, Schilling CH, Palsson BØ (1999) Metabolic flux balance analysis. In: Lee SY, Papoutsakis ET (eds) Metabolic engineering. Marcel Deker, New York, pp 13–57

    Google Scholar 

  • Edwards JS, Ibarra RU, Palsson BØ (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotech 19:125–130. doi:10.1038/84379

    CAS  Google Scholar 

  • Feinberg M, Horn FJM (1974) Dynamics of open chemical systems and the algebraic structure of the underlying reaction network. Chem Eng Sci 29:775–787. doi:10.1016/0009-2509(74)80195-8

    CAS  Google Scholar 

  • Franz A, Song HS, Ramkrishna D, Kienle A (2011) Experimental and theoretical analysis of poly (β-hydroxybutyrate) formation and consumption in Ralstonia eutropha. Biochem Eng J 55:49–58

    CAS  Google Scholar 

  • Gagneur J, Klamt S (2004) Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinform 5:175–195. doi:10.1186/1471-2105-5-175

    Google Scholar 

  • Gombert AK, Nielsen J (2000) Mathematical modelling of metabolism. Curr Opin Biotechnol 11:180–186. doi:10.1016/S0958-1669(00)00079-3

    CAS  Google Scholar 

  • Gruchattka E, Hädicke O, Klamt S, Schütz V, Kayser O (2013) In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpenoid factories. Microb Cell Fact 12:84–101. doi:10.1186/1475-2859-12-84

    Google Scholar 

  • Gudmundsson S, Thiele I (2010) Computationally efficient flux variability analysis. BMC Bioinform 11:489–591. doi:10.1186/1471-2105-11-489

    Google Scholar 

  • Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U (2006) COPASI—a complex Pathway simulator. Bioinformatics 22:3067–3074. doi:10.1093/bioinformatics/btl485 PMID 17032683

    CAS  Google Scholar 

  • Horvat P, Špoljarić IV, Lopar M, Atlić A, Koller M, Braunegg G (2013) Mathematical modelling and process optimization of a continuous 5-stage bioreactor cascade for production of poly [-(R)-3-hydroxybutyrate] by Cupriavidus necator. Bioprocess Biosyst Eng 36:1235–1250. doi:10.1007/s00449-012-0852-8

    CAS  Google Scholar 

  • Jeong Bo-Y, Wittmann C, Kato T, Park EY (2015) Comparative metabolic flux analysis of an Ashbya gossypii wild type strain and a high riboflavin-producing mutant strain. J Biosci Bioeng 119:101–106. doi:10.1016/j.jbiosc.2014.06.014

    CAS  Google Scholar 

  • Jevremovic D, Trinh CT, Srienc F, Boley D (2008) A simple rank test to distinguish extreme pathways from elementary modes in metabolic networks. Technical Report TR 08-029, Department of Computer Science and Engineering University of Minnesota, 4-192 EECS Building 200 Union Street SE Minneapolis, MN 55455-0159 USA

  • Jungers RM, Zamorano F, Blondel VD, Wouwer AV, Bastin G (2011) Fast computation of minimal elementary decompositions of metabolic flux vectors. Automatica 47:1255–1259. doi:10.1016/j.automatica.2011.01.011

    Google Scholar 

  • Kaleta C, de Figueiredo LF, Schuster S (2009a) Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns. Genome Res 19:1872–1883. doi:10.1101/gr.090639.108

    CAS  Google Scholar 

  • Kaleta C et al (2009b) EFMEvolver: computing elementary flux modes in genomescale metabolic networks. In: Grosse I, Neumann S, Posch S, Schreiber F, Stadler P (eds) Proceedings German conference on bioinformatics 2009, GI Edition, Lecture Notes in Informatics. Gesellschaft für Informatik, Bonn, 157:179–189

  • Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:D480–D484. doi:10.1093/nar/gkm882

    CAS  Google Scholar 

  • Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14:491–496. doi:10.1016/j.copbio.2003.08.001

    CAS  Google Scholar 

  • Klamt S, Stelling J (2002) Combinatorial complexity of pathway analysis in metabolic networks. Mol Biol Rep 29:233–236. doi:10.1023/A:1020390132244

    CAS  Google Scholar 

  • Klamt S, Stelling J (2003) Two approaches for metabolic pathway analysis. Trends Biotechnol 21:64–69. doi:10.1016/S0167-7799(02)00034-3

    CAS  Google Scholar 

  • Klamt S, Schuster S, Gilles ED (2002) Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnol Bioeng 77:734–751. doi:10.1002/bit.10153

    CAS  Google Scholar 

  • Klamt S, Stelling J, Ginkel M, Gilles ED (2003) FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps. Bioinformatics 19:261–269. doi:10.1093/bioinformatics/19.2.261

    CAS  Google Scholar 

  • Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with Cell NetAnalyzer. BMC Syst Biol 1:2. doi:10.1186/1752-0509-1-2

    Google Scholar 

  • Krömer JO, Wittmann C, Schroder H, Heinzle E (2006) Metabolic pathway analysis for rational design of l-methionine production by Escherichia coli and Corynebacterium glutamicum. Metab Eng 8:353–369. doi:10.1016/j.ymben.2006.02.001

    Google Scholar 

  • Kumar M, Saini S, Gayen K (2014) Elementary mode analysis reveals that Clostridium acetobutylicum modulates its metabolic strategy under external stress. Mol BioSyst 10:2090–2105. doi:10.1039/C4MB00126E

    CAS  Google Scholar 

  • Larhlimi A, Bockmayr A (2009) A new constraint-based description of the steady-state flux cone of metabolic networks. Discrete Appl Math 157:2257–2266. doi:10.1016/j.dam.2008.06.039

    Google Scholar 

  • Li S, Huang D, Li Y, Wen J, Jia X (2012) Rational improvement of the engineered isobutanol-producing Bacillus subtilis by elementary mode analysis. Microb Cell Fact 11:101–112. http://www.microbialcellfactories.com/content/11/1/101

  • Liao JC, Hou SY, Chao YP (1996) Pathway analysis, engineering, and physiological considerations for redirecting central metabolism. Biotechnol Bioeng 52:129–140. doi:10.1002/(SICI)1097-0290(19961005)52:1<129:AID-BIT13>3.0.CO;2-J

    CAS  Google Scholar 

  • Lopar M, Špoljarić IV, Atlić A, Koller M, Braunegg G, Horvat P (2013) Five-step continuous production of PHB analyzed by elementary flux, modes, yield space analysis and high structured metabolic model. Biochem Eng J 79:57–70. doi:10.1016/j.bej.2013.07.003

    CAS  Google Scholar 

  • Lopar M, Špoljarić IV, Cepanec N, Koller M, Braunegg G, Horvat P (2014) Study of metabolic network of Cupriavidus necator DSM 545 growing on glycerol by applying elementary flux modes and yield space analysis. J Ind Microbiol Biotechnol 41:913–930. doi:10.1007/s10295-014-1439-y

    CAS  Google Scholar 

  • Machado D, Soons Z, Patil KR, Ferreira EC, Rocha I (2012) Random sampling of elementary flux modes in large-scale metabolic networks. Bioinformatics 28:i515–i521. doi:10.1093/bioinformatics/bts401

    CAS  Google Scholar 

  • Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276. doi:10.1016/j.ymben.2003.09.002

    CAS  Google Scholar 

  • Matsuda F, Furusawa C, Kondo T, Ishii J, Shimizu H, Kondo K (2011) Engineering strategy of yeast metabolism for higher alcohol production. Microb Cell Fact 10–19:70–79. doi:10.1186/1475-2859-10-70

    Google Scholar 

  • McCloskey D, Palsson ØB, Feist AM (2013) Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol 9:661. doi:10.1038/msb.2013.18

    CAS  Google Scholar 

  • Mendes P (1993) GEPASI: A software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Appl Biosci CABIOS 9:563–571. doi:10.1093/bioinformatics/9.5.563

    CAS  Google Scholar 

  • Milner PC (1964) The possible mechanisms of complex reactions involving consecutive steps. J Electrochem Soc 111:228–232. doi:10.1149/1.2426089

    CAS  Google Scholar 

  • Nookaew I, Meechai A, Thammarongtham C, Laoteng K, Ruanglek V, Cheevadhanarak S, Nielsen J, Bhumiratana S (2007) Identification of flux regulation coefficients from elementary flux modes: a systems biology tool for analysis of metabolic networks. Biotechnol Bioeng 97:1535–1549. doi:10.1002/bit.21339

    CAS  Google Scholar 

  • Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. doi:10.1038/nbt.1614

    CAS  Google Scholar 

  • Osterlund T, Nookaew I, Nielsen J (2012) Fifteen years of large scale metabolic modeling of yeast: developments and impacts. Biotechnol Adv 30:979–988

    Google Scholar 

  • Papin JA, Price ND, Palsson BØ (2002) Extreme pathway lengths and reaction participation in genome-scale metabolic networks. Genome Res 12:1889–1900. doi:10.1101/gr.327702

    CAS  Google Scholar 

  • Papin JA, Stelling J, Price ND, Klamt S, Schuster S, Palsson BØ (2004) Comparison of network-based pathway analysis methods. Trends Biotechnol 22:400–405. doi:10.1016/j.tibtech.2004.06.010

    CAS  Google Scholar 

  • Park JM, Kim TY, Lee SY (2010) Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses. Proc Natl Acad Sci USA 107:14931–14936. doi:10.1073/pnas.1003740107

    CAS  Google Scholar 

  • Parmar JH, Bhartiya S, Venkatesh KV (2012) Quantification of metabolism in Saccharomyces cerevisiae under hyperosmotic conditions using elementary mode analysis. J Ind Microbiol Biotechnol 39:927–941. doi:10.1007/s10295-012-1090-4

    CAS  Google Scholar 

  • Pfeiffer T, Sanchez-Valdenebro I, Nuno J, Montero F, Schuster S (1999) METATOOL: for studying metabolic networks. Bioinformatics 15:251–257. doi:10.1093/bioinformatics/15.3.251

    CAS  Google Scholar 

  • Poblete-Castro I, Binger D, Oehlert R, Rohde M (2014) Comparison of mcl-Poly (3-hydroxyalkanoates) synthesis by different Pseudomonas putida strains from crude glycerol: citrate accumulates at high titer under PHA-producing conditions. BMC Biotechnol 14:962–972. doi:10.1186/s12896-014-0110-z

    Google Scholar 

  • Poolman MG, Fell DA, Raines CA (2003) Elementary modes analysis of photosynthate metabolism in the chloroplast stroma. Eur J Biochem 270:430–439. doi:10.1046/j.1432-1033.2003.03390.x

    CAS  Google Scholar 

  • Poolman MG, Venkatesh KV, Pidcock MK, Fell DA (2004) A method for the determination of flux in elementary modes, and its application to Lactobacillus rhamnosus. Biotechnol Bioeng 88:601–612. doi:10.1002/bit.20273

    CAS  Google Scholar 

  • Price ND, Reed JL, Papin JA, Wiback SJ, Palsson BØ (2003) Network-based analysis of metabolic regulation in the human red blood cell. J Theor Biol 225:185–194. doi:10.1016/S0022-5193(03)00237-6

    CAS  Google Scholar 

  • Price ND, Reed JL, Palsson BØ (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2:886–897. doi:10.1038/nrmicro1023

    CAS  Google Scholar 

  • Qian H, Beard DA (2005) Thermodynamics of stoichiometric biochemical networks in living systems far from equilibrium. Biophys Chem 114:213–220

    CAS  Google Scholar 

  • Qian H, Beard DA, Liang S (2003) Stoichiometric network theory for nonequilibrium biochemical systems. Eur J Biochem 270:415–421. doi:10.1046/j.1432-1033.2003.03357.x

    CAS  Google Scholar 

  • Rajvanshi M, Venkatesh KV (2011) Phenotypic characterization of Corynebacterium glutamicum under osmotic stress conditions using elementary mode analysis. J Ind Microbiol Biotechnol 38:1345–1357. doi:10.1007/s10295-010-0918-z

    CAS  Google Scholar 

  • Rezola A, de Figueiredo LF, Brock M, Pey J, Podhorski A, Wittmann C, Schuster S, Bockmayr A, Planes FJ (2011) Exploring metabolic pathways in genome-scale networks via generating flux modes. Bioinformatics 27:534–540. doi:10.1093/bioinformatics/btq681

    CAS  Google Scholar 

  • Roels JA (1983) Energetics and kinetics in biotechnology. Elsevier, Amsterdam. agris.fao.org. Samatova NF (2002) Parallel out-of-core algorithm for genome-scale enumeration of metabolic systemic pathways. Parallel and distributed processing symposium, proceedings international, IPDPS 2002, Abstracts and CD-ROM 185–192

  • Samatova NF (2002) Parallel out-of-core algorithm for genome-scale enumeration of metabolic systemic pathways. Parallel and distributed processing symposium, proceedings international, IPDPS 2002, Abstracts and CD-ROM 185–192

  • Schilling CH, Letscher D, Palsson BØ (2000) Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol 203:229–248. doi:10.1006/jtbi.2000.1073

    CAS  Google Scholar 

  • Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Molecular systems biology 3:119. doi:10.1038/msb4100162

    Google Scholar 

  • Schuster S, Hilgetag C (1994) On elementary flux modes in biochemical reaction systems at steady state. J Biol Syst 2:165–182. doi:10.1142/S0218339094000131

    Google Scholar 

  • Schuster S, Fell DA, Dandekar T (2000) A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol 18:326–332. doi:10.1038/73786

    CAS  Google Scholar 

  • Schuster S, Pfeiffer T, Moldenhauer F, Koch I, Dandekar T (2002) Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae. Bioinformatics 18:351–361. doi:10.1093/bioinformatics/18.2.351

    CAS  Google Scholar 

  • Schwartz J, Kanehisa M (2005) A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes. Bioinformatics 21:204–205. doi:10.1093/bioinformatics/bti1132

    Google Scholar 

  • Schwartz J-M, Kanehisa M (2006) Quantitative elementary mode analysis of metabolic pathways: the example of yeast glycolysis. BMC Bioinform 7:186. doi:10.1186/1471-2105-7-186

    Google Scholar 

  • Schwartz J-M, Taylor PN (2014) In silico prediction of elementary mode fluxes. Paper presented at: 2nd international work-conference on bioinformatics and biomedical engineering (IWBBIO), Granada (Spain). http://www.bioinf.manchester.ac.uk/schwartz/pdf/IWBBIO_2014a.pdf. Accessed 16 Mar 2015

  • Schwartz J-M, Gaugain C, Nacher JC, de Daruvar A, Kanehisa M (2007) Observing metabolic functions at the genome scale. Genome Biol 8:R123–R139. doi:10.1186/gb-2007-8-6-r123

    Google Scholar 

  • Schwarz R, Musch P, von Kamp A, Engels B, Schirmer H, Schuster S, Dandekar T (2005) YANA—a software tool for analyzing flux modes, gene-expression and enzyme activities. BMC Bioinform 6:135. doi:10.1186/1471-2105-6-135

    Google Scholar 

  • Schwarz R, Liang C, Kaleta C, Kuhnel M, Hoffmann E, Kuznetsov S, Hecker M, Griffiths G, Schuster S, Dandekar T (2007) Integrated network reconstruction, visualization and analysis using YANAsquare. BMC Bioinform 8:313–322. doi:10.1186/1471-2105-8-313

    Google Scholar 

  • Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. PNAS 99:15112–15117. doi:10.1073/pnas.232349399

    CAS  Google Scholar 

  • Song HS, Ramkrishna D (2009) Reduction of a set of elementary modes using yield analysis. Biotechnol Bioeng 102:554–568. doi:10.1002/bit.22062

    CAS  Google Scholar 

  • Song H-S, Ramkrishna D (2013) Complex nonlinear behavior in metabolic processes: global bifurcation analysis of Escherichia coli growth on multiple substrates. Processes 1:263–278. doi:10.3390/pr1030263

    CAS  Google Scholar 

  • Soons ZITA, Ferreira EC, Rocha I (2011) Identification of minimal metabolic pathway models consistent with phenotypic data. J Process Control 21:1483–1492. doi:10.1016/j.jprocont.2011.05.012

    CAS  Google Scholar 

  • Špoljarić IV, Lopar M, Koller M, Muhr A, Salerno A, Reiterer A, Horvat P (2013a) Mathematical modeling of poly [(R)-3-hydroxyalkanoate] synthesis by Cupriavidus necator DSM 545 on substrates stemming from biodiesel production. Biores Technol 133:482–494. doi:10.1016/j.biortech.2013.01.126

    Google Scholar 

  • Špoljarić IV, Lopar M, Koller M, Muhr A, Salerno A, Reiterer A, Horvat P (2013b) In silico optimization and low structured kinetic model of poly [(R)-3-hydroxybutyrate] synthesis by Cupriavidus necator DSM 545 by fed-batch cultivation on glycerol. J Biotechnol 168:625–635. doi:10.1016/j.jbiotec.2013.08.019

    Google Scholar 

  • Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED (2002) Metabolic network structure determines key aspects of functionality and regulation. Nature 420:190–193. doi:10.1038/nature01166

    CAS  Google Scholar 

  • Stephanopoulos GN, Aristidou AA, Nielsen J (1998) Metabolic engineering. Principles and methodologies. Academic Press, San Diego

    Google Scholar 

  • Sweetlove LJ, Ratcliffe RG (2011) Flux-balance modeling of plant metabolism. Front Plant Sci 2:38. doi:10.3389/fpls.2011.00038

    CAS  Google Scholar 

  • Tomar N, De RK (2013) Comparing methods for metabolic network analysis and an application to metabolic engineering. Gene 521:1–14. doi:10.1016/j.gene.2013.03.017

    CAS  Google Scholar 

  • Trinh CT, Carlson R, Wlaschin A, Srienc F (2006) Design, construction and performance of the most efficient biomass producing E. coli bacterium. Metab Eng 8:628–638. doi:10.1016/j.ymben.2006.07.006

    CAS  Google Scholar 

  • Trinh CT, Unrean P, Srienc F (2008) Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses. Appl Environ Microbiol 74:3634–3643. doi:10.1128/AEM.02708-07

    CAS  Google Scholar 

  • Trinh CT, Wlaschin A, Srienc F (2009) Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Appl Microbiol Biotechnol 81:813–826. doi:10.1007/s00253-008-1770-1

    CAS  Google Scholar 

  • Urbanczik R (2006) SNA—a toolbox for the stoichiometric analysis of metabolic networks. BMC Bioinform 7:129. doi:10.1186/1471-2105-7-129

    Google Scholar 

  • Urbanczik R, Wagner C (2005) Functional stoichiometric analysis of metabolic networks. Bioinformatics 21:4176–4180. doi:10.1093/bioinformatics/bti674

    CAS  Google Scholar 

  • Van Dien SJ, Lidstrom ME (2002) Stoichiometric model for evaluating the metabolic capabilities of the facultative methylotroph Methylobacterium extorquens AM1, with application to reconstruction of C3 and C4 metabolism. Biotechnol Bioeng 78:296–312. doi:10.1002/bit.10200

    Google Scholar 

  • Varma A, Palsson BØ (1994) Metabolic flux balancing, basic concepts, scientific and practical use. Biotechnology 12:994–998

    CAS  Google Scholar 

  • Vijayasankaran N, Carlson R, Srienc F (2005) Metabolic pathway structures for recombinant protein synthesis in Escherichia coli. Appl Microbiol Biotechnol 68:737–746. doi:10.1007/s00253-005-1920-7

    CAS  Google Scholar 

  • von Kamp A, Schuster S (2006) Metatool 5.0: fast and flexible elementary modes analysis. Bioinformatics 22:1930–1931. doi:10.1093/bioinformatics/btl267

    Google Scholar 

  • Wagner C (2004) Nullspace approach to determine the elementary modes of chemical reaction systems. J Phys Chem B 108:2425–2431. doi:10.1021/jp034523f

    CAS  Google Scholar 

  • Wang Q, Yang Y, Ma H, Zhao X (2007) Metabolic network properties help assign weights to elementary modes to understand physiological flux distributions. Bioinformatics 23:1049–1052. doi:10.1093/bioinformatics/btm074

    CAS  Google Scholar 

  • Wiback SJ, Mahadevan R, Palsson BØ (2003) Reconstructing metabolic flux vectors from extreme pathways: defining the alpha-spectrum. J Theor Biol 224:313–324. doi:10.1016/S0022-5193(03)00168-1

    CAS  Google Scholar 

  • Wiback SJ, Mahadevan R, Palsson BØ (2004) Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum. Biotechnol Bioeng 86:317–331. doi:10.1002/bit.20011

    CAS  Google Scholar 

  • Wiechert W (2001) 13C metabolic flux analysis. Metabolic Eng 3:195–206. doi:10.1038/nprot.2009.58

    CAS  Google Scholar 

  • Wiechert W, Möllney M, Petersen S, Graaf AA (2001) A universal framework for 13C metabolic flux analysis. Metabolic Eng 3:265–283. doi:10.1006/mben.2001.0188

    CAS  Google Scholar 

  • Wierzbicki MC (2013) Application of synthetic biology for increasing anaerobic biodiesel production in Escherichia coli. Master’s Thesis, University of Tennessee, 2013. http://trace.tennessee.edu/utk_gradthes/2475. Accsessed 16 Mar 2015

  • Wlaschin AP, Trinh CT, Carlson R, Srienc F (2006) The fractional contributions of elementary modes to the metabolism of Escherichia coli and their estimation from reaction entropies. Metab Eng 8:338–352. doi:10.1016/j.ymben.2006.01.007

    CAS  Google Scholar 

  • Wunderlich Z, Mirny Leonid (2006) Using the topology of metabolic networks to predict viability of mutant strains. Biophys J 91:2304–2311. doi:10.1529/biophysj.105.080572

    CAS  Google Scholar 

  • Xiong W, Gao C, Yan D, Wu C, Wu Q (2009) Double CO2 fixation in photosynthesis–fermentation model enhances algal lipid synthesis for biodiesel production. Biores Technol 10:2287–2293. doi:10.1016/j.biortech.2009.11.041

    Google Scholar 

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Acknowledgments

The authors gratefully acknlowlege the financial support provided by the European Commission by granting the Cooperative FP7 project contract no. 245084 ANIMPOL (“Biotechnological conversion of carbon containing wastes for eco-efficient production of high added value products”).

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Authors and Affiliations

  1. Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000, Zagreb, Croatia

    Predrag Horvat

  2. Office of Research Management and Service, Research Group Interfaces, NAWI Graz, Division of Physical and Theoretical Chemistry, c/o Institute of Chemistry, University of Graz, Heinrichstrasse 28/III, 8010, Graz, Austria

    Martin Koller

  3. ARENA - Association for Resource Efficient and Sustainable Technologies, Inffeldgasse 21b, 8010, Graz, Austria

    Martin Koller & Gerhart Braunegg

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  1. Predrag Horvat
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  2. Martin Koller
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  3. Gerhart Braunegg
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Correspondence to Martin Koller.

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Horvat, P., Koller, M. & Braunegg, G. Recent advances in elementary flux modes and yield space analysis as useful tools in metabolic network studies. World J Microbiol Biotechnol 31, 1315–1328 (2015). https://doi.org/10.1007/s11274-015-1887-1

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  • DOI: https://doi.org/10.1007/s11274-015-1887-1

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