References
- Aziz, R. K., Bartels, D., Best, A. A., De Jongh, M., Disz, T., Edwards, R. A., Formsma, K., Gerdes, S., Glass, E. M., Kubal, M. and Meyer, F. (2008). The RAST server: Rapid annotations using sub systems technology. BMC Genomics, 9(1), 75. https://doi.org/10.1186/1471-2164-9-75
- Becker, S., Feist, A.,Mo, M. L., Hannum, G., Palsson, B. Ø. and Herrgard, M. J. (2007). Quantitative prediction of cellular metabolism with constraint-based models: The COBRA toolbox. Nature Protocols, 2(3), 727–738. https://doi.org/10.1038/nprot.2007.99
- Benyamini, T., Folger, O., Ruppin, E., and Shlomi, T. (2010). Flux balance analysis accounting for metabolite dilution. Genome Biology, 11(4), R43. https://doi.org/10.1186/gb-2010-11-4-r43
- Boele, J., Olivier, B. G., & Teusink, B. (2012). FAME, the flux analysis and modeling environment. BMC System Biology, 6(1), 8. https://doi.org/10.1186/1752-0509-6-8
- Bonarius, H. P., Hatzimanikatis, V., Meesters, K. P., de Gooijer, C. D., Schmid, G., & Tramper, J. (1996). Metabolic flux analysis of hybridoma cells in different culture media using mass balances. Biotechnology and Bioengineering, 50(3), 299–318. https://doi.org/10.1002/(SICI)1097-0290(19960505)50:3<299::AID-BIT9>3.0.CO;2-B
- Calheiros Gomes, L., & Simões, M. (2012). 13C metabolic flux analysis: From the principle to recent applications. Current Bioinformatics, 7(1), 77–86.16. https://doi.org/10.2174/157489312799304404
- Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., Holland, T. A., Keseler, I. M., Kothari, A., Kubo, A. and Krummenacker, M. (2014). The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway/genome databases. Nucleic Acids Research, 42(D1), D459–D471. https://doi.org/10.1093/nar/gkt1103
- Chan, S. H., Wang, L., Dash, S., & Maranas, C. D. (2018). Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints. Bioinformatics, 34(24), 4248–4255.
- Chavali, A. K., Whittemore, J. D., Eddy, J. A., Williams, K. T., & Papin, J. A. (2008). Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major. Molecular Systems Biology, 4(1). https://doi.org/10.1038/msb.2008.15
- Chubukov, V., Mukhopadhyay, A., Petzold, C. J., Keasling, J. D., & Martín, H. G. (2016). Synthetic and systems biology for microbial production of commodity chemicals. NPJ Systems Biology and Applications, 2, 16009. https://doi.org/10.1038/npjsba.2016.9
- Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., & Robles, M. (2005). Blast 2 GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics, 21(18), 3674–3676. https://doi.org/10.1093/bioinformatics/bti610
- Covert, M. W., Schilling, C. H., Famili, I., Edwards, J. S., Goryanin, I. I., Selkov, E., & Palsson, B. O. (2001). Metabolic modeling of microbial strains in silico. Trends in Biochemical Sciences, 26(3), 179–186. https://doi.org/10.1016/S0968-0004(00)01754-0
- Dai, Z., & Locasale, J. W. (2017). Understanding metabolism with flux analysis: From theory to application. Metabolic Engineering, 43(1), 94–102. https://doi.org/10.1016/j.ymben.2016.09.005
- Delgado, J., & Liao, J. C. (1997). Inverse flux analysis for reduction of acetate excretion in Escherichia coli. Biotechnology Progress, 13(4), 361–367. https://doi.org/10.1021/bp970047x
- Edwards, J. S., Covert, M., & Palsson, B. (2002). Metabolic modelling of microbes: The flux‐ balance approach. Environmental Microbiology, 4(3), 133–140. https://doi.org/10.1046/j.1462-2920.2002.00282.x
- Edwards, J. S., Ibarra, R. U., & Palsson, B. O. (2001). In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnology, 19(2), 125. https://doi.org/10.1038/84379
- Edwards, J. S., & Palsson, B. O. (1998). How will bioinformatics influence metabolic engineering? Biotechnology and Bioengineering, 58(2‐ 3), 162–169. https://doi.org/10.1002/(SICI)1097-0290(19980420)58:2/3<162::AID-BIT8>3.0.CO;2-J
- Elkind, Y., Edwards, R., Mavandad, M., Hedrick, S. A., Ribak, O., Dixon, R. A., & Lamb, C. J. (1990). Abnormal plant development and down-regulation of phenylpropanoid biosynthesis in transgenic tobacco containing a heterologous phenylalanine ammonia-lyase gene. Proceedings of the National Academy of Sciences, 87(22), 9057–9061. https://doi.org/10.1073/pnas.87.22.9057
- Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Hatzimanikatis, V. and Palsson, B. Ø. (2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Molecular Systems Biology, 3(1), 121. https://doi.org/10.1038/msb4100155
- Feist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L., & Palsson, B. Ø. (2009). Reconstruction of biochemical networks in microorganisms. Nature Reviews. Microbiology, 7(2), 129. https://doi.org/10.1038/nrmicro1949
- Fell, D. A., & Small, J. R. (1986). Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochemical Journal, 238(3), 781–786. https://doi.org/10.1042/bj2380781
- Feng, X., Xu, Y., Chen, Y., and Tang, Y. J. (2012). MicrobesFlux: A web platform for drafting metabolic models from the KEGG database. BMC System Biology, 6(1), 94. https://doi.org/10.1186/1752-0509-6-94
- Feng, X., Xu, Y., Chen, Y., & Tang, Y. J. (2012). Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1. PLoS Computational Biology, 8(2), e1002376. https://doi.org/10.1371/journal.pcbi.1002376
- Fong, S. S., Burgard, A. P., Herring, C. D., Knight, E. M., Blattner, F. R., Maranas, C. D., & Palsson, B. O. (2005). In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnology and Bioengineering, 91(5), 643–648. https://doi.org/10.1002/bit.20542
- Fong, S. S., Joyce, A. R., & Palsson, B. O. (2005). Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Resource, 15(10), 1365–1372. https://doi.org/10.1101/gr.3832305
- Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R. D., & Bairoch, A. (2003). ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Research, 31(13), 3784–3788. https://doi.org/10.1093/nar/gkg563
- Gianchandani, E. P., Chavali, A. K., & Papin, J. A. (2010). The application of flux balance analysis in systems biology. Wiley Interdisciplinary Reviews. Systems Biology and Medicine, 2(3), 372–382. https://doi.org/10.1002/wsbm.60
- Henson, M. A., & Hanly, T. J. (2014). Dynamic flux balance analysis for synthetic microbial communities. IET Systems Biology, 8(5), 214–229. https://doi.org/10.1049/iet-syb.2013.0021
- Höffner, K., Harwood, S. M., & Barton, P. I. (2013). A reliable simulator for dynamic flux balance analysis. Biotechnology and Bioengineering, 110(3), 792–802. https://doi.org/10.1002/bit.24748
- Hoppe, A., Hoffmann, S., Gerasch, A., Gille, C., & Holzhütter, H. G. (2011). FASIMU: Flexible software for flux-balance computation series in large metabolic networks. BMC Bioinformatics, 12(1), 28. https://doi.org/10.1186/1471-2105-12-28
- Hu, W. J., Harding, S. A., Lung, J., Popko, J. L., Ralph, J., Stokke, D. D., & Chiang, V. L. (1999). Repression of lignin biosynthesis promotes cellulose accumulation and growth in transgenic trees. Nature Biotechnology, 17(8), 808. https://doi.org/10.1038/11758
- Ingraham, J. L., Maaløe, O., & Neidhardt, F. C. (1983). Growth of the bacterial cell. Sinauer Associates.
- Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Research, 40(D1), D109–D114. https://doi.org/10.1093/nar/gkr988
- Klamt, S., Saez-Rodriguez, J., & Gilles, E. D. (2007). Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC System Biology, 1(1), 2. https://doi.org/10.1186/1752-0509-1-2
- Kumar, D., & Budman, H. (2017). Applications of polynomial chaos expansions in optimization and control of bioreactors based on dynamic metabolic flux balance models. Chemical Engineering Science, 167(1), 18–28. https://doi.org/10.1016/j.ces.2017.03.035
- Kümmel, A., Panke, S., & Heinemann, M. (2006). Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics, 7(1), 512. https://doi.org/10.1186/1471-2105-7-512
- Lakshmanan, M., Koh, G., Chung, B. K., & Lee, D. Y. (2012). Software applications for flux balance analysis. Briefings in Bioinformatics, 15(1), 108–122. https://doi.org/10.1093/bib/bbs069
- LCSB JENKINS. https://prince.lcsb.uni.lu/
- Lee, D. Y., Yun, H., Park, S., and Lee, S. Y. (2003). MetaFluxNet: The management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics, 19(16), 2144–2146. https://doi.org/10.1093/bioinformatics/btg271
- Lee, K. H., Park, J. H., Kim, T. Y., Kim, H. U., & Lee, S. Y. (2007). Systems metabolic engineering of Escherichia coli for L-threonine production. Molecular Systems Biology, 3(1), 149. https://doi.org/10.1038/msb4100196
- Lee, S. J., Lee, D. Y., Kim, T. Y., Kim, B. H., Lee, J., & Lee, S. Y. (2005). Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Applied and Environmental Microbiology, 71(12), 7880–7887. https://doi.org/10.1128/AEM.71.12.7880-7887.2005
- Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., … Weitz, K. K. (2010). Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models. Molecular Systems Biology, 6(1), 390. https://doi.org/10.1038/msb.2010.47
- Li, L., Zhou, Y., Cheng, X., Sun, J., Marita, J. M., Ralph, J., & Chiang, V. L. (2003). Combinatorial modification of multiple lignin traits in trees through multigene cotransformation. Proceedings of the National Academy of Sciences, 100(8), 4939–4944. https://doi.org/10.1073/pnas.0831166100
- Li, X., Deng, Y., Yang, Y., Wei, Z., Cheng, J., Cao, L., Mu, D., Luo, S., Zheng, Z., Jiang, S. and Wu, X. (2017). Fermentation process and metabolic flux of ethanol production from the detoxified hydrolyzate of cassava residue. Frontiers in Microbiology, 8(1), 1603. https://doi.org/10.3389/fmicb.2017.01603
- Majewski, R. A., & Domach, M. M. (1990). Simple constrained‐ optimization view of acetate overflow in E. coli. Biotechnology and Bioengineering, 35(7), 732–738. https://doi.org/10.1002/bit.260350711
- Manichaikul, A., Ghamsari, L., Hom, E. F., Lin, C., Murray, R. R., Chang, R. L., & Thiele, I. (2009). Metabolic network analysis integrated with transcript verification for sequenced genomes. Nature Methods, 6(8), 589. https://doi.org/10.1038/nmeth.1348
- McConville, M. J., De Souza, D., Saunders, E., Likic, V. A., & Naderer, T. (2007). Living in a phagolysosome; metabolism of Leishmania amastigotes. Trends in Parasitology, 23(8), 368–375. https://doi.org/10.1016/j.pt.2007.06.009
- Milne, C. B., Kim, P. J., Eddy, J. A., & Price, N. D. (2009). Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology. Biotechnology Journal: Healthcare Nutrition Technology, 4(12), 1653–1670. https://doi.org/10.1002/biot.200900234
- Mo, M. L., Palsson, B. Ø., & Herrgard, M. J. (2009). Connecting extracellular measurements to intracellular flux states in yeast. BMC System Biology, 3(1), 37. https://doi.org/10.1186/1752-0509-3-37
- Neidhardt, F. C., Ingraham, J. L., & Schaechter, M. (1990). Physiology of the bacterial cell: A molecular approach (Vol. 20). Sinauer Associates.
- Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis? Nature Biotechnology, 28(3), 245. https://doi.org/10.1038/nbt.1614
- Overbeek, R., Olson, R., Pusch, G. D., Olsen, G. J., Davis, J. J., Disz, T., Edwards, R. A., Gerdes, S., Parrello, B., Shukla, M. and Vonstein, V. (2013). The SEED and the rapid annotation of microbial genomes using sub systems technology (RAST). Nucleic Acids Research, 42(D1), D206–D214. https://doi.org/10.1093/nar/gkt1226
- Pan, D. T., Wang, X. D., Shi, H. Y., Yuan, D. C., & Xiu, Z. L. (2018). Dynamic flux balance analysis for microbial conversion of glycerol into 1, 3-propanediol by Klebsiella pneumoniae. Bioprocess and Biosystems Engineering, 41(12), 1793–1805. https://doi.org/10.1007/s00449-018-2002-4
- 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. Proceedings of the National Academy of Sciences, 104(19), 7797–7802. https://doi.org/10.1073/pnas.0702609104
- Pharkya, P., Burgard, A. P., & Maranas, C. D. (2004). OptStrain: A computational framework for redesign of microbial production systems. Genome Research, 14(11), 2367–2376. https://doi.org/10.1101/gr.2872004
- Poolman, M. G., Assmus, H. E., & Fell, D. A. (2004). Applications of metabolic modelling to plant metabolism. Journal of Experimental Botany, 55(400), 1177–1186. https://doi.org/10.1093/jxb/erh090
- Price, N. D., Reed, J. L., & Palsson, B. Ø. (2004). Genome-scale models of microbial cells: Evaluating the consequences of constraints. Nature Reviews. Microbiology, 2(11), 886. https://doi.org/10.1038/nrmicro1023
- Ramalingam, S., Vikram, M., Vigneshbabu, M. P., & Sivasankari, M. (2011). Flux balance analysis for maximizing polyhydroxyalkanoate production in Pseudomonas putida. International Journal of Chemical Sciences, 8(5), S1–S15. http://nopr.niscair.res.in/handle/123456789/10954
- Raman, K., & Chandra, N. (2009). Flux balance analysis of biological systems: Applications and challenges. Briefings in Bioinformatics, 10(4), 435–449. https://doi.org/10.1093/bib/bbp011
- Raman, K., Rajagopalan, P., & Chandra, N. (2005). Flux balance analysis of mycolic acid pathway: Targets for anti-tubercular drugs. PLoS Computational Biology, 1(5), e46. https://doi.org/10.1371/journal.pcbi.0010046
- Rios-Estepa, R., & Lange, B. M. (2007). Experimental and mathematical approaches to modeling plant metabolic networks. Phytochemistry, 68(16–18), 2351–2374. https://doi.org/10.1016/j.phytochem.2007.04.021
- Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., Pinto, J. P., Nielsen, J., Patil, K. R., Ferreira, E. C. and Rocha, M. (2010). OptFlux: An open-source software platform for in silico metabolic engineering. BMC System Biology, 4(1), 45. https://doi.org/10.1186/1752-0509-4-45
- Santos, F., Boele, J., & Teusink, B. (2011). A practical guide to genome-scale metabolic models and their analysis. In Methods in enzymology (Vol. 500, pp. 509–532). Academic Press. Elsevier.
- Sauer, U., Hatzimanikatis, V., Bailey, J. E., Hochuli, M., Szyperski, T., & Wüthrich, K. (1997). Metabolic fluxes in riboflavin-producing Bacillus subtilis. Nature Biotechnology, 15(5), 448. https://doi.org/10.1038/nbt0597-448
- Savinell, J. M., & Palsson, B. O. (1992). Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism. Journal of Theoretical Biology, 154(4), 421–454. https://doi.org/10.1016/S0022-5193(05)80161-4
- Schellenberger, J., Que, R., Fleming, R. M., Thiele, I., Orth, J. D., Feist, A. M., & Kang, J. (2011). Quantitative prediction of cellular metabolism with constraint-based models: The COBRA toolbox v2. 0. Nature Protocols, 6(9), 1290. https://doi.org/10.1038/nprot.2011.308
- Schomburg, I., Chang, A., & Schomburg, D. (2002). BRENDA, Enzyme data and metabolic information. Nucleic Acids Research, 30(1), 47–49. https://doi.org/10.1093/nar/30.1.47
- Schuetz, R., Kuepfer, L., & Sauer, U. (2007). Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Molecular Systems Biology, 3(1), 119. https://doi.org/10.1038/msb4100162
- Schuster, S., Pfeiffer, T., & Fell, D. A. (2008). Is maximization of molar yield in metabolic networks favored by evolution? Journal of Theoretical Biology, 252(3), 497–504. https://doi.org/10.1016/j.jtbi.2007.12.008
- Schwender, J. (2009). Plant metabolic networks. (B. H. Junker, Ed.). Springer.
- Scott, F., Wilson, P., Conejeros, R., & Vassiliadis, V. S. (2018). Simulation and optimization of dynamic flux balance analysis models using an interior point method reformulation. Computers & Chemical Engineering, 119(1), 152–170. https://doi.org/10.1016/j.compchemeng.2018.08.041
- Seemann, T. (2014). Prokka: Rapid prokaryotic genome annotation. Bioinformatics, 30(14), 2068–2069. https://doi.org/10.1093/bioinformatics/btu153
- Shameer, S., Vallarino, J. G., Fernie, A. R., Ratcliffe, R. G., & Sweetlove, L. J. (2020). Flux balance analysis of metabolism during growth by osmotic cell expansion and its application to tomato fruits. The Plant Journal, 103(1), 68–82. https://doi.org/10.1111/tpj.14707
- Shen, C. R., & Liao, J. C. (2013). Synergy as design principle for metabolic engineering of 1-propanol production in Escherichia coli. Metabolic Engineering, 17(1), 12–22. https://doi.org/10.1016/j.ymben.2013.01.008
- Stephanopoulos, G., Aristidou, A. A., & Nielsen, J. (1998). Metabolic engineering: Principles and methodologies. Elsevier.
- Sylvain, A. H., Thomas, A., Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S. N., Richelle, A., Heinken, A., Haraldsdóttir, H. S., Wachowiak, J., Keating, S.M., Vlasov, V. and Magnusdóttir, S. (2019). Creation and analysis of biochemical constraint-based models: The COBRA Toolbox v3.0.. Nature Protocols, 14(3), 639–702. https://doi.org/10.1038/s41596-018-0098-2
- Teusink, B., Wiersma, A., Jacobs, L., Notebaart, R. A., & Smid, E. J. (2009). Understanding the adaptive growth strategy of lactobacillus plantarum by in silico optimisation. PLoS Computational Biology, 5(6), e1000410. https://doi.org/10.1371/journal.pcbi.1000410
- Tobes, R., Pareja-Tobes, P., Manrique, M., Pareja-Tobes, E., Kovach, E., Alekhin, A., and Pareja, E. (2015). Gene calling and bacterial genome annotation with BG7. In Bacterial Pan-genomics (pp. 177–189). New York, NY: Humana Press. Springer.
- Toroghi, M. K., Cluett, W. R., & Mahadevan, R. (2016). A multi-scale model of the whole human body based on dynamic parsimonious flux balance analysis. IFAC-PapersOnLine, 49(7), 937–942. https://doi.org/10.1016/j.ifacol.2016.07.319
- Urbanczik, R. (2006). SNA – A toolbox for the stoichiometric analysis of metabolic networks. BMC Bioinformatics, 7(1), 129.https://doi.org/10.1186/1471-2105-7-129
- Varma, A., Boesch, B. W., & Palsson, B. O. (1993). Biochemical production capabilities of Escherichia coli. Biotechnology and Bioengineering, 42(1), 59–73. https://doi.org/10.1002/bit.260420109
- Varma, A., & Palsson, B. O. (1993). Metabolic capabilities of Escherichia coli II. Optimal growth patterns. Journal of Theoretical Biology, 165(4), 503–522. https://doi.org/10.1006/jtbi.1993.1203
- Varma, A., & Palsson, B. O. (1994a). Metabolic flux balancing: Basic concepts, scientific and practical use. Bio/technology, 12(10), 994. https://doi.org/10.1038/nbt1094-994
- Varma, A., & Palsson, B. O. (1994b). Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Applied and Environmental Microbiology, 60(10), 3724–3731. https://doi.org/10.1128/AEM.60.10.3724-3731.1994
- Vitkin, E., Gillis, A., Polikovsky, M., Bender, B., Golberg, A., & Yakhini, Z. (2020). Distributed flux balance analysis simulations of serial biomass fermentation by two organisms. PloS One, 15(1), e0227363. https://doi.org/10.1371/journal.pone.0227363
- Vlassis, N., Pacheco, M. P., & Sauter, T. (2014). Fast reconstruction of compact context-specific metabolic network models. PLoS Computational Biology, 10(1), e1003424. https://doi.org/10.1371/journal.pcbi.1003424
- Wang, J. P., Matthews, M. L., Naik, P. P., Williams, C. M., Ducoste, J. J., Sederoff, R. R., & Chiang, V. L. (2019). Flux modeling for monolignol biosynthesis. Current Opinion in Biotechnology, 56(1), 187–192. https://doi.org/10.1016/j.copbio.2018.12.003
- Wang, J. P., Matthews, M. L., Williams, C. M., Shi, R., Yang, C., Tunlaya-Anukit, S., & Naik, P. (2018). Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis. Nature Communications, 9(1), 1579. https://doi.org/10.1038/s41467-018-03863-z
- Wang, J. P., Tunlaya‐Anukit, S., Shi, R., Yeh, T. F., Chuang, L., Isik, F., & Naik, P. P. (2016). A proteomic‐based quantitative analysis of the relationship between monolignol biosynthetic protein abundance and lignin content using transgenic populus trichocarpa. Recent Advances in Polyphenol Research, 5, 89–107. https://books.google.co.in/books?hl=en&lr=&id=BByfDQAAQBAJ&oi=fnd&pg=PA89&dq=Wang,+J.+P.,+Tunlaya%E2%80%90Anukit,+S.,+Shi,+R.,+Yeh,+T.+F.,+Chuang,+L.,+Isik,+F.,+%26+Naik,+P.+P.+(2016).+A+proteomic%E2%80%90based+quantitative+analysis+of+the+relationship+between+monolignol+biosynthetic+protein+abundance+and+lignin+content+using+transgenic+populus+&ots=32Gr1QwU42&sig=W07AHx9L1KoLJnxFm2E3DK4bfEk&redir_esc=y#v=onepage&q&f=false
- Zomorrodi, A. R., & Maranas, C. D. (2010). Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC System Biology, 4(1), 178. https://doi.org/10.1186/1752-0509-4-178
- Zupke, C., & Stephanopoulos, G. (1994). Modeling of isotope distributions and intracellular fluxes in metabolic networks using atom mapping matrixes. Biotechnology Progress, 10(5), 489–498. https://doi.org/10.1021/bp00029a006
- Zupke, C., & Stephanopoulos, G. (1995). Intracellular flux analysis in hybridomas using mass balances and in vitro 13C NMR. Biotechnology and Bioengineering, 45(4), 292–303. https://doi.org/10.1002/bit.260450403