Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- General Notations
- 1 Introduction
- 2 Enzymatic Adaptation
- 3 Early Development of Cybernetic Models
- 4 Revisiting Cybernetic Laws via Optimal Control Theory
- 5 Toward Modeling of Metabolic Networks
- 6 The Hybrid Cybernetic Model (HCM)
- 7 The Lumped Hybrid Cybernetic Model (L-HCM)
- 8 Predicting Dynamic Behavior of Mutant Strains with L-HCM
- 9 Nonlinear Analysis of Cybernetic Models
- 10 Metabolic Modeling Landscape
- References
- Index
- References
References
Published online by Cambridge University Press: 29 September 2018
Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- General Notations
- 1 Introduction
- 2 Enzymatic Adaptation
- 3 Early Development of Cybernetic Models
- 4 Revisiting Cybernetic Laws via Optimal Control Theory
- 5 Toward Modeling of Metabolic Networks
- 6 The Hybrid Cybernetic Model (HCM)
- 7 The Lumped Hybrid Cybernetic Model (L-HCM)
- 8 Predicting Dynamic Behavior of Mutant Strains with L-HCM
- 9 Nonlinear Analysis of Cybernetic Models
- 10 Metabolic Modeling Landscape
- References
- Index
- References
Summary
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- Cybernetic Modeling for Bioreaction Engineering , pp. 252 - 265Publisher: Cambridge University PressPrint publication year: 2018
References
Agrawal, P., Lee, C., Lim, H. C., and Ramkrishna, D. (1982). Theoretical investigations of dynamic behavior of isothermal continuous stirred tank biological reactors. Chemical Engineering Science, 37, 453–462.CrossRefGoogle Scholar
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle, in Second International Symposium on Information Theory, ed. Petrov, B. N. and Csaki, F., 267–281, Budapest: Akademiai Kiado.Google Scholar
Alexander, M. (1990). Cybernetic Modeling of Bacterial Metabolite Production. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Alexander, M. L., and Ramkrishna, D. (1991). Cybernetic modeling of iron-limited growth and siderophore production. Biotechnology and Bioengineering, 38, 637–652.CrossRefGoogle ScholarPubMed
Aris, R. (1965). Prolegomena to the rational analysis of systems of chemical reactions. Archive for Rational Mechanics and Analysis, 19, 81–99.CrossRefGoogle Scholar
Axley, M. J., Grahame, D. A., and Stadtman, T. C. (1990). Escherichia coli formate-hydrogen lyase: purification and properties of the selenium-dependent formate dehydrogenase component. Journal of Biological Chemistry, 265, 18213–18218.CrossRefGoogle ScholarPubMed
Bader, F. G. (1982). Kinetics of double-substrate-limited growth. In Microbial Population Dynamics, ed. Bazin, M. J., 1–32, Boca Raton, FL: CRC Press.Google Scholar
Bader, F. G. (1978). Analysis of double-substrate limited growth. Biotechnology and Bioengineering, 20, 183–202.Google Scholar
Badsha, M. B., Tsuboi, R., and Kurata, H. (2014). Complementary elementary modes for fast and efficient analysis of metabolic networks. Biochemical Engineering Journal, 90, 121–130.Google Scholar
Bailey, J. E. (1991). Toward a Science of Metabolic Engineering. Science, 252, 1668–1675.Google Scholar
Bailey, J. E. (1998). Mathematical modeling and analysis in biochemical engineering: past accomplishments and future opportunities. Biotechnology Progress, 14, 8–20.CrossRefGoogle ScholarPubMed
Baloo, S. (1990). Modeling of Metabolic Regulation in Bacterial Continuous Cultures. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Baloo, S., and Ramkrishna, D. (1991a). Metabolic regulation in bacterial continuous cultures: I. Biotechnology and Bioengineering, 38, 1337–1352.Google Scholar
Baloo, S., and Ramkrishna, D. (1991b). Metabolic regulation in bacterial continuous cultures: II. Biotechnology and Bioengineering, 38, 1353–1363.CrossRefGoogle ScholarPubMed
Baltzis, B. C., and Fredrickson, A. G. (1988). Limitation of growth-rate by 2 complementary nutrients: some elementary but neglected considerations. Biotechnology and Bioengineering, 31, 75–86.Google Scholar
Barber, C. B., Dobkin, D. P., and Huhdanpaa, H. (1996). The Quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 22, 469–483.CrossRefGoogle Scholar
Baroukh, C., Munoz-Tamayo, R., Bernard, O., and Steyer, J. P. (2015). Mathematical modeling of unicellular microalgae and cyanobacteria metabolism for biofuel production. Current Opinion in Biotechnology, 33, 198–205.CrossRefGoogle ScholarPubMed
Barron, A., Rissanen, J., and Yu, B. (1998). The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory, 44, 2743–2760.CrossRefGoogle Scholar
Batt, B. C., and Kompala, D. S. (1989). A structured kinetic modeling framework for the dynamics of hybridoma growth and monoclonal antibody production in continuous suspension cultures. Biotechnology and Bioengineering, 34, 515–531.Google Scholar
Behre, J., Wilhelm, T., Von Kamp, A., Ruppin, E., and Schuster, S. (2008). Structural robustness of metabolic networks with respect to multiple knockouts. Journal of Theoretical Biology, 252, 433–441.Google Scholar
Berrios-Rivera, S. J., Bennett, G. N., and San, K. Y. (2002). Metabolic engineering of Escherichia coli: increase of NADH availability by overexpressing an NAD+-dependent formate dehydrogenase. Metabolic Engineering, 4, 217–229.Google Scholar
Bilous, O., and Amundson, N. R. (1955). Chemical reactor stability and sensitivity. AIChE Journal, 1, 513–521.Google Scholar
Bohl, K., de Figueiredo, L. F., Hdicke, O., Klamt, S., Kost, C., Schuster, S., and Kaleta, C. (2010). CASOP GS: computing intervention strategies targeted at production improvement in genome-scale metabolic networks, in Proceedings of the 25th German Conference on Bioinformatics, ed. Schomburg, D. and, Grote, A., 71–80, Bonn: Gesellschaft für Informatik.Google Scholar
Burgard, A. P., Pharkya, P., and Maranas, C. D. (2003). OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering, 84, 647–657.Google Scholar
Çakir, T., Arga, K. Y., Altintas, M. M., and Ülgen, K. Ö. (2004a). Flux analysis of recombinant Saccharomyces cerevisiae YPB-G utilizing starch for optimal ethanol production. Process Biochemistry, 39, 2097–2108.Google Scholar
Çakir, T., Kirdar, B., and Ülgen, K. Ö. (2004b). Metabolic pathway analysis of yeast strengthens the bridge between transcriptornics and metabolic networks. Biotechnology and Bioengineering, 86, 251–260.Google Scholar
Carlson, R., and Srienc, F. (2004). Fundamental Escherichia coli biochemical pathways for biomass and energy production: identification of reactions. Biotechnology and Bioengineering, 85, 1–19.Google Scholar
Castaño-Cerezo, S., Pastor, J. M., Renilla, S., Bernal, V., Iborra, J. L., and Canovas, M. (2009). An insight into the role of phosphotransacetylase (pta) and the acetate/acetyl-CoA node in Escherichia coli. Microbial Cell Factories, 8, Article number 54.Google Scholar
Chan, S. H. J., and Ji, P. (2011). Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks. Bioinformatics, 27, 2256–2262.Google Scholar
Chan, S. H. J., Solem, C., Jensen, P. R., and Ji, P. (2014). Estimating biological elementary flux modes that decompose a flux distribution by the minimal branching property. Bioinformatics, 30, 3232–3239.Google Scholar
Chandrasekaran, S., and Price, N. D. (2010). Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proceedings of the National Academy of Sciences of the United States of America, 107, 17845– 17850.Google Scholar
Charalampopoulos, D., Vazquez, J. A., and Pandiella, S. S. (2009). Modelling and validation of Lactobacillus plantarum fermentations in cereal-based media with different sugar concentrations and buffering capacities. Biochemical Engineering Journal, 44, 96–105.Google Scholar
Chassagnole, C., Noisommit-Rizzi, N., Schmid, J. W., Mauch, K., and Reuss, M. (2002). Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnology and Bioengineering, 79, 53–73.Google Scholar
Choon, Y. W., Mohamad, M. S., Deris, S., Illias, R. M., Chong, C. K., and Chai, L. E. (2014). A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains. Bioprocess and Biosystems Engineering, 37, 521–532.Google Scholar
Colijn, C., Brandes, A., Zucker, J., Lun, D. S., Weiner, B., Farhat, M. R., Cheng, T. Y., Moody, D. B., Murray, M., and Galagan, J. E. (2009). Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLOS Computational Biology, 5, Article number e1000489.Google Scholar
Contiero, J., Beatty, C., Kumari, S., Desanti, C. L., Strohl, W. R., and Wolfe, A. (2000). Effects of mutations in acetate metabolism on high-cell-density growth of Escherichia coli. Journal of Industrial Microbiology & Biotechnology, 24, 421–430.Google Scholar
Cortassa, S., Aon, J. C., and Aon, M. A. (1995). Fluxes of carbon, phosphorylation, and redox intermediates during growth of Saccharomyces cerevisiae on different carbon sources. Biotechnology and Bioengineering, 47, 193–208.Google Scholar
Covert, M. W., and Palsson, B. Ø. (2002). Transcriptional regulation in constraints-based metabolic models of Escherichia coli. Journal of Biological Chemistry, 277, 28058–28064.Google Scholar
Covert, M. W., Xiao, N., Chen, T. J., and Karr, J. R. (2008). Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics, 24, 2044–2050.Google Scholar
Cruz, H. J., Moreira, J. L., and Carrondo, M. J. T. (1999). Metabolic shifts by nutrient manipulation in continuous cultures of BHK cells. Biotechnology and Bioengineering, 66, 104–113.Google Scholar
Davis, B. D., Dulbecco, R., Eisen, H. N., Ginsberg, H. S., and Wood, W. B. (1967). Microbiology: A Text Emphasizing Molecular and Genetic Aspects of Microbiology and Immunology, and the Relations of Bacteria, Fungi and Viruses to Human Diseases, New York: Harper and Row.Google Scholar
de Figueiredo, L. F., Podhorski, A., Rubio, A., Kaleta, C., Beasley, J. E., Schuster, S., and Planes, F. J. (2009). Computing the shortest elementary flux modes in genome-scale metabolic networks. Bioinformatics, 25, 3158–3165.Google Scholar
De Mey, M., De Maeseneire, S., Soetaert, W., and Vandamme, E. (2007). Minimizing acetate formation in E-coli fermentations. Journal of Industrial Microbiology & Biotechnology, 34, 689–700.Google Scholar
Devilbiss, F. (2016). Is Metabolism Goal-Directed? Investigating the Validity of Modeling Biological Systems with Cybernetic Control Via Omic Data. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Dhurjati, P. (1982). Cybernetic Modeling of the Growth of Microorganisms in Multiple Substrate Environments. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Dhurjati, P., Ramkrishna, D., Flickinger, M. C., and Tsao, G. T. (1985). A cybernetic view of microbial-growth: modeling of cells as optimal strategists. Biotechnology and Bioengineering, 27, 1–9.Google Scholar
Duarte, N. C., Herrgard, M. J., and Palsson, B. Ø. (2004). Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Research, 14, 1298–1309.Google Scholar
Edwards, J. S., Ramakrishna, R., and Palsson, B. Ø. (2002). Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnology and Bioengineering, 77, 27–36.Google Scholar
Egli, T. (1995). The ecological and physiological significance of the growth of heterotrophic microorganisms with mixtures of substrates. Advances in Microbial Ecology, 14, 305–386.CrossRefGoogle Scholar
Europa, A. F., Gambhir, A., Fu, P. C., and Hu, W. S. (2000). Multiple steady states with distinct cellular metabolism in continuous culture of mammalian cells. Biotechnology and Bioengineering, 67, 25–34.Google Scholar
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, Article number 121.Google Scholar
Finn, R. K., and Wilson, R. E. (1954). Fermentation process control: population dynamics of a continuous propagator for microorganisms. Journal of Agricultural and Food Chemistry, 2, 66–69.CrossRefGoogle Scholar
Follstad, B. D., Balcarcel, R. R., Stephanopoulos, G., and Wang, D. I. C. (1999). Metabolic flux analysis of hybridoma continuous culture steady state multiplicity. Biotechnology and Bioengineering, 63, 675–683.Google Scholar
Franz, A., Song, H.-S., Ramkrishna, D., and Kienle, A. (2011). Experimental and theoretical analysis of poly(beta-hydroxybutyrate) formation and consumption in Ralstonia eutropha. Biochemical Engineering Journal, 55, 49–58.Google Scholar
Fredrickson, A. G. (1976). Formulation of structured growth models. Biotechnology and Bioengineering, 18, 1481–1486.CrossRefGoogle ScholarPubMed
Fredrickson, A. G. (1991). Segregated, structured, distributed models and their role in microbial ecology: a case study based on work done on the filter-feeding ciliate Tetrahymena pyriformis. Microbial Ecology, 22, 139–159.Google Scholar
Gadkar, K. G., Doyle, F. J., Crowley, T. J., and Varner, J. D. (2003). Cybernetic model predictive control of a continuous bioreactor with cell recycle. Biotechnology Progress, 19, 1487–1497.Google Scholar
Gagneur, J., and Klamt, S. (2004). Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinformatics, 5, Article number 175.Google Scholar
Gauch, H. G. (2003). Scientific Method in Practice, Cambridge, UK: Cambridge University Press.Google Scholar
Gayen, K., and Venkatesh, K. V. (2006). Analysis of optimal phenotypic space using elementary modes as applied to Corynebacterium glutamicum. BMC Bioinformatics, 7, Article number 445.Google Scholar
Geng, J., Song, H.-S., Yuan, J. Q., and Ramkrishna, D. (2012). On enhancing productivity of bioethanol with multiple species. Biotechnology and Bioengineering, 109, 1508–1517.Google Scholar
Gernaey, K. V., Lantz, A. E., Tufvesson, P., Woodley, J. M., and Sin, G. (2010). Application of mechanistic models to fermentation and biocatalysis for next-generation processes. Trends in Biotechnology, 28, 346–354.Google Scholar
Gerstl, M. P., Jungreuthmayer, C., and Zanghellini, J. (2015). tEFMA: computing thermodynam-ically feasible elementary flux modes in metabolic networks. Bioinformatics, 31, 2232–2234.Google Scholar
Gupta, S., and Clark, D. P. (1989). Escherichia coli derivatives lacking both alcohol-dehydrogenase and phosphotransacetylase grow anaerobically by lactate fermentation. Journal of Bacteriology, 171, 3650–3655.Google Scholar
Hädicke, O., and Klamt, S. (2010). CASOP: a computational approach for strain optimization aiming at high productivity. Journal of Biotechnology, 147, 88–101.Google Scholar
Herbert, D., Elsworth, R., and Telling, R. C. (1956). The continuous culture of bacteria: a theoretical and experimental study. Journal of General Microbiology, 14, 601–622.Google Scholar
Herbert, D., Phipps, P. J., and Tempest, D. W. (1965). The chemostat: design and instrumentation. Laboratory Practice, 14, 1150–1161.Google ScholarPubMed
Herrnstein, R. J. (1997). The Matching Law: Papers in Psychology and Economics, ed. Rachlin, H. and Laibson, D. I., Cambridge, MA: Harvard University Press.Google Scholar
Hjersted, J. L., Henson, M. A., and Mahadevan, R. (2007). Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture. Biotechnology and Bioengineering, 97, 1190–1204.Google Scholar
Hoefnagel, M. H. N., Starrenburg, M. J. C., Martens, D. E., Hugenholtz, J., Kleerebezem, M., Van Swam, I. I., Bongers, R., Westerhoff, H. V., and Snoep, J. L. (2002). Metabolic engineering of lactic acid bacteria, the combined approach: kinetic modelling, metabolic control and experimental analysis. Microbiology-Sgm, 148, 1003–1013.Google Scholar
Holtzclaw, W. D., and Chapman, L. F. (1975). Degradative acetolactate synthase of Bacillus subtilis: purification and properties. Journal of Bacteriology, 121, 917–922.Google Scholar
Hunt, K. A., Folsom, J. P., Taffs, R. L., and Carlson, R. P. (2014). Complete enumeration of elementary flux modes through scalable demand-based subnetwork definition. Bioinformatics, 30, 1569–1578.Google Scholar
Jones, K. D., and Kompala, D. S. (1999). Cybernetic model of the growth dynamics of Saccharomyces cerevisiae in batch and continuous cultures. Journal of Biotechnology, 71, 105–131.Google Scholar
Jungers, R. M., Zamorano, F., Blondel, V. D., Vande Wouwer, A., and Bastin, G. (2011). Fast computation of minimal elementary decompositions of metabolic flux vectors. Automatica, 47, 1255–1259.Google Scholar
Jungreuthmayer, C., Ruckerbauer, D. E., and Zangnellini, J. (2013). regEfmtool: speeding up elementary flux mode calculation using transcriptional regulatory rules in the form of three-state logic. Biosystems, 113, 37–39.Google Scholar
Kabir, M. M., Ho, P. Y., and Shimizu, K. (2005). Effect of ldhA gene deletion on the metabolism of Escherichia coli based on gene expression, enzyme activities, intracellular metabolite concentrations, and metabolic flux distribution. Biochemical Engineering Journal, 26, 1–11.Google Scholar
Kaleta, C., de Figueiredo, L. F., Behre, J., and Schuster, S. (2009). EFMEvolver: computing elementary flux modes in genome-scale metabolic networks, in Lecture Notes in Informatics-Proceedings, 179–189.Google Scholar
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., and Covert, M. W. (2012). A whole-cell computational model predicts phenotype from genotype. Cell, 150, 389–401.Google Scholar
Katoh, T., Yuguchi, D., Yoshii, H., Shi, H. D., and Shimizu, K. (1999). Dynamics and modeling on fermentative production of poly (β-hydroxybutyric acid) from sugars via lactate by a mixed culture of Lactobacillus delbrueckii and Alcaligenes eutrophus. Journal of Biotechnology, 67, 113–134.Google Scholar
Khodayari, A., and Maranas, C. D. (2016). A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Nature Communications, 7, Article number 13806.Google Scholar
Khodayari, A., Zomorrodi, A. R., Liao, J. C., and Maranas, C. D. (2014). A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metabolic Engineering, 25, 50–62.Google Scholar
Kim, B. M., Kim, S. W., and Yang, D. R. (2003). Cybernetic modeling of the cephalosporin C fermentation process by Cephalosporium acremonium. Biotechnology Letters, 25, 611–616.Google Scholar
Kim, J. I. (2008). A Hybrid Cybernetic Modeling for the Growth of Escherichia coli in Glucose-pyruvate Mixtures. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Kim, J. I., Song, H.-S., Sunkara, S. R., Lali, A., and Ramkrishna, D. (2012). Exacting predictions by cybernetic model confirmed experimentally: steady state multiplicity in the chemostat. Biotechnology Progress, 28, 1160–1166.CrossRefGoogle ScholarPubMed
Kim, J. I., Varner, J. D., and Ramkrishna, D. (2008). A hybrid model of anaerobic E. coli GJT001: combination of elementary flux modes and cybernetic variables. Biotechnology Progress, 24, 993–1006.Google Scholar
Kim, J. I., Song, H.-S., Sunkara, S. R., Lali, A., and Ramkrishna, D. (2012). Exacting predictions by cybernetic model confirmed experimentally: steady state multiplicity in the chemostat. Biotechnology Progress, 28, 1160–1166.Google Scholar
Kitano, H. (2007). Towards a theory of biological robustness. Molecular Systems Biology, 3, Article number 137.Google Scholar
Klamt, S. (2006). Generalized concept of minimal cut sets in biochemical networks. Biosystems, 83, 233–247.Google Scholar
Klamt, S., and Gilles, E. D. (2004). Minimal cut sets in biochemical reaction networks. Bioinformatics, 20, 226–234.Google Scholar
Klamt, S., Saez-Rodriguez, J., and Gilles, E. D. (2007). Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Systems Biology, 1, Article number 2.Google Scholar
Klamt, S., and Stelling, J. (2002). Combinatorial complexity of pathway analysis in metabolic networks. Molecular Biology Reports, 29, 233–236.Google Scholar
Klamt, S., and Stelling, J. (2003). Two approaches for metabolic pathway analysis? Trends in Biotechnology, 21, 64–69.Google Scholar
Klamt, S., and Von Kamp, A. (2011). An application programming interface for CellNetAnalyzer. Biosystems, 105, 162–168.Google Scholar
Kompala, D. (1984). Bacterial Growth on Multiple Substrates. Experimental Verification of Cybernetic Models. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Kompala, D. S., Ramkrishna, D., Jansen, N. B., and Tsao, G. T. (1986). Investigation of bacterial growth on mixed substrates: experimental evaluation of cybernetic models. Biotechnology and Bioengineering, 28, 1044–1055.Google Scholar
Kompala, D. S., Ramkrishna, D., and Tsao, G. T. (1984). Cybernetic modeling of microbial-growth on multiple substrates. Biotechnology and Bioengineering, 26, 1272–1281.Google Scholar
Kotte, O., Zaugg, J. B., and Heinemann, M. (2010). Bacterial adaptation through distributed sensing of metabolic fluxes. Molecular Systems Biology, 6, Article number 355.Google Scholar
Krishnan, M. S. (1996). Process Development of Fuel Ethanol Production from Lignocellulosic Sugars Using Genetically Engineered Yeasts. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Krishnan, M. S., Xia, Y., Ho, N. W. Y., and Tsao, G. T. (1997). Fuel ethanol production from lignocellulosic sugars: studies using a genetically engineered Saccharomyces yeast. Fuels and Chemicals from Biomass, 666, 74–92.Google Scholar
Kümmel, A., Panke, S., and Heinemann, M. (2006). Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Molecular Systems Biology, 2, Article number 0034.Google Scholar
Kurata, H., Zhao, Q. Y., Okuda, R., and Shimizu, K. (2007). Integration of enzyme activities into metabolic flux distributions by elementary mode analysis. BMC Systems Biology, 1, Article number 31.Google Scholar
Lee, A. L., Ataai, M. M., and Shuler, M. L. (1984). Double-substrate-limited growth of Escherichia-coli. Biotechnology and Bioengineering, 26, 1398–1401.Google Scholar
Lee, S. B., and Bailey, J. E. (1984). Genetically structured models for lac promoter-operator Function in the Escherichia coli chromosome and in multicopy plasmids: Lac operator function. Biotechnology and Bioengineering, 26, 1372–1382.CrossRefGoogle ScholarPubMed
Lin, H., Bennett, G. N., and San, K. Y. (2005). Effect of carbon sources differing in oxidation state and transport route on succinate production in metabolically engineered Escherichia coli. Journal of Industrial Microbiology & Biotechnology, 32, 87–93.Google Scholar
Llaneras, F., and Pico, J. (2010). Which metabolic pathways generate and characterize the flux space? A comparison among elementary modes, extreme pathways and minimal generators. Journal of Biomedicine and Biotechnology, 2010, Article number 753904.Google Scholar
Machado, D., Soons, Z., Patil, K. R., Ferreira, E. C., and Rocha, I. (2012). Random sampling of elementary flux modes in large-scale metabolic networks. Bioinformatics, 28, i515–i521.Google Scholar
Magasanik, B. (1982). Genetic control of nitrogen assimilation in bacteria. Annual Review of Genetics, 16, 135–168.Google Scholar
Mahadevan, R., Edwards, J. S., and Doyle, F. J. (2002). Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophysical Journal, 83, 1331–1340Google Scholar
Mandli, A. R., and Modak, J. M. (2014). Cybernetic modeling of adaptive prediction of environmental changes by microorganisms. Mathematical Biosciences, 248, 40–45Google Scholar
Marashi, S. A., David, L., and Bockmayr, A. (2012). Analysis of metabolic subnetworks by flux cone projection. Algorithms for Molecular Biology, 7, Article number 17.Google Scholar
Marr, A. G., Nilson, E. H., and Clark, D. J. (1963). The maintenance requirement of Escherichia coli. Annals of the New York Academy of Sciences, 102, 536–548.Google Scholar
Mayr, E. (1961). Cause and effect in biology: kinds of causes, predictability, and teleology are viewed by a practicing biologist. Science, 134, 1501–1506.Google Scholar
Mayr, E. (1982). The Growth of Biological Thought: Diversity, Evolution, and Inheritance, Cambridge, MA: Harvard University Press.Google Scholar
McDonald, C. P., and Urban, N. R. (2010). Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models. Ecological Modelling, 221, 428–432.Google Scholar
Mitchell, A., Romano, G. H., Groisman, B., Yona, A., Dekel, E., Kupiec, M., Dahan, O., and Pilpel, Y. (2009). Adaptive prediction of environmental changes by microorganisms. Nature, 460, 220–224.Google Scholar
Monod, J. (1942). Recherches sur la Croissance des Cultures Bactériennes. Paris: Hermann.Google Scholar
Monod, J. (1949). The growth of bacterial cultures. Annual Review of Microbiology, 3, 371–394.CrossRefGoogle Scholar
Namjoshi, A. (2003). A Mathematical Investigation of the Consequences of Metabolic Regulation in Complex Pathways: The Cybernetic Approach. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Namjoshi, A. A., Hu, W. S., and Ramkrishna, D. (2003). Unveiling steady-state multiplicity in hybridoma cultures: the cybernetic approach. Biotechnology and Bioengineering, 81, 80–91.Google Scholar
Namjoshi, A. A., and Ramkrishna, D. (2001). Multiplicity and stability of steady states in continuous bioreactors: dissection of cybernetic models. Chemical Engineering Science, 56, 5593–5607.Google Scholar
Narang, A. (1994). The Dynamics of Microbial Growth on Mixtures of Substrates. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Narang, A., Konopka, A., and Ramkrishna, D. (1997a). Dynamic analysis of the cybernetic model for diauxic growth. Chemical Engineering Science, 52, 2567–2578.Google Scholar
Narang, A., Konopka, A., and Ramkrishna, D. (1997b). New patterns of mixed-substrate utilization during batch growth of Escherichia coli K12. Biotechnology and Bioengineering, 55, 747–757.Google Scholar
Neidhardt, F. C., Ingraham, J. L., and Schaechter, M. (1990). Physiology of the Bacterial Cell: A Molecular Approach, Sunderland, MA: Sinauer Associates.Google Scholar
Neijssel, O. M., and Tempest, D. W. (1975). Regulation of carbohydrate metabolism in Klebsiella aerogenes NCTC 418 organisms, growing in chemostat culture. Archives of Microbiology, 106, 251–258.Google Scholar
Nissen, T. L., Schulze, U., Nielsen, J., and Villadsen, J. (1997). Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae. Microbiology-Uk, 143, 203–218.Google Scholar
Nizam, S. A., and Shimizu, K. (2008). Effects of arcA and arcB genes knockout on the metabolism in Escherichia coli under anaerobic and microaerobic conditions. Biochemical Engineering Journal, 42, 229–236.Google Scholar
Oreilly, A. M., and Scott, J. A. (1995). Defined coimmobilization of mixed microorganism cultures. Enzyme and Microbial Technology, 17, 636–646.Google Scholar
Orth, J. D., Thiele, I., and Palsson, B. Ø. (2010). What is flux balance analysis? Nature Biotechnology, 28, 245–248.Google Scholar
Papin, J. A., Price, N. D., Wiback, S. J., Fell, D. A., and Palsson, B. Ø. (2003). Metabolic pathways in the post-genome era. Trends in Biochemical Sciences, 28, 250–258.CrossRefGoogle ScholarPubMed
Pavlou, S., and Fredrickson, A. G. (1989). Growth of microbial-populations in nonminimal media: some considerations for modeling. Biotechnology and Bioengineering, 34, 971–989.Google Scholar
Penny, W. D. (2012). Comparing dynamic causal models using AIC, BIC and free energy. Neuroimage, 59, 319–330.Google Scholar
Pey, J., Villar, J. A., Tobalina, L., Rezola, A., Garcia, J. M., Beasley, J. E., and Planes, F. J. (2015). TreeEFM: calculating elementary flux modes using linear optimization in a tree-based algorithm. Bioinformatics, 31, 897–904.Google Scholar
Pinchuk, G. E., Hill, E. A., Geydebrekht, O. V., De Ingeniis, J., Zhang, X. L., Osterman, A., Scott, J. H., Reed, S. B., Romine, M. F., Konopka, A. E., Beliaev, A. S., Fredrickson, J. K., and Reed, J. L. (2010). Constraint-based model of Shewanella oneidensis MR-1 metabolism: a Tool for data analysis and hypothesis generation. PLOS Computational Biology, 6, Article number e1000822.Google Scholar
Pirt, S. J. (1965). The maintenance energy of bacteria in growing cultures. Proceedings of the Royal Society of London B: Biological Sciences, 163, 224–231.Google Scholar
Pirt, S. J. (1982). Maintenance energy: a general model for energy-limited and energy-sufficient growth. Archives of Microbiology, 133, 300–302.Google Scholar
Pitkänen, J. P., Aristidou, A., Salusjarvi, L., Ruohonen, L., and Penttila, M. (2003). Metabolic flux analysis of xylose metabolism in recombinant Saccharomyces cerevisiae using continuous culture. Metabolic Engineering, 5, 16–31.Google Scholar
Pittendrigh, C. S. (1958). Adaptation, natural selection and behavior. Behavior and Evolution, ed. Roe, A., Simpson, G. G., 390–416. New Haven: Yale.Google Scholar
Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., and Mishchenko, E. F. (1962). The Mathematical Theory of Optimal Processes. New York: Interscience.Google Scholar
Preiss, J. (1989) Chemistry and metabolism of intracellular reserves, in Bacteria in Nature, ed. Leadbetter, E. R. 3, 189–258, New York: Plenum Press.Google Scholar
Provost, A., and Bastin, G. (2004). Dynamic metabolic modelling under the balanced growth condition. Journal of Process Control, 14, 717–728.Google Scholar
Provost, A., Bastin, G., Agathos, S. N., and Schneider, Y. J. (2006). Metabolic design of macroscopic bioreaction models: application to Chinese hamster ovary cells. Bioprocess and Biosystems Engineering, 29, 349–366.Google Scholar
Provost, A., Bastin, G., and Schneider, Y. J. (2007). From metabolic networks to minimal dynamic bioreaction models. IFAC Proceedings, 40, 1–6.Google Scholar
Quek, L. E., and Nielsen, L. K. (2014). A depth-first search algorithm to compute elementary flux modes by linear programming. BMC Systems Biology, 8, Article number 294.Google Scholar
Ramakrishna, R. (1996). Cybernetic Modeling of Microbial Growth on Substitutable Substrates: Applications in Bioremediation. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Ramakrishna, R., Ramkrishna, D., and Konopka, A. E. (1996). Cybernetic modeling of growth in mixed, substitutable substrate environments: preferential and simultaneous utilization. Biotechnology and Bioengineering, 52, 141–151.Google Scholar
Raman, K., and Chandra, N. (2009). Flux balance analysis of biological systems: applications and challenges. Briefings in Bioinformatics, 10, 435–449.Google Scholar
Ramkrishna, D. (1979). Statistical models of cell populations. Advances in Biochemical Engineering, 11, 1–47.Google Scholar
Ramkrishna, D. (1983). A cybernetic perspective of microbial-growth. ACS Symposium Series, 207, 161–178.Google Scholar
Ramkrishna, D. (2003). On modeling of bioreactors for control. Journal of Process Control, 13, 581–589.Google Scholar
Ramkrishna, D., Frederickson, A. G., and Tsuchiya, H. M. (1966). Dynamics of microbial propagation models considering endogenous metabolism. Journal of General and Applied Microbiology, 12, 311–327.Google Scholar
Ramkrishna, D., Frederickson, A. G., and Tsuchiya, H. M. (1967). Dynamics of microbial propagation: models considering inhibitors and variable cell composition. Biotechnology and Bioengineering, 9, 129–170.Google Scholar
Ramkrishna, D., and Song, H.-S. (2008). A rationale for Monod’s biochemical growth kinetics. Industrial & Engineering Chemistry Research, 47, 9090–9098.Google Scholar
Ramkrishna, D., and Song, H.-S. (2012). Dynamic models of metabolism: review of the cybernetic approach. AIChE Journal, 58, 986–997.Google Scholar
Rardin, R. L. (1998). Optimization in Operations Research, Upper Saddle River, NJ: Prentice Hall.Google Scholar
Rissanen, J. (2007). Information and Complexity in Statistical Modeling, NewYork, NY: Springer.Google Scholar
Roos, T. (2011). Short course: introduction to information-theoretic modeling, Fifth Brazilian Conference on Statistical Modelling in Insurance and Finance, Maresias, Brazil.Google Scholar
Saa, P. A., and Nielsen, L. K. (2017). Formulation, construction and analysis of kinetic models of metabolism: a review of modelling frameworks. Biotechnology Advances, 35, 981–1003.Google Scholar
Sánchez, A. M., Bennett, G. N., and San, K. Y. (2005). Efficient succinic acid production from glucose through overexpression of pyruvate carboxylase in an Escherichia coli alcohol dehydrogenase and lactate dehydrogenase mutant. Biotechnology Progress, 21, 358–365.Google Scholar
Sauer, U., Lasko, D. R., Fiaux, J., Hochuli, M., Glaser, R., Szyperski, T., Wuthrich, K., and Bailey, J. E. (1999). Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. Journal of Bacteriology, 181, 6679–6688.Google Scholar
Sawers, G., and Watson, G. (1998). A glycyl radical solution: oxygen-dependent interconversion of pyruvate formate-lyase. Molecular Microbiology, 29, 945–954.Google Scholar
Schellenberger, J., Park, J. O., Conrad, T. M., and Palsson, B. Ø. (2010). BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics, 11, Article number 213.Google Scholar
Schilling, C. H., Letscher, D., and Palsson, B. Ø. (2000). Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. Journal of Theoretical Biology, 203, 229–248.Google Scholar
Schuster, S., Fell, D. A., and Dandekar, T. (2000). A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnology, 18, 326–332.Google Scholar
Schuster, S., and Hilgetag, C. (1994). On elementary flux modes in biochemical reaction systems at steady state. Journal of Biological Systems, 2, 165–182.Google Scholar
Schuster, S., Hilgetag, C., Woods, J. H., and Fell, D. A. (2002). Reaction routes in biochemical reaction systems: algebraic properties, validated calculation procedure and example from nucleotide metabolism. Journal of Mathematical Biology, 45, 153–181.Google Scholar
Schütte, H., Flossdorf, J., Sahm, H., and Kula, M. R. (1976). Purification and properties of formaldehyde dehydrogenase and formate dehydrogenase from Candida boidinii. European Journal of Biochemistry, 62, 151–160.Google Scholar
Schwartz, J. M., and Kanehisa, M. (2005). A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes. ACS Bioinformatics, 21, 204–205.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.Google Scholar
Segre, D., Vitkup, D., and Church, G. M. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America, 99, 15112–15117.Google Scholar
Senior, P. J. (1975). Regulation of nitrogen metabolism in Escherichia coli and Klebsiella aerogenes: studies with the continuous-culture technique. Journal of Bacteriology, 123, 407–418.Google Scholar
Serres, M. H., and Riley, M. (2006). Genomic analysis of carbon source metabolism of Shewanella oneidensis MR-1: predictions versus experiments. Journal of Bacteriology, 188, 4601–4609.Google Scholar
Shlomi, T., Berkman, O., and Ruppin, E. (2005). Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proceedings of the National Academy of Sciences of the United States of America, 102, 7695–7700.Google Scholar
Sidoli, F. R., Mantalaris, A., and Asprey, S. P. (2005). Toward global parametric estimability of a large-scale kinetic single-cell model for mammalian cell cultures. Industrial & Engineering Chemistry Research, 44, 868–878.Google Scholar
Song, H.-S., and Ramkrishna, D. (2013). Complex nonlinear behavior in metabolic processes: global bifurcation analysis of Escherichia coli growth on multiple substrates. Processes, 1, 263–278.Google Scholar
Song, H.-S., Devilbiss, F., and Ramkrishna, D. (2013a). Modeling metabolic systems: the need for dynamics. Current Opinion in Chemical Engineering, 2, 373–382.Google Scholar
Song, H.-S., Goldberg, N., Mahajan, A., and Ramkrishna, D. (2017). Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming. Bioinformatics, 33, 2345–2353.Google Scholar
Song, H.-S., Kim, S. J., and Ramkrishna, D. (2012). Synergistic optimal integration of continuous and fed-batch reactors for enhanced productivity of lignocellulosic bioethanol. Industrial & Engineering Chemistry Research, 51, 1690–1696.Google Scholar
Song, H.-S., and Liu, C. (2015). Dynamic metabolic modeling of denitrifying bacterial growth: the cybernetic approach. Industrial & Engineering Chemistry Research, 54, 10221–10227.Google Scholar
Song, H.-S., Morgan, J. A., and Ramkrishna, D. (2009). Systematic development of hybrid cybernetic models: application to recombinant yeast co-consuming glucose and xylose. Biotechnology and Bioengineering, 103, 984–1002.Google Scholar
Song, H.-S., and Ramkrishna, D. (2009a). Reduction of a set of elementary modes using yield analysis. Biotechnology and Bioengineering, 102, 554–568.Google Scholar
Song, H.-S., and Ramkrishna, D. (2009b). When is the quasi-steady-state approximation admissible in metabolic modeling? When admissible, what models are desirable? Industrial & Engineering Chemistry Research, 48, 7976–7985.Google Scholar
Song, H.-S., and Ramkrishna, D. (2010). Prediction of metabolic function from limited data: Lumped Hybrid Cybernetic Modeling (L-HCM). Biotechnology and Bioengineering, 106, 271–284.CrossRefGoogle ScholarPubMed
Song, H.-S., and Ramkrishna, D. (2011). Cybernetic models based on lumped elementary modes accurately predict strain-specific metabolic function. Biotechnology and Bioengineering, 108, 127–140.Google Scholar
Song, H.-S., and Ramkrishna, D. (2012). Prediction of dynamic behavior of mutant strains from limited wild-type data. Metabolic Engineering, 14, 69–80.Google Scholar
Song, H.-S., and Ramkrishna, D. (2016). Comment on “Mathematical modeling of unicellular microalgae and cyanobacteria metabolism for biofuel production” by Baroukh et al. [Curr Opin Biotechnol. 2015, 33:198–205]. Current Opinion in Biotechnology, 38, 198–199.Google Scholar
Song, H.-S., Ramkrishna, D., Pinchuk, G. E., Beliaev, A. S., Konopka, A. E., and Fredrickson, J. K. (2013b). Dynamic modeling of aerobic growth of Shewanella oneidensis. Predicting triauxic growth, flux distributions, and energy requirement for growth. Metabolic Engineering, 15, 25–33.Google Scholar
Song, H.-S., Reifman, J., and Wallqvist, A. (2014). Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle. PLOS One, 9, Article number e112524.Google Scholar
Song, H.-S., Renslow, R. S., Fredrickson, J. K., and Lindemann, S. R. (2015). Integrating ecological and engineering concepts of resilience in microbial communities. Frontiers in Microbiology, 6, Article number 1298.Google Scholar
Song, H.-S., Thomas, D. G., Stegen, J. C., Li, M. J., Liu, C. X., Song, X. H., Chen, X. Y., Fredrickson, J. K., Zachara, J. M., and Scheibe, T. D. (2017b). Regulation-structured dynamic metabolic model provides a potential mechanism for delayed enzyme response in denitrification Process. Frontiers in Microbiology, 8, Article number 1866.Google Scholar
Soons, Z. I., Ferreira, E. C., and Rocha, I. (2010). Selection of elementary modes for bioprocess control. IFAC Proceedings, 43, 156–161.Google Scholar
Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S., and Gilles, E.D. (2002). Metabolic network structure determines key aspects of functionality and regulation. Nature, 420, 190–193.Google Scholar
Stephanopoulos, G., Aristidou, A. A., and Nielsen, J. (1998). Metabolic Engineering: Principles and Methodologies, San Diego: Academic press.Google Scholar
Steuer, R., Gross, T., Selbig, J., and Blasius, B. (2006). Structural kinetic modeling of metabolic networks. Proceedings of the National Academy of Sciences of the United States of America, 103, 11868–11873.Google Scholar
Straight, J. (1991). Microbial Growth in Continuous Cultures subject to Single and Multiple Limitations Involving Carbon and/or Nitrogen. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Straight, J. V., and Ramkrishna, D. (1991) Complex growth dynamics in batch cultures: experiments and cybernetic models. Biotechnology and Bioengineering, 37, 895–909.Google Scholar
Straight, J. V., and Ramkrishna, D. (1994a) Cybernetic modeling and regulation of metabolic pathways. Growth on complementary nutrients. Biotechnology Progress, 10, 574–587.Google Scholar
Straight, J. V., and Ramkrishna, D. (1994b). Modeling of bacterial-growth under multiply-limiting conditions: experiments under carbon-limiting or/and nitrogen-limiting conditions. Biotechnology Progress, 10, 588–605.Google Scholar
Ström, T. (1975). On logarithmic norms. SIAM Journal on Numerical Analysis, 12, 741–753.Google Scholar
Symonds, M. R. E., and Moussalli, A. (2011). A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology, 65, 13–21.Google Scholar
Szambelan, K., Nowak, J., and Czarnecki, Z. (2004). Use of Zymomonas mobilis and Saccharomyces cerevisiae mixed with Kluyveromyces fragilis for improved ethanol production from Jerusalem artichoke tubers. Biotechnology Letters, 26, 845–848.Google Scholar
Tang, Y. J., Martin, H. G., Deutschbauer, A., Feng, X. Y., Huang, R., Llora, X., Arkin, A., and Keasling, J. D. (2009). Invariability of central metabolic flux distribution in Shewanella oneidensis MR-1 under environmental or genetic perturbations. Biotechnology Progress, 25, 1254–1259.Google Scholar
Tang, Y. J., Meadows, A. L., and Keasling, J. D. (2007). A kinetic model describing Shewanella oneidensis MR-1 growth, substrate consumption, and product secretion. Biotechnology and Bioengineering, 96, 125–133.Google Scholar
Tempest, D. W., Neijssel, O. M., and Zevenboom, W. (1983). Properties and performance of microorganisms in laboratory culture; their relevance to growth in natural ecosystems. Symposia of the Society for General Microbiology, 34, 119–152.Google Scholar
Terzer, M., Maynard, N. D., Covert, M. W., and Stelling, J. (2009). Genome-scale metabolic networks. Wiley Interdisciplinary Reviews-Systems Biology and Medicine, 1, 285–297.Google Scholar
Terzer, M., and Stelling, J. (2008). Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24, 2229–2235.Google Scholar
Terzer, M., and Stelling, J. (2010). Parallel extreme ray and pathway computation. Parallel Processing and Applied Mathematics, Part II 6068, 300–309.Google Scholar
Trinh, C. T., Carlson, R., Wlaschin, A., and Srienc, F. (2006). Design, construction and performance of the most efficient biomass producing E-coli bacterium. Metabolic Engineering, 8, 628–638.Google Scholar
Trinh, C. T., Unrean, P., and Srienc, F. (2008). Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses. Applied and Environmental Microbiology, 74, 3634–3643.Google Scholar
Trinh, C. T., Wlaschin, A., and Srienc, F. (2009). Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Applied Microbiology and Biotechnology, 81, 813–826.Google Scholar
Tsuchiya, H. M., Fredrickson, A. G., and Aris, R. (1966). Dynamics of microbial cell populations. Advances in Chemical Engineering, 6, 125–206.Google Scholar
Turner, B. (1986). Cybernetic Modeling of Bacterial Cultures at Low Growth Rates. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Turner, B. G., Ramkrishna, D., and Jansen, N. B. (1989). Cybernetic modeling of bacteriol cultures at low growth rates: single-substrate systems. Biotechnology and Bioengineering, 34, 252–261.Google Scholar
Tyler, B. (1978). Regulation of the assimilation of nitrogen compounds. Annual Review of Biochemistry, 47, 1127–1162.Google Scholar
Uppal, A., Ray, W. H., and Poore, A. B. (1974). Dynamic behavior of continuous stirred tank reactors. Chemical Engineering Science, 29, 967–985.Google Scholar
Urbanczik, R., and Wagner, C. (2005a). Functional stoichiometric analysis of metabolic networks. Bioinformatics, 21, 4176–4180.Google Scholar
Urbanczik, R., and Wagner, C. (2005b). An improved algorithm for stoichiometric network analysis: theory and applications. Bioinformatics, 21, 1203–1210.Google Scholar
Varner, J. (1997). Metabolic Engineering from a Cybernetic Perspective. A Conceptual Framework. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Varner, J., and Ramkrishna, D. (1998). Application of cybernetic models to metabolic engineering: investigation of storage pathways. Biotechnology and Bioengineering, 58, 282–291.Google Scholar
Varner, J., and Ramkrishna, D. (1999a). Metabolic engineering from a cybernetic perspective. 1. Theoretical preliminaries. Biotechnology Progress, 15, 407–425.Google Scholar
Varner, J., and Ramkrishna, D. (1999b). Metabolic engineering from a cybernetic perspective. 2. Qualitative investigation of nodal architectures and their response to genetic perturbation. Biotechnology Progress, 15, 426–438.Google Scholar
Varner, J., and Ramkrishna, D. (1999c). Metabolic engineering from a cybernetic perspective: aspartate family of amino acids. Metabolic Engineering, 1, 88–116.Google Scholar
Vazquez, J. A., and Murado, M. A. (2008). Unstructured mathematical model for biomass, lactic acid and bacteriocin production by lactic acid bacteria in batch fermentation. Journal of Chemical Technology and Biotechnology, 83, 91–96.Google Scholar
Verduyn, C. (1991). Physiology of yeasts in relation to biomass yields. Antonie Van Leeuwenhoek International Journal of General and Molecular Microbiology, 60, 325–353.Google Scholar
Verduyn, C., Postma, E., Scheffers, W. A., and Vandijken, J. P. (1990). Energetics of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures. Journal of General Microbiology, 136, 405–412.Google Scholar
Villadsen, J., Nielsen, J., and Lidn, G. (2011). Bioreaction Engineering Principles, 29, 3rd edition, New York: Springer.Google Scholar
von Kamp, A., and Schuster, S. (2006). Metatool 5.0: fast and flexible elementary modes analysis. Bioinformatics, 22, 1930–1931.Google Scholar
von Meyenburg, H. K. (1969). Katabolit-Repression under Sprossungszklus von Saccharomyces cerevisiae. PhD dissertation, Zurich: ETH.Google Scholar
Wagner, C. (2004). Nullspace approach to determine the elementary modes of chemical reaction systems. Journal of Physical Chemistry B, 108, 2425–2431.Google Scholar
Wagner, C., and Urbanczik, R. (2005). The geometry of the flux cone of a metabolic network. Biophysical Journal, 89, 3837–3845.Google Scholar
Wang, L. P., Ridgway, D., Gu, T. Y., and Moo-Young, M. (2009). Kinetic modeling of cell growth and product formation in submerged culture of recombinant Aspergillus niger. Chemical Engineering Communications, 196, 481–490.Google Scholar
Wang, L. Q., Birol, I., and Hatzimanikatis, V. (2004). Metabolic control analysis under uncertainty: framework development and case studies. Biophysical Journal, 87, 3750–3763.Google Scholar
Wiback, S. J., Mahadevan, R., and Palsson, B. Ø. (2004). Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum. Biotechnology and Bioengineering, 86, 317–331.Google Scholar
Wiener, N. (1948) Cybernetics: Or Control and Communication in the Animal and the Machine. Paris: Hermann et Cie, Cambridge Mass.: MIT Press (2nd revised ed. 1961).Google Scholar
Wilhelm, T., Behre, J., and Schuster, S. (2004). Analysis of structural robustness of metabolic networks. Systems Biology, 1, 114–120.Google Scholar
Yang, Y. T., Aristidou, A. A., San, K. Y., and Bennett, G. N. (1999a). Metabolic flux analysis of Escherichia coli deficient in the acetate production pathway and expressing the Bacillus subtilis acetolactate synthase. Metabolic Engineering, 1, 26–34.Google Scholar
Yang, Y. T., San, K. Y., and Bennett, G. N. (1999b). Redistribution of metabolic fluxes in Escherichia coli with fermentative lactate dehydrogenase overexpression and deletion. Metabolic Engineering, 1, 141–152.Google Scholar
Yang, Y. T., Bennett, G. N., and San, K. Y. (2001). The effects of feed and intracellular pyruvate levels on the redistribution of metabolic fluxes in Escherichia coli. Metabolic Engineering, 3, 115–123.Google Scholar
Yoo, S., and Kim, W. S. (1994). Cybernetic model for synthesis of poly-β-hydroxybutyric acid in Alcaligenes eutrophus. Biotechnology and Bioengineering, 43, 1043–1051.Google Scholar
Young, J. D. (2005). A System-Level Mathematical Description of Metabolic Regulation Combining Aspects of Elementary Mode Analysis with Cybernetic Control Laws. PhD dissertation, West Lafayette, IN: Purdue University.Google Scholar
Young, J. D. (2015). Learning from the steersman: a natural history of cybernetic models. Industrial & Engineering Chemistry Research, 54, 10162–10169.Google Scholar
Young, J. D., Henne, K. L., Morgan, J. A., Konopka, A. E., and Ramkrishna, D. (2008). Integrating cybernetic modeling with pathway analysis provides a dynamic, systems-level description of metabolic control. Biotechnology and Bioengineering, 100, 542–559.Google Scholar
Young, J. D., and Ramkrishna, D. (2007). On the matching and proportional laws of cybernetic models. Biotechnology Progress, 23, 83–99.Google Scholar
Yun, N. R., San, K. Y., and Bennett, G. N. (2005). Enhancement of lactate and succinate formation in adhE or pta-ackA mutants of NADH dehydrogenase-deficient Escherichia coli. Journal of Applied Microbiology, 99, 1404–1412.Google Scholar
Zhu, J. F., and Shimizu, K. (2005). Effect of a single-gene knockout on the metabolic regulation in Escherichia coli for D-lactate production under microaerobic condition. Metabolic Engineering, 7, 104–115.Google Scholar