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  • 1
    Electronic Resource
    Electronic Resource
    New York, NY [u.a.] : Wiley-Blackwell
    Biotechnology and Bioengineering 42 (1993), S. 59-73 
    ISSN: 0006-3592
    Keywords: Escherichia coli ; amino acids ; nucleotides ; biosynthesis ; linear optimization ; metabolic fluxes ; metabolic engineering ; stoichiometry ; Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: Microbial metabolism provides at mechanism for the conversion of substrates into useful biochemicals. Utilization of microbes in industrial processes requires a modification of their natural metabolism in order to increase the efficiency of the desired conversion. Redirection of metabolic fluxes forms the basis of the newly defined field of metabolic engineering. In this study we use a flux balance based approach to study the biosynthesis of the 20 amino acids and 4 nucleotides as biochemical products. These amino acids and nucleotides are primary products of biosynthesis as well as important industrial products and precursors for the production of other biochemicals. The biosynthetic reactions of the bacterium Escherichia coli have been formulated into a metabolic network, and growth has been defined as a balanced drain on the metabolite pools corresponding to the cellular composition. Theoretical limits on the conversion of glucose, glycerol, and acetate substrates to biomass as well as the biochemical products have been computed. The substrate that results in the maximal carbon conversion to a particular product is identified. Criteria have been developed to identify metabolic constraints in the optimal solutions. The constraints of stoichiometry, energy, and redox have been determined in the conversions of glucose, glycerol, and acetate substrates into the biochemicals. Flux distributions corresponding to the maximal production of the biochemicals are presented. The goals of metabolic engineering are the optimal redirection of fluxes from generating biomass toward producing the desired biochemical. Optimal biomass generation is shown to decrease in a piecewise linear manner with increasing product formation. In some cases, synergy is observed between biochemical production and growth, leading to an increased overall carbon conversion. Balanced growth and product formation are important in a bioprocess, particularly for nonsecreted products. © 1993 John Wiley & Sons, Inc.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    New York, NY [u.a.] : Wiley-Blackwell
    Biotechnology and Bioengineering 58 (1998), S. 162-169 
    ISSN: 0006-3592
    Keywords: bioinformatics ; metabolic engineering ; genetic engineering ; mathematical analysis ; stoichiometry ; enzyme kinetics ; modal analysis ; genetic circuits ; Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: Ten microbial genomes have been fully sequenced to date, and the sequencing of many more genomes is expected to be completed before the end of the century. The assignment of function to open reading frames (ORFs) is progressing, and for some genomes over 70% of functional assignments have been made. The majority of the assigned ORFs relate to metabolic functions. Thus, the complete genetic and biochemical functions of a number of microbial cells may be soon available. From a metabolic engineering standpoint, these developments open a new realm of possibilities. Metabolic analysis and engineering strategies can now be built on a sound genomic basis. An important question that now arises; how should these tasks be approached? Flux-balance analysis (FBA) has the potential to play an important role. It is based on the fundamental principle of mass conservation. It requires only the stoichiometric matrix, the metabolic demands, and some strain specific parameters. Importantly, no enzymatic kinetic data is required. In this article, we show how the genomically defined microbial metabolic genotypes can be analyzed by FBA. Fundamental concepts of metabolic genotype, metabolic phenotype, metabolic redundancy and robustness are defined and examples of their use given. We discuss the advantage of this approach, and how FBA is expected to find uses in the near future. FBA is likely to become an important analysis tool for genomically based approaches to metabolic engineering, strain design, and development. © 1998 John Wiley & Sons, Inc. Biotechnol Bioeng 58:162-169, 1998.
    Additional Material: 4 Ill.
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  • 3
    Electronic Resource
    Electronic Resource
    New York, NY [u.a.] : Wiley-Blackwell
    Biotechnology and Bioengineering 45 (1995), S. 69-79 
    ISSN: 0006-3592
    Keywords: E. coli ; linear optimization ; metabolic fluxes ; stoichiometry ; sensitivity analysis ; Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: Stoichiometrically based flux balance models provide a method to quantify the metabolic pathway fluxes within a living cell. Predictions of flux balance models are expected to have applications in pathway engineering as well as in bioprocess design and control. These models utilize optimality principles applied to metabolic pathway stoichiometry along with the metabolic requirements for growth to determine the flux distribution in a metabolic network. A flux balance model has been developed for Escherichia coli W3110 using five experimentally determined strain-specific parameters. In this report, we determine the sensitivity of the predictions of the flux balance model to these five strain-specific parameters. Model predictions are shown to be sensitive to the two parameters describing metabolic capacity, while they are relatively insensitive to the three parameters that describe the metabolic requirements for growth. Thus, when stoichiometrically based models are formulated for additional strains one needs to measure the metabolic capacity (maximum rates of nutrient and oxygen utilization) accurately. Determination of metabolic capacity from batch experiments is relatively easy to perform. On the other hand, the harder to determine maintenance parameters need not be as accurately determined. © 1995 John Wiley & Sons, Inc.
    Additional Material: 8 Ill.
    Type of Medium: Electronic Resource
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