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Journal ArticleDOI

Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism.

21 Feb 1992-Journal of Theoretical Biology (J Theor Biol)-Vol. 154, Iss: 4, pp 421-454
TL;DR: Analysis of metabolic networks using linear optimization theory allows one to quantify and understand the limitations imposed on the cell by its metabolic stoichiometry, and to understand how the flux through each pathway influences the overall behavior of metabolism.
About: This article is published in Journal of Theoretical Biology.The article was published on 1992-02-21 and is currently open access. It has received 255 citations till now.
Citations
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Journal ArticleDOI
TL;DR: A flux balance based approach is used to study the biosynthesis of the 20 amino acids and 4 nucleotides as biochemical products and finds synergy is observed between biochemical production and growth, leading to an increased overall carbon conversion.
Abstract: 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.

223 citations

Journal ArticleDOI
TL;DR: It is concluded that analysis of metabolic network stoichiometry is a useful tool to detect metabolic limits and to guide process intensification studies.
Abstract: Using available biochemical information, metabolic networks have been constructed to describe the biochemistry of growth of Saccharomyces cerevisiae and Candida utilis on a wide variety of carbon substrates. All networks contained only two fitted parameters, the P/O ratio and a maintenance coefficient. It is shown that with a growth-associated maintenance coefficient, K, of 1.37 mol ATP/ C-mol protein for both yeasts and P/O ratios of 1.20 and 1.53 for S. cerevisiae and C. utilis, respectively, measured biomass yields could be described accurately. A metabolic flux analysis of aerobic growth of S. cerevisiae on glucose/ethanol mixtures predicted five different metabolic flux regimes upon transition from 100% glucose to 100% ethanol. The metabolic network constructed for growth of S. cerevisiae on glucose was applied to perform a theoretical exercise on the overproduction of amino acids. It is shown that theoretical operational product yield values can be substantially lower than calculated maximum product yields. A practical case of lysine production was analyzed with respect to theoretical bottlenecks limiting product formation. Predictions of network-derived irreversibility limits for Y(sp) (mu) functions were compared with literature data. The comparisons show that in real systems such irreversibility constraints may be of relevance. It is concluded that analysis of metabolic network stoichiometry is a useful tool to detect metabolic limits and to guide process intensification studies. (c) 1995 John Wiley & Sons, Inc.

212 citations

Journal ArticleDOI
TL;DR: The reconstructed cellular metabolic network of Mus musculus, based on annotated genomic data, pathway databases, and currently available biochemical and physiological information, is presented and represents the first attempt to collect and characterize the metabolicnetwork of a mammalian cell on the basis of genomic data.
Abstract: The reconstructed cellular metabolic network of Mus musculus, based on annotated genomic data, pathway databases, and currently available biochemical and physiological information, is presented. Although incomplete, it represents the first attempt to collect and characterize the metabolic network of a mammalian cell on the basis of genomic data. The reaction network is generic in nature and attempts to capture the carbon, energy, and nitrogen metabolism of the cell. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, including transport reactions between the compartments and the extracellular medium. The reaction list consists of 872 internal metabolites involved in a total of 1220 reactions, whereof 473 relate to known open reading frames. Initial in silico analysis of the reconstructed model is presented.

201 citations

Journal ArticleDOI
TL;DR: An optimization-based framework is introduced for testing whether experimental flux data are consistent with different hypothesized objective functions, and results reveal that the CoIs for both growth conditions are strikingly similar, which is consistent with the presence of a single metabolic objective driving the flux distributions in both cases.
Abstract: An optimization-based framework is introduced for testing whether experimental flux data are consistent with different hypothesized objective functions Specifically, we examine whether the maximization of a weighted combination of fluxes can explain a set of observed experimental data Coefficients of importance (CoIs) are identified that quantify the fraction of the additive contribution of a given flux to a fitness (objective) function with an optimization that can explain the experimental flux data A high CoI value implies that the experimental flux data are consistent with the hypothesis that the corresponding flux is maximized by the network, whereas a low value implies the converse This framework (ie, ObjFind) is applied to both an aerobic and anaerobic set of Escherichia coli flux data derived from isotopomer analysis Results reveal that the CoIs for both growth conditions are strikingly similar, even though the flux distributions for the two cases are quite different, which is consistent with the presence of a single metabolic objective driving the flux distributions in both cases Interestingly, the CoI associated with a biomass production flux, complete with energy and reducing power requirements, assumes a value 9 and 15 times higher than the next largest coefficient for the aerobic and anaerobic cases, respectively

199 citations


Cites background from "Network analysis of intermediary me..."

  • ...…(i) the maximization of metabolite (Varma et al., 1993a) or ATP (Majewski and Domach, 1990; Ramakrishna et al., 2001) production; and (ii) the minimization of the Euclidean norm (i.e., sum of the fluxes) (Bonarius et al., 1996), nutrient uptake, or redox production (Savinell and Palsson, 1992)....

    [...]

  • ...Other postulated objective functions include: (i) the maximization of metabolite (Varma et al., 1993a) or ATP (Majewski and Domach, 1990; Ramakrishna et al., 2001) production; and (ii) the minimization of the Euclidean norm (i.e., sum of the fluxes) (Bonarius et al., 1996), nutrient uptake, or redox production ( Savinell and Palsson, 1992 )....

    [...]

Journal ArticleDOI
TL;DR: Large-scale (13)C metabolic flux analysis, which is required to improve confidence in the network models and their predictions, remains a major challenge, but advances in both modeling and analytical techniques are bringing this challenge within sight.

191 citations


Cites background from "Network analysis of intermediary me..."

  • ...This suggested that pyruvate dehydrogenase could be a rigid node, or may be related to a bottleneck in the import of NADH from cytoplasm into mitochondria (Paredes et al., 1998; Savinell and Palsson, 1992)....

    [...]

References
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Book
01 Jan 1984
TL;DR: Strodiot and Zentralblatt as discussed by the authors introduced the concept of unconstrained optimization, which is a generalization of linear programming, and showed that it is possible to obtain convergence properties for both standard and accelerated steepest descent methods.
Abstract: This new edition covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. One major insight is the connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve a problem. This was a major theme of the first edition of this book and the fourth edition expands and further illustrates this relationship. As in the earlier editions, the material in this fourth edition is organized into three separate parts. Part I is a self-contained introduction to linear programming. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. It is possible to go directly into Parts II and III omitting Part I, and, in fact, the book has been used in this way in many universities.New to this edition is a chapter devoted to Conic Linear Programming, a powerful generalization of Linear Programming. Indeed, many conic structures are possible and useful in a variety of applications. It must be recognized, however, that conic linear programming is an advanced topic, requiring special study. Another important topic is an accelerated steepest descent method that exhibits superior convergence properties, and for this reason, has become quite popular. The proof of the convergence property for both standard and accelerated steepest descent methods are presented in Chapter 8. As in previous editions, end-of-chapter exercises appear for all chapters.From the reviews of the Third Edition: this very well-written book is a classic textbook in Optimization. It should be present in the bookcase of each student, researcher, and specialist from the host of disciplines from which practical optimization applications are drawn. (Jean-Jacques Strodiot, Zentralblatt MATH, Vol. 1207, 2011)

4,908 citations

Journal ArticleDOI
TL;DR: Analysis of oxidative pathways of glutamine and glutamate showed that extramitochondrial malate is oxidized almost quantitatively to pyruvate + CO2 by NAD(P)+-linked malic enzyme, present in the mitochondria of all tumors tested, but absent in heart, liver, and kidney mitochondria.

374 citations

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Linear optimization theory is a mathematical formalism used to analyze metabolic networks and understand the limitations and behavior of metabolism.