Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism.
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.
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TL;DR: This work describes how metabolic networks can be represented using stoichiometric matrices and well‐defined constraints on metabolic fluxes, and describes techniques for integrating these approaches with models of other biological processes.
Abstract: During the last decade, models have been developed to characterize cellular metabolism at the level of an entire metabolic network. The main concept that underlies whole-network metabolic modeling is the identification and mathematical definition of constraints. Here, we review large-scale metabolic network modeling, in particular, stoichiometric- and constraint-based approaches. Although many such models have been reconstructed, few networks have been extensively validated and tested experimentally, and we focus on these. We describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints on metabolic fluxes. We then discuss relatively successful approaches, including flux balance analysis (FBA), pathway analysis, and common extensions or modifications to these approaches. Finally, we describe techniques for integrating these approaches with models of other biological processes.
134 citations
TL;DR: This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks and highlights the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.
133 citations
Cites background from "Network analysis of intermediary me..."
...A central optimization task in metabolic networks is flux balance analysis (FBA) (Fell and Small, 1986; Savinell and Palsson, 1992; Varma and Palsson, 1993))....
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...A number of different studies have evaluated and compared various hypotheses for the cellular objective function (Feist and Palsson, 2010; Knorr et al., 2007; Ow et al., 2009; Pramanik and Keasling, 1997; Savinell and Palsson, 1992; Schuetz et al., 2007)....
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TL;DR: In this article, the authors investigated the effects of waste production and energy loads on the cell growth rate using linear optimization and showed that neither the maintenance demand for ATP nor the antibody production rate limit growth rate at normal experimental conditions.
131 citations
TL;DR: A model was developed to study the stoichiometric demands for nutrients for the synthesis of cell mass and product in animal cell growth and can be potentially employed to analyze results from animal cell cultures, to improve the performance of culture processes, and to design new process rationally.
Abstract: Animal cell cultivation in vitro has been studied for more than 40 years. However, the culture medium composition has not been designed on the basis of the stoichiometric nutritional demands for animal cell growth. In this article, a model was developed to study the stoichiometric demands for nutrients (including glucose, 20 amino acids, and 10 vitamins)for the synthesis of cell mass and product. The coefficients for these nutrients in the stoichiometric equation governing animal cell growth were determined based on cell composition. In addition, a detailed analysis of the nutrients' roles in the synthesis of cell mass and product was also performed. Applications of the stoichiometric analysis in animal cell cultivation, such as culture medium design, supplemental medium formulation, and feeding strategy will also be discussed. The stoichiometric analysis can be potentially employed to analyze results from animal cell cultures, to improve the performance of culture processes, and to design new process rationally. It can also help to provide a better understanding of animal cell metabolism. Simplifications on the cellular energy metabolism were made in order to simplify the model and to provide the preliminary bases to test the process performance. However, this could introduce inaccuracies for the model and results in errors in the calculations of glucose and glutamine concentrations when employed in medium design. (c) 1994 John Wiley & Sons, Inc.
122 citations
TL;DR: A stoichiometric model of central metabolism was developed based on new information regarding metabolism in this bacterium to evaluate the steady-state growth capabilities of the serine cycle facultative methylotroph Methylobacterium extorquens AM1 during growth on methanol, succinate, and pyruvate.
Abstract: A stoichiometric model of central metabolism was developed based on new information regarding metabolism in this bacterium to evaluate the steady-state growth capabilities of the serine cycle facultative methylotroph Methylobacterium extorquens AM1 during growth on methanol, succinate, and pyruvate. The model incorporates 20 reversible and 47 irreversible reactions, 65 intracellular metabolites, and experimentally-determined biomass composition. The flux space for this underdetermined system of equations was defined by finding the elementary modes, and constraints based on experimental observations were applied to determine which of these elementary modes give a reasonable description of the flux distribution for each growth substrate. The predicted biomass yield, on a carbon atom basis, is 49.8%, which agrees well with the range of published experimental yield measurements (37-50%). The model predicts the cell to be limited by reduced pyridine nucleotide availability during methylotrophic growth, but energy-limited when growing on multicarbon substrates. Mutation and phenotypic analysis was used to explore a previously unknown region of the metabolic map and to confirm the stoichiometry of the pathways in this region used in the metabolic model. Based on genome sequence data and simulation results, three enzymes involved in C 3 -C 4 interconversion pathways were predicted to be mutually redundant: malic enzyme, phosphoenolpyruvate carboxykinase, and phosphoenolpyruvate synthase. Insertion mutations in the genes predicted to encode these enzymes were made and these mutants were capable of growing on all substrates tested, confirming the redundancy of these pathways. Likewise, pathway analysis suggests that the TCA cycle enzymes citrate synthase and succinate dehydrogenase are essential for all growth substrates. In keeping with these predictions, null mutants could not be obtained in these genes. Finally, a similar model was developed for the ribulose monophosphate pathway obligate methylotroph Methylobacillus flagellatum KT to compare the efficiency of carbon utilization in the two types of methylotrophic carbon utilization pathways. The predicted yield for this organism on methanol is 65.9%.
120 citations
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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
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