scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
TL;DR: A novel, flexible, optimization-based mathematical framework for the modeling of arbitrarily complex metabolic networks: topological metabolic analysis (TMA), adapted from state-space approaches used by Manousiouthakis and co-workers is introduced.

8 citations

Journal ArticleDOI
TL;DR: Progress has been made in many aspects such as the identification of rate-controlling steps, applications of optimization principles, and stoichiometric analyses, which have led to an improved understanding of metabolic pathways and have facilitated their manipulation.

7 citations

Book ChapterDOI
TL;DR: The use of analytical tools such as elementary flux modes, linear optimization of metabolic models, and control analysis are described to help refine the grasp of biologically meaningful behaviors and model reliability.
Abstract: The advent of "big data" in biology (e.g., genomics, proteomics, metabolomics), holding the promise to reveal the nature of the formidable complexity in cellular and organ makeup and function, has highlighted the compelling need for analytical and integrative computational methods to interpret and make sense of the patterns and changes in those complex networks. Computational models need to be built on sound physicochemical mechanistic principles in order to integrate, interpret, and simulate high-throughput experimental data. Energy transduction processes have been traditionally studied with thermodynamic, kinetic, or thermo-kinetic models, with the latter proving superior to understand the control and regulation of mitochondrial energy metabolism and its interactions with cytoplasmic and other cellular compartments. In this work, we survey the methods to be followed to build a computational model of mitochondrial energetics in isolation or integrated into a network of cellular processes. We describe the use of analytical tools such as elementary flux modes, linear optimization of metabolic models, and control analysis, to help refine our grasp of biologically meaningful behaviors and model reliability. The use of these tools should improve the design, building, and interpretation of steady-state behaviors of computational models while assessing validation criteria and paving the way to prediction.

7 citations

Journal ArticleDOI
TL;DR: A systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data.
Abstract: Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough).

7 citations

Book ChapterDOI
01 Jan 2000
TL;DR: The analysis of metabolic fluxes provides a unique perspective on the functioning of mammalian cells and enables the prediction of cellular responses to changes in nutrient composition, the response to the genetic events involved in transformation, and the response in response to external stimuli and signals.
Abstract: The analysis of metabolic fluxes provides a unique perspective on the functioning of mammalian cells. Metabolic flux analysis is the use of a mathematical description of central metabolism to quantify the flow of carbon through the cell. Compared to other biochemical techniques, which reduce cellular function to specific enzymes and pathways, metabolic flux analysis views the cell as a whole. Concurrently measuring the fluxes through multiple biochemical pathways can identify interactions in the regulatory machinery of the cell. When pathways are observed independently, their interactions must be inferred. However, associations between pathways can only be seen when their respective fluxes are compared. Understanding the interactions between pathways enables the prediction of cellular responses to changes in nutrient composition, the response to the genetic events involved in transformation, and the response to external stimuli and signals.

7 citations

References
More filters
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

Trending Questions (1)
What are some of the theories that support linear kind of development?

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