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: A heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions is devised, which implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways.
56 citations
TL;DR: A role for the time-invariant structure of the metabolic reaction network, expressed as stoichiometric and thermodynamic constraints, in shaping the cellular metabolic objective is pointed to.
54 citations
TL;DR: Effectively, this work can focus its curation efforts into establishing better model annotations and gene-protein-reaction associations within the core metabolism, while relying on genome and proteome databases to build new annotations for peripheral pathways, which may bear less relevance to the modeling interest.
Abstract: Genome-scale metabolic modeling is a systems-based approach that attempts to capture the metabolic complexity of the whole cell, for the purpose of gaining insight into metabolic function and regulation. This is achieved by organizing the metabolic components and their corresponding interactions into a single context. The reconstruction process is a challenging and laborious task, especially during the stage of manual curation. For the mouse genome-scale metabolic model, however, we were able to rapidly reconstruct a compartmentalized model from well-curated metabolic databases online. The prototype model was comprehensive. Apart from minor compound naming and compartmentalization issues, only nine additional reactions without gene associations were added during model curation before the model was able to simulate growth in silico. Further curation led to a metabolic model that consists of 1399 genes mapped to 1757 reactions, with a total of 2037 reactions compartmentalized into the cytoplasm and mitochondria, capable of reproducing metabolic functions inferred from literatures. The reconstruction is made more tractable by developing a formal system to update the model against online databases. Effectively, we can focus our curation efforts into establishing better model annotations and gene-protein-reaction associations within the core metabolism, while relying on genome and proteome databases to build new annotations for peripheral pathways, which may bear less relevance to our modeling interest.
53 citations
Cites background from "Network analysis of intermediary me..."
..., linear optimization [14]) to check whether known metabolic functions can be reproduced in silico....
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TL;DR: An historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted, and a critical perspective on the overall genome-scale modeling procedure is provided.
Abstract: Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.
53 citations
TL;DR: The metabolism of 293SF cells grown in serum‐free and low‐protein medium was analyzed, finding glucose utilization was more efficient, as it entered the TCA cycle more efficiently, which led to a twofold higher ATP production in the low‐ protein medium.
Abstract: In this study the metabolism of 293SF cells grown in serum-free and low-protein medium was analyzed This cell line is known for its ability to replicate recombinant adenovirus, mainly used in gene tzherapy applications A complete model composed of the main glycolytic, glutaminolytic, and amino acids pathways, as well as the internalization fluxes of certain compounds into the mitochondria, is used for metabolic flux calculations The pentose-phosphate cycle is also added to the biochemical reactions set and was independently measured with labeled 14C-glucose Different feeding strategies in two different media were analyzed with the model, and the theoretical ATP production was also calculated The two media were similar in their glucose and amino acid composition, but one contained BSA at 1g/L whereas the other had a very low protein content Use of low-protein medium resulted in up to fourfold higher adenoviral vector production In this medium, glucose utilization was more efficient, as it entered the TCA cycle more efficiently Also, lower glutamine and amino acids consumption were observed as well as lower lactate and ammonia production This increased TCA activity led to a twofold higher ATP production in the low-protein medium © 2002 John Wiley & Sons, Inc Biotechnol Bioeng 77: 91–104, 2002
52 citations
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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