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 manually curated genome-scale metabolic model of L. reuteri JCM 1112T is constructed that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.
Abstract: Background: Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.
Results: A genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden-Meyerhof-Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden-Meyerhof-Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.
Conclusion: We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.
3 citations
Cites methods from "Network analysis of intermediary me..."
...2.5 Flux balance analysis Flux balance analysis (FBA) was used to analyze the genome-scale metabolic model (Fell & Small, 1986; Savinell & Palsson, 1992) by constraining exchange reactions in the model with experimental values of substrate uptake and secretion rates....
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TL;DR: An overview of a number of quantitative tools from systems theory will be presented as enabling methodologies for unraveling biological regulatory systems, with an emphasis on sensitivity analysis, identification methods, and optimization approaches.
3 citations
Dissertation•
05 Nov 2007
TL;DR: This thesis is concerned with the study of the regulatory processes involved in the adaptation of metabolic systems to environmental and genetic changes.
Abstract: new insights through the quantitative description of regulatory processes Sergio Rossell This thesis has been reviewed by: prof. dr. Abbreviations and symbols 2PG 2-phosphoglycerate 3PG 3-phosphoglycerate AA aminoacids synthesis pathways ACE acetaldehyde ADH alcohol dehydrogenase –E.C. 1.1.1.1 ALD fluctose bisphosphate aldolase –E.C. 4.1.2.13 BPG 1,3-bisphosphoglycerate C J ei flux control coefficient of enzyme i C xj ei concentration control coefficient of enzyme i on metabolite x j DHAP dihydroxyacetonephosphate ε vi xj elasticity of the rate v i for the metabolite x j ENO enolase –E.C. 2.7.1.40 EtOH ethanol F6P fructose-6-phosphate F16P fructose-1,6-bisphosphate Γ mass-actio ratio G6P glucose-6-phosphate GAP glyceraldehyde-3-phosphate GAPDH glyceraldehyde-3-phosphate dehydrogenase –E.C.1.2.1.12 GLC glucose GLCi intracellular glucose GLT glucose transporter HK hexokinase –E.C. 2.7.1.1 J steady-state flux k cat catalytic constant of an enzyme k deg first order kinetic constant of the rate of protein degradation K Eq equilibrium constant K m Michaelis-Menten constant ii Abbreviations and symbols k trans first order kinetic constant of the rate of translation P reaction of pathway product v P J xi partitioned response coefficient for the metabolite x i PEP phosphoenolpyruvate PDC pyruvate decarboxylase –E. PPP pentose phosphate pathway PYR pyruvate ρ dd,protein degradation/dilution regulation coefficient for a protein ρ dd,Vmax degradation/dilution regulation coefficient for V max ρ h hierarchical regulation coefficient ρ m metabolic regualtion coefficient ρ mRN A,f lux transcriptional regulation coefficient for an enzyme flux ρ mRN A,protein transcriptional regulation coefficient for a protein ρ mRN A,Vmax transcriptional regulation coefficient for V max ρ P T,V max posttranslational regulation coefficient for V max ρ trans,Vmax translational regulation coeffcient for V max ρ trans,protein translational regulation coefficient for a protein R j i response coefficent of the variable or parameter i on the variable j S reaction or pathway substrate SD standard deviation SEM standard error of the mean SC storage carbohydrates –glycogen and trehalose TPI triose-phosphate isomerase –E.C. 5.3.1.1. v enzyme rate v trans rate of translation v deg rate of protein degradation v dil rate of protein dilution due to growth Summary This thesis is concerned with the study of the regulatory processes involved in the adaptation of metabolic systems to environmental and genetic changes. The study of regulation is an endeavor unique to biology. It addresses systems of a complexity that is unparalleled in the inanimate realm. More importantly, these systems are adaptive: living cells modulate their system properties in response to environmental changes. These modulations …
2 citations
TL;DR: This work proposes a method that reduces genome-scale metabolic networks models using data from real experiments instead of relying on predefined phenotypes, circumventing the use of a priori information and guaranteeing that the network is capable to describe all observed phenotypes and can be reliably used for estimation, prediction, and optimization.
2 citations
01 Jan 2005
TL;DR: In this article, a revision of methodologies of consistency and metabolic flux analysis and their application to fermentation systems is presented, which is particularly useful in connection with studies of metabolite production, where the objective is to direct as much carbon as it is possible from a substrate towards a metabolic product.
Abstract: Stoichiometric balances in fermentation processes are esential for their understanding and/or application. The calculation of metabolic flows is fundamental in quantitative studies of cellular physiology. Metabolic flux analysis is a powerful tool for the determination of the flows in the network of biochemical reactions. Intracellular fluxes are calculated using a stoichiometric model that describes the biochemistry of the microorganism. Metabolic flux analysis is particularly useful in connection with studies of metabolite production, where the objective is to direct as much carbon as it is possible from a substrate towards a metabolic product, besides to allow the calculation of nonmeasured extracellular fluxes and maximum theoretical yields, identification of alternating metabolic pathways and branched nodes of metabolic control. This work presents a revision of methodologies of consistency and metabolic flux analysis and their application to fermentation systems.
2 citations
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
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