Composite control of cell function: metabolic pathways behaving as single control units

Kholodenko, Boris N.; Schuster, Stefan; Rohwer, Johann M.; Cascante, Marta; Westerhoff, Hans V.

doi:10.1016/0014-5793(95)00562-n

Cited by 35 publications

(19 citation statements)

References 21 publications

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“…Furthermore, mathematical models have so far only been applied to relatively small parts of plant metabolism (Gombert and Nielsen, 1999), and the paradigm of steady-state conditions that forms another basis of metabolic control analysis is also questionable (Giersch, 2000). In order to build models for large biochemical networks, some further simplifications have to be carried out, such as using matrix calculations of overall substrate-product stoichiometry (Kholodenko et al, 1995). Cornish-Bowden and Eisenthal (2000) emphasized the importance of the reliability of stoichiometric ratios when used for computer simulations and demonstrated that with such calculations, new links or non-obvious links in biochemical pathways could be found.…”

Section: Modelling Based On Biochemical Stoichiometrymentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,612

1,337

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Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and 'proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

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Section: Modelling Based On Biochemical Stoichiometrymentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,612

1,337

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“…Each of these motifs is then be replaced by supernodes that act similarly, in a way that is mathematically comprehensive. Hierarchical integration can lead to understanding of complex networks [17,23,15,16].…”

Section: Introductionmentioning

confidence: 99%

Computing Algebraic Functions with Biochemical Reaction Networks

et al. 2009

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In biological organisms, networks of chemical reactions control the processing of information in a cell. A general approach to study the behavior of these networks is to analyze common modules. Instead of this analytical approach to study signaling networks, we construct functional motifs from the bottom up. We formulate conceptual networks of biochemical reactions that implement elementary algebraic operations over the domain and range of positive real numbers. We discuss how the steady state behavior relates to algebraic functions, and study the stability of the networks' fixed points. The primitive networks are then combined in feed-forward networks, allowing us to compute a diverse range of algebraic functions, such as polynomials. With this systematic approach, we explore the range of mathematical functions that can be constructed with these networks.

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“…To this end, the identification of blocked reactions (i.e., reactions incapable of carrying flux due to the stoichiometry of the metabolic network under steady-state conditions) and enzyme subsets (i.e., groups of reactions that operate together in fixed flux proportions under steady-state conditions) in metabolic models has attracted considerable interest in recent years (Kholodenko et al 1995;Rohwer et al 1996;Pfeiffer et al 1999;. The output of these analyses provides significant biological insight as to which reactions are potentially missing from metabolic models, as well as which reactions may be under coordinated regulation, alluding to a mechanism for the continuous refinement of metabolic reconstructions through an iterative model-building process.…”

mentioning

confidence: 99%

Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions

Burgard¹,

Nikolaev²,

Schilling³

et al. 2004

Genome Res.

342

406

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In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v 1 and v 2 , are (1) directionally coupled, if a non-zero flux for v 1 implies a non-zero flux for v 2 but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v 1 implies a non-zero, though variable, flux for v 2 and vice versa; or (3) fully coupled, if a non-zero flux for v 1 implies not only a non-zero but also a fixed flux for v 2 and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations.[Supplemental material is available online at www.genome.org.]An overarching attribute of metabolic networks is their inherent robustness and ability to cope with ever-changing environmental conditions. Despite this flexibility, network stoichiometry and connectivity do establish limits/barriers to the coordination and accessibility of reactions. The recent abundance of complete genome sequences has enabled the generation of genome-scale metabolic reconstructions for various microorganisms (Covert et al. 2001;Price et al. 2003;. These models provide a largely complete skeleton of the metabolic reactions present in an organism. Examination of the structural and topological properties of metabolic networks is important at both the conceptual level, to reveal the organizational principles of metabolic interactions within cellular networks, and at the practical level for more effectively focusing engineering interventions and ensuring the consistency of the underlying reconstructions.To this end, the identification of blocked reactions (i.e., reactions incapable of carrying flux due to the stoichiometry of the metabolic network under steady-state conditions) and enzyme subsets (i.e., groups of reactions that operate together in fixed flux proportions under steady-state conditions) in metabolic models has attracted considerable interest

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Composite control of cell function: metabolic pathways behaving as single control units

Cited by 35 publications

References 21 publications

Metabolomics — the link between genotypes and phenotypes

Metabolomics — the link between genotypes and phenotypes

Computing Algebraic Functions with Biochemical Reaction Networks

Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions

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