Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering

Schuster, Stefan; Dandekar, Thomas; Fell, David A.

doi:10.1016/s0167-7799(98)01290-6

Cited by 593 publications

(417 citation statements)

References 24 publications

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“…In 86% of the cases studied, the effects of gene knockouts in E. coli could be shown correctly by growth rate data of E. coli mutants reported in the literature. In a series of related papers, Pfeiffer et al (1999), Schuster (1999) and Schuster et al (1999Schuster et al ( , 2000 described the concept of elementary flux mode. Biochemical pathways were rationalized into thermodynamically and stoichiometrically feasible subsets of enzymes that each generate valid steady states.…”

Section: Modelling Based On Biochemical Stoichiometrymentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

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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

1,624

1,337

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“…In this connection, Young and coworkers 16,17 drew on the concept of elementary flux modes, first propounded by Clarke 18 referring to various routines in the network through which external substrate is converted into various fermentation products and biomass. Further, Young and coworkers 16,17 has resolved other difficulties with the cybernetic formulation of large networks by (i) decomposing the pathways into elementary flux modes as done by Schuster et al, 19,20 (ii) by generalizing cybernetic laws using optimal control theory as shown in Young and Ramkrishna 21 to include more general objective functions, and (iii) by incorporation of global objectives such as maximizing biomass or substrate uptake rate among the various modes and to further control of reactions in each mode to enforce the maximum flux throughput. The resulting models are robust and capable of describing dynamic behavior of multiple strains as shown by Young and coworkers.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo-) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.

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“…The stoichiometric data and models available provide the basis for various in silico applications differing in the methodology and algorithms used. Major contributions for C. glutamicum have been obtained from flux balancing [66] and elementary (flux) mode analysis [67]. In short, flux balance analysis is a constraint based method to analyze stoichiometric networks.…”

Section: Stoichiometric Modelingmentioning

confidence: 99%

Analysis and Engineering of Metabolic Pathway Fluxes in Corynebacterium glutamicum

Wittmann

2010

Biosystems Engineering I

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The Gram-positive soil bacterium Corynebacterium glutamicum was discovered as a natural overproducer of glutamate about 50 years ago. Linked to the steadily increasing economical importance of this microorganism for production of glutamate and other amino acids, the quest for efficient production strains has been an intense area of research during the past few decades. Efficient production strains were created by applying classical mutagenesis and selection and especially metabolic engineering strategies with the advent of recombinant DNA technology. Hereby experimental and computational approaches have provided fascinating insights into the metabolism of this microorganism and directed strain engineering. Today, C. glutamicum is applied to the industrial production of more than 2 million tons of amino acids per year. The huge achievements in recent years, including the sequencing of the complete genome and efficient post genomic approaches, now provide the basis for a new, fascinating era of research -analysis of metabolic and regulatory properties of C. glutamicum on a global scale towards novel and superior bioprocesses.

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Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering

Cited by 593 publications

References 24 publications

Metabolomics — the link between genotypes and phenotypes

Metabolomics — the link between genotypes and phenotypes

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Analysis and Engineering of Metabolic Pathway Fluxes in Corynebacterium glutamicum

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