Computation of elementary modes: a unifying framework and the new binary approach

Gagneur, Julien; Klamt, Steffen

doi:10.1186/1471-2105-5-175

Cited by 214 publications

(98 citation statements)

References 37 publications

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“…An elementary flux mode is a feasible flux distribution [an element v ʦ ker(Y I a ) പ Ն0 r ] with a maximum number of zero entries. If, as in our setup, we consider only positive reaction rates, elementary flux modes are equivalent to extreme rays (21). Formally, the extreme rays E i of ker(Y I a ) പ Ն0 r are defined as follows:…”

Section: Positive Steady States and Elementary Fluxesmentioning

confidence: 99%

See 1 more Smart Citation

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Conradi

Flockerzi

Raisch

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

102

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In analyzing and mathematical modeling of complex (bio)chemical reaction networks, formal methods that connect network structure and dynamic behavior are needed because often, quantitative knowledge of the networks is very limited. This applies to many important processes in cell biology. Chemical reaction network theory allows for the classification of the potential network behavior-for instance, with respect to the existence of multiple steady states-but is computationally limited to small systems. Here, we show that by analyzing subnetworks termed elementary flux modes, the applicability of the theory can be extended to more complex networks. For an example network inspired by cell cycle control in budding yeast, the approach allows for model discrimination, identification of key mechanisms for multistationarity, and robustness analysis. The presented methods will be helpful in modeling and analyzing other complex reaction networks.reaction network ͉ structure ͉ elementary flux mode ͉ bistability

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Section: Positive Steady States and Elementary Fluxesmentioning

confidence: 99%

“…. , r} ͉ E ji Ͼ 0,} denotes the support of vector E j ; i.e., the set of indices where E j has nonzero values (21). We call a set of nonnegative vectors {E 1 , .…”

Section: Positive Steady States and Elementary Fluxesmentioning

confidence: 99%

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Conradi

Flockerzi

Raisch

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

102

View full text Add to dashboard Cite

show abstract

“…Elementary flux mode analysis systematically enumerates all independent minimal pathways through a network, each a unique elementary mode, that are stoichiometrically and thermodynamically feasible [70]. All possible steady-state flux distributions through the metabolic network are nonnegative linear combinations of the set of elementary modes [71]. This can be exploited to extract key properties from metabolic networks such as maximum …”

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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“…However, despite the development of novel methods using state of the art computational techniques expediting their application in larger networks [11], this family of algorithms fails on GSMNs using standard computers, because of the combinatorial explosion in the number of EFMs [4]. In this light, several methods have been recently proposed to determine a subset of EFMs in GSMNs [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…There are many algorithms to compute EFMs in metabolic networks being the proposal of [15] considered the fastest one.…”

Section: Introductionmentioning

confidence: 99%

A new approach to obtaining EFMs using graph methods based on the shortest path between end nodes

Hidalgo¹,

Guil²,

García³

2016

Genomics Comput Biol

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Genome-scale metabolic networks let us understand the behaviour of the metabolism in the cells of living organisms. The availability of great amounts of such data gives the scientific community the opportunity to infer in silico new metabolic knowledge. Elementary Flux Modes (EFM) are minimal contained pathways or subsets of a metabolic network that are very useful to achieving the comprehension of a very specific metabolic function (as well as dysfunctions), and to get the knowledge to develop new drugs. Metabolic networks can have large connectivity and, therefore, EFMs resolution faces a combinational explosion challenge to be solved. In this paper we propose a new approach to obtain EFMs based on graph theory, the balanced graph concept and the shortest path between end nodes.Our proposal uses the shortest path between end nodes (input and output nodes) that finds all the pathways in the metabolic network and is able to prioritise the pathway search accounting the biological mean pursued. Our technique has two phases, the exploration phase and the characterisation one, and we show how it works in a well-known case study. We also demonstrate the relevance of the concept of balanced graph to achieve to the full list of EFMs.

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Computation of elementary modes: a unifying framework and the new binary approach

Cited by 214 publications

References 37 publications

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Analysis and Engineering of Metabolic Pathway Fluxes in Corynebacterium glutamicum

A new approach to obtaining EFMs using graph methods based on the shortest path between end nodes

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