A critical view of metabolic network adaptations

Papp, Balázs; Teusink, Bas; Notebaart, Richard A.

doi:10.2976/1.3020599

Cited by 55 publications

(52 citation statements)

References 76 publications

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“…GSMRs give an opportunity to explore these properties, such as existence of loops [146,147], optimal pathway usage [148], metabolic connectivity [149][150][151], and pathway redundancy [152,153]. Understanding the evolutionary relationships is another application of GSMRs, a new aspect of this approach and there are only a small number of papers in this field [154][155][156].…”

Section: Applicationsmentioning

confidence: 99%

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Masoudi‐Nejad

Salehzadeh‐Yazdi

Akbari‐Birgani

et al. 2013

Biological Knowledge Discovery Handbook

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INTRODUCTIONGenome-scale metabolic reconstruction is the ultimate goal of system biologists for finding out the genotype-phenotype relationship. For achieving this purpose, we need enough knowledge about the metabolic pathways and genome annotation. During the present study, we aim to describe the foundational concepts, central to omics data, model formulation, history, method, and applications of metabolic network reconstruction as well as some related sources. In Section 45.2, we describe omics data and high-throughput technologies for gaining these data. In section 45.3, we present different ways for metabolic network modeling. In section 45.4, we summarize the history of genome-scale modeling as one of the most important methods for metabolic network modeling. In sections 45.5 and 45.6, we elucidate how genome-scale metabolic models could be generated and what their applications are. Finally, in section 45.7, we review biochemical pathways and genome annotation databases. OMICs ERAInnovative omics technologies such as genomics, transcriptomics, proteomics, and metabolomics facilitate a strategy toward the simultaneous analysis of the large number of genes, transcripts, proteins, and metabolites. Therefore, a huge volume of data has generated about the make-up of cells and their behavior at various cellular levels and different environmental conditions, which enable us to reconstruct genome-scale biomolecular networks (e.g., transcriptional regulatory networks, interactomic networks, and metabolic networks)Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, First Edition. Edited by Mourad Elloumi and Albert Y. Zomaya.

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Section: Applicationsmentioning

confidence: 99%

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Masoudi‐Nejad

Salehzadeh‐Yazdi

Akbari‐Birgani

et al. 2013

Biological Knowledge Discovery Handbook

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“…[73]. On the other hand, there is mounting evidence that biochemical aspects, such as similarities among catalyzed reactions and their coupling in pathways influences the evolutionary patterns of the many gene families that encode metabolic enzymes [75,76].…”

Section: Evolution Of Metabolismsmentioning

confidence: 99%

Evolution of metabolic networks: a computational frame-work

et al. 2010

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Background: The metabolic architectures of extant organisms share many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a universe of chemical reaction upon which the metabolism is built, an explicit representation of the enzymes that implement the metabolism, of a genetic system that encodes these enzymes, and of a fitness function that can be selected for. Results: We describe here a simulation environment that implements all these components in a simplified ways so that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters. Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is determined by an ad hoc mapping between their secondary structure and the transition state graphs that they stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the complete system and first simulation results. Conclusions: The simulation system presented here allows coherent investigations into the evolutionary mechanisms of the first steps of metabolic evolution using a self-consistent toy universe.

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“…Ignoring any hierarchy of functional modules, the uptake reactions, reactions contributing to biomass generation and all the reactions in-between result in a layered organization of metabolism. These complications present a formidable challenge in any attempt to explore the interrelations between network topology and dynamical function for metabolic systems [41,42].…”

Section: Network Representations Of Metabolic Systemsmentioning

confidence: 99%

“…In the majority, these analyses considered metabolism as an undirected network (see earlier studies [39,43,44] and for a critique of the projection of metabolic networks, see recent studies [41,45]). …”

Section: Network Representations Of Metabolic Systemsmentioning

confidence: 99%

Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

Beber

Fretter

Jain

et al. 2012

J. R. Soc. Interface.

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Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over-or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.

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A critical view of metabolic network adaptations

Cited by 55 publications

References 76 publications

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Evolution of metabolic networks: a computational frame-work

Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

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