ArticleMetabolites2012
A topological characterization of medium-dependent essential metabolic reactions.
Article in Metabolites, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Intrinsic limitations in mainstream methods of identifying network motifs in biology.BMC bioinformatics · 2020Article
- Understanding genetic variation - the value of systems biology.British journal of clinical pharmacology · 2014Review
- Knowledge discovery in spectral data by means of complex networks.Metabolites · 2013Article
- Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks.Journal of the Royal Society, Interface · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions.
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Registered trials
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