ArticlePloS one2011
Randomizing genome-scale metabolic networks.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation.Frontiers in molecular biosciences · 2022Review
- Network-Based Metabolism-Centered Screening of Potential Drug Targets inFrontiers in cellular and infection microbiology · 2019Article
- An enormous potential for niche construction through bacterial cross-feeding in a homogeneous environment.PLoS computational biology · 2018Article
- Flux-dependent graphs for metabolic networks.NPJ systems biology and applications · 2018Article
- Metabolic modeling with Big Data and the gut microbiome.Applied & translational genomics · 2016Review
- Topology of molecular interaction networks.BMC systems biology · 2013Review
- Probabilistic inference of biochemical reactions in microbial communities from metagenomic sequences.PLoS computational biology · 2013Article
- Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.Molecular systems biology · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have "unusual" properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.