ArticleTheScientificWorldJournal2014
Modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis: how good are genome-scale metabolic networks at predicting the differences?
Article in TheScientificWorldJournal, 2014. 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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Who cites it
8 citing papers in PubMed.
- Microbial proliferation deteriorates the corrosion inhibition capability, lubricity, and stability of cutting fluid.Frontiers in microbiology · 2025Article
- Determination of the genome-scale metabolic network ofFrontiers in bioengineering and biotechnology · 2025Article
- Bacteria and Methanogens in the Human Microbiome: a Review of Syntrophic Interactions.Microbial ecology · 2022Review
- CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design.BMC bioinformatics · 2018Article
- Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications.Iranian journal of biotechnology · 2018Article
- In silico engineering ofPeerJ · 2018Article
- A Genome-Scale Metabolic Reconstruction ofFrontiers in genetics · 2018Article
- Efficient Reconstruction of Predictive Consensus Metabolic Network Models.PLoS computational biology · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.
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