ArticlePloS one2017
Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 55 citations in OpenAlex.
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- From Single Organisms to Communities: Modeling Methanotrophs and Their Satellites.Microorganisms · 2025Review
- Multiscale metabolic engineering in biological lignin valorization.Innovation (Cambridge (Mass.)) · 2025Review
- Differences in GenBank and RefSeq annotations may affect genomics data interpretation formSphere · 2025Article
- Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Genome-scale metabolic network model and phenome of solvent-tolerant Pseudomonas putida S12.BMC genomics · 2024Article
- Reconstruction and metabolic profiling of the genome-scale metabolic network model ofSynthetic and systems biotechnology · 2023Article
- CAVE: a cloud-based platform for analysis and visualization of metabolic pathways.Nucleic acids research · 2023Article
- Article
- A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics.Nature communications · 2021Article
- Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition.PLoS computational biology · 2021Article
- Industrial biotechnology of Pseudomonas putida: advances and prospects.Applied microbiology and biotechnology · 2020Review
- Article
- MEMOTE for standardized genome-scale metabolic model testing.Nature biotechnology · 2020Article
- Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.NPJ systems biology and applications · 2020Article
- High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities.Environmental microbiology · 2020Article
- Large-scale kinetic metabolic models ofBiotechnology for biofuels · 2020Article
- A systematic assessment of current genome-scale metabolic reconstruction tools.Genome biology · 2019Article
- Genome-scale metabolic model analysis indicates low energy production efficiency in marine ammonia-oxidizing archaea.AMB Express · 2018Article
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.
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