ArticlePLoS computational biology2017
Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed.
- Nutrient control enables metabolic reconstruction ofbioRxiv : the preprint server for biology · 2026Article
- MCC: automated mass and charge curation at the genome scale applied toMicrobiology spectrum · 2026Article
- Metabolic modeling unveils potential probiotic roles ofISME communications · 2026Article
- Genome-scale metabolic modelling of the priority fungal pathogenmicroLife · 2026Article
- Article
- Genome-scale metabolic modeling ofmSystems · 2025Article
- Genome-scale metabolic model ofmSystems · 2025Article
- MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models.Genome biology · 2025Article
- Minimal cut sets in metabolic networks: from conceptual foundations to applications in metabolic engineering and biomedicine.Briefings in bioinformatics · 2025Review
- A Guide to Metabolic Network Modeling for Plant Biology.Plants (Basel, Switzerland) · 2025Review
- MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism.Bioinformatics advances · 2025Article
- Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Exploring the metabolic profile of A. baumannii for antimicrobial development using genome-scale modeling.PLoS pathogens · 2024Article
- Genome-scale model ofMicrobiology spectrum · 2024Article
- A new metabolic model ofLife science alliance · 2023Article
- Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome.Nature communications · 2023Article
- CAVE: a cloud-based platform for analysis and visualization of metabolic pathways.Nucleic acids research · 2023Article
- A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism.Bioengineering (Basel, Switzerland) · 2023Article
- Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning.Nature communications · 2023Article
- New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells.PLoS computational biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Energy metabolism is central to cellular biology. Thus, genome-scale models of heterotrophic unicellular species must account appropriately for the utilization of external nutrients to synthesize energy metabolites such as ATP. However, metabolic models designed for flux-balance analysis (FBA) may contain thermodynamically impossible energy-generating cycles: without nutrient consumption, these models are still capable of charging energy metabolites (such as ADP→ATP or NADP+→NADPH). Here, we show that energy-generating cycles occur in over 85% of metabolic models without extensive manual curation, such as those contained in the ModelSEED and MetaNetX databases; in contrast, such cycles are rare in the manually curated models of the BiGG database. Energy generating cycles may represent model errors, e.g., erroneous assumptions on reaction reversibilities. Alternatively, part of the cycle may be thermodynamically feasible in one environment, while the remainder is thermodynamically feasible in another environment; as standard FBA does not account for thermodynamics, combining these into an FBA model allows erroneous energy generation. The presence of energy-generating cycles typically inflates maximal biomass production rates by 25%, and may lead to biases in evolutionary simulations. We present efficient computational methods (i) to identify energy generating cycles, using FBA, and (ii) to identify minimal sets of model changes that eliminate them, using a variant of the GlobalFit algorithm.
Indexed as
Identifiers
What OpenQuestion holds
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.