ArticleiScience2020
OptFill: A Tool for Infeasible Cycle-Free Gapfilling of Stoichiometric Metabolic Models.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction.Communications biology · 2026Article
- ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.iScience · 2025Article
- Fermi calculations enable quick downselection of target genes and process optimization in photosynthetic systems.Plant physiology · 2025Review
- Article
- Dissecting metabolic landscape of alveolar macrophage.Scientific reports · 2024Article
- Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase inmSystems · 2024Article
- Article
- A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation.iScience · 2023Article
- Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information.iScience · 2022Article
- Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.iScience · 2022Article
- Using EuGeneCiD and EuGeneCiM computational tools for synthetic biology.STAR protocols · 2021Article
- A gap-filling algorithm for prediction of metabolic interactions in microbial communities.PLoS computational biology · 2021Article
- Article
- PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models.STAR protocols · 2021Article
- Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis ofSTAR protocols · 2020Article
- Article
- Inferring composition and function of the human gut microbiome in time and space: A review of genome-scale metabolic modelling tools.Computational and structural biotechnology journal · 2020Review
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2 authors.
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Abstract
Stoichiometric metabolic modeling, particularly genome-scale models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions but cannot avoid thermodynamically infeasible cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill, which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle-free gapfilling solutions. In addition, OptFill can be adapted to automate inherent TICs identification in any GSM. Overall, OptFill can address critical issues in automated development of high-quality GSMs.
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