ReviewCell systems2021
Path to improving the life cycle and quality of genome-scale models of metabolism.
Review in Cell systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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.
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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging genome-scale metabolic models to understand aerobic methanotrophs.The ISME journal · 2024Pooled it
- Reconstruction of human metabolic models with large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- MCC: automated mass and charge curation at the genome scale applied toMicrobiology spectrum · 2026Article
- Influence of Amino Acids on Quorum Sensing-Related Pathways inMetabolites · 2025Article
- Auxotrophy-based curation improves the consensus genome-scale metabolic model of yeast.Synthetic and systems biotechnology · 2024Article
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
- Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments.Synthetic and systems biotechnology · 2023Article
- Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance.Cell reports · 2023Article
- Lifespan-extending interventions induce consistent patterns of fatty acid oxidation in mouse livers.Communications biology · 2023Article
- Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning.Nature communications · 2023Article
- Quantitative systems pharmacology of the eye: Tools and data for ocular QSP.CPT: pharmacometrics & systems pharmacology · 2023Review
- Genome-scale modeling of yeast metabolism: retrospectives and perspectives.FEMS yeast research · 2022Review
- Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks.Nature machine intelligence · 2022Article
- Recent Progress on Systems and Synthetic Biology of Diatoms for Improving Algal Productivity.Frontiers in bioengineering and biotechnology · 2022Review
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
Abstract
Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.
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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.