ArticleCell systems2020
Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.
Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.FEMS microbiology reviews · 2025Review
- Emerging tools and best practices for studying gut microbial community metabolism.Nature metabolism · 2024Review
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- A computational model of Pseudomonas syringae metabolism unveils a role for branched-chain amino acids in Arabidopsis leaf colonization.PLoS computational biology · 2023Article
- Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.Current opinion in chemical biology · 2023Review
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Extracting functionally accurate context-specific models of Atlantic salmon metabolism.NPJ systems biology and applications · 2023Article
- Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning.Nature communications · 2023Article
- Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities.Quantitative biology (Beijing, China) · 2023Review
- Deep Learning Concepts and Applications for Synthetic Biology.GEN biotechnology · 2022Review
- Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models.PLoS computational biology · 2022Article
- Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases.Frontiers in cardiovascular medicine · 2022Review
- Review
- Article
- Bacteriophage self-counting in the presence of viral replication.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- The E. coli Whole-Cell Modeling Project.EcoSal Plus · 2021Article
- A gap-filling algorithm for prediction of metabolic interactions in microbial communities.PLoS computational biology · 2021Article
- PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models.STAR protocols · 2021Article
- gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models.Genome biology · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. Here, we develop a solution to this problem for genome-scale metabolic models. We generate an ensemble of models, each equally consistent with experimental data, then perform simulations with them. We apply machine learning to the simulation output to identify model structure variation that maximally influences simulations. These variants are high-priority candidates for curation through removal, addition, or reannotation in the model. We apply this approach, automated metabolic model ensemble-driven elimination of uncertainty with statistical learning (AMMEDEUS), to 29 bacterial species to improve gene essentiality predictions. We explore targets for individual species and compile pan-species targets to improve the database used during model construction. AMMEDEUS is an automated and performance-driven recommendation system that complements intuition during curation of biochemical knowledgebases.
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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.