ArticleCell systems2018
Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts.
Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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.
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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
49 citing papers in PubMed, 95 citations in OpenAlex.
- Towards the construction of a virtual yeast.Nature · 2026Review
- Energy-translation coupling limits anaerobic yeast growth.Nature communications · 2026Article
- Metab8D: a metabolic regulome network from multiomics and machine learning.Communications biology · 2026Article
- The return of metabolism: biochemistry and physiology of glycolysis.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Yeast Stress Response to Synthetic Constructs.ACS synthetic biology · 2026Review
- Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation.Genome research · 2026Article
- DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks.Briefings in bioinformatics · 2025Article
- A deep learning architecture for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Engineering strategies for enhanced heterologous protein production by Saccharomyces cerevisiae.Microbial cell factories · 2024Review
- CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing.Bioinformatics advances · 2024Article
- Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.ACS synthetic biology · 2023Article
- Genetic effects on molecular network states explain complex traits.Molecular systems biology · 2023Article
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Yeast increases glycolytic flux to support higher growth rates accompanied by decreased metabolite regulation and lower protein phosphorylation.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Unravelling metabolic cross-feeding in a yeast-bacteria community usingMolecular systems biology · 2023Article
- Plant and microbial sciences as key drivers in the development of metabolomics research.Proceedings of the National Academy of Sciences of the United States of America · 2023Review
- The Knowns and Unknowns in Protein-Metabolite Interactions.International journal of molecular sciences · 2023Review
- Fungal resilience and host-pathogen interactions: Future perspectives and opportunities.Parasite immunology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 5 institutions in 5 countries.
Funding
Abstract
A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.
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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.