ArticlePLoS computational biology2017
Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- Metab8D: a metabolic regulome network from multiomics and machine learning.Communications biology · 2026Article
- Therapeutic potential in rheumatic diseases of extracellular vesicles derived from mesenchymal stromal cells.Nature reviews. Rheumatology · 2023Review
- Disentangling the roles of aneuploidy, chromosomal instability and tumour heterogeneity in developing resistance to cancer therapies.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2023Review
- Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases.PLoS computational biology · 2022Article
- Proteome allocations change linearly with the specific growth rate of Saccharomyces cerevisiae under glucose limitation.Nature communications · 2022Article
- Personalized phosphoproteomics identifies functional signaling.Nature biotechnology · 2022Article
- Involvement of a Multidrug Efflux Pump and Alterations in Cell Surface Structure in the Synergistic Antifungal Activity of Nagilactone E and Anethole against Budding YeastAntibiotics (Basel, Switzerland) · 2021Article
- Effects of A6E Mutation on Protein Expression and Supramolecular Assembly of Yeast Asparagine Synthetase.Biology · 2021Article
- CRISPRi screens reveal genes modulating yeast growth in lignocellulose hydrolysate.Biotechnology for biofuels · 2021Article
- Multiscale dynamic modeling and simulation of a biorefinery.Biotechnology and bioengineering · 2019Article
- Footprint-based functional analysis of multiomic data.Current opinion in systems biology · 2019Review
- Identify gene expression pattern change at transcriptional and post-transcriptional levels.Transcription · 2019Article
- Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human.Methods in molecular biology (Clifton, N.J.) · 2019Article
- A resource of variant effect predictions of single nucleotide variants in model organisms.Molecular systems biology · 2018Article
- Trans-omic Analysis Reveals Selective Responses to Induced and Basal Insulin across Signaling, Transcriptional, and Metabolic Networks.iScience · 2018Article
- Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts.Cell systems · 2018Article
- Article
- Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism.Frontiers in plant science · 2018Article
- Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells.Metabolic engineering · 2018Article
- Designing and interpreting 'multi-omic' experiments that may change our understanding of biology.Current opinion in systems biology · 2017Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.
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