ArticleProceedings of the National Academy of Sciences of the United States of America2019
Predicting growth rate from gene expression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Yeast Stress Response to Synthetic Constructs.ACS synthetic biology · 2026Review
- Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves.Scientific reports · 2025Article
- Integrating yeast biodiversity and machine learning for predictive metabolic engineering.FEMS yeast research · 2025Review
- Screening of high glucose tolerant Escherichia coli for L-valine fermentation by autonomous evolutionary mutation.Applied microbiology and biotechnology · 2025Article
- Microbial reaction rate estimation using proteins and proteomes.The ISME journal · 2025Article
- Microbial reaction rate estimation using proteins and proteomes.bioRxiv : the preprint server for biology · 2024Article
- Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
- Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions.Frontiers in microbiology · 2023Article
- Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations.PLoS genetics · 2022Article
- A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences.PLoS computational biology · 2021Article
- Metabolic engineering of microorganisms for the production of multifunctional non-protein amino acids: γ-aminobutyric acid and δ-aminolevulinic acid.Microbial biotechnology · 2021Review
- Beyond the chemical master equation: Stochastic chemical kinetics coupled with auxiliary processes.PLoS computational biology · 2021Article
- A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants.NPJ systems biology and applications · 2020Article
- Distinguishing cell phenotype using cell epigenotype.Science advances · 2020Article
- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth-rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this work, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from
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