ArticlePLoS computational biology2014
Phenotypic signatures arising from unbalanced bacterial growth.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- 400 nm femtosecond laser irradiation eradicates mature MRSA biofilms and inhibits their formation via quorum sensing dysregulation.Scientific reports · 2026Article
- A Machine Learning Approach to Microcalorimetric Pattern Classification of Pathogens in Synovial Fluid.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.Nature communications · 2023Article
- Optimal dose of lactoferrin reduces the resilience of in vitro Staphylococcus aureus colonies.PloS one · 2022Article
- Predicting plasmid persistence in microbial communities by coarse-grained modeling.BioEssays : news and reviews in molecular, cellular and developmental biology · 2021Article
- Temporal encoding of bacterial identity and traits in growth dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Robust, linear correlations between growth rates and β-lactam-mediated lysis rates.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Quantitative and synthetic biology approaches to combat bacterial pathogens.Current opinion in biomedical engineering · 2017Article
- Article
- Bacterial temporal dynamics enable optimal design of antibiotic treatment.PLoS computational biology · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify "phenotypic signatures" by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains.
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