Author

J. B. Rattray

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 5

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 5 Approach:deep learning· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

S. P. Brown· 5 shared J. Gurney· 4 shared S. A. Thomas· 4 shared E. Molotkova· 2 shared Y. Wang· 2 shared A. El-Zayat· 1 shared C. Solis-Lemus· 1 shared D. Pimentel-Alarcon· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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5 shared papersMicrobiology

S. P. Brown

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

The las and rhl Quorum Sensing Systems in Pseudomonas aeruginosa Form a Multi-Signal Reciprocal Network Which Can Tune Reactivity to Variations in Physical and Social Environments

S. A. Thomas, A. El-Zayat, J. Gurney, J. B. Rattray, S. P. Brown

The paper shows that the las and rhl quorum sensing systems in Pseudomonas aeruginosa interact reciprocally and cooperatively, enhancing their responsiveness to environmental changes.

bioRxiv · Feb 23, 2023 · PreprintMicrobiology

The dynamic response of quorum-sensing to density is robust to signal supplementation and signal synthase knockouts

J. B. Rattray, P. Kramer, J. Gurney, S. A. Thomas, S. P. Brown

This study investigates how quorum-sensing (QS) in Pseudomonas aeruginosa responds to genetic and signal perturbations, showing robustness in density-dependent gene expression despite signal supplementation or synthase knockouts.

bioRxiv · Sep 12, 2022 · PreprintMicrobiology

Machine learning identification of Pseudomonas aeruginosa strains from colony image data

J. B. Rattray, R. J. Lowhorn, R. Walden, P. Marquez Zacarias, E. Molotkova, G. Perron, C. Solis-Lemus, D. Pimentel-Alarcon, S. P. Brown

This paper uses deep learning to classify Pseudomonas aeruginosa strains based on colony morphology, achieving high accuracy despite limited data.

bioRxiv · Sep 2, 2022 · PreprintMicrobiology

Bacterial quorum sensing allows graded and bimodal cellular responses to variations in population density

J. B. Rattray, S. A. Thomas, Y. Wang, E. Molotkova, J. Gurney, J. Varga, S. P. Brown

This study shows Pseudomonas aeruginosa uses quorum sensing to produce graded and bimodal responses to population density changes, without a clear threshold for gene expression activation.

bioRxiv · Dec 15, 2021 · PreprintMicrobiology

In silico bacteria evolve robust cooperation via complex quorum-sensing strategies

Y. Wang, J. B. Rattray, S. A. Thomas, J. Gurney, S. P. Brown

The paper uses agent-based simulations to study how environmental factors shape the evolution of quorum-sensing strategies in bacteria, revealing how genetic constraints influence cooperation and resilience against cheaters.

bioRxiv · Apr 3, 2019Microbiology