Author

E. S. King

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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Evolutionary Biology· 2 Microbiology· 2

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 4 Approach:deep learning· 3 Organism:E. coli· 3 Approach:statistical model· 2 Approach:experimental assay· 1 Assay:fluorescence / live-cell microscopy· 1 Assay:proteomics· 1

Frequent Co-Authors

J. G. Scott· 4 shared D. T. Weaver· 2 shared A. E. Stacey· 1 shared A. E. Stacy· 1 shared B. Pierce· 1 shared E. Dolson· 1 shared J. A. Maltas· 1 shared J. Maltas· 1 shared

Activity by Year

2022-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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4 shared papersMicrobiology

J. G. Scott

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

A low-footprint, fluorescence-based bacterial time-kill assay for estimating dose-dependent cell death dynamics

E. S. King, A. E. Stacey, J. G. Scott

This paper introduces a fluorescence-based time-kill assay using resazurin to measure dose-dependent cell death in bacteria, showing improved accuracy and reduced resource use compared to existing methods.

bioRxiv · Mar 8, 2024 · PreprintMicrobiology

Diverse mutant selection windows shape spatialheterogeneity in evolving populations

E. S. King, B. Pierce, M. Hinczewski, J. G. Scott

This paper introduces fitness seascapes to model genotype-by-environment interactions, enabling more accurate prediction of drug resistance evolution by analyzing spatially heterogeneous mutant selection windows.

PLOS Computational Biology · Feb 22, 2024Peer-reviewedEvolutionary Biology

Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.

D. T. Weaver, E. S. King, J. Maltas, J. G. Scott

This study uses reinforcement learning to develop optimal antibiotic treatment strategies that reduce antimicrobial resistance in simulated E. coli populations.

bioRxiv · Jan 12, 2023Microbiology

Fitness seascapes are necessary for realistic modeling of the evolutionary response to drug therapy

E. S. King, A. E. Stacy, D. T. Weaver, J. A. Maltas, R. Barker-Clarke, E. Dolson, J. G. Scott

The paper introduces a fitness seascape model to study drug resistance in E. coli by mapping genotypes to dose-response functions and shows that higher mutation supply and early drug adherence reduce resistance.

bioRxiv · Jun 10, 2022Evolutionary Biology