Mapping single‐cell responses to population‐level dynamics during antibiotic treatment

Author:

Kim Kyeri12ORCID,Wang Teng12,Ma Helena R12ORCID,Şimşek Emrah12ORCID,Li Boyan3,Andreani Virgile45ORCID,You Lingchong1267ORCID

Affiliation:

1. Department of Biomedical Engineering Duke University Durham NC USA

2. Center for Quantitative Biodesign Duke University Durham NC USA

3. Integrated Science Program, Yuanpei College Peking University Beijing China

4. Biomedical Engineering Department Boston University Boston MA USA

5. Biological Design Center Boston University Boston MA USA

6. Center for Genomic and Computational Biology Duke University Durham NC USA

7. Department of Molecular Genetics and Microbiology Duke University School of Medicine Durham NC USA

Abstract

AbstractTreatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters.

Funder

National Institutes of Health

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Information Systems

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