Temporal encoding of bacterial identity and traits in growth dynamics
Abstract
Microbiology has traditionally been defined by the study of the phenotypic traits of microorganisms. While some traits can be easily explained with a direct genetic basis, most are a result of complex interactions between the organism and its environment. We demonstrate that phenotypes with a sufficiently high information content can distinguish strains and predict traits such as antibiotic response. This has implications for how clinicians can better identify and treat bacterial infections. In particular, our results highlight that both phenotype-based and sequence-based approaches contribute valuable information and can be used alongside one another in practice.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 03, 2020
- Source ID
- 10.1073/pnas.2008807117
Entities
People
- Carolyn Zhang
- Deverick Anderson
- Helena R Ma
- Joshua T. Thaden
- Lingchong You
- Minfeng Xiao
- Vance G Fowler
- Wenchen Song
- Xiao Peng
Organizations
- Army Research Office
- BGI Genomics
- Centers for Disease Control and Prevention
- David and Lucile Packard Foundation
- Duke University
- National Institutes of Health
- National Science Foundation
- United States Agency for Healthcare Research and Quality