Building better microbial infection models: a call to do the “field” experiments
- 1School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
- 2Emory-Children’s Cystic Fibrosis Center, Atlanta, Georgia, USA
Abstract
Experimental model systems are essential in microbiology. However, models are often described as “biologically relevant” without a clear explanation of what that means or how relevance was established. Here, I argue that the missing piece in model development is benchmarking. New technologies and increasingly elaborate model systems can be powerful, but they do not guarantee that a model better represents the environment it is meant to reproduce. The guarantee that is implied cannot be made explicit until these models are evaluated against measurements of microbial behavior and function made directly in those environments. These “field” experiments are often messy, expensive, low throughput, and technically challenging, but provide the benchmarks needed to determine what a model captures, what it misses, and which questions it can tackle. While chemical measurements and quantification of physical features can guide model construction, the most important readout is the behavior of the microbes in the natural environment.
Related
- Building better citation practices in public health research: Practical considerations for student researchers, from source discovery to crafting and positioning claims
- Quantifying microbial fitness in high-throughput experiments
- The influence of nutrient enrichment and fish addition on microbial communities in water and fish guts during mesocosm experiments conducted in a tropical freshwater lagoon (Lagoon Aghien, Cote dIvoire)
- Advancing Cadmium Remediation via Rhizosphere Engineering: From Microbial Mechanisms to Field Deployment
- Metabolic network restoration, not donor microbial engraftment, associates with resolution of recurrent Clostridioides difficile infection
BibTeX
@article{whiteley2026building,
title = {{Building better microbial infection models: a call to do the “field” experiments}},
author = {Marvin Whiteley},
journal = {mBio},
year = {2026},
month = aug,
doi = {10.1128/mbio.01768-26},
url = {https://doi.org/10.1128/mbio.01768-26},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Building better microbial infection models: a call to do the “field” experiments
AU - Marvin Whiteley
PY - 2026
DA - 2026/08/24
JO - mBio
DO - 10.1128/mbio.01768-26
UR - https://doi.org/10.1128/mbio.01768-26
AB - Experimental model systems are essential in microbiology. However, models are often described as “biologically relevant” without a clear explanation of what that means or how relevance was established. Here, I argue that the missing piece in model development is benchmarking. New technologies and increasingly elaborate model systems can be powerful, but they do not guarantee that a model better represents the environment it is meant to reproduce. The guarantee that is implied cannot be made explicit until these models are evaluated against measurements of microbial behavior and function made directly in those environments. These “field” experiments are often messy, expensive, low throughput, and technically challenging, but provide the benchmarks needed to determine what a model captures, what it misses, and which questions it can tackle. While chemical measurements and quantification of physical features can guide model construction, the most important readout is the behavior of the microbes in the natural environment.
SN - 2150-7511
ER -