Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case

Abstract

Laboratory models have become a cornerstone of modern microbiology. However, the accuracy of even the most commonly used models has never been evaluated. Here, we propose a quantitative framework based on gene expression data to evaluate model performance and apply it to models of Pseudomonas aeruginosa cystic fibrosis lung infection. We discovered that these models captured different aspects of P. aeruginosa infection physiology, and we identify which functional categories are and are not captured by each model. These methods will provide researchers with a solid basis to choose among laboratory models depending on the scientific question of interest and will help improve existing experimental models.