Model selection for Pseudomonas aeruginosa CF infections

Laboratory models are essential to modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, the choice among laboratory models is often based on weak scientific rationale. We propose a general quantitative framework to assess model accuracy from RNA-seq data and use this framework to evaluate models of Pseudomonas aeruginosa cystic fibrosis (CF) lung infection. These methods provide researchers with an evidence-based approach to select and improve laboratory models.