An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infections in vivo.

ABSTRACT: The management of bacterial diseases calls for a detailed knowledge about the dynamic changes in host-bacteria interactions. Biological insights are gained by integrating experimental data with mechanistic mathematical models to infer experimentally unobservable quantities. This inter-disciplinary field would benefit from experiments with maximal information content yielding high-precision inference. Here, we present a computationally efficient tool for optimizing experimental design in terms of parameter inference in studies using isogenic-tagged strains. We study the effect of three experimental design factors: number of biological replicates, sampling timepoint selection and number of copies per tagged strain. We conduct a simulation study to establish the relationship between our optim

SUBMITTER: Vlazaki M 

PROVIDER: S-EPMC7811585 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature