An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infections in vivo
Myrto Vlazaki, David J Price, Olivier Restif
Journal of The Royal Society Interface | ROYAL SOC | Published : 2020
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 cop..View full abstract
Awarded by BBSRC
M.V. is jointly funded by a Newnham College Major Studentship and a Vergottis Award from the Cambridge Trust. O.R. is funded by the ALBORADA Trust. O.R. and D.J.P. acknowledge research grant no. BB/M020193 from the BBSRC.