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

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Vlazaki, Myrto 
Price, David J 

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 optimality criterion and the size of parameter estimate confidence intervals, and showcase its application in a range of biological scenarios reflecting different dynamics patterns observed in experimental infections. We show that in low-variance systems with low killing and replication rates, predicting high-precision experimental designs is consistently achieved; higher replicate sizes and strategic timepoint selection yield more precise estimates. Finally, we address the question of resource allocation under constraints; given a fixed number of host animals and a constraint on total inoculum size per host, infections with fewer strains at higher copies per strain lead to higher-precision inference.

experimental design, likelihood-free, mathematical model, microbiology, parameter inference, within-host dynamics, Animals, Bacterial Infections, Computer Simulation, Models, Biological, Models, Theoretical, Research Design
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J R Soc Interface
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The Royal Society
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Biotechnology and Biological Sciences Research Council (BB/M020193/1)