PT - JOURNAL ARTICLE AU - David J. Price AU - Alexandre Breuzé AU - Richard Dybowski AU - Olivier Restif TI - An Efficient Moments-Based Inference Method for Within-Host Bacterial Infection Dynamics AID - 10.1101/116319 DP - 2017 Jan 01 TA - bioRxiv PG - 116319 4099 - http://biorxiv.org/content/early/2017/03/13/116319.short 4100 - http://biorxiv.org/content/early/2017/03/13/116319.full AB - Over the last ten years, isogenic tagging (IT) has revolutionised the study of bacterial infection dynamics in laboratory animal models. However, quantitative analysis of IT data has been hindered by the piecemeal development of relevant statistical models. The most promising approach relies on stochastic Markovian models of bacterial population dynamics within and among organs. Here we present an efficient numerical method to fit such stochastic dynamic models to in vivo experimental IT data. A common approach to statistical inference with stochastic dynamic models relies on producing large numbers of simulations, but this remains a slow and inefficient method for all but simple problems. Instead, we derive and solve the systems of ordinary differential equations for the two lower-order moments of the stochastic variables (mean, variance and covariance). For any given model structure, and assuming linear dynamic rates, we demonstrate how the model parameters can be efficiently and accurately estimated by divergence minimisation. We then apply our method to an experimental dataset and compare the estimates and goodness-of-fit to those obtained by maximum likelihood estimation. This flexible framework can easily be applied to a range of experimental systems. Its computational efficiency paves the way for model comparison and optimal experimental design.Anumber of animalsTnumber of tagged strainsnnumber of organsNinumber of bacteria in organ imijmigration rate from organ i to organ jkikilling rate in organ irireplication rate in organ iτiobservation time iA, B, Cmatricesλvector of transition ratesBNumber of bootstrap samplesθ*MDE parameter estimateABCapproximate Bayesian computationITisogenic taggingLVlive vaccineMAREmean absolute relative errorMDEminimum divergence estimateMLEmaximum likelihood estimateqPCRquantitative polymerase chain reactionWITSwildtype isogenic tagged strain