PT  - JOURNAL ARTICLE
AU  - Liam P. Shaw
AU  - Hassan Bassam
AU  - Chris P. Barnes
AU  - A. Sarah Walker
AU  - Nigel Klein
AU  - Francois Balloux
TI  - Modelling microbiome recovery after antibiotics using a stability landscape framework
AID  - 10.1101/222398
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 222398
4099  - http://biorxiv.org/content/early/2019/03/04/222398.short
4100  - http://biorxiv.org/content/early/2019/03/04/222398.full
AB  - Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiome’s diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a ‘stability landscape’: the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to data from a previous study of the year-long effects of short courses of four common antibiotics on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes, and further validate our model using data from another study looking at the impact of a combination of last-resort antibiotics on the gut microbiome. Using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of certain antibiotics in both the gut and oral microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.