Maintenance and recovery of key beneficial services by complex microbial communities in the face of disturbance is fundamental to health.  Yet, stability and resilience vary in, and between different individuals, and are poorly understood.  We seek to identify features of the human microbiome that predict microbial community stability and resilience following disturbance.  Towards this end, we are developing novel statistical and mathematical methods for data integration (sparse, nonlinear multi-table methods), testing existing ecological theories using multiple operational definitions of relevant concepts, and applying statistical learning strategies to allow data-driven investigation of ecological and clinical properties that determine and predict stability and/or resilience.  The breadth and magnitude of this project’s impact are significant:  the ability to predict microbiome stability and resilience will enable clinicians to take rational steps to stabilize beneficial microbial interactions and alter detrimental ones, so as to restore resilience, and to anticipate and manage responses to various interventions, thus, avoiding debilitating episodes of disease.

Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment

Julia Fukuyama , Laurie Rumker , Kris Sankaran , Pratheepa Jeganathan, Les Dethlefsen, David A. Relman , Susan P. Holmes (2017) PLOS Computational Biology.

[Anaylsis Files hosted at Stanford Digital Repository]

Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation

Les Dethlefsena and David A. Relman (2011) PNAS.