https://www.sciencedaily.com/releases/2016/06/160627125308.htm
MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses
full text here: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0980-6
A team of investigators from Brigham and Women's Hospital and the University of Massachusetts have developed a suite of computer algorithms that can accurately predict the behavior of the microbiome -- the vast collection of microbes living on and inside the human body.
In a paper published in Genome Biology, the authors show how their algorithms can be applied to develop new treatments for serious diarrheal infections, includingClostridium difficile, and inflammatory bowel disease. The team also shows how to identify bacteria most crucial for a healthy and stable microbial community, which could inform the development of probiotics and other therapies.
MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses
full text here: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0980-6
Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors.
Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations.