We study what human-associated microbial communities (the “microbiome”) tell us about their host and how they affect its health. We aim to develop personalized microbiome-based and -guided therapeutics and diagnostics.
We do so by examining the sum of microbial species, genes, metabolites and other microbial factors, and studying how they act in tandem, and along with other host factors, to reflect or affect health and disease.
… developing data analysis methods that go beyond microbiome composition (who the microbes are) and examine microbiome activity (what the microbes do) directly from human microbiome data.
We are ...
… studying microbial metabolism, pursuing potentially causal or mechanistic insights by applying optimization methods that incorporate biochemical knowledge (knowledge-driven approaches) and machine learning methods that decipher the metabolic network structure directly from data (data-driven approaches).
... studying genomic plasticity and adaptation in the microbiome as a way to understand various selective pressures in the ecosystem, using high resolution bioinformatic algorithms.
… aspiring to develop personally-tailored therapies that act through the microbiome (microbiome-based therapeutics) or to use the microbiome in order to personalize healthcare (microbiome-guided medicine).
... applying our methodology in diverse clinical settings, with a special focus on the reproductive system and adverse pregnancy outcomes.
Tuesday, June 29, 2021
Chen (Martin) Liu wins outstanding MA thesis award from the Department of Biomedical Informatics.
Monday, May 31, 2021
Yoli Meydan joined the lab as a research assistant.