Computational models and software connect metagenomics to metabolic network reconstruction, assess metabolic complementarity between species, and identify critical species associated to functions of interest.
A novel metabolic network analysis method enables large-scale computational predictions of biosynthetic capabilities across the human oral microbiome, revealing a unique cluster of fastidious microorganisms and potential metabolic interdependencies.
Human gut bacteria alter their metabolism in response to each other's presence, which causes their community dynamics to deviate from predictions that are based on mono-culture data.
Modeling weighted transfer ratios enable statistical analysis of maternal–infant transfer at a more general level and can indicate whether any transfer is persistent, transient, or originates from alternate sources.
A computational method is presented that quantifies the effect that specific bacteria in the gut have on the immune system and guides the design of therapeutically potent microbial consortia to cure auto-immune disease.
A data-driven within-host model reveals that different antibiotics are associated with divergent effects on antibiotic resistance carriage and abundance in hospitalised patients, with important implications for antibiotic stewardship.
Killing their neighbors allows bacteria to steal genes, including antibiotic resistance genes, which we observed under a microscope, quantified, modeled, and predicted potentially guiding strategies to combat it.
A multi-cohort analysis of 2,500 gut microbiomes and five major diseases discovers that disease-microbiome associations display specific age-centric trends, with diseases characterized by age-centric trends of species gain/loss.
Properties of various microbial communities time series, such as the noise color and neutrality, are captured by stochastic generalized Lotka-Volterra equations, even in the absence of interactions.
