New research from ISB’s Gibbons Lab shows that computer models of gut metabolism can predict whether probiotics will successfully establish in an individual’s microbiome and how dietary prebiotics shape health-related molecules in the gut. The findings point toward a future of more personalized, predictable microbiome-based interventions.
Postdoctoral fellows are the lifeblood of research institutions, yet their contributions are often underrecognized. At the Institute for Systems Biology (ISB), postdocs enter the scientific workforce with passion, vision, and advanced training – bringing the fresh ideas that fuel discovery.
Researchers at ISB have developed personalized models to predict C. diff colonization risk and test targeted probiotics. This approach could prevent infection before it starts and paves the way for precision microbiome therapies tailored to individual gut ecosystems.
ISB scientists have developed a simple, low-cost method to measure the total amount of bacteria in the gut using existing sequencing data. This breakthrough could speed up microbiome research and unlock new insights into health and disease.
ISB’s Gibbons Lab developed a breakthrough method that analyzes food-derived DNA in fecal metagenomes, allowing for data-driven diet tracking without the need for burdensome questionnaires.
ISB has been selected as a winner of the 2024 Amazon Web Services (AWS) IMAGINE Grant. The grant will support ISB’s continued development of My Digital Gut, an online decision-support platform that will help make microbiome-informed nutrition and healthcare personalized, predictive, and preventive.
Everybody poops, but not every day. An ISB-led research team examined the clinical, lifestyle, and multi-omic data of more than 1,400 healthy adults. How often people poop, they found, can have a large influence on one’s physiology and health.
ISB researchers have developed a novel way to simulate personalized, microbiome-mediated responses to diet. They use a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific short-chain fatty acid production rates in response to different dietary, prebiotic, and probiotic inputs.