“Creating novel computational models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applying these findings to develop new diagnostic tests and therapeutic interventions to improve patient care.“
email: ggerber#bwh.harvard.edu, physical address: Hale Building 8002M, 60 Fenwood Road, Boston, MA 02115
Positions
- Post Doctoral Fellow in AI/Deep Learning for MicrobiomeThe Gerber Lab (http://gerber.bwh.harvard.edu) is a multidisciplinary group at Brigham and Women’s Hospital/Harvard Medical School that develops novel computational models and high-throughput experimental systems to understand the role of the microbiota in human diseases, and applies these findings to develop new diagnostic tests and therapies. A long-standing and continuing focus of the lab is on… Read more: Post Doctoral Fellow in AI/Deep Learning for Microbiome
- Graduate StudentsI am always excited to work with talented graduate students with interests relevant to my lab, which focuses on developing novel machine learning/computational biology/wet lab approaches to further understanding of the microbiome–the trillions of microbes living on and within us. This fascinating, complex and dynamic ecosystem is crucial for human health, and when disrupted may… Read more: Graduate Students
Lab News
- Learning ecosystem-scale dynamics from microbiome data with MDSINE2Work led by Travis Gibson, PhD – see our new manuscript. Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and… Read more: Learning ecosystem-scale dynamics from microbiome data with MDSINE2
- Dr. Gerber to speak at AAAS Annual meeting on Feb. 14, 2025 in BostonDr. Gerber will speak at the American Association for Advancement of Science Annual meeting, “Science Shaping Tomorrow” at the Hynes Convention Center in Boston.
- MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics dataWork led by Jen Dawkins – see our new manuscript. Metabolite production, consumption, and exchange are intimately involved with host health and disease, as well as being key drivers of host-microbiome interactions. Despite the increasing prevalence of datasets that jointly measure microbiome composition and metabolites, computational tools for linking these data to the status of… Read more: MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics data
- MCSPACE: inferring microbiome spatiotemporal dynamics from high-throughput co-localization dataWork led by Gary Uppal – see our new manuscript. Recent advances in high-throughput approaches for estimating co-localization of microbes, such as SAMPL-seq, allow characterization of the biogeography of the gut microbiome longitudinally and at unprecedented scale. However, these high-dimensional data are complex and have unique noise properties. To address these challenges, we developed MCSPACE,… Read more: MCSPACE: inferring microbiome spatiotemporal dynamics from high-throughput co-localization data
- “AI in microbiome research: Where have we been, where are we going?”Dr. Gerber lays out his perspective on how AI will impact microbiome research in this Cell Host & Microbe piece.