Gerber Lab

Work 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 interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled. Our open-source software package provides multiple tools for interpreting learned models, including phylogeny/taxonomy of modules, and stability, interaction topology and keystoneness. To benchmark MDSINE2, we generated microbiome timeseries data from two murine cohorts that received faecal transplants from human donors and were then subjected to dietary and antibiotic perturbations. MDSINE2 outperforms state-of-the-art methods and identifies interaction modules that provide insights into ecosystems-scale interactions in the gut microbiome.

Work 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 the host remain limited. To address these limitations, we developed MMETHANE, an open-source software package that implements a purpose-built deep learning model for predicting host status from paired microbial sequencing and metabolomic data. MMETHANE incorporates prior biological knowledge, including phylogenetic and chemical relationships, and is intrinsically interpretable, outputting an English-language set of rules that explains its decisions. Using a compendium of six datasets with paired microbial composition and metabolomics measurements, we showed that MMETHANE always performed at least on par with existing methods, including blackbox machine learning techniques, and outperformed other methods on >80% of the datasets evaluated. We additionally demonstrated through two cases studies analyzing inflammatory bowel disease gut microbiome datasets that MMETHANE uncovers biologically meaningful links between microbes, metabolites, and disease status.

Dr. Gerber lays out his perspective on how AI will impact microbiome research in this Cell Host & Microbe piece.

The Festival is designed principally for scientists and clinicians who are working in the fields of genomics, other omics, and/or using biodata , to further research, drug discovery, healthcare and – ultimately – deliver better patient outcomes.

Topics covered include genomics, single cell/spatial biology, multiomics, biodata, cancer research, drug discovery, AI, microbiome, proteomics, liquid biopsy and so much more.