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Today our all-hands EMBRIO research seminar, 3-4pm EST, features Ph.D. candidate Javier Muñoz (Brubaker/Green Lab) presenting Systems Modeling for Multiomics Analysis Integration in Inflammatory Bowel Disease (abstract below).
Check out the online training opportunity by the creators of Imaris happening Dec. 7th, 11AM ET, "Trainable AI Image Analysis for Everyone!". Details and Registration link below.
Purdue labs planning for undergraduate researchers next summer (and our partner institutions who have students who want to come to Purdue for a summer research experience), the SURF REU project portal is open for faculty to submit your projects. Please indicate your project as affiliated with EMBRIO Institute. EMBRIO will cover the PI portion of the cost.
Mark your calendar for the NSF BII conference January 22-23. This year’s conference will be a hybrid, with most attending via zoom, and a smaller team in person at NSF headquarters. View the draft agenda.
Systems modeling for Multiomics AnalysisIntegration in Inflammatory Bowel Disease.
Javier Munoz-Briones and Doug Brubaker
Crohn’s disease and ulcerative colitis are chronic inflammatory bowel diseases (IBD) with a rising global prevalence, influenced by clinical and demographics factors. The pathogenesis of IBD involves complexinteractions between gut microbiome dysbiosis, epithelial cell barrierdisruption, and immune hyperactivity, which are poorly understood. This necessitates the development of novel approaches to integrate and model multiple clinical and molecular data modalities from patients, animal models, and in vitro systems to discover effectivebiomarkers for diseaseprogression and drug response. For this presentation, my focus is on the lack of understanding of regarding the composition and functional interactions of the gut microbiome in IBD. As sequencing technologies advance, the amount of molecular and compositional data from pairedmeasurements of host and microbiome systems is exploding. While it is become routine to generate such rich, deep datasets, tools for their interpretation lag behind. Here, I presenta computational framework for integrative modelingof microbiome multi-omics data: Latent Interacting Variable Effects (LIVE) modeling. LIVE combines various types of microbiome multi-omics data using single-omic latent variables (LV) into a structured meta-model to determine the most predictive combinations of multi-omics features predicting an outcome, patient group, or phenotype. I implemented and tested LIVE using publicly available metagenomic and metabolomics data set from Crohn’s Disease(CD) and ulcerative colitis (UC) status patients in the PRISM and LLDeep cohorts. The findings show that LIVE reduced the number of features interactions from the originaldatasets for CD to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes, clinical variables, and a disease status outcome. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the meta-model integrating -omic data types.
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