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Feb 27 2023

2/27 – Peter E. Larsen, Loyola University, Chicago

CBQB Seminar (in-person event)

February 27, 2023

12:00 PM - 1:00 PM


COMRB 8175


909 S Wolcott Ave, Chicago, IL 60612

This is an in-person event in room COMRB 8175 on west campus (directions).  You can also watch seminar live here>>


Peter E. Larsen, PhD
Assistant Director
Loyola Genomics Facility
Loyola University


Predictive Computational Modeling of Host-Microbiome Interaction


Host-microbiome interactions are known to have substantial effects on human health, but the diversity of the human microbiome makes it difficult to definitively attribute specific microbiome features to a host phenotype. One approach to overcoming this challenge is to use animal models of host-microbiome interaction, but it must be determined that relevant aspects of host-microbiome interactions are reflected in the animal model. One such experimental validation is an experiment by Ridura et. al. In that experiment, transplanting a microbiome from a human into a mouse also conferred the human donor’s obesity phenotype. We have aggregated a collection of previously published host-microbiome mouse-model experiments and combined it with thousands of sequenced and annotated bacterial genomes and metametabolomic pathways. Three computational models were generated, each model reflecting an aspect of host-microbiome interactions: (i) Predict the change in microbiome community structure in response to host diet using a community interaction network, (ii) Predict metagenomic data from microbiome community structure, and (iii) Predict host obesogenesis from modeled microbiome metagenomic data. These computationally validated models were combined into an integrated model of host-microbiome-diet interactions and used to replicate the Ridura experiment in silico. The results of the computational models indicate that network-based models are significantly more predictive than similar but non-network-based models. Network-based models also provide additional insight into the molecular mechanisms of host-microbiome interaction by highlighting metabolites and metabolic pathways proposed to be associated with microbiome-based obesogenesis. While the models generated in this study are likely too specific to the animal models and experimental conditions used to train our models to be of general utility in a broader understanding of obesogenesis, the approach detailed here is expected to be a powerful tool of investigating multiple types of host-microbiome interactions.


UIC Biomedical Engineering

Date posted

Feb 14, 2023

Date updated

Feb 14, 2023