Within the iHead initiative, we integrate high-throughput data generated by the consortium by combining mechanistic models with statistical and machine learning techniques. Using data-integrative genome-scale metabolic modeling, we explore host-microbe metabolic interdependencies across various consortia previously identified by the iHead initiative. Our analysis will generate quantitative predictions about metabolic pathway activities, which are then subjected to classical statistical and machine learning methods.

This allows us to elucidate how immunity-related compounds modulate the host-microbiota metabolic system and to identify common and unique metabolic response patterns in photoautotrophic and heterotrophic host-microbe systems.

Furthermore, our models will provide experimentally testable hypotheses regarding the evolutionary interface between metabolism and immune systems, and whether they share common co-metabolism principles across different kingdoms of life or are subject to kingdom-specific solutions.