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Related Experiment Video

Updated: Jan 14, 2026

Author Spotlight: Advanced Enteroid Model for Studying Host-Pathogen Interactions

Author Spotlight: Advanced Enteroid Model for Studying Host-Pathogen Interactions

Published on: April 5, 2024

Metabolic modeling of host-microbe interactions.

Natchapon Srinak¹, Florian Krüger¹, Christoph Kaleta¹

¹Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany.

Computational and Structural Biotechnology Journal

|October 23, 2025

Summary

Genome-scale metabolic models (GEMs) provide a systems-level view of host-microbe interactions, revealing metabolic dependencies. This review explores GEM applications, challenges, and future directions for studying these complex biological relationships.

Keywords:

Community modeling Constrained-based reconstruction and analysis (COBRA)Genome scale metabolic modeling Host-microbe interactions Multi-species modeling

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Published on: November 18, 2022

Area of Science:

Microbiology
Systems Biology
Metabolic Engineering

Background:

Host-microbe interactions are crucial for eukaryotic life, impacting metabolism and immunity.
Understanding these complex dynamics requires integrative approaches considering host and microbial genetics.
Current methods struggle to fully capture the intricate and dynamic nature of these relationships.

Purpose of the Study:

To review the application of Genome-scale metabolic models (GEMs) in studying host-microbe interactions.
To highlight how GEMs reveal reciprocal metabolic influences between hosts and microbes.
To discuss challenges and future directions for using GEMs in this field.

Main Methods:

Utilizing Genome-scale metabolic models (GEMs) to simulate metabolic fluxes and cross-feeding.
Integrating GEMs with experimental data for hypothesis generation.
Analyzing metabolic interdependencies and emergent community functions.

Main Results:

GEMs enable systems-level investigation of host-microbe metabolic interdependencies.
GEMs facilitate the exploration of cross-feeding relationships and community metabolism.
Recent applications demonstrate the power of GEMs in uncovering reciprocal metabolic influences.

Conclusions:

GEMs offer a powerful framework for dissecting complex host-microbe metabolic interactions.
Addressing technical challenges and leveraging available tools will advance GEM applications.
Future research should focus on refining GEMs for more comprehensive insights into host-microbe dynamics.