The Future Exploring of Gut Microbiome-Immunity Interactions: From In Vivo/Vitro Models to In Silico Innovations

Abstract

Investigating the complex interactions between microbiota and immunity is crucial for a fruitful understanding progress of human health and disease. This review assesses animal models, next-generation in vitro models, and in silico approaches that are used to decipher the microbiome-immunity axis, evaluating their strengths and limitations. While animal models provide a comprehensive biological context, they also raise ethical and practical concerns. Conversely, modern in vitro models reduce animal involvement but require specific costs and materials. When considering the environmental impact of these models, in silico approaches emerge as promising for resource reduction, but they require robust experimental validation and ongoing refinement. Their potential is significant, paving the way for a more sustainable and ethical future in microbiome-immunity research.

Keywords: immunity; in silico models; in vitro models; in vivo models; inflammation; microbiome.

Figure 1

Schematic overview of the main in-silico models and tools to study metabolic interactions, disease etiology and disease modeling. The figure summarizes the three prominent types of in-silico models used to evaluate the MIA that will be discussed in detail in the next paragraphs: (1) multi-species ecosystem models/Genome scale metabolic models (GEMs), (2) machine learning-based models and (3) agent-based simulation (ABS) tools.

Figure 2

Integration of single-species models with host metabolism models to study interactions between the gut microbiota (GM) and the host through a comprehensive supra-organism model. (A) Each colored network represents the metabolic model of a single microbial species. (B) By combining multiple single-species models, a multi-species model can be constructed, simulating functional exchanges between different microbial species within a community. (C) Integrating this multi-species model with host biological data creates a microbe-host model, enabling the study of interactions between microbes and the host.