Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

Area of Science:

• Microbiology
• Computational Biology
• Systems Biology

Background:

• Intestinal microbiota is vital for human health, aiding digestion and pathogen defense.
• Understanding microbiome interactions is key to modulating host health.
• Microbiome research informs strategies for health improvement, including dietary changes and bacterial colonization.

Purpose of the Study:

• To develop an agent-based model simulating interactions between bacterial species and the gut environment.
• To investigate the stability and recovery of the intestinal microbial system following antibiotic disturbance.
• To analyze the impact of feedback mechanisms, mutation, and spatial structure on bacterial community dynamics.

Main Methods:

• Agent-based modeling of two bacterial species and their gut interactions.
• Simulation of polysaccharide fermentation pathways and antibiotic treatment as a disturbance.
• Analysis of system recovery based on feedback interactions, therapy duration, and antibiotic dosage.
• Modeling bacterial mutation to antibiotic resistance as a stochastic process.

Main Results:

• Feedback mechanisms are essential for system functionality and stability post-disturbance.
• Antibiotic treatment induces high mutation rates, but sensitive strains can re-emerge.
• The fitness cost of antibiotic resistance influences the recovery dynamics of bacterial strains.
• The model visualizes emergent spatial structures and their role in bacterial adaptation.

Conclusions:

• The study confirms the necessity of feedback mechanisms for microbial community stability.
• Bacterial communities can recover from antibiotic disturbances, with sensitive strains often dominating post-recovery.
• Spatial structure plays a critical role in bacterial survival and adaptation to environmental changes.