Gi-MAPS: a quantitative engineering framework for AI-guided pediatric gut microbiome ecological interpretation and...

Area of Science:

• Microbiology
• Bioinformatics
• Systems Biology

Background:

• Quantitative microbiome analysis faces challenges due to fragmented workflows.
• Lack of standardized anaerobic sampling and absolute quantification hinders reproducibility.
• Patent-documented engineering integration is crucial for population-scale microbiome analytics.

Purpose of the Study:

• To develop and validate Gi-MAPS, an integrated analytical system for quantitative microbiome analysis.
• To address limitations in anaerobic sampling, absolute quantification, and AI inference.
• To establish a reproducible framework for large-scale microbiome studies.

Main Methods:

• Gi-MAPS integrates patented innovations: anaerobic sample preservation, multiplex qPCR for absolute quantification of Bifidobacterium, and a digital-twin engine for microbiota simulation.
• Explainable ensemble AI models are coupled with these modules for a quantitative and simulation-enabled framework.
• Subsystems were validated under granted patents to define performance boundaries and reproducibility.

Main Results:

• Anaerobic preservation demonstrated <0.1% residual oxygen stability.
• Multiplex qPCR achieved detection sensitivity down to five genomic copies/µL.
• The system showed high accuracy in ecological maturity estimation (AUC > 0.97), disease-risk classification (89%), and digital-twin forecasting (95% concordance).

Conclusions:

• Gi-MAPS offers a patent-anchored, standardized engineering framework for quantitative, function-aware microbiome analysis.
• Key innovations include oxygen-controlled anaerobic sampling, absolute microbial quantification, and digital-twin ecological simulation.
• This system provides a reproducible foundation for large-scale cohort deployment, longitudinal monitoring, and future multi-omics integration.