Gi-MAPS: a quantitative engineering framework for AI-guided pediatric gut microbiome ecological interpretation and digital-twin simulation
Year: 2026
Type: article
Abstract: Background: Quantitative and reproducible microbiome analysis is limited by fragmented workflows lacking standardized anaerobic sampling, absolute quantification methods, and transparent AI inference. Patent-documented engineering integration is required for reliable microbiome analytics at population scale. Methods: species in a single reaction, and (iii) a CIT-Net-based digital-twin engine that supports forward simulation of gut microbiota ecological trajectories. These modules are coupled with explainable ensemble artificial intelligence models to form a fully quantitative and simulation-enabled microbiome analysis framework. Each subsystem was validated under granted patents to define engineering performance boundaries and reproducibility specifications. Results: System validation demonstrated <0.1% residual oxygen stability for anaerobic preservation, detection sensitivity down to five genomic copies per microliter, AUC > 0.97 for ecological maturity estimation, 89% accuracy for disease-risk classification, and 95% concordance for digital-twin forecasting. Execution-layer software copyright modules and filed patents extend automation, visualization, and future application domains. Conclusion: Gi-MAPS provides a patent-anchored, standardized engineering framework whose key novelties lie in oxygen-controlled anaerobic sampling, absolute microbial quantification via multiplex qPCR, and digital-twin ecological simulation, enabling quantitative, function-aware, and prospective microbiome analysis. It establishes a reproducible foundation enabling large-scale cohort deployment, longitudinal ecological monitoring, digital-twin simulation, and future multi-omics integration.
Source: Frontiers in Microbiology
Language: English
Topic: Gut microbiota and health
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7140192561