Modelling A Digital Twin of The Human Gut Microbiome Towards Precision Medication: A Data Driven Approach

The human gut microbiome plays a pivotal role in health and disease, influencing conditions ranging from gastrointestinal disorders to systemic diseases. The emergence of digital twin technology provides a transformative approach to understanding and predicting complex interactions between the gut microbiota and medications. This paper presents a comprehensive framework for creating a digital twin of the human gut microbiome, using data-driven methodologies to enable personalized medicine. The proposed system models the dynamics of the gut microbiome, predicts drug-microbe interactions using a Random Forest machine learning approach, and estimates the changes in the microbial population through mathematical modeling. By integrating genomic data, drug-specific chemical properties, and dose-response relationships, it simulates the microbiome responses to drugs, presenting results through interactive 3D visualizations and graphical interfaces. Performance evaluations highlight the model’s robustness, achieving a ROC-AUC of 0.97 and PR-AUC of 0.91, outperforming other machine learning approaches. This research underscores the potential of digital twins in advancing personalized gut health treatments, paving the way for precision medicine.