Designing an Intelligent Cyber-Biological System for Enhanced Analysis and Monitoring of Organoids

Abstract

Organoids replicate key aspects of organ function and present a microcosmic representation of the in-vivo state, offering unprecedented opportunities for disease modeling, drug testing, and personalized medicine. However, the complexity of these heterogeneous biological models requires innovative approaches to optimize the analysis and monitoring. This work outlines an Intelligent Cyber-Biological System (ICBS) with a multi-layered MAPE-K structure for enhanced analysis and real-time organoid monitoring. Starting with the Laboratory Layer, the ICBS harnesses real-time data from organoid cultures, which the Data Layer processes and archives for insight extraction. The Intelligence Layer employs advanced machine learning (ML) models for pattern recognition and predictive analytics, identifying critical development stages and treatment responses. In the Decision Layer, perceptions derived from ML-generated guide strategic decision-making for culture condition adjustments and further examinations. The ICBS framework engages researchers in its findings, fostering decision implementation and a responsive feedback system. Its real-world application in the intestinal organoid case study demonstrates the feasibility and effectiveness of the proposed solution, increasing the efficiency of experimental processes and the predictive accuracy of treatment outcomes.