A modular computational framework for medical digital twins

Area of Science:

• Computational Biology
• Bioinformatics
• Software Engineering

Background:

• Precision medicine aims to tailor treatments to individual patients.
• Medical digital twins are computational models of disease processes calibrated to individual patients.
• Developing these models requires integrating diverse data and component models from multiple sources.

Purpose of the Study:

• To present a modular, open-source software platform for constructing and integrating computational models for precision medicine.
• To facilitate a decentralized, community-based approach to developing and updating medical digital twins.
• To demonstrate the platform's utility through a case study.

Main Methods:

• Development of a modular software architecture for computational modeling.
• Integration of mechanistic and data-driven modeling techniques.
• Implementation of a case study involving an animal model of respiratory fungal infection.

Main Results:

• A scalable, open-source platform for the integration and simulation of diverse computational models was developed.
• The platform supports a decentralized, community-driven model-building process.
• Successful application of the platform in a case study demonstrated its feasibility.

Conclusions:

• The presented software platform supports the large-scale development of medical digital twins for precision medicine.
• The modular and open-source nature fosters collaboration and accelerates the implementation of personalized healthcare solutions.
• This approach has the potential to significantly improve diagnosis, prognosis, and treatment across various medical conditions.