New study on the use of digital twins in precision medicine
13.01.2025
An international team led by researchers from the University of Padua's Padua Center for Network Medicine has proposed a new conceptual framework for the use of digital twins in precision medicine. The digital twin is a virtual model of a physical object that follows the object's life cycle and uses real-time data sent by sensors on the object itself to simulate its behavior and monitor operations.
The research results, titled "Challenges and opportunities for digital twins in precision medicine from a complex systems perspective," were published in the scientific journal "NPJ Digital Medicine" from the Nature Publishing Group. These results lay the foundation for a new generation of diagnostic and therapeutic tools that combine network science, computational biology, and digital medicine.
"Our approach is not a mere exercise in predictive modeling," explains Manlio De Domenico, the first author of the study and a professor at the Department of Physics and Astronomy at the University of Padua. "It is based on complex computational models guided by explicit biological hypotheses, allowing for transparent and interpretable simulation and analysis of therapeutic interventions, improving the understanding of the mechanisms underlying biological processes. The challenge now is to make them communicate well with each other."
The study stands out for its interdisciplinarity, as it integrates concepts and techniques from statistical physics with biology and medicine. The digital twins described in the work are not mere statistical reproductions of clinical data but actual explanatory models that, in principle, can replicate in-silico the behavior of cells, organs, or entire organisms using simulations based on multiscale and multilevel biological mechanisms. This allows for the exploration of dynamic therapeutic strategies and the optimization of clinical decisions in real-time.
The research, conducted in collaboration with Ca' Foscari University of Venice, Binghamton University (USA), the London Institute for Mathematical Sciences, and the Universidade Católica Portuguesa in Lisbon, highlights how these models can bridge the gaps of "opaque" AI-based techniques. "Opaque" AI is defined as such because its complexity prevents human users from fully understanding and explaining the mechanisms driving it, causing some distrust in its use. This complexity hinders the spread of AI in crucial sectors like medicine and security.
By using hypothesis-driven generative models, this approach promises to improve the effectiveness of personalized therapies, reducing the risks associated with suboptimal diagnoses and treatments. The integration of biological, historical, and environmental "big data" also enables capturing the complexity of biological interactions and the exposome (the set of environmental stimuli that come into contact with the body), opening new possibilities in the fight against complex diseases such as cancer, neurodegenerative diseases, and many chronic conditions.
This research represents a meeting point between complex systems physics, medicine, and systems biology, outlining new perspectives for developing a more equitable, effective, and sustainable medicine. The commitment of the Padua Center for Network Medicine at the University of Padua in this direction underscores the central role of interdisciplinary research in transforming modern medicine.
