Digital-twins for personalized medicine

Digital twins for personalized medicine aim to provide predictive, patient-specific representations of biological systems by combining clinical data, experimental observations, and physics-based modeling. In contrast to purely data-driven approaches, this vision relies on mechanistic digital twins, grounded in the laws of continuum mechanics, transport phenomena, and multi-physics coupling.

Building on porous media mechanics, biological tissues are modeled as complex, heterogeneous, and evolving media where solid deformation, interstitial fluid flow, and biochemical transport are strongly coupled. This framework is particularly suited to describe soft tissues and tumors, where growth, remodeling, permeability changes, and mechanical stresses play a key role in disease progression and therapeutic response.

At smaller scales, microfluidic and organ-on-chip systems provide controlled experimental platforms to reproduce key features of the tumor microenvironment. These systems enable quantitative calibration and validation of digital twins, allowing inverse identification of biomechanical and transport parameters that are otherwise inaccessible in vivo.

Within the field of oncophysics, such digital twins capture the interplay between tumor cells, stromal components, and vascular or interstitial flows, linking cellular-scale processes to organ-scale behavior. By integrating multi-scale modeling, experimental microfluidics, and patient-specific data, digital twins open the way to robust predictions of tumor evolution, invasion patterns, and treatment outcomes.

Ultimately, this approach supports personalized medicine by transforming physical understanding into actionable clinical insight, enabling optimized therapeutic strategies tailored to the mechanical, transport, and biological signature of each patient.