Towards Digital Twins for personalized cancer treatment.

How can heterogeneous multi-omics data be integrated into interpretable models for precision oncology? In a review published in Frontiers in Systems Biology, we explore multilayer network approaches as a mechanistic framework for Digital Twins in cancer research.

Advances in omics technologies now generate large amounts of biological data describing multiple levels of cellular organization. One of the major challenges in precision oncology is integrating these heterogeneous datasets into biologically interpretable models.

In this review published in Frontiers in Systems Biology, authors describe how multilayer networks provide a powerful framework for integrating genomics, transcriptomics, proteomics, metabolomics, and other molecular data while preserving their biological relationships.

The review further discusses how these network-based models could serve as the foundation for patient-specific Digital Twins capable of simulating disease progression and treatment response.

The next steps involve integrating longitudinal and multimodal data, developing more comprehensive mechanistic models, and validating these models in clinical cohorts.

Collaborations et partnerships

Funding bodies and supporters :

• Evotec – In Silico R&D
• CARe