Multilayer network approaches to omics data integration in digital twins for cancer research
6 affiliations
- 1Equipe Labellisée Ligue Nationale Contre le
- 2Evotec
- 3Univ Toulouse, INSERM, CNRS, CRCT
- 4CRCT - Centre de Recherches en Cancérologie de Toulouse (Centre de Recherches en Cancérologie de Toulouse 2 Avenue Hubert Curien 31037 Toulouse Cedex 1 FRANCE - France)
- 5Evotec, Abingdon
- 6Evotec [Toulouse] (Campus Curie, 195, route d‘Espagne, 31036 Toulouse, France - France)
Abstract
How can we effectively integrate and represent heterogeneous multi-omics data to better understand complex biological systems and support the development of personalized digital models? The growing availability of high-dimensional omics data across different molecular scales presents both opportunities and challenges in biomedical research. Traditional approaches often treat each omics layer in isolation or rely on concatenation strategies that obscure the interactions between different regulatory layers. In this review, we discuss the multilayer network-based framework as an extensive representation of different omics data types, capturing the modularity, redundancy and cross-talk between layers, and providing a more faithful interpretable view of the biological system. We explore how this approach can be used as a basis for the construction of Digital Twins, computational replicas of individual biological systems capable of simulating disease progression and treatment outcomes. In contrast to existing multi-omics integration reviews), we emphasize the role of multilayer networks as a mechanistic and interpretable scaffold for Digital Twins development. We discuss key methodological considerations, benefits and potential applications of this approach, highlighting its promise for advancing both our understanding of biological complexity and our ability to design personalized interventions.
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BibTeX
@article{chenel2026multilayer,
title = {{Multilayer network approaches to omics data integration in digital twins for cancer research}},
author = {Hugo Chenel and Malvina Marku and Tim J. James and Andrei Y. Zinovyev and Véra Pancaldi},
journal = {Frontiers in Systems Biology},
year = {2026},
month = jun,
doi = {10.3389/fsysb.2026.1776941},
url = {https://doi.org/10.3389/fsysb.2026.1776941},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multilayer network approaches to omics data integration in digital twins for cancer research
AU - Hugo Chenel
AU - Malvina Marku
AU - Tim J. James
AU - Andrei Y. Zinovyev
AU - Véra Pancaldi
PY - 2026
DA - 2026/06/04
JO - Frontiers in Systems Biology
DO - 10.3389/fsysb.2026.1776941
UR - https://doi.org/10.3389/fsysb.2026.1776941
AB - How can we effectively integrate and represent heterogeneous multi-omics data to better understand complex biological systems and support the development of personalized digital models? The growing availability of high-dimensional omics data across different molecular scales presents both opportunities and challenges in biomedical research. Traditional approaches often treat each omics layer in isolation or rely on concatenation strategies that obscure the interactions between different regulatory layers. In this review, we discuss the multilayer network-based framework as an extensive representation of different omics data types, capturing the modularity, redundancy and cross-talk between layers, and providing a more faithful interpretable view of the biological system. We explore how this approach can be used as a basis for the construction of Digital Twins, computational replicas of individual biological systems capable of simulating disease progression and treatment outcomes. In contrast to existing multi-omics integration reviews), we emphasize the role of multilayer networks as a mechanistic and interpretable scaffold for Digital Twins development. We discuss key methodological considerations, benefits and potential applications of this approach, highlighting its promise for advancing both our understanding of biological complexity and our ability to design personalized interventions.
SN - 2674-0702
ER -