Article Dans Une Revue npj Systems Biology and Applications Année : 2024

Immune digital twins for complex human pathologies: applications, limitations, and challenges

1 Lifeware - Computational systems biology and optimization
2 CBI - Centre de Biologie Intégrative
3 UF - University of Florida [Gainesville]
4 Larner College of Medicine [University of Vermont, Burlington]
5 Hadassah Hebrew University Medical Center [Jerusalem]
6 UCL - University College of London [London]
7 NTNU - Norwegian University of Science and Technology [Trondheim]
8 Fraunhofer IZI - Fraunhofer Institute for Cell Therapy and Immunology
9 YSM - Yale School of Medicine [New Haven, Connecticut]
10 KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
11 Université de Liège
12 King‘s College London
13 UConn Health [Farmington, Connecticut, CT, USA]
14 INMI - Istituto Nazionale di Malattie Infettive "Lazzaro Spallanzani"
15 UBI - University of Beira Interior [Portugal]
16 BSC-CNS - Barcelona Supercomputing Center - Centro Nacional de Supercomputacion
17 Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe
18 UT - Université de Toulouse
19 University of Richmond
20 University of Arkansas [Fayetteville]
21 Maastricht University [Maastricht]
22 ANTIQUE - Analyse Statique par Interprétation Abstraite
23 Andalusian Public Foundation Progress [Séville]
24 uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg
25 University of Idaho [Moscow, USA]
26 Novadiscovery [Lyon]
27 University of Patras
28 IISc Bangalore - Indian Institute of Science [Bangalore]
29 Örebro University
30 Sol Plaatje University [Kimberley]
31 Imperial College London
32 URMC - University of Rochester Medical Center
33 SIB - Swiss Institute of Bioinformatics [Genève]
34 Ghent University Hospital
35 Evotec [Toulouse]
36 Indiana University [Bloomington]
Luis L Fonseca
Leonard A Harris
Rahuman S Malik Sheriff
Juilee Thakar
van Du T. Tran

Résumé

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.
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hal-04909272 , version 1 (23-01-2025)

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Anna Niarakis, Reinhard Laubenbacher, Gary An, Yaron Ilan, Jasmin Fisher, et al.. Immune digital twins for complex human pathologies: applications, limitations, and challenges. npj Systems Biology and Applications, 2024, 10 (1), pp.141. ⟨10.1038/s41540-024-00450-5⟩. ⟨hal-04909272⟩
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