Humans; Immune System/immunology; Computer Simulation; Drug Discovery/methods; Precision Medicine/methods; Drug Discovery; Immune System; Precision Medicine; Modeling and Simulation; Biochemistry, Genetics and Molecular Biology (all); Computer Science Applications; Applied Mathematics
Abstract :
[en] 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.
Precision for document type :
Review article
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Niarakis, Anna ; Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France. anna.niaraki@univ-tlse3.fr ; Lifeware Group, Inria, Saclay-île de France, Palaiseau, France. anna.niaraki@univ-tlse3.fr
Laubenbacher, Reinhard ; Department of Medicine, University of Florida, Gainesville, FL, USA
An, Gary; Department of Surgery, University of Vermont Larner College of Medicine, Vermont, USA
Ilan, Yaron ; Faculty of Medicine Hebrew University, Hadassah Medical Center, Jerusalem, Israel
Fisher, Jasmin; UCL Cancer Institute, University College London, Paul O'Gorman Building, 72 Huntley Street, London, WC1E 6BT, UK
Flobak, Åsmund; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway ; The Cancer Clinic, St Olav's University Hospital, Trondheim, Norway ; Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
Reiche, Kristin; Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany ; Institute of Clinical Immunology, Medical Faculty, University Hospital, University of Leipzig, Leipzig, Germany ; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany
Rodríguez Martínez, María; Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, USA
Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven, Belgium
Maia Ladeira, Luiz Carlos ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique ; Université de Liège - ULiège > GIGA > GIGA Molecular & Computational Biology - Biomechanics & Computationel Tissues Engineering
Veschini, Lorenzo; Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, London, UK ; Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, 47408, USA
Blinov, Michael L ; Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, 06030, USA
Messina, Francesco; Department of Epidemiology, Preclinical Research and Advanced Diagnostic, National Institute for Infectious Diseases 'Lazzaro Spallanzani' - I.R.C.C.S., Rome, Italy
Fonseca, Luis L ; Department of Medicine, University of Florida, Gainesville, FL, USA
Ferreira, Sandra; Mathematics Department and Center of Mathematics, University of Beira Interior, Covilhã, Portugal
Montagud, Arnau; Barcelona Supercomputing Center (BSC), Barcelone, Spain ; Institute for Integrative Systems Biology (I2SysBio), CSIC-UV, Valencia, Spain
Noël, Vincent ; Institut Curie, Université PSL, F-75005, Paris, France ; INSERM, U900, F-75005, Paris, France ; Mines ParisTech, Université PSL, F-75005, Paris, France
Marku, Malvina; Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
Tsirvouli, Eirini; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway ; Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
Torres, Marcella M; Department of Mathematics and Statistics, University of Richmond, Richmond, VA, USA
Harris, Leonard A ; Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, USA ; Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, USA ; Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
Sego, T J; Department of Medicine, University of Florida, Gainesville, FL, USA
Cockrell, Chase ; Department of Surgery, University of Vermont Larner College of Medicine, Vermont, USA
Shick, Amanda E; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
Balci, Hasan; Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
Salazar, Albin; INRIA Paris/CNRS/École Normale Supérieure/PSL Research University, Paris, France
Rian, Kinza; Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
Hemedan, Ahmed Abdelmonem ; Bioinformatics Core Unit, Luxembourg Centre of Systems Biomedicine LCSB, Luxembourg University, Esch-sur-Alzette, Luxembourg
Esteban-Medina, Marina; Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
Staumont, Bernard ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique
Hernandez-Vargas, Esteban; Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, 83844-1103, USA
Martis B, Shiny; Novadiscovery, Lyon, France
Madrid-Valiente, Alejandro; Barcelona Supercomputing Center (BSC), Barcelone, Spain
Karampelesis, Panagiotis; Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
Sordo Vieira, Luis; Department of Medicine, University of Florida, Gainesville, FL, USA
Harlapur, Pradyumna ; Department of Bioengineering, Indian Institute of Science, Bengaluru, India
Kulesza, Alexander; Novadiscovery, Lyon, France
Nikaein, Niloofar; School of Medical Sciences, Faculty of Medicine and Health, Örebro University, SE-70182, Örebro, Sweden ; X-HiDE - Exploring Inflammation in Health and Disease Consortium, Örebro University, Örebro, Sweden
Garira, Winston; Multiscale Mathematical Modelling of Living Systems program (M3-LSP), Kimberley, South Africa ; Department of Mathematical Sciences, Sol Plaatje University, Kimberley, South Africa ; Private Bag X5008, Kimberley, 8300, South Africa
Malik Sheriff, Rahuman S ; European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, Cambridge, UK ; Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
Thakar, Juilee ; Department of Microbiology & Immunology and Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, 14642, USA
Tran, Van Du T ; Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
Carbonell-Caballero, Jose; Barcelona Supercomputing Center (BSC), Barcelone, Spain
Safaei, Soroush; Institute of Biomedical Engineering and Technology, Ghent University, Gent, Belgium ; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
Valencia, Alfonso ; Barcelona Supercomputing Center (BSC), Barcelone, Spain ; ICREA, 23 Passeig Lluís Companys, 08010, Barcelona, Spain
Zinovyev, Andrei; In silico R&D, Evotec, 31400, Toulouse, France
Glazier, James A; Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, 47408, USA
This work is based on work during the three-week workshop on Building Immune Digital Twins, which was made possible by the support of the Institut Pascal, University of Paris-Saclay, France, via the program Investissements d\u2019avenir, ANR-11-IDEX-0003-0. We would also like to thank Genopole for supporting the workshop. Lastly, the authors thank Dr Laurence Calzone for her insightful comments. The authors also acknowledge the following support: AN was supported by a public-private partnership grant (CIFRE contract, no. 2020/0766) with SANOFI-AVENTIS R&D. R.L. acknowledges financial support from the Defense Advanced Research Projects Agency (grant HR00112220038), and the National Institutes of Health (grants R01 GM127909, R01 AI135128, and R01 HL169974). L.C.M., B.S., and L.G. acknowledge support from the European Commission (grants H2020-SC1-BHC-11-2020 963845 and DigitalEurope EDITH-CSA 101083771). M.L.B. acknowledges support from the National Institutes of Health (grants R24 GM137787 and P41 EB023912). L.L.F. acknowledges financial support from the Defense Advanced Research Projects Agency (grant HR00112220038). L.H. acknowledged support from NIH/NCI Transition Career Development Award to Promote Diversity (K22-CA237857). L.S.V. acknowledges support from the National Institutes of Health (grants R01 AI135128, R01 HL169974, K25 AI175668). P.H. acknowledges support from the Prime Ministers\u2019 Research Fellowship (PMRF). H.B. acknowledges the support of ZonMw (Grant No. 10430012010015) N.N. acknowledges support from the Knowledge Foundation (20200017) and The Swedish Fund for Research Without Animal Experiments. A.M., J.C.C., and A.V. acknowledge support from the European Commission (grants CREXDATA 101092749, PerMedCoE 951773, and EDITH-CSA 101083771). F.M. acknowledges support from the Italian Ministry of Health (grant \u201CRicerca Corrente\u201D Linea 4 Project 5 and \u201C5 per 1000\u20132021\u201D Grant No. 5M-2021-23683787) and the European Commission with HORIZON programme (Grant No. 101046203\u2014BY-COVID). J.A.G. acknowledges support from the National Institutes of Health (grants U24 EB028887) and the National Science Foundation (grants NSF 2303695, NSF 2120200, and NSF 1720625). G.A. and C.C. acknowledge support from the National Institutes of Health Award UO1EB025825 and the Defense Advanced Research Projects Agency through Cooperative Agreement D20AC00002 awarded by the U.S. Department of the Interior (DOI), Interior Business Center. K.R. was supported by the BMBF (Federal Ministry of Education and Research) in DAAD project 57616814 (SECAI, School of Embedded and Composite AI) as part of the program Konrad Zuse Schools of Excellence in Artificial Intelligence. Further, KR was supported by the imSAVAR and the CERTAINTY project. imSAVAR received funding from the Innovative Medicine Initiative 2 Joint Undertaking (JU) under grant agreement No 853988. The JU receives support from the European Union\u2019s Horizon 2020 research and innovation programme and EFPIA and JDRF INTERNATIONAL. The CERTAINTY project is funded by the European Union (Grant Agreement 101136379).
Committee on Foundational Research Gaps and Future Directions for Digital Twins, Board on Mathematical Sciences and Analytics, Committee on Applied and Theoretical Statistics, Computer Science and Telecommunications Board, Board on Life Sciences, Board on Atmospheric Sciences and Climate, et al. Foundational Research Gaps and Future Directions for Digital Twins (National Academies Press, 2023).
Consortium E. Edith C. S. A. Deliverable 3.2: first draft of the VHT roadmap. Zenodo (2023).
Viceconti, M., De Vos, M., Mellone, S. Geris, L. Position paper From the digital twins in healthcare to the Virtual Human Twin: a moon-shot project for digital health research. IEEE J Biomed Health Inform. 28, 491–501 (2023).
R. Sahal S.H. Alsamhi K.N. Brown Personal digital twin: a close look into the present and a step towards the future of personalised healthcare industry Sensors 2022 22 5918 1:CAS:528:DC%2BB38XitFGmtbfF 35957477 9371419 10.3390/s22155918
B. Björnsson et al. Digital twins to personalize medicine Genome Med. 2019 12 31892363 6938608 10.1186/s13073-019-0701-3
H.-C. Chang et al. Understanding the need for digital twins’ data in patient advocacy and forecasting oncology Front. Artif. Intell. 2023 6 1260361 38028666 10667907 10.3389/frai.2023.1260361
A. Chaudhuri et al. Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas Front. Artif. Intell. 2023 6 1222612 37886348 10598726 10.3389/frai.2023.1222612
P.E. Heudel F. Renard A. Attye [Digital twins in cancer research and treatment: a future for personalized medicine] Bull. Cancer 2023 110 1085 1087 37661550 10.1016/j.bulcan.2023.07.007
J.D. Iqbal M. Krauthammer N. Biller-Andorno The use and ethics of digital twins in medicine J. Law Med. Ethics 2022 50 583 596 36398633 10.1017/jme.2022.97
A. Rahmim et al. Theranostic digital twins for personalized radiopharmaceutical therapies: reimagining theranostics via computational nuclear oncology Front. Oncol. 2022 12 1062592 36591527 9797662 10.3389/fonc.2022.1062592
F. Pesapane A. Rotili S. Penco L. Nicosia E. Cassano Digital twins in radiology J. Clin. Med. 2022 11 6553 36362781 9655926 10.3390/jcm11216553
A. Jung M.A.F. Gsell C.M. Augustin G. Plank An integrated workflow for building digital twins of cardiac electromechanics-a multi-fidelity approach for personalising active mechanics Mathematics (Basel) 2022 10 823 35295404 7612499
J. Corral-Acero et al. The “Digital Twin” to enable the vision of precision cardiology Eur. Heart J. 2020 41 4556 4564 32128588 7774470 10.1093/eurheartj/ehaa159
H. Abdollahi et al. Radiopharmaceutical therapy on-a-chip: a perspective on microfluidic-driven digital twins towards personalized cancer therapies Sci. Bull. (Beijing) 2023 68 1983 1988 1:CAS:528:DC%2BB3sXhslels7zJ 37573246 10.1016/j.scib.2023.08.009
Trayanova, N. A. Prakosa, A. Up digital and personal: how heart digital twins can transform heart patient care. Heart Rhythm. 21, 89–99 (2023).
Zhang, Y. et al. Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins. medRxiv https://doi.org/10.1101/2023.05.31.23290587 (2023).
P.D. Winter T.J.A. Chico Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework to identify barriers and facilitators for the implementation of digital twins in cardiovascular medicine Sensors 2023 23 6333 37514627 10385429 10.3390/s23146333
M.A. Clarke J. Fisher Executable cancer models: successes and challenges Nat. Rev. Cancer 2020 20 343 354 1:CAS:528:DC%2BB3cXhtVGgt7vO 32341552 10.1038/s41568-020-0258-x
N.K. Chakshu I. Sazonov P. Nithiarasu Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis Biomech. Model Mechanobiol. 2021 20 449 465 33064221 10.1007/s10237-020-01393-6
G. Coorey et al. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field npj Digital Med. 2022 5 10.1038/s41746-022-00640-7
K. Gillette et al. A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs Med. Image Anal. 2021 71 102080 33975097 10.1016/j.media.2021.102080
P. Shamanna et al. Type 2 diabetes reversal with digital twin technology-enabled precision nutrition and staging of reversal: a retrospective cohort study Clin. Diabetes Endocrinol. 2021 7 34776010 8591797 10.1186/s40842-021-00134-7
Kovatchev, B. The artificial pancreas in 2017: the year of transition from research to clinical practice. Nat. Rev. Endocrinol. 14, 74–76 (2018).
S.A. Brown et al. Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes N. Engl. J. Med. 2019 381 1707 1717 1:CAS:528:DC%2BC1MXitFWktr3P 31618560 7076915 10.1056/NEJMoa1907863
P. Armeni et al. Digital twins in healthcare: is it the beginning of a new era of evidence-based medicine? a critical review J. Pers. Med. 2022 12 1255 36013204 9410074 10.3390/jpm12081255
A. Handel N.L. La Gruta P.G. Thomas Simulation modelling for immunologists Nat. Rev. Immunol. 2020 20 186 195 1:CAS:528:DC%2BC1MXitlajt73I 31804613 10.1038/s41577-019-0235-3
R. Laubenbacher et al. Building digital twins of the human immune system: toward a roadmap npj Digital Med. 2022 5 1:STN:280:DC%2BB2Mnks1GksA%3D%3D 10.1038/s41746-022-00610-z
M.D. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship Sci. Data 2016 3 26978244 4792175 10.1038/sdata.2016.18
M.D. Wilkinson et al. Addendum: the FAIR Guiding Principles for scientific data management and stewardship Sci. Data 2019 6 30890711 6427092 10.1038/s41597-019-0009-6
P. Rocca-Serra et al. The FAIR Cookbook—the essential resource for and by FAIR doers Sci. Data 2023 10 37208467 10198982 10.1038/s41597-023-02166-3
S.M. Keating et al. SBML Level 3: an extensible format for the exchange and reuse of biological models Mol. Syst. Biol. 2020 16 e9110 32845085 8411907 10.15252/msb.20199110
N. Le Novère et al. The systems biology graphical notation Nat. Biotechnol. 2009 27 735 741 19668183 10.1038/nbt.1558
E. Demir et al. The BioPAX community standard for pathway data sharing Nat. Biotechnol. 2010 28 935 942 1:CAS:528:DC%2BC3cXhtFegsbnL 20829833 3001121 10.1038/nbt.1666
D. Waltemath et al. Reproducible computational biology experiments with SED-ML-the simulation experiment description markup language BMC Syst. Biol. 2011 5 22172142 3292844 10.1186/1752-0509-5-198
D. Waltemath et al. The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE) J. Integr. Bioinform 2020 17 20200005 32598315 7756615 10.1515/jib-2020-0005
FAIRsharing [Internet]. Available from https://fairsharing.org/4787.
J. Masison et al. A modular computational framework for medical digital twins Proc. Natl Acad. Sci. USA 2021 118 e2024287118 1:CAS:528:DC%2BB3MXhtFaqtrvK 33972437 8157963 10.1073/pnas.2024287118
M.L. Neal et al. A reappraisal of how to build modular, reusable models of biological systems PLoS Comput. Biol. 2014 10 e1003849 25275523 4183381 10.1371/journal.pcbi.1003849
L.P. Smith et al. SBML level 3 package: hierarchical model composition, version 1 release 3 J. Integr. Bioinform. 2015 12 268 26528566 10.1515/jib-2015-268
B. Gruening et al. Recommendations for the packaging and containerizing of bioinformatics software. [version 2; peer review: 2 approved, 1 approved with reservations] F1000Res 2018 7 ISCB Comm J 742 31543945 10.12688/f1000research.15140.1
W. Garira The research and development process for multiscale models of infectious disease systems PLoS Comput. Biol. 2020 16 e1007734 1:CAS:528:DC%2BB3cXhtFajtrjN 32240165 7156109 10.1371/journal.pcbi.1007734
W. Garira A complete categorization of multiscale models of infectious disease systems J. Biol. Dyn. 2017 11 378 435 28849734 10.1080/17513758.2017.1367849
Montagud, A., Ponce de León, M. Valencia, A. Systems biology at the giga-scale: large multi-scale models of complex, heterogeneous multicellular systems. Curr. Opin. Syst. Biol. 28, 100385 (2021).
Kumar, R. Sikander A. Review and Analysis of Model Order Reduction Techniques for High-dimensional Complex Systems (Microsystem Technologies, 2024).
J. Wagner et al. A single-cell atlas of the tumor and immune ecosystem of human breast Cancer Cell 2019 177 1330 1345.e18 1:CAS:528:DC%2BC1MXnt1Clurs%3D
M. Manica et al. Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders Mol. Pharm. 2019 16 4797 4806 1:CAS:528:DC%2BC1MXhvFKktL%2FO 31618586 10.1021/acs.molpharmaceut.9b00520
An, G. Cockrell, C. Generating synthetic multidimensional molecular time series data for machine learning: considerations. Front Syst. Biol. 3 (2023).
A. Zhang L. Xing J. Zou J.C. Wu Shifting machine learning for healthcare from development to deployment and from models to data Nat. Biomed. Eng. 2022 6 1330 1345 35788685 10.1038/s41551-022-00898-y
C.V. Theodoris et al. Transfer learning enables predictions in network biology Nature 2023 618 616 624 1:CAS:528:DC%2BB3sXhtFaqtrbL 37258680 10949956 10.1038/s41586-023-06139-9
A. Weber J. Born M. Rodriguez Martínez TITAN: T-cell receptor specificity prediction with bimodal attention networks Bioinformatics 2021 37 i237 i244 1:CAS:528:DC%2BB3MXitlGnurzM 34252922 8275323 10.1093/bioinformatics/btab294
N. Rieke et al. The future of digital health with federated learning npj Digital Med. 2020 3 10.1038/s41746-020-00323-1
I. Dayan et al. Federated learning for predicting clinical outcomes in patients with COVID-19 Nat. Med. 2021 27 1735 1743 1:CAS:528:DC%2BB3MXitVequ7bN 34526699 9157510 10.1038/s41591-021-01506-3
G. Novakovsky N. Dexter M.W. Libbrecht W.W. Wasserman S. Mostafavi Obtaining genetics insights from deep learning via explainable artificial intelligence Nat. Rev. Genet 2023 24 125 137 1:CAS:528:DC%2BB38XisFCgurbP 36192604 10.1038/s41576-022-00532-2
M. Bordukova N. Makarov R. Rodriguez-Esteban F. Schmich M.P. Menden Generative artificial intelligence empowers digital twins in drug discovery and clinical trials Expert Opin. Drug Discov. 2024 19 33 42 1:CAS:528:DC%2BB3sXit1OlsL3M 37887266 10.1080/17460441.2023.2273839
Tejada-Lapuerta, A. Causal machine learning for single-cell genomics. Preprint at https://arxiv.org/abs/2310.14935 (2023).
M. Dong et al. Causal identification of single-cell experimental perturbation effects with CINEMA-OT Nat. Methods 2023 20 1769 1779 1:CAS:528:DC%2BB3sXit1OnurrO 37919419 10630139 10.1038/s41592-023-02040-5
X. Qiu et al. Mapping transcriptomic vector fields of single cells Cell 2022 185 690 711.e45 1:CAS:528:DC%2BB38XisFOlurs%3D 35108499 9332140 10.1016/j.cell.2021.12.045
W. Garira K. Muzhinji Application of the replication-transmission relativity theory in the development of multiscale models of infectious disease dynamics J. Biol. Dyn. 2023 17 2255066 37708175 10.1080/17513758.2023.2255066
W. Garira K. Muzhinji The universal theory for multiscale modelling of infectious disease dynamics Mathematics 2023 11 3874 10.3390/math11183874
M. Raissi P. Perdikaris G.E. Karniadakis Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations J. Comput Phys. 2019 378 686 707 10.1016/j.jcp.2018.10.045
H.A. Elmarakeby et al. Biologically informed deep neural network for prostate cancer discovery Nature 2021 598 348 352 1:CAS:528:DC%2BB3MXitV2itrzK 34552244 8514339 10.1038/s41586-021-03922-4
J. Ma et al. Using deep learning to model the hierarchical structure and function of a cell Nat. Methods 2018 15 290 298 1:CAS:528:DC%2BC1cXjvVOlurc%3D 29505029 5882547 10.1038/nmeth.4627
Martínez, M. R., Barberis, M. Niarakis, A. Computational modelling of immunological mechanisms: from statistical approaches to interpretable machine learning. ImmunoInformatics 12, 100029 (2023).
D.E. Kirschner C.A. Hunt S. Marino M. Fallahi-Sichani J.J. Linderman Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models Wiley Interdiscip. Rev. Syst. Biol. Med 2014 6 289 309 1:CAS:528:DC%2BC2cXhtVSqsL7K 24810243 4102180 10.1002/wsbm.1270
S. Marino I.B. Hogue C.J. Ray D.E. Kirschner A methodology for performing global uncertainty and sensitivity analysis in systems biology J. Theor. Biol. 2008 254 178 196 18572196 2570191 10.1016/j.jtbi.2008.04.011
L. Rodriguez-Rodriguez L. Gillet B. Machiels Shaping of the alveolar landscape by respiratory infections and long-term consequences for lung immunity Front Immunol. 2023 14 1149015 1:CAS:528:DC%2BB3sXps1Smsr0%3D 37081878 10112541 10.3389/fimmu.2023.1149015
F. Hou K. Xiao L. Tang L. Xie Diversity of macrophages in lung homeostasis and diseases Front Immunol. 2021 12 753940 1:CAS:528:DC%2BB3MXis1ylsL7K 34630433 8500393 10.3389/fimmu.2021.753940
Kumar, V. Pulmonary innate immune response determines the outcome of inflammation during pneumonia and sepsis-associated acute lung injury. Front Immunol. 4, 1722 (2020).
Q. Guo et al. Rheumatoid arthritis: pathological mechanisms and modern pharmacologic therapies Bone Res 2018 6 15 29736302 5920070 10.1038/s41413-018-0016-9
F. Humby et al. Rituximab versus tocilizumab in anti-TNF inadequate responder patients with rheumatoid arthritis (R4RA): 16-week outcomes of a stratified, biopsy-driven, multicentre, open-label, phase 4 randomised controlled trial Lancet 2021 397 305 317 1:CAS:528:DC%2BB3MXltVyrtbw%3D 33485455 7829614 10.1016/S0140-6736(20)32341-2
Zerrouk, N., Aghakhani, S., Singh, V., Augé, F. Niarakis, A. A mechanistic cellular atlas of the rheumatic joint. Front. Syst. Biol. 2 (2022).
V. Singh et al. RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis Database (Oxford) 2020 2020 baaa017 1:CAS:528:DC%2BB3cXitFGksrvI 32311035 10.1093/database/baaa017
S. Aghakhani S. Soliman A. Niarakis Metabolic reprogramming in rheumatoid arthritis synovial fibroblasts: a hybrid modeling approach PLoS Comput. Biol. 2022 18 e1010408 1:CAS:528:DC%2BB38Xjt1WlsbvI 36508473 9779668 10.1371/journal.pcbi.1010408
N. Zerrouk R. Alcraft B.A. Hall F. Augé A. Niarakis Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis NPJ Syst. Biol. Appl 2024 10 1:CAS:528:DC%2BB2cXitFyntrc%3D 38272919 10811231 10.1038/s41540-024-00337-5
Q. Miagoux et al. Inference of an integrative, executable network for rheumatoid arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map J. Pers. Med. 2021 11 785 34442429 8400381 10.3390/jpm11080785
V. Singh A. Naldi S. Soliman A. Niarakis A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synoviocytes predicts drug synergies in the arthritic joint NPJ Syst. Biol. Appl 2023 9 1:CAS:528:DC%2BB3sXhsVymsbbF 37454172 10349856 10.1038/s41540-023-00294-5
F.R. Macfarlane M.A.J. Chaplain R. Eftimie Quantitative predictive modelling approaches to understanding rheumatoid arthritis: a brief review Cells 2019 9 74 31892234 7016994 10.3390/cells9010074
N. Moise A. Friedman Rheumatoid arthritis—a mathematical model J. Theor. Biol. 2019 461 17 33 30347191 10.1016/j.jtbi.2018.10.039
J. Kerkhofs et al. A qualitative model of the differentiation network in chondrocyte maturation: a holistic view of chondrocyte hypertrophy PLoS ONE 2016 11 e0162052 27579819 5007039 10.1371/journal.pone.0162052
R. Lesage et al. An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis BMC Biol. 2022 20 36352408 9648005 10.1186/s12915-022-01451-8
Aghakhani, S., Niarakis, A. Soliman, S. MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps. J. Integr. Bioinform. 21, 20230048 (2024).
A. Niarakis et al. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches Front Immunol. 2023 14 1282859 1:CAS:528:DC%2BB2cXlslCgtrs%3D 38414974 10.3389/fimmu.2023.1282859
A.P. Creagh et al. Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis npj Digital Med. 2024 7 10.1038/s41746-024-01013-y
C.W. Seymour et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3) JAMA 2016 315 762 774 1:CAS:528:DC%2BC28XhtFGqsbbN 26903335 5433435 10.1001/jama.2016.0288
J.C. Schefold D. Hasper H.D. Volk P. Reinke Sepsis: time has come to focus on the later stages Med. Hypotheses 2008 71 203 208 18448264 10.1016/j.mehy.2008.03.022
G. An In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling Crit. Care Med 2004 32 2050 2060 1:CAS:528:DC%2BD2cXosVSltr8%3D 15483414 10.1097/01.CCM.0000139707.13729.7D
G. An Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation Theor. Biol. Med Model 2008 5 18505587 2442588 10.1186/1742-4682-5-11
Cockrell, C., Schobel-McHugh, S., Lisboa, F., Vodovotz, Y. An, G. Generating synthetic data with a mechanism-based Critical Illness digital twin: demonstration for post traumatic acute respiratory distress syndrome. BioRxiv https://doi.org/10.1101/2022.11.22.517524 (2022).
An, G. Cockrell C. A design specification for Critical Illness Digital Twins to cure sepsis: responding to the National Academies of Sciences, Engineering and Medicine Report: Foundational Research Gaps and Future Directions for Digital Twins. Preprint at https://arxiv.org/abs/2405.05301 (2024).
C. Cockrell D. Larie G. An Preparing for the next pandemic: simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents Front. Immunol. 2022 13 995395 1:CAS:528:DC%2BB38XjtVKnsr7M 36479109 9720328 10.3389/fimmu.2022.995395
P. Aldo G. Marusov D. Svancara J. David G. Mor Simple Plex(TM): a novel multi-analyte, automated microfluidic immunoassay platform for the detection of human and mouse cytokines and chemokines Am. J. Reprod. Immunol. 2016 75 678 693 1:CAS:528:DC%2BC28Xot1Oltb0%3D 27170460 5084752 10.1111/aji.12512
F.L. Ferreira D.P. Bota A. Bross C. Mélot J.L. Vincent Serial evaluation of the SOFA score to predict outcome in critically ill patients JAMA 2001 286 1754 1758 1:STN:280:DC%2BD3Mrmt1Slsg%3D%3D 11594901 10.1001/jama.286.14.1754
E.P. Raith et al. Prognostic accuracy of the SOFA Score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit JAMA 2017 317 290 300 28114553 10.1001/jama.2016.20328
C. Cockrell G. An Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model J. Theor. Biol. 2017 430 157 168 28728997 5635265 10.1016/j.jtbi.2017.07.016
D. Hanahan R.A. Weinberg Hallmarks of cancer: the next generation Cell 2011 144 646 674 1:CAS:528:DC%2BC3MXjsFeqtrk%3D 21376230 10.1016/j.cell.2011.02.013
A.D. Waldman J.M. Fritz M.J. Lenardo A guide to cancer immunotherapy: from T cell basic science to clinical practice Nat. Rev. Immunol. 2020 20 651 668 1:CAS:528:DC%2BB3cXpvVant7c%3D 32433532 7238960 10.1038/s41577-020-0306-5
A.R. Kumar A.R. Devan B. Nair B.S. Vinod L.R. Nath Harnessing the immune system against cancer: current immunotherapy approaches and therapeutic targets Mol. Biol. Rep. 2021 48 8075 8095 1:CAS:528:DC%2BB3MXit1yktbzF 34671902 8605995 10.1007/s11033-021-06752-9
D.M. Pardoll The blockade of immune checkpoints in cancer immunotherapy Nat. Rev. Cancer 2012 12 252 264 1:CAS:528:DC%2BC38XksVegtrw%3D 22437870 4856023 10.1038/nrc3239
E.A. Stahlberg et al. Exploring approaches for predictive cancer patient digital twins: opportunities for collaboration and innovation Front Digit Health 2022 4 1007784 36274654 9586248 10.3389/fdgth.2022.1007784
C. Luchini R.T. Lawlor M. Milella A. Scarpa Molecular tumor boards in clinical practice Trends Cancer 2020 6 738 744 32517959 10.1016/j.trecan.2020.05.008 Sep
Patel, V. G., Characterizing patterns of disease progression in patients with genitourinary cancers treated with immune checkpoint inhibitors. J. Clin. Oncol. 38, 482–482 (2020).
J. Hue Z. Valinciute S. Thavaraj L. Veschini Multifactorial estimation of clinical outcome in HPV-associated oropharyngeal squamous cell carcinoma via automated image analysis of routine diagnostic H&E slides and neural network modelling Oral. Oncol. 2023 141 106399 37098302 10.1016/j.oraloncology.2023.106399
M. Rade et al. A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer Mol. Med. 2024 30 19 1:CAS:528:DC%2BB2cXislyju7k%3D 38302875 10835874 10.1186/s10020-024-00789-9
R. Howell et al. Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling Sci. Adv. 2023 9 eadd1992 1:CAS:528:DC%2BB3sXotF2gt74%3D 37043573 10096595 10.1126/sciadv.add1992
P. Kreuzaler et al. Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling Proc. Natl Acad. Sci. USA 2019 116 22399 22408 1:CAS:528:DC%2BC1MXitVOhtLfE 31611367 6825310 10.1073/pnas.1903485116
A. Montagud et al. Patient-specific Boolean models of signalling networks guide personalised treatments eLife 2022 11 e72626 1:CAS:528:DC%2BB38XhvF2gsrnI 35164900 9018074 10.7554/eLife.72626
M. Ruscone et al. Multiscale model of the different modes of cancer cell invasion Bioinformatics 2023 39 btad374 1:CAS:528:DC%2BB3sXitFWqt7rM 37289551 10293590 10.1093/bioinformatics/btad374
S. Chuah V. Chew High-dimensional immune-profiling in cancer: implications for immunotherapy J. Immunother. Cancer 2020 8 e000363 32034066 7057482 10.1136/jitc-2019-000363
Meet Your Digital Twin: The Coming Revolution In Drug Development [Internet]. [cited 2024 Feb 25]. Available from https://www.forbes.com/sites/ganeskesari/2021/09/29/meet-your-digital-twin-the-coming-revolution-in-drug-development/?sh=39e3b552745f.
G. An C. Cockrell Drug development digital twins for drug discovery, testing and repurposing: A schema for requirements and development Front Syst. Biol. 2022 2 928387 35935475 9351294 10.3389/fsysb.2022.928387
Sanofi taps digital twin tech to design its new vaccine plants [Internet]. [cited 2024 Feb 25]. Available from https://www.fiercepharma.com/manufacturing/sanofi-harnesses-dassaults-digital-twin-technology-optimize-production-future-vaccine.
P. Moingeon M. Chenel C. Rousseau E. Voisin M. Guedj Virtual patients, digital twins and causal disease models: Paving the ground for in silico clinical trials Drug Discov. Today 2023 28 103605 1:CAS:528:DC%2BB3sXpsFKgsrk%3D 37146963 10.1016/j.drudis.2023.103605
G. Russo et al. Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB BMC Bioinform. 2020 21 10.1186/s12859-020-03762-5
Letter of support for UISS-TB-DR. https://www.ema.europa.eu/en/documents/other/letter-support-universal-immune-system-simulator-tuberculosis-disease-model-uiss-tb-dr_en.pdf.
Viceconti, M. Emili, L., eds. toward Good Simulation Practice: Best Practices for the Use of Computational Modelling and Simulation in the Regulatory Process of Biomedical Products (Springer Nature Switzerland, 2024).
R. Madabushi et al. The US Food and Drug Administration’s Model-Informed Drug Development Paired Meeting Pilot Program: early experience and impact Clin. Pharm. Ther. 2019 106 74 78 10.1002/cpt.1457
Y. Wang et al. Model-Informed Drug Development: current US regulatory practice and future considerations Clin. Pharm. Ther. 2019 105 899 911 10.1002/cpt.1363
S. Marshall et al. Model-informed drug discovery and development: current industry good practice and regulatory expectations and future perspectives CPT Pharmacomet. Syst. Pharm. 2019 8 87 96 1:CAS:528:DC%2BC1MXit1KhtLk%3D 10.1002/psp4.12372
A. Värri P. Kranz-Zuppan R. de la Cruz IEC 62304 ed. 2: software life cycle standard for health software Stud. Health Technol. Inf. 2019 264 868 872
Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submissions | FDA [Internet]. [cited 2024 Aug 5]. Available from https://www.fda.gov/regulatory-information/search-fda-guidance-documents/assessing-credibility-computational-modeling-and-simulation-medical-device-submissions.
A. Niarakis et al. Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology Brief. Bioinform. 2022 23 bbac212 35671510 9294410 10.1093/bib/bbac212
A.A. Sinaci et al. From raw data to FAIR data: the fairification workflow for health research Methods Inf. Med. 2020 59 e21 e32 32620019 10.1055/s-0040-1713684
K. Tiwari et al. Reproducibility in systems biology modelling Mol. Syst. Biol. 2021 17 e9982 33620773 7901289 10.15252/msb.20209982
F.T. Bergmann et al. COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project BMC Bioinform. 2014 15 10.1186/s12859-014-0369-z
P.-H. Huang K.-H. Kim M. Schermer Ethical issues of digital twins for personalized health care service: preliminary mapping study J. Med. Internet Res. 2022 24 e33081 35099399 8844982 10.2196/33081
S. Reddy S. Allan S. Coghlan P. Cooper A governance model for the application of AI in health care J. Am. Med Inf. Assoc. 2020 27 491 497 10.1093/jamia/ocz192
K. Bruynseels F. Santoni de Sio J. van den Hoven Digital twins in health care: ethical implications of an emerging engineering paradigm Front. Genet. 2018 9 31 29487613 5816748 10.3389/fgene.2018.00031
A. Semakova N. Zvartau Data-driven identification of hypertensive patient profiles for patient population simulation Procedia Comput. Sci. 2018 136 433 442 10.1016/j.procs.2018.08.269
Martinez-Velazquez, R., Gamez, R. Saddik, A. E. "Cardio Twin: A Digital Twin of the human heart running onthe edge," 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Istanbul,Turkey, 2019, pp. 1-6, https://doi.org/10.1109/MeMeA.2019.8802162.
