1
|
Zhang K, Zhou HY, Baptista-Hon DT, Gao Y, Liu X, Oermann E, Xu S, Jin S, Zhang J, Sun Z, Yin Y, Razmi RM, Loupy A, Beck S, Qu J, Wu J. Concepts and applications of digital twins in healthcare and medicine. PATTERNS (NEW YORK, N.Y.) 2024; 5:101028. [PMID: 39233690 PMCID: PMC11368703 DOI: 10.1016/j.patter.2024.101028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Abstract
The digital twin (DT) is a concept widely used in industry to create digital replicas of physical objects or systems. The dynamic, bi-directional link between the physical entity and its digital counterpart enables a real-time update of the digital entity. It can predict perturbations related to the physical object's function. The obvious applications of DTs in healthcare and medicine are extremely attractive prospects that have the potential to revolutionize patient diagnosis and treatment. However, challenges including technical obstacles, biological heterogeneity, and ethical considerations make it difficult to achieve the desired goal. Advances in multi-modal deep learning methods, embodied AI agents, and the metaverse may mitigate some difficulties. Here, we discuss the basic concepts underlying DTs, the requirements for implementing DTs in medicine, and their current and potential healthcare uses. We also provide our perspective on five hallmarks for a healthcare DT system to advance research in this field.
Collapse
Affiliation(s)
- Kang Zhang
- National Clinical Eye Research Center, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China
- Institute for Clinical Data Science, Wenzhou Medical University, Wenzhou 325000, China
- Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
- Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou 325000, China
| | - Hong-Yu Zhou
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02138, USA
| | - Daniel T. Baptista-Hon
- Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
- School of Medicine, University of Dundee, DD1 9SY Dundee, UK
| | - Yuanxu Gao
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100000, China
| | - Xiaohong Liu
- Cancer Institute, University College London, WC1E 6BT London, UK
| | - Eric Oermann
- NYU Langone Medical Center, New York University, New York, NY 10016, USA
| | - Sheng Xu
- Department of Chemical Engineering and Nanoengineering, University of California San Diego, San Diego, CA 92093, USA
| | - Shengwei Jin
- Institute for Clinical Data Science, Wenzhou Medical University, Wenzhou 325000, China
- Department of Anesthesia and Critical Care, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou 325000, China
| | - Jian Zhang
- National Clinical Eye Research Center, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China
- Department of Anesthesia and Critical Care, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou 325000, China
| | - Zhuo Sun
- Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou 325000, China
| | - Yun Yin
- Faculty of Business and Health Science Institute, City University of Macau, Macau 999078, China
| | | | - Alexandre Loupy
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, 75015 Paris, France
| | - Stephan Beck
- Cancer Institute, University College London, WC1E 6BT London, UK
| | - Jia Qu
- National Clinical Eye Research Center, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China
- Institute for Clinical Data Science, Wenzhou Medical University, Wenzhou 325000, China
| | - Joseph Wu
- Cardiovascular Research Institute, Stanford University, Standford, CA 94305, USA
| | - International Consortium of Digital Twins in Medicine
- National Clinical Eye Research Center, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China
- Institute for Clinical Data Science, Wenzhou Medical University, Wenzhou 325000, China
- Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02138, USA
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100000, China
- Cancer Institute, University College London, WC1E 6BT London, UK
- NYU Langone Medical Center, New York University, New York, NY 10016, USA
- Department of Chemical Engineering and Nanoengineering, University of California San Diego, San Diego, CA 92093, USA
- Department of Anesthesia and Critical Care, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou 325000, China
- Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou 325000, China
- Faculty of Business and Health Science Institute, City University of Macau, Macau 999078, China
- Zoi Capital, New York, NY 10013, USA
- Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration, 75015 Paris, France
- Cardiovascular Research Institute, Stanford University, Standford, CA 94305, USA
- School of Medicine, University of Dundee, DD1 9SY Dundee, UK
| |
Collapse
|
2
|
Alsalloum GA, Al Sawaftah NM, Percival KM, Husseini GA. Digital Twins of Biological Systems: A Narrative Review. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:670-677. [PMID: 39184962 PMCID: PMC11342927 DOI: 10.1109/ojemb.2024.3426916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/07/2024] [Accepted: 07/08/2024] [Indexed: 08/27/2024] Open
Abstract
The concept of Digital Twins (DTs), software models that mimic the behavior and interactions of physical or conceptual objects within their environments, has gained traction in recent years, particularly in medicine and healthcare research. DTs technology emerges as a pivotal tool in disease modeling, integrating diverse data sources to computationally model dynamic biological systems. This narrative review explores potential DT applications in medicine, from defining DTs and their history to constructing DTs, modeling biologically relevant systems, as well as discussing the benefits, risks, and challenges in their application. The influence of DTs extends beyond healthcare and can revolutionize healthcare management, drug development, clinical trials, and various biomedical research fields.
Collapse
Affiliation(s)
- Ghufran A. Alsalloum
- Department of Biosciences and Bioengineering, College of EngineeringAmerican University of SharjahSharjah26666UAE
| | - Nour M. Al Sawaftah
- Department of Material Science and Engineering, College of EngineeringAmerican University of SharjahSharjah26666UAE
| | - Kelly M. Percival
- Department of Chemical and Biological Engineering, College of EngineeringAmerican University of SharjahSharjah26666UAE
| | - Ghaleb A. Husseini
- Department of Chemical and Biological Engineering, College of EngineeringAmerican University of SharjahSharjah26666UAE
| |
Collapse
|
3
|
Vallée A. Challenges and directions for digital twin implementation in otorhinolaryngology. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08662-5. [PMID: 38703196 DOI: 10.1007/s00405-024-08662-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/05/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. PURPOSE These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. METHODS Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. RESULTS Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. CONCLUSION These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.
Collapse
Affiliation(s)
- Alexandre Vallée
- Department of Epidemiology and Public Health, Foch Hospital, 92150, Suresnes, France.
| |
Collapse
|
4
|
Böttcher L, Fonseca LL, Laubenbacher RC. Control of Medical Digital Twins with Artificial Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585589. [PMID: 38562787 PMCID: PMC10983973 DOI: 10.1101/2024.03.18.585589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.
Collapse
|
5
|
Laubenbacher R, Adler F, An G, Castiglione F, Eubank S, Fonseca LL, Glazier J, Helikar T, Jett-Tilton M, Kirschner D, Macklin P, Mehrad B, Moore B, Pasour V, Shmulevich I, Smith A, Voigt I, Yankeelov TE, Ziemssen T. Forum on immune digital twins: a meeting report. NPJ Syst Biol Appl 2024; 10:19. [PMID: 38365857 PMCID: PMC10873299 DOI: 10.1038/s41540-024-00345-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/25/2024] [Indexed: 02/18/2024] Open
Abstract
Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.
Collapse
Affiliation(s)
| | - Fred Adler
- Department of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, UT, USA
| | - Gary An
- Department of Surgery, University of Vermont, Burlington, VT, USA
| | - Filippo Castiglione
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, UAE
| | - Stephen Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Luis L Fonseca
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - James Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Tomas Helikar
- Department of Biochemistry, University of Nebraska, Lincoln, NE, USA
| | | | - Denise Kirschner
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Borna Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Beth Moore
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Virginia Pasour
- U.S. Army Research Office, Research Triangle Park, Raleigh, NC, USA
| | | | - Amber Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Isabel Voigt
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, The University of Texas, Austin, TX, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany
| |
Collapse
|
6
|
Michael CT, Almohri SA, Linderman JJ, Kirschner DE. A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons. FRONTIERS IN SYSTEMS BIOLOGY 2024; 3:1283341. [PMID: 39310676 PMCID: PMC11415237 DOI: 10.3389/fsysb.2023.1283341] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Computational models of disease progression have been constructed for a myriad of pathologies. Typically, the conceptual implementation for pathology-related in-silico intervention studies has been ad-hoc and similar in design to experimental studies. We introduce a multi-scale interventional design (MID) framework toward two key goals: tracking of disease dynamics from within-body to patient to population scale; and tracking impact(s) of interventions across these same spatial scales. Our MID framework prioritizes investigation of impact on individual patients within virtual pre-clinical trials, instead of replicating the design of experimental studies. We apply a MID framework to develop, organize, and analyze a cohort of virtual patients for the study of tuberculosis (TB) as an example disease. For this study, we use HostSim: our next-generation whole patient-scale computational model of individuals infected with Mycobacterium tuberculosis. HostSim captures infection within lungs by tracking multiple granulomas, together with dynamics occurring with blood and lymph node compartments, the compartments involved during pulmonary TB. We extend HostSim to include a simple drug intervention as an example of our approach and use our MID framework to quantify the impact of treatment at cellular and tissue (granuloma), patient (lungs, lymph nodes and blood), and population scales. Sensitivity analyses allow us to determine which features of virtual patients are the strongest predictors of intervention efficacy across scales. These insights allow us to identify patient-heterogeneous mechanisms that drive outcomes across scales.
Collapse
Affiliation(s)
- Christian T. Michael
- Department of Microbiology & Immunology, University of Michigan - Michigan Medicine, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Sayed Ahmad Almohri
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | | | - Denise E. Kirschner
- Department of Microbiology & Immunology, University of Michigan - Michigan Medicine, Ann Arbor, MI, USA
| |
Collapse
|
7
|
Sego TJ. SimService: a lightweight library for building simulation services in Python. Bioinformatics 2024; 40:btae009. [PMID: 38237907 PMCID: PMC10809901 DOI: 10.1093/bioinformatics/btae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/27/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
SUMMARY Integrative biological modeling requires software infrastructure to launch, interconnect, and execute simulation software components without loss of functionality. SimService is a software library that enables deploying simulations in integrated applications as memory-isolated services with interactive proxy objects in the Python programming language. SimService supports customizing the interface of proxies so that simulation developers and users alike can tailor generated simulation instances according to model, method, and integrated application. AVAILABILITY AND IMPLEMENTATION SimService is written in Python, is freely available on GitHub under the MIT license at https://github.com/tjsego/simservice, and is available for download via the Python Package Index (package name "simservice") and conda (package name "simservice" on the conda-forge channel).
Collapse
Affiliation(s)
- T J Sego
- Department of Medicine, University of Florida, Gainesville, FL 32610-0225, United States
| |
Collapse
|
8
|
Velleuer E, Domínguez-Hüttinger E, Rodríguez A, Harris LA, Carlberg C. Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia. Front Genet 2023; 14:1254966. [PMID: 38028610 PMCID: PMC10652399 DOI: 10.3389/fgene.2023.1254966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.
Collapse
Affiliation(s)
- Eunike Velleuer
- Department of Cytopathology, Heinrich Heine University, Düsseldorf, Germany
- Center for Child and Adolescent Health, Helios Klinikum, Krefeld, Germany
| | - Elisa Domínguez-Hüttinger
- Departamento Düsseldorf Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
| | - Alfredo Rodríguez
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
- Instituto Nacional de Pediatría, Ciudad México, Mexico
| | - Leonard A. Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, United States
- Cancer Biology Program, Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Carsten Carlberg
- Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, Olsztyn, Poland
- School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| |
Collapse
|
9
|
McMahon‑Cole H, Johnson A, Sadat Aghamiri S, Helikar T, Crawford LB. Modeling and Remodeling the Cell: How Digital Twins and HCMV Can Elucidate the Complex Interactions of Viral Latency, Epigenetic Regulation, and Immune Responses. CURRENT CLINICAL MICROBIOLOGY REPORTS 2023; 10:141-151. [PMID: 37901689 PMCID: PMC10601359 DOI: 10.1007/s40588-023-00201-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 10/31/2023]
Abstract
Purpose of Review Human cytomegalovirus (HCMV), while asymptomatic in most, causes significant complications during fetal development, following transplant or in immunosuppressed individuals. The host-virus interactions regulating viral latency and reactivation and viral control of the cellular environment (immune regulation, differentiation, epigenetics) are highly complex. Understanding these processes is essential to controlling infection and can be leveraged as a novel approach for understanding basic cell biology. Recent Findings Immune digital twins (IDTs) are digital simulations integrating knowledge of human immunology, physiology, and patient-specific clinical data to predict individualized immune responses and targeted treatments. Recent studies used IDTs to elucidate mechanisms of T cells, dendritic cells, and epigenetic control-all key to HCMV biology. Summary Here, we discuss how leveraging the unique biology of HCMV and IDTs will clarify immune response dynamics, host-virus interactions, and viral latency and reactivation and serve as a powerful IDT-validation platform for individualized and holistic health management.
Collapse
Affiliation(s)
- Hana McMahon‑Cole
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Alicia Johnson
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Lindsey B. Crawford
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
- Nebraska Center for Virology, Lincoln, NE, USA
- Nebraska Center for Integrated Biomolecular Communication, Lincoln, NE, USA
| |
Collapse
|
10
|
Sigawi T, Ilan Y. Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems. Biomimetics (Basel) 2023; 8:359. [PMID: 37622964 PMCID: PMC10452845 DOI: 10.3390/biomimetics8040359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.
Collapse
Affiliation(s)
| | - Yaron Ilan
- Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem P.O. Box 12000, Israel;
| |
Collapse
|
11
|
Wang RS, Huang S, Waldo SW, Hess E, Gokhale M, Johnson SW, Zeder K, Choudhary G, Leopold JA, Oldham WM, Kovacs G, Freiberg MS, Tedford RJ, Maron BA, Brittain EL. Elevated Pulmonary Arterial Compliance Is Associated with Survival in Pulmonary Hypertension: Results from a Novel Network Medicine Analysis. Am J Respir Crit Care Med 2023; 208:312-321. [PMID: 37276608 PMCID: PMC10395727 DOI: 10.1164/rccm.202211-2097oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023] Open
Abstract
Rationale: Predictors of adverse outcome in pulmonary hypertension (PH) are well established; however, data that inform survival are lacking. Objectives: We aim to identify clinical markers and therapeutic targets that inform the survival in PH. Methods: We included data from patients with elevated mean pulmonary artery pressure (mPAP) diagnosed by right heart catheterization in the U.S. Veterans Affairs system (October 1, 2006-September 30, 2018). Network medicine framework was used to subgroup patients when considering an N of 79 variables per patient. The results informed outcome analyses in the discovery cohort and a sex-balanced validation right heart catheterization cohort from Vanderbilt University (September 24, 1998-December 20, 2013). Measurements and Main Results: From an N of 4,737 complete case patients with mPAP of 19-24 mm Hg, there were 21 distinct subgroups (network modules) (all-cause mortality range = 15.9-61.2% per module). Pulmonary arterial compliance (PAC) drove patient assignment to modules characterized by increased survival. When modeled continuously in patients with mPAP ⩾19 mm Hg (N = 37,744; age, 67.2 yr [range = 61.7-73.8 yr]; 96.7% male; median follow-up time, 1,236 d [range = 570-1,971 d]), the adjusted all-cause mortality hazard ratio was <1.0 beginning at PAC ⩾3.0 ml/mm Hg and decreased progressively to ∼7 ml/mm Hg. A protective association between PAC ⩾3.0 ml/mm Hg and mortality was also observed in the validation cohort (N = 1,514; age, 60.2 yr [range = 49.2-69.1 yr]; 48.0% male; median follow-up time, 2,485 d [range = 671-3,580 d]). The association was strongest in patients with precapillary PH at the time of catheterization, in whom 41% (95% confidence interval, 0.55-0.62; P < 0.001) and 49% (95% confidence interval, 0.38-0.69; P < 0.001) improvements in survival were observed for PAC ⩾3.0 versus <3.0 ml/mm Hg in the discovery and validation cohorts, respectively. Conclusions: These data identify elevated PAC as an important parameter associated with survival in PH. Prospective studies are warranted that consider PAC ⩾3.0 ml/mm Hg as a therapeutic target to achieve through proven interventions.
Collapse
Affiliation(s)
- Rui-Sheng Wang
- Division of Cardiovascular Medicine
- Channing Division of Network Medicine, and
| | | | - Stephen W. Waldo
- Department of Medicine, Cardiology Section, Rocky Mountain Regional VA Medical Center, Aurora, Colorado
- Veterans Affairs Clinical Assessment, Reporting, and Tracking Program, Veterans Health Administration Office of Quality and Patient Safety, Washington, DC
- Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado
| | - Edward Hess
- Department of Medicine, Cardiology Section, Rocky Mountain Regional VA Medical Center, Aurora, Colorado
| | - Madhura Gokhale
- Department of Medicine, Cardiology Section, Rocky Mountain Regional VA Medical Center, Aurora, Colorado
| | - Shelsey W. Johnson
- Department of Pulmonary and Critical Care, Boston Medical Center, Boston, Massachusetts
| | - Katarina Zeder
- Department of Pulmonology, Medical University of Graz and Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
| | - Gaurav Choudhary
- Providence Veterans Affairs Medical Center and Division of Cardiovascular Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | | | - William M. Oldham
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Gabor Kovacs
- Department of Pulmonology, Medical University of Graz and Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
| | - Matthew S. Freiberg
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Geriatric Research Education and Clinical Centers (GRECC), Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee
| | - Ryan J. Tedford
- Division of Cardiology, Medical Department of Medicine, University of South Carolina, Charleston, South Carolina; and
| | - Bradley A. Maron
- Division of Cardiovascular Medicine
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - Evan L. Brittain
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| |
Collapse
|
12
|
Sun T, Wang J, Suo M, Liu X, Huang H, Zhang J, Zhang W, Li Z. The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases. Bioengineering (Basel) 2023; 10:627. [PMID: 37370558 DOI: 10.3390/bioengineering10060627] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/12/2023] [Accepted: 05/20/2023] [Indexed: 06/29/2023] Open
Abstract
Due to the high prevalence and rates of disability associated with musculoskeletal system diseases, more thorough research into diagnosis, pathogenesis, and treatments is required. One of the key contributors to the emergence of diseases of the musculoskeletal system is thought to be changes in the biomechanics of the human musculoskeletal system. However, there are some defects concerning personal analysis or dynamic responses in current biomechanical research methodologies. Digital twin (DT) was initially an engineering concept that reflected the mirror image of a physical entity. With the application of medical image analysis and artificial intelligence (AI), it entered our lives and showed its potential to be further applied in the medical field. Consequently, we believe that DT can take a step towards personalized healthcare by guiding the design of industrial personalized healthcare systems. In this perspective article, we discuss the limitations of traditional biomechanical methods and the initial exploration of DT in musculoskeletal system diseases. We provide a new opinion that DT could be an effective solution for musculoskeletal system diseases in the future, which will help us analyze the real-time biomechanical properties of the musculoskeletal system and achieve personalized medicine.
Collapse
Affiliation(s)
- Tianze Sun
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Jinzuo Wang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Moran Suo
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Xin Liu
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Huagui Huang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Jing Zhang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Wentao Zhang
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| | - Zhonghai Li
- Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116600, China
- Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian 116000, China
| |
Collapse
|
13
|
Asediya V, Anjaria P. Quantum leap: is the conflux of artificial intelligence, personalized medicine, nutrigenetics and nutrigenomics the path to optimal health? Epigenomics 2023. [PMID: 37191059 DOI: 10.2217/epi-2023-0084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023] Open
Affiliation(s)
- Varun Asediya
- Animal Nutrition Research Station, Anand Agricultural University, Anand, 388001, India
| | - Pranav Anjaria
- Department of Veterinary Public Health & Epidemiology, College of Veterinary Science & Animal Husbandry, Kamdhenu University, Anand, 388001, India
| |
Collapse
|
14
|
Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
Collapse
Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
| |
Collapse
|
15
|
Scott RT, Sanders LM, Antonsen EL, Hastings JJA, Park SM, Mackintosh G, Reynolds RJ, Hoarfrost AL, Sawyer A, Greene CS, Glicksberg BS, Theriot CA, Berrios DC, Miller J, Babdor J, Barker R, Baranzini SE, Beheshti A, Chalk S, Delgado-Aparicio GM, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Kalantari J, Khezeli K, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Garcia Martin H, Mason CE, Matar M, Mias GI, Myers JG, Nelson C, Oribello J, Parsons-Wingerter P, Prabhu RK, Qutub AA, Rask J, Saravia-Butler A, Saria S, Singh NK, Snyder M, Soboczenski F, Soman K, Van Valen D, Venkateswaran K, Warren L, Worthey L, Yang JH, Zitnik M, Costes SV. Biomonitoring and precision health in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00617-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
|
16
|
|
17
|
India's Opportunities and Challenges in Establishing a Twin Registry: An Unexplored Human Resource for the World's Second-Most Populous Nation. Twin Res Hum Genet 2022; 25:156-164. [PMID: 35786423 DOI: 10.1017/thg.2022.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Nature and nurture have always been a prerogative of evolutionary biologists. The environment's role in shaping an organism's phenotype has always intrigued us. Since the inception of humankind, twinning has existed with an unsettled parley on the contribution of nature (i.e. genetics) versus nurture (i.e. environment), which can influence the phenotypes. The study of twins measures the genetic contribution and that of the environmental influence for a particular trait, acting as a catalyst, fine-tuning the phenotypic trajectories. This is further evident because a number of human diseases show a spectrum of clinical manifestations with the same underlying molecular aberration. As of now, there is no definite way to conclude just from the genomic data the severity of a disease or even to predict who will get affected. This greatly justifies initiating a twin registry for a country as diverse and populated as India. There is an unmet need to set up a nationwide database to carefully curate the information on twins, serving as a valuable biorepository to study their overall susceptibility to disease. Establishing a twin registry is of paramount importance to harness the wealth of human information related to the biomedical, anthropological, cultural, social and economic significance.
Collapse
|
18
|
An G, Cockrell C. Drug Development Digital Twins for Drug Discovery, Testing and Repurposing: A Schema for Requirements and Development. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:928387. [PMID: 35935475 PMCID: PMC9351294 DOI: 10.3389/fsysb.2022.928387] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
There has been a great deal of interest in the concept, development and implementation of medical digital twins. This interest has led to wide ranging perceptions of what constitutes a medical digital twin. This Perspectives article will provide 1) a description of fundamental features of industrial digital twins, the source of the digital twin concept, 2) aspects of biology that challenge the implementation of medical digital twins, 3) a schematic program of how a specific medical digital twin project could be defined, and 4) an example description within that schematic program for a specific type of medical digital twin intended for drug discovery, testing and repurposing, the Drug Development Digital Twin (DDDT).
Collapse
Affiliation(s)
- Gary An
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT, United States
| | - Chase Cockrell
- Department of Surgery, University of Vermont Larner College of Medicine, Burlington, VT, United States
| |
Collapse
|
19
|
Linka K, Cavinato C, Humphrey JD, Cyron CJ. Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning. Acta Biomater 2022; 147:63-72. [PMID: 35643194 DOI: 10.1016/j.actbio.2022.05.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 01/15/2023]
Abstract
Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological analysis, modern in situ imaging, and biomechanical testing have deepened our understanding of these complex interrelations, yet two key questions remain: (1) Given the specific microstructure, can one predict the macroscopic mechanical properties without mechanical testing? (2) Can one quantify individual contributions of the different microstructural features to the macroscopic mechanical properties in an automated, systematic and largely unbiased way? Here we propose a bidirectional deep learning architecture to address these two questions. Our architecture uses data from standard histological analyses, two-photon microscopy and biaxial biomechanical testing. Its capabilities are demonstrated by predicting with high accuracy (R2=0.92) the evolving mechanical properties of the murine aorta during maturation and aging. Moreover, our architecture reveals that the extracellular matrix composition and organization are the most prominent factors governing the macroscopic mechanical properties of the tissues studied herein. STATEMENT OF SIGNIFICANCE: .
Collapse
Affiliation(s)
- Kevin Linka
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany
| | - Cristina Cavinato
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Jay D Humphrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT, USA
| | - Christian J Cyron
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany; Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany.
| |
Collapse
|
20
|
Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA. Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med 2022; 5:64. [PMID: 35595830 PMCID: PMC9122990 DOI: 10.1038/s41746-022-00610-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.
Collapse
Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - A Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France.,Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - T Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - G An
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - B Shapiro
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - R S Malik-Sheriff
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, Cambridge, UK
| | - T J Sego
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - A Knapp
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - P Macklin
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - J A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| |
Collapse
|
21
|
Li X, Lee EJ, Lilja S, Loscalzo J, Schäfer S, Smelik M, Strobl MR, Sysoev O, Wang H, Zhang H, Zhao Y, Gawel DR, Bohle B, Benson M. A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets. Genome Med 2022; 14:48. [PMID: 35513850 PMCID: PMC9074288 DOI: 10.1186/s13073-022-01048-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 04/13/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. METHODS We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. RESULTS Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. CONCLUSIONS We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.
Collapse
Affiliation(s)
- Xinxiu Li
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Eun Jung Lee
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
- Department of Otorhinolaryngology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sandra Lilja
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Samuel Schäfer
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Martin Smelik
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Maria Regina Strobl
- Department of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Oleg Sysoev
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linkoping University, Linköping, Sweden
| | - Hui Wang
- Jiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Huan Zhang
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Yelin Zhao
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Danuta R Gawel
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden
| | - Barbara Bohle
- Department of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Mikael Benson
- Centre for Personalized Medicine, Linköping University, Linköping, Sweden.
- Crown Princess Victoria Children's Hospital, Linköping University Hospital, Linköping, Sweden.
- Division of ENT Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
| |
Collapse
|
22
|
Dirnagl U, Duda GN, Grainger DW, Reinke P, Roubenoff R. Reproducibility, relevance and reliability as barriers to efficient and credible biomedical technology translation. Adv Drug Deliv Rev 2022; 182:114118. [PMID: 35066104 DOI: 10.1016/j.addr.2022.114118] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/14/2022] [Accepted: 01/15/2022] [Indexed: 12/23/2022]
Abstract
Biomedical research accuracy and relevance for improving healthcare are increasingly identified as costly problems. Basic research data quality, reporting and methodology, and reproducibility are common factors implicated in this challenge. Preclinical models of disease and therapy, largely conducted in rodents, have known deficiencies in replicating most human conditions. Their translation to human results is acknowledged to be poor for decades. Clinical data quality and quantity is also recognized as deficient; gold standard randomized clinical trials are expensive. Few solid conclusions from clinical studies are replicable and many remain unpublished. The translational pathway from fundamental biomedical research through to innovative solutions handed to clinical practitioners is therefore highly inefficient and costly in terms of wasted resources, early claims from fundamental discoveries never witnessed in humans, and few new, improved solutions available clinically for myriad diseases. Improving this biomedical research strategy and resourcing for reliability, translational relevance, reproducibility and clinical impact requires careful analysis and consistent enforcement at both funding and peer review levels.
Collapse
Affiliation(s)
- Ulrich Dirnagl
- Department of Experimental Neurology, Charité - Universitätsmedizin Berlin, Germany; QUEST Center for Responsible Research, Berlin Institute of Health, Germany
| | - Georg N Duda
- Berlin Institute of Health (BIH) Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany; Julius Wolff Institute for Biomechanics and Musculoskeletal Regeneration, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany
| | - David W Grainger
- Department of Pharmaceutics and Pharmaceutical Chemistry, Health Sciences, University of Utah, Salt Lake City, UT 84112 USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112 USA.
| | - Petra Reinke
- Berlin Institute of Health (BIH) Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Germany; Berlin Center for Advanced Therapies (BeCAT), Charité - Universitaetsmedizin Berlin, 13353 Berlin, Germany
| | - Ronenn Roubenoff
- Novartis Institutes for Biomedical Research, Cambridge, Basel, Massachusetts, Switzerland
| |
Collapse
|
23
|
Sordo Vieira L, Laubenbacher RC. Computational models in systems biology: standards, dissemination, and best practices. Curr Opin Biotechnol 2022; 75:102702. [PMID: 35217296 DOI: 10.1016/j.copbio.2022.102702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/15/2021] [Accepted: 02/03/2022] [Indexed: 11/29/2022]
Abstract
Mathematical and computational models are a key technology in systems biology. Progress in the field depends on the replicability and reproducibility of their properties and behavior. For this, an essential requirement is a set of clear standards for model specification and dissemination. This review covers existing standards, and it highlights the most important areas where further work is required. This includes the specification of agent-based models, an increasingly common modeling approach.
Collapse
Affiliation(s)
- Luis Sordo Vieira
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Florida, Gainesville, FL 32610, United States; Department of Psychiatry, University of Florida, Gainesville, FL 32610, United States
| | - Reinhard C Laubenbacher
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Florida, Gainesville, FL 32610, United States.
| |
Collapse
|
24
|
Buchsbaum JC, Jaffray DA, Ba D, Borkon LL, Chalk C, Chung C, Coleman MA, Coleman CN, Diehn M, Droegemeier KK, Enderling H, Espey MG, Greenspan EJ, Hartshorn CM, Hoang T, Hsiao HT, Keppel C, Moore NW, Prior F, Stahlberg EA, Tourassi G, Willcox KE. Predictive Radiation Oncology - A New NCI-DOE Scientific Space and Community. Radiat Res 2022; 197:434-445. [PMID: 35090025 DOI: 10.1667/rade-22-00012.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/10/2022] [Indexed: 11/03/2022]
Abstract
With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops-and a final concluding "World Café" on March 29, 2021-focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops. Participants identified twelve key questions-and collaborative parallel ideas-as the focus of work going forward to advance the field. These were then used to define short-term and longer-term "Blue Sky" goals. In addition, the group determined key success factors for predictive oncology in the context of radiation oncology, if not the future of all of medicine. These are: cross-discipline collaboration, targeted talent development, development of mechanistic mathematical and computational models and tools, and access to high-quality multiscale data that bridges mechanisms to phenotype. The workshop participants reported feeling energized and highly motivated to pursue next steps together to address the unmet needs in radiation oncology specifically and in cancer research generally and that NCI and DOE project goals align at the convergence of radiation therapy and advanced computing.
Collapse
Affiliation(s)
| | - David A Jaffray
- The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
| | - Demba Ba
- Harvard University, Cambridge, Massachusetts 02138
| | - Lynn L Borkon
- Frederick National Laboratory for Cancer Research, Frederick, Maryland, 21701
| | | | - Caroline Chung
- The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
| | | | | | | | | | - Heiko Enderling
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612
| | | | | | | | - Thuc Hoang
- U.S. Department of Energy, Washington, DC 20585
| | - H Timothy Hsiao
- American Society for Radiation Oncology (ASTRO), Arlington, Virginia 22202
| | | | - Nathan W Moore
- Sandia National Laboratories, Albuquerque, New Mexico 87123
| | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205
| | - Eric A Stahlberg
- Frederick National Laboratory for Cancer Research, Frederick, Maryland, 21701
| | | | | |
Collapse
|
25
|
Douthwaite JA, Lesage B, Gleirscher M, Calinescu R, Aitken JM, Alexander R, Law J. A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics. Front Robot AI 2022; 8:758099. [PMID: 34977162 PMCID: PMC8719333 DOI: 10.3389/frobt.2021.758099] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 11/19/2021] [Indexed: 11/13/2022] Open
Abstract
Digital twins offer a unique opportunity to design, test, deploy, monitor, and control real-world robotic processes. In this paper we present a novel, modular digital twinning framework developed for the investigation of safety within collaborative robotic manufacturing processes. The modular architecture supports scalable representations of user-defined cyber-physical environments, and tools for safety analysis and control. This versatile research tool facilitates the creation of mixed environments of Digital Models, Digital Shadows, and Digital Twins, whilst standardising communication and physical system representation across different hardware platforms. The framework is demonstrated as applied to an industrial case-study focused on the safety assurance of a collaborative robotic manufacturing process. We describe the creation of a digital twin scenario, consisting of individual digital twins of entities in the manufacturing case study, and the application of a synthesised safety controller from our wider work. We show how the framework is able to provide adequate evidence to virtually assess safety claims made against the safety controller using a supporting validation module and testing strategy. The implementation, evidence and safety investigation is presented and discussed, raising exciting possibilities for the use of digital twins in robotic safety assurance.
Collapse
Affiliation(s)
- J A Douthwaite
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - B Lesage
- Department of Computer Science, University of York, York, United Kingdom
| | - M Gleirscher
- Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - R Calinescu
- Department of Computer Science, University of York, York, United Kingdom
| | - J M Aitken
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
| | - R Alexander
- Department of Computer Science, University of York, York, United Kingdom
| | - J Law
- Department of Computer Science and the Advanced Manufacturing Research Centre, University of Sheffield, Sheffield, United Kingdom
| |
Collapse
|
26
|
Digital Twins: From Personalised Medicine to Precision Public Health. J Pers Med 2021; 11:jpm11080745. [PMID: 34442389 PMCID: PMC8401029 DOI: 10.3390/jpm11080745] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 01/19/2023] Open
Abstract
A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology's history and main concepts. A number of application examples of digital twins for personalised medicine, public health, and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.
Collapse
|