Reinhard LaubenbacherDepartment of Medicine, University of Florida, Gainesville, FL, Fred AdlerDepartment of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, UT, Gary AnDepartment of Surgery, University of Vermont, Burlington, VT, Filippo CastiglioneBiotechnology Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates, Stephen EubankBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, Luis L. FonsecaDepartment of Medicine, University of Florida, Gainesville, FL, James GlazierDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Tomas HelikarDepartment of Biochemistry, University of Nebraska, Lincoln, NE, Marti Jett-TiltonU.S. Walter Reed Army Institute of Research, Silver Spring, MD, Denise KirschnerDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Paul MacklinDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Borna MehradDepartment of Medicine, University of Florida, Gainesville, FL, Beth MooreDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Virginia PasourU.S. Army Research Office, Research Triangle Park, NC, Ilya ShmulevichInstitute for Systems Biology, Seattle, WA, Amber SmithDepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, Isabel VoigtCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany, Thomas E. YankeelovDepartment of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, The University of Texas, Austin, TX, and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Tjalf ZiemssenCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany
{"title":"Forum on immune digital twins: a meeting report","authors":"Reinhard LaubenbacherDepartment of Medicine, University of Florida, Gainesville, FL, Fred AdlerDepartment of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, UT, Gary AnDepartment of Surgery, University of Vermont, Burlington, VT, Filippo CastiglioneBiotechnology Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates, Stephen EubankBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, Luis L. FonsecaDepartment of Medicine, University of Florida, Gainesville, FL, James GlazierDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Tomas HelikarDepartment of Biochemistry, University of Nebraska, Lincoln, NE, Marti Jett-TiltonU.S. Walter Reed Army Institute of Research, Silver Spring, MD, Denise KirschnerDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Paul MacklinDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, IN, Borna MehradDepartment of Medicine, University of Florida, Gainesville, FL, Beth MooreDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, Virginia PasourU.S. Army Research Office, Research Triangle Park, NC, Ilya ShmulevichInstitute for Systems Biology, Seattle, WA, Amber SmithDepartment of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, Isabel VoigtCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany, Thomas E. YankeelovDepartment of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, Oncology, The University of Texas, Austin, TX, and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Tjalf ZiemssenCenter for Clinical Neuroscience, Carl Gustav Carus University Hospital, Dresden, Germany","doi":"arxiv-2310.18374","DOIUrl":null,"url":null,"abstract":"Medical digital twins are computational models of human biology relevant to a\ngiven medical condition, which can be tailored to an individual patient,\nthereby predicting the course of disease and individualized treatments, an\nimportant goal of personalized medicine. The immune system, which has a central\nrole in many diseases, is highly heterogeneous between individuals, and thus\nposes a major challenge for this technology. If medical digital twins are to\nfaithfully capture the characteristics of a patient's immune system, we need to\nanswer many questions, such as: What do we need to know about the immune system\nto build mathematical models that reflect features of an individual? What data\ndo we need to collect across the different scales of immune system action? What\nare the right modeling paradigms to properly capture immune system complexity?\nIn February 2023, an international group of experts convened in Lake Nona, FL\nfor two days to discuss these and other questions related to digital twins of\nthe immune system. The group consisted of clinicians, immunologists,\nbiologists, and mathematical modelers, representative of the interdisciplinary\nnature of medical digital twin development. A video recording of the entire\nevent is available. This paper presents a synopsis of the discussions, brief\ndescriptions of ongoing digital twin projects at different stages of progress.\nIt also proposes a 5-year action plan for further developing this technology.\nThe main recommendations are to identify and pursue a small number of promising\nuse cases, to develop stimulation-specific assays of immune function in a\nclinical setting, and to develop a database of existing computational immune\nmodels, as well as advanced modeling technology and infrastructure.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"2006 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2310.18374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Medical digital twins are computational models of human biology relevant to a
given medical condition, which can be 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. If medical digital twins are to
faithfully capture the characteristics of a patient's immune system, we need to
answer many questions, such as: What do we need to know about the immune system
to build mathematical models that reflect features of an individual? What data
do we need to collect across the different scales of immune system action? What
are the right modeling paradigms to properly capture immune system complexity?
In February 2023, an international group of experts convened in Lake Nona, FL
for two days to discuss these and other questions related to digital twins of
the immune system. 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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
