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Improving the physiological realism of experimental models
Author(s) -
Kalyan C. Vinnakota,
Youngjin Chae,
Patrik Rorsman,
Robert S. Balaban,
André La Gerche,
Richard WadeMartins,
Daniel Beard,
Jeroen A. L. Jeneson
Publication year - 2016
Publication title -
interface focus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 49
eISSN - 2042-8901
pISSN - 2042-8898
DOI - 10.1098/rsfs.2015.0076
Subject(s) - computer science , computational model , function (biology) , in silico , human disease , field (mathematics) , data science , disease , machine learning , risk analysis (engineering) , artificial intelligence , medicine , biology , pathology , biochemistry , mathematics , pure mathematics , gene , evolutionary biology
The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease

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