Virtual physiological human: training challenges

Lawford, Patricia; Narracott, Andrew; McCormack, Keith; Bisbal, Jesús; Martín, Carlos; Brook, Bindi S.; Zachariou, Margarita; Kohl, Peter; Fletcher, Katherine; Díaz‐Zuccarini, Vanessa

doi:10.1098/rsta.2010.0082

Cited by 14 publications

(5 citation statements)

References 6 publications

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“…For instance, in so-called personalized medicine, an aim is to devise different intervention strategies for a broad spectrum of conditions for individual patients. This requires large amounts of information to be combined quantitatively, ranging from, e.g., lower levels of genetic and environmental influence on metabolic reactions and organelle functions, 33,37 to higher levels of organ and whole-body integration, as in the Physiome project [38][39][40][41] (www.physiome.org.nz) and the related Virtual Physiological Human (VPH) project [34][35][36][42][43][44][45] (www.vph-noe.eu).…”

Section: Multivariate Metamodeling In Biologymentioning

confidence: 99%

Analyzing complex mathematical model behavior by partial least squares regression‐based multivariate metamodeling

Tøndel

Martens

2014

WIREs Computational Stats

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The increasing complexity of mathematical models of complex systems like living cells has created a need for methods to reduce computational demand, maintain overview of the capabilities and feasibility of the models, compare alternative models, and obtain more reliable and effective fitting of models to experimental data. Metamodeling-statistical modeling of the behavior of complex mathematical models, also called 'surrogate modeling'-is well established in many scientific disciplines, such as mechanical engineering and process simulation, and has recently also found use in computational biology, as well as other fields of bioscience. Many of these are based on partial least squares regression (PLSR) and various nonlinear and N-way extensions of the PLSR. This is a versatile family of multivariate data modeling methods that combines a simple, flexible model structure (low-rank bilinear subspace regression) and an intuitively attractive optimization criterion (maximized explained input-output covariance) to provide both predictive ability and graphical insight. This review summarizes the background for PLSR-based metamodeling, and the use of PLSR and related methods in the main application areas of metamodeling: reduction of computational demand, sensitivity analysis, model comparison, and parameterization of models in relation to measured data. The methodology is generic, but here illustrated by examples from computational biology. The advantages and limitations of metamodeling for analyzing complex model behavior are discussed.Conflict of interest: The authors have declared no conflicts of interest for this article. main application areas to date. Subsequently, methods for multivariate metamodeling are described, with particular focus on the partial least squares regression (PLSR) version of bilinear regression.Finally, examples of applications of multivariate metamodeling are shown, with special focus on the use of metamodeling in biology and medicine. These are fields where it is increasingly important to enable scientists to handle high-complexity systems with understandable and robust mathematical models. The Methods section gives technical details of the generic methodology, the Applications section provides illustrations of practical use of the metamodeling, while 440 Mathematical model behavior by partial least squares regression-based multivariate metamodeling behavior of model M(.) is observed under all relevant conditions. If model M(.) has K different Volume 6, November/December 2014 Overview and direct parameter estimation State trajectories/ calculated metrics Parameters = I(Metrics) Original model M Metrics = C(Parameters) Overview, sensitivity analysis, and computational speed-up Inputs Prediction Parameters, initial conditions Inverse metamodel, I Classical metamodel, C Prediction Measured metrics Outputs FIGURE 1 | Classical and inverse multivariate metamodeling of a mathematical model M(.). 2 While the classical metamodel C(.) has the same input-output direction as the original model, the inve...

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Section: Multivariate Metamodeling In Biologymentioning

confidence: 99%

Analyzing complex mathematical model behavior by partial least squares regression‐based multivariate metamodeling

Tøndel

Martens

2014

WIREs Computational Stats

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“…This second Theme Issue of 'The virtual physiological human: computer simulation for integrative biomedicine' continues to explore in detail the potential of the virtual physiological human (VPH) approach (Fenner et al 2008). The initial editorial communication on the VPH training challenges (Lawford et al 2010) illustrates some of the tasks and solutions for an integrated (across Europe) approach towards a key requirement for sustainability of VPH efforts: the training of current and future 'specialists' in what could be regarded as a 'generalist' research direction. 'Squaring this circle' will continue to remain a core challenge for the VPH.…”

Section: Introductionmentioning

confidence: 99%

“…The initial editorial communication on the VPH training challenges (Lawford et al 2010) illustrates some of the tasks and solutions for an integrated (across Europe) approach towards a key requirement for sustainability of VPH efforts: the training of current and future 'specialists' in what could be regarded as a 'generalist' research direction. 'Squaring this circle' will continue to remain a core challenge for the VPH.…”

Section: The Virtual Physiological Human: Computer Simulation For Intmentioning

confidence: 99%

The virtual physiological human: computer simulation for integrative biomedicine II

Kohl

Viceconti

2010

Phil. Trans. R. Soc. A.

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“…There is still no formal postgraduate training for researchers wishing to specialize in this field [20]. The VPH MultiInstitutional Graduate Programme (VPH-MIP) has been funded through the EU Lifelong Learning programme to develop a flexible, multidisciplinary course in computational biomedicine for students at Master's level.…”

Section: Rsfsroyalsocietypublishingorg Interface Focus 3: 20130003mentioning

confidence: 99%

Integrative approaches to computational biomedicine

et al. 2013

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One contribution of 25 to a Theme Issue 'The virtual physiological human: integrative approaches to computational biomedicine'. The new discipline of computational biomedicine is concerned with the application of computer-based techniques and particularly modelling and simulation to human health. Since 2007, this discipline has been synonymous, in Europe, with the name given to the European Union's ambitious investment in integrating these techniques with the eventual aim of modelling the human body as a whole: the virtual physiological human. This programme and its successors are expected, over the next decades, to transform the study and practice of healthcare, moving it towards the priorities known as '4P's': predictive, preventative, personalized and participatory medicine.

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Virtual physiological human: training challenges

Cited by 14 publications

References 6 publications

Analyzing complex mathematical model behavior by partial least squares regression‐based multivariate metamodeling

Analyzing complex mathematical model behavior by partial least squares regression‐based multivariate metamodeling

The virtual physiological human: computer simulation for integrative biomedicine II

Integrative approaches to computational biomedicine

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