The modelome of line curvature: Many nonlinear models approximated by a single bi-linear metamodel with verbal profiling

Isaeva, Julia; Martens, Magni; Sæbø, Solve; Wyller, John; Martens, Harald

doi:10.1016/j.physd.2012.02.002

Cited by 8 publications

(18 citation statements)

References 11 publications

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“…1) was applied for the sensitivity analysis, predicting the model outputs as functions of the input parameters using regression methodology (Kleijnen, 2007;Tøndel et al 2010Tøndel et al , 2011Tøndel et al , 2012Tøndel et al , 2013Vik et al 2011;Isaeva et al 2012aIsaeva et al , 2012bIsaeva et al , 2012cMartens et al 2013) (when the input parameters are predicted as functions of the model outputs, the approach is referred to as inverse metamodelling (Tøndel et al 2012;Isaeva et al 2012b)). The regression coefficients can then be used as measures of the sensitivity of the model outputs to variations in the input parameters and the Ca transient.…”

Section: Sensitivity Analysis Methodologymentioning

confidence: 99%

Quantifying inter‐species differences in contractile function through biophysical modelling

Tøndel

Land

Niederer

et al. 2015

The Journal of Physiology

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Key pointsr To facilitate translation of data from animal models into clinical applications, it is important to analyse and quantify the differences and relevance of specific physiological mechanisms between species.r We propose a novel approach for the quantification of inter-species differences in terms of biophysical model parameters and apply this to elucidate the differences in cardiac contraction mechanisms between mouse, rat and human.r Our results indicate that the parameters related to the sensitivity and cooperativity of calcium binding to troponin C and the activation and relaxation rates of tropomyosin/crossbridge binding kinetics differ most significantly between mouse, rat and human.r Our results predict crossbridge binding to be slowest in human and fastest in mouse.Abstract Animal models and measurements are frequently used to guide and evaluate clinical interventions. In this context, knowledge of inter-species differences in physiology is crucial for understanding the limitations and relevance of animal experimental assays for informing clinical applications. Extensive effort has been put into studying the structure and function of cardiac contractile proteins and how differences in these translate into the functional properties of muscles. However, integrating this knowledge into a quantitative description, formalising and highlighting inter-species differences both in the kinetics and in the regulation of physiological mechanisms, remains challenging. In this study we propose and apply a novel approach for the quantification of inter-species differences between mouse, rat and human. Assuming conservation of the fundamental physiological mechanisms underpinning contraction, biophysically based computational models are fitted to simulate experimentally recorded phenotypes from multiple species. The phenotypic differences between species are then succinctly quantified as differences in the biophysical model parameter values. This provides the potential of quantitatively establishing the human relevance of both animal-based experimental and computational models for application in a clinical context. Our results indicate that the parameters related to the sensitivity and cooperativity of calcium binding to troponin C and the activation and relaxation rates of tropomyosin/crossbridge binding kinetics differ most significantly between mouse, rat and human, while for example the reference tension, as expected, shows only minor differences between the species. Hence, while confirming expected inter-species differences in calcium sensitivity due to large differences in the observed calcium transients, our results also indicate more unexpected differences in the cooperativity mechanism. Specifically, the decrease in the unbinding rate of calcium to troponin C with increasing active tension was much lower for mouse than for rat and human. Our results also predicted crossbridge binding to be slowest in human and fastest in mouse.

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Section: Sensitivity Analysis Methodologymentioning

confidence: 99%

Quantifying inter‐species differences in contractile function through biophysical modelling

Tøndel

Land

Niederer

et al. 2015

The Journal of Physiology

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“…In summary, inverse multivariate metamodeling reveals covariation patterns—expected as well as unexpected—among the numerous Outputs . Moreover, it can also simplify parameter estimation and help identify ambiguities in the input–output relationships, and thereby provide a basis for future model improvement, as shown in Refs , and Tøndel et al (Submitted). A combination of classical and inverse multivariate metamodeling constitutes a powerful tool to analyze and describe the dynamic model behavior …”

Section: Introductionmentioning

confidence: 89%

“…Multivariate metamodeling based on PLSR is a relatively new methodological framework, in which many input variables and many output variables are related to each other simultaneously, via their most relevant patterns of systematic intercorrelations. The framework draws on recent developments from various research groups including our own . The illustrations in this review are taken mainly from our own papers.…”

Section: Introductionmentioning

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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“…Then it is better to compare the experimental data directly to the raw simulation data. We have called that the Direct Look-Up method Isaeva et al (2012a) [32] and (2012b) [34]-which is a simple version of so-called Case-based Reasoning.…”

Section: Multivariate Metamodeling: Models Of Modelsmentioning

confidence: 99%

“…For instance, from the modelome-of-line-curvature data (Figure 12), Isaeva et al (2012a) [32] submitted print-outs from a representative subset of the thousands of simulated curves to quantitative sensory descriptive analysis, using a sensory panel from food science. Once the panelists had developed a suitable vocabulary and used it to profile the selected curves, a PLS regression model was developed to describe sensory profile Y from the curves' joint metamodel scores X.…”

Section: Combining Human and Technical Datamentioning

confidence: 99%

Quantitative Big Data: where chemometrics can contribute

Martens¹

2015

Journal of Chemometrics

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The modelome of line curvature: Many nonlinear models approximated by a single bi-linear metamodel with verbal profiling

Cited by 8 publications

References 11 publications

Quantifying inter‐species differences in contractile function through biophysical modelling

Quantifying inter‐species differences in contractile function through biophysical modelling

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

Quantitative Big Data: where chemometrics can contribute

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