Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

Xy, Zhang; Mn, Trame; Lj, Lesko; Schmidt, Stephan

doi:10.1002/psp4.6

Cited by 308 publications

(247 citation statements)

References 55 publications

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“…Sobol' analysis partitions the total variance in model output to the contribution of each individual input variable (i.e., first-order effects), as well as their interactions (i.e., higher-order effects), as follows (equation adapted from Zhang et al, 2015):…”

Section: Discussionmentioning

confidence: 99%

“…Latin hypercube sampling (LHS) was used for this purpose due to its effectiveness in covering multi-dimensional input spaces (Osidele and Beck, 2001;Sieber and Uhlenbrook, 2005;Tang et al, 2007b). According to Nossent et al (2011) and Zhang et al (2015), the sample size was selected to ensure the convergence of the first-and total-order Sobol' sensitivity indices, which occurs when the width of the 95 % confidence intervals from 1000-fold bootstrap re-sampling of the each index is below 10 % of the corresponding mean obtained from bootstrapping. Specifically, we generated different sizes of LHS samples of climate perturbations with the historical climate data from one study site, from which the PET responses were estimated using the Penman-Monteith model.…”

Section: Representing Plausible Changes In the Climatic Variablesmentioning

confidence: 99%

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Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones

Guo

Westra

Maier

2017

Hydrol. Earth Syst. Sci.

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Abstract. Assessing the factors that have an impact on potential evapotranspiration (PET) sensitivity to changes in different climate variables is critical to understanding the possible implications of climatic changes on the catchment water balance. Using a global sensitivity analysis, this study assessed the implications of baseline climate conditions on the sensitivity of PET to a large range of plausible changes in temperature (T ), relative humidity (RH), solar radiation (R s ) and wind speed (u z ). The analysis was conducted at 30 Australian locations representing different climatic zones, using the Penman-Monteith and Priestley-Taylor PET models. Results from both models suggest that the baseline climate can have a substantial impact on overall PET sensitivity. In particular, approximately 2-fold greater changes in PET were observed in cool-climate energy-limited locations compared to other locations in Australia, indicating the potential for elevated water loss as a result of increasing actual evapotranspiration (AET) in these locations. The two PET models consistently indicated temperature to be the most important variable for PET, but showed large differences in the relative importance of the remaining climate variables. In particular for the Penman-Monteith model, wind and relative humidity were the second-most important variables for dry and humid catchments, respectively, whereas for the PriestleyTaylor model solar radiation was the second-most important variable, with the greatest influence in warmer catchments. This information can be useful to inform the selection of suitable PET models to estimate future PET for different climate conditions, providing evidence on both the structural plausibility and input uncertainty for the alternative models.

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Section: Discussionmentioning

confidence: 99%

Section: Representing Plausible Changes In the Climatic Variablesmentioning

confidence: 99%

Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones

Guo

Westra

Maier

2017

Hydrol. Earth Syst. Sci.

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“…Sobol sensitivity analysis has been used to characterize systems pharmacology models (59,60). Sensitivity analysis has also been presented as an important method to support QSP workflows (8), and here we illustrate how to run a Sobol global sensitivity analysis using a test function that can be solved quickly, the six-parameter Sobol g-function (61).…”

Section: Example 7: Sobol Global Sensitivity Analysismentioning

confidence: 99%

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Cheng

Thalhauser

Smithline

et al. 2017

AAPS J

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Abstract. Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

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“…In this section we apply GSA to the total model which comprises bending and torsional parameters all together. The input parameters are defined in (20).…”

Section: Gsa Of Drive Train High Speed (Hss) Shaft Subsystemmentioning

confidence: 99%

“…Reviews on different GSA methods can be found in [16][17][18][19][20]. Some examples of sensitivity analysis are derivative-based Morris one-at-atime (OAT) local sensitivity method [21] and Sobol/Saltelli method providing the variance based sensitivity [22,23], whereas the Sobol/Saltelli variance based sensitivity method provides indices that quantify the relative contribution of each input structural parameter to set of objective functions [22,23].…”

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of High Speed Shaft Subsystem of a Wind Turbine Drive Train

Asadi

Berbyuk

Johansson

2018

International Journal of Rotating Machinery

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The wind turbine dynamics are complex and critical area of study for the wind industry. Quantification of the effective factors to wind turbine performance is valuable for making improvements to both power performance and turbine health. In this paper, the global sensitivity analysis of validated mathematical model for high speed shaft drive train test rig has been developed in order to evaluate the contribution of systems input parameters to the specified objective functions. The drive train in this study consists of a 3-phase induction motor, flexible shafts, shafts' coupling, bearing housing, and disk with an eccentric mass. The governing equations were derived by using the Lagrangian formalism and were solved numerically by Newmark method. The variance based global sensitivity indices are introduced to evaluate the contribution of input structural parameters correlated to the objective functions. The conclusion from the current research provides informative beneficial data in terms of design and optimization of a drive train setup and also can provide better understanding of wind turbine drive train system dynamics with respect to different structural parameters, ultimately designing more efficient drive trains. Finally, the proposed global sensitivity analysis (GSA) methodology demonstrates the detectability of faults in different components.

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Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

Cited by 308 publications

References 55 publications

Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones

Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Global Sensitivity Analysis of High Speed Shaft Subsystem of a Wind Turbine Drive Train

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