A global sensitivity analysis approach for morphogenesis models

Boas, Sonja E. M.; Jimenez, Maria I. Navarro; Merks, Roeland; Blom, Joke

doi:10.1186/s12918-015-0222-7

Cited by 13 publications

(11 citation statements)

References 45 publications

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“…[12]; the exact values of these parameters do not qualitatively affect the results of the model, and have modest quantitative impact; for a detailed sensitivity analysis see Refs. [12,47].…”

Section: Resultsmentioning

confidence: 99%

Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression

et al. 2016

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Angiogenesis involves the formation of new blood vessels by sprouting or splitting of existing blood vessels. During sprouting, a highly motile type of endothelial cell, called the tip cell, migrates from the blood vessels followed by stalk cells, an endothelial cell type that forms the body of the sprout. To get more insight into how tip cells contribute to angiogenesis, we extended an existing computational model of vascular network formation based on the cellular Potts model with tip and stalk differentiation, without making a priori assumptions about the differences between tip cells and stalk cells. To predict potential differences, we looked for parameter values that make tip cells (a) move to the sprout tip, and (b) change the morphology of the angiogenic networks. The screening predicted that if tip cells respond less effectively to an endothelial chemoattractant than stalk cells, they move to the tips of the sprouts, which impacts the morphology of the networks. A comparison of this model prediction with genes expressed differentially in tip and stalk cells revealed that the endothelial chemoattractant Apelin and its receptor APJ may match the model prediction. To test the model prediction we inhibited Apelin signaling in our model and in an in vitro model of angiogenic sprouting, and found that in both cases inhibition of Apelin or of its receptor APJ reduces sprouting. Based on the prediction of the computational model, we propose that the differential expression of Apelin and APJ yields a “self-generated” gradient mechanisms that accelerates the extension of the sprout.

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Section: Resultsmentioning

confidence: 99%

Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression

et al. 2016

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“…Several previous studies applied GSA in the calibration of the complex models, such as environmental and biological models. However, ranking of parameter sensitivities is the usual method in the GSA approach (Boas et al, 2015 ), and most of the past studies did not provide specific criteria that could be used to distinguish influential and non-influential parameters. Additionally, some studies only focused on the variation in the steady state or integrated measures (e.g., area under the curve) rather than full time-course behaviors (Safta et al, 2015 ; Zhang et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

“…Unlike local SA, global SA (GSA) calculates the contribution from the variety of all model parameters, including both single parameter effects and multiple parameter interactions. This approach has been widely applied to biological models (McNally et al, 2011 ; Boas et al, 2015 ; Loizou et al, 2015 ; Lumen et al, 2015 ). For example, McNally et al ( 2011 ) provided a GSA workflow for PBPK models that begins with preliminary screening from elementary effect (EE)-based Morris method to eliminate (i.e., fix at nominal values) the parameters with negligible impact on the model output, and then used variance-based GSA to determine the influential parameters in the PBPK model.…”

Section: Introductionmentioning

confidence: 99%

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling

et al. 2018

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Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

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“…In vivo, the tip cell is regularly replaced by trailing endothelial cells in the sprout, a phenomenon called "tip cell overtaking" [22]. In our models such tip cell overtaking can occur as a side effect of sprout extensions [23], suggesting the possibility that tip cell overtaking is non-functional and that Dll4-Notch signaling acts to ensure that the cell at the tip assumes the tip cell phenotype, as opposed to a model where the tip cell actively migrates to the tip.…”

Section: Mechanical Cell-ecm Interactionsmentioning

confidence: 96%

Cell-based modeling of cell-matrix interactions in angiogenesis

Merks

2015

ITM Web of Conferences

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Abstract. The self-organization of endothelial cells into blood vessel networks and sprouts can be studied using computational, cell-based models. These take as input the behavior of individual, endothelial cells, as observed in experiments, and gives as output the resulting, collective behavior, i.e. the formation of shapes and tissue structures. Many cell-based models ignore the extracellular matrix, i.e., the fibrous or homogeneous materials that surround cells and gives tissue structural support. In this extended abstract, we highlight two approaches that we have taken to explore the role of the extracellular matrix in our cellular Potts models of blood vessel formation (angiogenesis): first we discuss a model considering chemical endothelial cell-matrix interactions, then we discuss a model that include mechanical cell-matrix interactions. We end by discussing some potential new directions.

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A global sensitivity analysis approach for morphogenesis models

Cited by 13 publications

References 45 publications

Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression

Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling

Cell-based modeling of cell-matrix interactions in angiogenesis

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