A methodology for performing global uncertainty and sensitivity analysis in systems biology.

By Simeone Marino, Ian B. Hogue, Christian Ray, Denise E. Kirschner. Journal of Theoretical Biology, 2008

2,876 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2aa3f4d2d19d2afb5fe179ad36f8e66ac4e06449

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In Genetics

1. B. Gomero
2. Myron Zalucki
3. Zhike Zi

In Molecular Biology

1. B. Gomero
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In Statistics and Probability

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Citations in the corpus

• Sensitivity analysis of infectious disease models: methods, advances and their application (2013, methodology): “PRCCs were calculated following the procedures described previously [21].”
• Latin Hypercube Sampling and Partial Rank Correlation Coefficient Analysis Applied to an Optimal Control Problem (2012, methodology): “In Chapter 2, we will outline the procedure for using a combination of methods developed by Sally Blower and Denise Kirschner [18] to perform the LHS/PRCC sensitivity analysis.”
• Global analysis of a Holling type II predator–prey model with a constant prey refuge (2014, methodology): “To address the effects of all the parameters on OQ1, sensitivity analyses were performed by evaluating the partial rank correlation coefficients (PRCCs) [2, 25] for various input parameters against the threshold condition OQ1, and then the most significant parameters were determined.”
• Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory (2018, methodology): “Global uncertainty analysis (Marino et al., 2008; Thiele et al., 2014) was used to investigate responses of model outputs (utilization distribution; mean proportion of eggs laid per agent on day five; median egg density per grass/pasture patch; median egg density per road ROWs; total eggs laid in…”
• Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, methodology): “Both methods are robust measures as long as input factors are uncorrelated (Marino et al. 2008).”
• Sensitivity analysis approaches applied to systems biology models. (2011, methodology): “PRCC analysis is a sensitivity analysis method that calculates the partial rank correlation coefficient for the model inputs (sampled by Latin hypercube sampling method) and outputs [11, 38, 53, 54].”
• Variance-based sensitivity analysis of a forest growth model (2012, methodology): “Global SAs have been applied to several different kinds of models in forestry (Wu et al., 2011; Xu et al., 2009), hydrology (Nossent et al., 2011; Tang et al., 2007), ecology (Makler-Pick et al., 2011) and biology (Marino et al., 2008).”