Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. III. Analysis of the approximations

Cukier, Robert I.; Schaibly, John H.; Shuler, Kurt E.

doi:10.1063/1.431440

Cited by 227 publications

(147 citation statements)

References 5 publications

Supporting

Mentioning

144

Contrasting

Unclassified

Order By: Relevance

“…The implementations of the FAST method are complicated and they are available in [54,59,60,62]. The computational cost for the FAST method is very high because it needs a large number of model evaluations.…”

Section: Fourier Amplitude Sensitivity Test (Fast)mentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

View full text Add to dashboard Cite

With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

show abstract

Section: Fourier Amplitude Sensitivity Test (Fast)mentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

View full text Add to dashboard Cite

show abstract

“…In this approach, 'exact' (Downing et al 1985) sensitivities are calculated as defined by partial derivatives. The Fourier amplitude sensitivity test (FAST) (Cukier et al 1973(Cukier et al , 1978 has been used to solve coupled nonlinear rate equations by Schaibly and Shuler (1973) and Cukier et al, (1975). FAST has also been used by Dickinson and Gelinas (1976) to study sensitivities of parameters used to model atmospheric chemical kinetics, by Koda et al (1979) to study atmospheric diffusion, and several others (Leipmann and Stephanopoulos, 1985;Kim etal., 1988).…”

Section: Introductionmentioning

confidence: 99%

A review of techniques for parameter sensitivity analysis of environmental models

Hamby

1994

Environ Monit Assess

1,563

852

View full text Add to dashboard Cite

Abstract. Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a 'sensitivity analysis'. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.

show abstract

“…Variance-based methods for SA were first employed by chemists in the 1970s (Cukier et al, 1973(Cukier et al, , 1975(Cukier et al, , 1978. Later, Sobol' (1993) In general, one may conclude the features of variance-based methods as:…”

Section: Variance-based Methodsmentioning

confidence: 99%