In silico identification of the key components and steps in IFN‐γ induced JAK‐STAT signaling pathway

Zi, Zhike; Cho, Kwang-Hyun; Sung, Myong-Hee; Xia, Xuefeng; Zheng, Jiashun; Sun, Zhirong

doi:10.1016/j.febslet.2005.01.009

Cited by 106 publications

(120 citation statements)

References 35 publications

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“…It is a sensitivity approach based on MonteCarlo filtering. Cho et al and Zi et al [34,35] first applied this method to study cell signalling pathways. The MPSA method was later used in the analysis of other systems biology models [11,36,37].…”

Section: Multi-parametric Sensitivity Analysis (Mpsa)mentioning

confidence: 99%

“…Correspondingly the cumulative frequency is calculated for both acceptable and unacceptable cases. Finally, parameter sensitivities are measured by the maximum vertical distance of the two cumulative frequency curves based the KolmogorovSmirnov statistics [35]. The calculated MPSA sensitivities are between 0 and 1.…”

Section: Multi-parametric Sensitivity Analysis (Mpsa)mentioning

confidence: 99%

“…Larger parameter sensitivity indicates that the corresponding parameter variation has large impact on the defined model output. The detailed algorithm and implementation of the MPSA method have been described before [34,35].…”

Section: Multi-parametric Sensitivity Analysis (Mpsa)mentioning

confidence: 99%

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Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

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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.

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Section: Multi-parametric Sensitivity Analysis (Mpsa)mentioning

confidence: 99%

Section: Multi-parametric Sensitivity Analysis (Mpsa)mentioning

confidence: 99%

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Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

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“…Their role and elements of interferon induced signaling pathways are subjects of ongoing research (see, e.g., the reviews (Levy and Darnell, 2002;Sen, 2001;Pestka et al, 2004)) . However, there are very few attempts to model the processes involved in the signaling pathways activated by IFN, and they are mostly concerned with IFN-γ that is produced only by activated lymphocytes (Yamada et al, 2003;Zi et al, 2005). In this section, the mathematical model description of early processes activated by IFN-β, a cytokine produced by most cell types following viral infection, will be briefly introduced.…”

Section: Early Stages Of An Interferon-β Activated Pathwaymentioning

confidence: 99%

Model Based Analysis of Signaling Pathways

Śmieja

2008

International Journal of Applied Mathematics and Computer Science

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The paper is concerned with application of mathematical modeling to the analysis of signaling pathways. Two issues, deterministic modeling of gene transcription and model-driven discovery of regulatory elements, are dealt with. First, the biological background is given and the importance of the stochastic nature of biological processes is addressed. The assumptions underlying deterministic modeling are presented. Special emphasis is put on describing gene transcription. A framework for including unknown processes activating gene transcription by means of first-order lag elements is introduced and discussed. Then, a particular interferon-β induced pathway is introduced, limited to early events that precede activation of gene transcription. It is shown how to simplify the system description based on the goals of modeling. Further, a computational analysis is presented, facilitating better understanding of the mechanisms underlying regulation of key components in the pathway. The analysis is illustrated by a comparison of simulation and experimental data.

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“…[18][19][20]) and their application to signaling pathways analysis (e.g. [17,21]). Usually, for signaling pathways models parameter values are randomly generated from one of two possible distributions:…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Sensitivity analysis of deterministic signaling pathways models

Puszyński¹,

Lachor²,

Kardyńska³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. The paper is focused on application of sensitivity methods to analysis of signaling pathway models. Two basic methods are compared: local, based on standard sensitivity functions, and global, based on Sobol indices. Firstly, a general outline of modeling of signaling pathways by means of ordinary differential equations is briefly described. Afterwards, the methods of sensitivity analysis, known from literature, are introduced and illustrated with a simple example of a dynamical system of the second order. Subsequently, the analysis of the p53/Mdm2 regulatory module, which is a key element of any pathway involving p53 protein, is presented. The results of this analysis suggest that no single method should be chosen for investigation of any signaling pathway model but several of them should be applied to answer important questions about sources of heterogeneity in cells behavior, robustness of signaling pathways and possible molecular drug targets.

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In silico identification of the key components and steps in IFN‐γ induced JAK‐STAT signaling pathway

Cited by 106 publications

References 35 publications

Sensitivity analysis approaches applied to systems biology models

Sensitivity analysis approaches applied to systems biology models

Model Based Analysis of Signaling Pathways

Sensitivity analysis of deterministic signaling pathways models

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