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Sensitivity analysis for reproducible candidate values of model parameters in signaling hub model

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  • Kentaro Inoue

Abstract

Mathematical models for signaling pathways are helpful for understanding molecular mechanism in the pathways and predicting dynamic behavior of the signal activity. To analyze the robustness of such models, local sensitivity analysis has been implemented. However, such analysis primarily focuses on only a certain parameter set, even though diverse parameter sets that can recapitulate experiments may exist. In this study, we performed sensitivity analysis that investigates the features in a system considering the reproducible and multiple candidate values of the model parameters to experiments. The results showed that although different reproducible model parameter values have absolute differences with respect to sensitivity strengths, specific trends of some relative sensitivity strengths exist between reactions regardless of parameter values. It is suggested that (i) network structure considerably influences the relative sensitivity strength and (ii) one might be able to predict relative sensitivity strengths specified in the parameter sets employing only one of the reproducible parameter sets.

Suggested Citation

  • Kentaro Inoue, 2019. "Sensitivity analysis for reproducible candidate values of model parameters in signaling hub model," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0211654
    DOI: 10.1371/journal.pone.0211654
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    References listed on IDEAS

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    1. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    2. Edward Kent & Stefan Neumann & Ursula Kummer & Pedro Mendes, 2013. "What Can We Learn from Global Sensitivity Analysis of Biochemical Systems?," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-13, November.
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