Inference method for developing mathematical models of cell signaling pathways using proteomic datasets

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Abstract

The progress in proteomics technologies has led to a rapid accumulation of large-scale proteomic datasets in recent years, which provides an unprecedented opportunity and valuable resources to understand how living organisms perform necessary functions at systems levels. This work presents a computational method for designing mathematical models based on proteomic datasets. Using the mitogen-activated protein (MAP) kinase pathway as the test system, we first develop a mathematical model including the cytosolic and nuclear subsystems. A key step of modeling is to apply a genetic algorithm to infer unknown model parameters. Then the robustness property of mathematical models is used as a criterion to select appropriate rate constants from the estimated candidates. Moreover, quantitative information such as the absolute protein concentrations is used to further refine the mathematical model. The successful application of this inference method to the MAP kinase pathway suggests that it is a useful and powerful approach for developing accurate mathematical models to gain important insights into the regulatory mechanisms of cell signaling pathways.

Original languageEnglish
Title of host publicationBioinformatics
Subtitle of host publicationVolume II: Structure, Function, and Applications
EditorsJonathan M. Keith
Place of PublicationNew York NY USA
PublisherHumana Press
Pages329-344
Number of pages16
Volume1526
Edition2
ISBN (Electronic)9781493966134
ISBN (Print)9781493966110
DOIs
Publication statusPublished - 2017

Publication series

NameMethods in Molecular Biology
PublisherHumana Press
Volume1526
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Cell signaling pathway
  • Proteomics
  • Reverse engineering
  • Robustness

Cite this

Tian, T., & Song, J. (2017). Inference method for developing mathematical models of cell signaling pathways using proteomic datasets. In J. M. Keith (Ed.), Bioinformatics: Volume II: Structure, Function, and Applications (2 ed., Vol. 1526, pp. 329-344). (Methods in Molecular Biology; Vol. 1526). Humana Press. https://doi.org/10.1007/978-1-4939-6613-4_18