Model simplification of signal transduction pathway networks via a hybrid inference strategy

Jianfang Jia, Hong Yue

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)
11 Downloads (Pure)

Abstract

A full-scale mathematical model of cellular networks normally involves a large number of variables and parameters. How to effectively develop manageable and reliable models is crucial for effective computation, analysis and design of such systems. The aim of model simplification is to eliminate parts of a model that are unimportant for the properties of interest. In this work, a model reduction strategy via hybrid inference is proposed for signal pathway networks. It integrates multiple techniques including conservation analysis, local sensitivity analysis, principal component analysis and flux analysis to identify the reactions and variables that can be considered to be eliminated from the full-scale model. Using an I·B-NF-·B signalling pathway model as an example, simulation analysis demonstrates that the simplified model quantitatively predicts the dynamic behaviours of the network.
Original languageEnglish
Title of host publicationProceedings of the 17th IFAC World Congress
Place of PublicationSeoul, Korea
Pages10307-10312
Number of pages6
Volume41
DOIs
Publication statusPublished - 2008

Publication series

NameIFAC Proceedings
PublisherIFAC
Number2
Volume41

    Fingerprint

Keywords

  • cellular
  • metabolic
  • cardiovascular
  • neurosystems
  • Model formulation
  • experimental design

Cite this

Jia, J., & Yue, H. (2008). Model simplification of signal transduction pathway networks via a hybrid inference strategy. In Proceedings of the 17th IFAC World Congress (Vol. 41, pp. 10307-10312). (IFAC Proceedings; Vol. 41, No. 2). Seoul, Korea. https://doi.org/10.3182/20080706-5-KR-1001.01744