Parameter estimation of signal transduction pathways using probability density function of measurement

T.Y. Liu, J.F. Jia, H. Wang, H. Yue

Research output: Contribution to conferencePaper

Abstract

Parameter estimation of signal transduction pathway models is a challenging task as such models are normally nonlinear, high dimensional, and the measurement data is limited and corrupted by noise. In this paper, a novel method for parameter estimation is proposed, in which the distance between the probability density function (PDF) of the model output and the PDF of the measurement data is minimized. This method has been applied to estimate unknown parameters of a TNFalpha- mediated NF-kappaB signal transduction pathway model. The simulation results show the effectiveness of this new algorithm.
Original languageEnglish
Pages464-467
Number of pages4
DOIs
Publication statusPublished - 2007
EventThe 1st International Conference on Biomedical Engineering, 2007. ICBBE 2007 - Wuhan, China
Duration: 6 Jul 2007 → …

Conference

ConferenceThe 1st International Conference on Biomedical Engineering, 2007. ICBBE 2007
CountryChina
CityWuhan
Period6/07/07 → …

    Fingerprint

Keywords

  • parameter estimation
  • signal transduction
  • probability density

Cite this

Liu, T. Y., Jia, J. F., Wang, H., & Yue, H. (2007). Parameter estimation of signal transduction pathways using probability density function of measurement. 464-467. Paper presented at The 1st International Conference on Biomedical Engineering, 2007. ICBBE 2007 , Wuhan, China. https://doi.org/10.1109/ICBBE.2007.120