An approach to estimating protein networks of cell cycle based on least-squares methods for periodic signals

Takehito Azuma, Mayumi Ito, Shuichi Adachi

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

Abstract

This paper considers a least-squares method for state space models by using periodic signals and two theoretical properties of the least-squares method are shown. Moreover the least-squares method considered in this paper is applied to an estimation problem of protein networks for cell cycle in budding yeast. The derived properties of the least-squares method are verified in the estimation problem and the approach to estimate protein networks for cell cycle is demonstrated. Finally two mathematical models are derived based on the estimated protein network.

Original languageEnglish
Title of host publication2011 5th International Conference on Sensing Technology, ICST 2011
Pages458-463
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event2011 5th International Conference on Sensing Technology, ICST 2011 - Palmerston North, New Zealand
Duration: 2011 Nov 282011 Dec 1

Publication series

NameProceedings of the International Conference on Sensing Technology, ICST
ISSN (Print)2156-8065
ISSN (Electronic)2156-8073

Other

Other2011 5th International Conference on Sensing Technology, ICST 2011
CountryNew Zealand
CityPalmerston North
Period11/11/2811/12/1

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Keywords

  • Cell Cycle
  • Least-squares method
  • Protein networks
  • Systems biology

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Azuma, T., Ito, M., & Adachi, S. (2011). An approach to estimating protein networks of cell cycle based on least-squares methods for periodic signals. In 2011 5th International Conference on Sensing Technology, ICST 2011 (pp. 458-463). [6137021] (Proceedings of the International Conference on Sensing Technology, ICST). https://doi.org/10.1109/ICSensT.2011.6137021