By Topic

Budding yeast cell cycle modeled by context-sensitive probabilistic Boolean network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ronaldo Fumio Hashimoto ; Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão 1010, CEP 05508-090, Brazil ; Henrique Stagni ; Carlos Henrique Aguena Higa

The yeast (Saccharomyces cerevisiae) cell cycle has been studied for years, providing us a good knowledge about this cellular process. However, behind this process, there are still complex interactions between genes and proteins that are not fully understood. In this paper, we present a yeast cell cycle modeled by a context-sensitive probabilistic Boolean network (cPBN). The importance of understanding the cell cycle process under this model is that this knowledge may be useful for inferring other gene regulatory networks (cPBNs) from biological data. Furthermore, this work shows an application of the cPBN model for a real biological system.

Published in:

2009 IEEE International Workshop on Genomic Signal Processing and Statistics

Date of Conference:

17-21 May 2009