Skip Navigation



Bioinformatics Advance Access published online on August 11, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti624
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/20/3873    most recent
bti624v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Bhasi, K.
Right arrow Articles by Ramanathan, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bhasi, K.
Right arrow Articles by Ramanathan, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 18, 2005
Revised July 25, 2005
Accepted August 10, 2005

Article

SPLINDID, a semi-parametric, model-based method for obtaining transcription rates and gene regulation parameters from genomic and proteomic expression profiles

Kavitha Bhasi 1, Alan Forrest 1, and Murali Ramanathan 1*

1 Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY 14260-1200

* To whom correspondence should be addressed.
Murali Ramanathan, E-mail: murali{at}acsu.buffalo.edu


  Abstract

Purpose: To evaluate a semi-parametric, model-based approach for obtaining transcription rates from mRNA and protein expression.

Methods: The transcription profile input was modeled using an exponential function of a cubic spline and the dynamics of translation, mRNA and protein degradation were modeled using the Hargrove-Schmidt model. The transcription rate profile and the translation and mRNA and protein degradation rate constants were estimated by the maximum likelihood method.

Results: Simulated data sets generated from the stochastic, transit compartment and dispersion signaling models were used to test the approach. The approach satisfactorily fit the mRNA and protein data and accurately recapitulated the parameter and the normalized transcription rate profile values. The approach was successfully used to model published data on tyrosine aminotransferase pharmacodynamics.

Conclusions: The semi-parametric approach is effective and could be useful for delineating the genomic effects of drugs.

Availability: Code suitable for use with the ADAPT software program is available from the corresponding author.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
T. Tian, S. Xu, J. Gao, and K. Burrage
Simulated maximum likelihood method for estimating kinetic rates in gene expression
Bioinformatics, January 1, 2007; 23(1): 84 - 91.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.