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Bioinformatics Advance Access originally published online on August 11, 2005
Bioinformatics 2005 21(20):3873-3879; doi:10.1093/bioinformatics/bti624
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

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

Kavitha Bhasi , Alan Forrest and Murali Ramanathan *

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

*To whom correspondence should be addressed at Department of Pharmaceutical Sciences, 543 Cooke Hall, State University of New York at Buffalo, Buffalo, NY 14260-1200, USA

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 datasets 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.

Contact: murali{at}acsu.buffalo.edu

Received on May 18, 2005; revised on July 25, 2005; accepted on August 10, 2005

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