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

  • Authors:

  • Kavitha Bhasi;Alan Forrest;Murali Ramanathan

  • Affiliations:

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

  • Venue:
  • Bioinformatics
  • Year:
  • 2005

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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 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@acsu.buffalo.edu