Abstract
We present a methodology aimed at partial validation and accuracy-precision assessment of a mathematical model of gene transcription at the cellular level. The method is based on the analysis of time-series measurements aggregated over a large number of cells. Such measurements are typically obtained via reverse transcriptase-polymerase chain reaction (RT-PCR) experiments. The validation procedure presented herein uses as an example data on L1 retrotransposon gene in HeLa cells. The procedure compares model predicted values with the RT-PCR data for L1 by means of the standard Bayesian statistical techniques with the help of modern Markov-Chain Monte-Carlo methodology.
Original language | English (US) |
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Pages (from-to) | 339-349 |
Number of pages | 11 |
Journal | Journal of Computational Biology |
Volume | 14 |
Issue number | 3 |
DOIs | |
State | Published - Apr 2007 |
Keywords
- Aggregated data
- Gene transcription model
- L1 retrotransposon
- Model validation
- Posterior confidence bounds
- RT-PCR
- Reaction rate equation
ASJC Scopus subject areas
- Modeling and Simulation
- Molecular Biology
- Genetics
- Computational Mathematics
- Computational Theory and Mathematics