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Issue No. 02 - March/April (2012 vol. 9)
ISSN: 1545-5963
pp: 430-437
Daifeng Wang , Dept. of Electr. & Comput. Eng., Univ. of Texas, Austin, TX, USA
M. K. Markey , Dept. of Biomed. Eng., Univ. of Texas, Austin, TX, USA
C. O. Wilke , Center for Comput. Biol. & Bioinf., Univ. of Texas, Austin, TX, USA
A. Arapostathis , Dept. of Electr. & Comput. Eng., Univ. of Texas, Austin, TX, USA
High-throughput methods systematically measure the internal state of the entire cell, but powerful computational tools are needed to infer dynamics from their raw data. Therefore, we have developed a new computational method, Eigen-genomic System Dynamic-pattern Analysis (ESDA), which uses systems theory to infer dynamic parameters from a time series of gene expression measurements. As many genes are measured at a modest number of time points, estimation of the system matrix is underdetermined and traditional approaches for estimating dynamic parameters are ineffective; thus, ESDA uses the principle of dimensionality reduction to overcome the data imbalance. Since degradation rates are naturally confounded by self-regulation, our model estimates an effective degradation rate that is the difference between self-regulation and degradation. We demonstrate that ESDA is able to recover effective degradation rates with reasonable accuracy in simulation. We also apply ESDA to a budding yeast data set, and find that effective degradation rates are normally slower than experimentally measured degradation rates. Our results suggest that either self-regulation is widespread in budding yeast and that self-promotion dominates self-inhibition, or that self-regulation may be rare and that experimental methods for measuring degradation rates based on transcription arrest may severely overestimate true degradation rates in healthy cells.
Bioinformatics, Degradation, Genomics, Gene expression, Eigenvalues and eigenfunctions, Oscillators, Time series analysis

Daifeng Wang, M. K. Markey, C. O. Wilke and A. Arapostathis, "Eigen-Genomic System Dynamic-Pattern Analysis (ESDA): Modeling mRNA Degradation and Self-Regulation," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 430-437, 2012.
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