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Eigen-Genomic System Dynamic-Pattern Analysis (ESDA): Modeling mRNA Degradation and Self-Regulation
March/April 2012 (vol. 9 no. 2)
pp. 430-437
ASCII Text
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Daifeng Wang, Mia K. Markey, Claus O. Wilke, Ari Arapostathis, "Eigen-Genomic System Dynamic-Pattern Analysis (ESDA): Modeling mRNA Degradation and Self-Regulation," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 430-437, March/April, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2011.150,
author = {Daifeng Wang and Mia K. Markey and Claus O. Wilke and Ari Arapostathis},
title = {Eigen-Genomic System Dynamic-Pattern Analysis (ESDA): Modeling mRNA Degradation and Self-Regulation},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {9},
number = {2},
issn = {1545-5963},
year = {2012},
pages = {430-437},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.150},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Eigen-Genomic System Dynamic-Pattern Analysis (ESDA): Modeling mRNA Degradation and Self-Regulation
IS - 2
SN - 1545-5963
SP430
EP437
EPD - 430-437
A1 - Daifeng Wang,
A1 - Mia K. Markey,
A1 - Claus O. Wilke,
A1 - Ari Arapostathis,
PY - 2012
KW - Eigenvalues and eigenvectors
KW - genome-wide gene expression
KW - systems theory
KW - singular value decomposition.
VL - 9
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Daifeng Wang, The University of Texas at Austin, Austin
Mia K. Markey, The University of Texas at Austin, Austin
Claus O. Wilke, The University of Texas at Austin, Austin
Ari Arapostathis, The University of Texas at Austin, Austin
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.

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Index Terms:
Eigenvalues and eigenvectors, genome-wide gene expression, systems theory, singular value decomposition.
Citation:
Daifeng Wang, Mia K. Markey, Claus O. Wilke, Ari Arapostathis, "Eigen-Genomic System Dynamic-Pattern Analysis (ESDA): Modeling mRNA Degradation and Self-Regulation," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 430-437, March-April 2012, doi:10.1109/TCBB.2011.150
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