Abstract
One of the challenges in constructing biological models involves resolving meaningful data patterns from which the mathematical models will be generated. For models that describe the change of mRNA in response to drug administration, questions exist whether the correct genes have been selected given the myriad transcriptional effects that may occur. Oftentimes, different algorithms will select or cluster different groups of genes from the same data set. A new approach was developed that focuses on identifying the underlying global dynamics of the system instead of selecting individual genes. The procedure was applied to microarray genomic data obtained from rat liver after a large single dose of methylprednisolone in 52 adrenalectomized rats. Twelve clusters of at least 30 genes each were selected, reflecting the major changes over time. This method along with isolating the underlying dynamics of the system also extracts and clusters the genes that make up this global dynamic for further analysis as to the contributions of specific mechanisms affected by the drug.
Footnotes
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This work was supported by National Science Foundation Grant 0519563 and the Environmental Protection Agency Grant GAD R 832721-010 (to E.Y. and I.P.A.) and National Institutes of General Medical Sciences, National Institutes of Health Grant GM-24211 (to R.R.A., D.C.D., and W.J.J.).
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Article, publication date, and citation information can be found at http://jpet.aspetjournals.org.
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doi:10.1124/jpet.107.133074.
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ABBREVIATIONS: MPL, methylprednisolone; PK/PD, pharmacokinetic/pharmacodynamic; ADX, adrenalectomized; CDF, cumulative distribution function; K-S, Kolmogorov-Smirnov; SNR, signal-to-noise ratio; LTI, linear time invariant; CS, corticosteroid; BS, intermediate biosignal.
- Received October 16, 2007.
- Accepted December 14, 2007.
- The American Society for Pharmacology and Experimental Therapeutics
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