The Community for Technology Leaders
RSS Icon
Issue No.02 - March/April (2012 vol.9)
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,singular value decomposition., Eigenvalues and eigenvectors, genome-wide gene expression, systems theory
Daifeng Wang, M. K. Markey, C. O. Wilke, A. 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
[1] O. Shalem, O. Dahan, M. Levo, M.R. Martinez, I. Furman, E. Segal, and Y. Pilpel, “Transient Transcriptional Responses to Stress Are Generated by Opposing Effects of mRNA Production and Degradation,” Molecular Systems Biology, vol. 4, p. 223, 2008.
[2] Y. Wang, C.L. Liu, J.D. Storey, R.J. Tibshirani, D. Herschlag, and P.O. Brown, “Precision and Functional Specificity in mRNA Decay,” Proc. Nat'l Academy of Sciences USA, vol. 99, pp. 5860-5865, Apr. 2002.
[3] E. Yang, E. van Nimwegen, M. Zavolan, N. Rajewsky, M. Schroeder, M. Magnasco, and J.E. DarnellJr, “Decay Rates of Human mRNAs: Correlation with Functional Characteristics and Sequence Attributes,” Genome Research, vol. 13, pp. 1863-1872, Aug. 2003.
[4] C. Vogel et al., “Sequence Signatures and mRNA Concentration Can Explain Two-Thirds of Protein Abundance Variation in a Human Cell Line,” Molecular Systems Biology, vol. 6, p. 400, Aug. 2010.
[5] C.F. Chin, A.C. Shih, and K.C. Fan, “Influence of mRNA Decay Rates on the Computational Prediction of Transcription Rate Profiles from Gene Expression Profiles,” J. Biosciences, vol. 32, pp. 1251-1262, Dec. 2007.
[6] L. Farina, A. De Santis, S. Salvucci, G. Morelli, and I. Ruberti, “Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data,” PLoS Computational Biology, vol. 4, p. e1000141, 2008.
[7] T. Chen, H.L. He, and G.M. Church, “Modeling Gene Expression with Differential Equations,” Proc. Pacific Symp. Biocomputing, pp. 29-40, 1999.
[8] R.H. Singer and S. Penman, “Messenger RNA in HeLa Cells: Kinetics of Formation and Decay,” J. Molecular Biology, vol. 78, pp. 321-34, Aug. 1973.
[9] J. Ross, “mRNA Stability in Mammalian Cells,” Microbiological Rev., vol. 59, pp. 423-450, Sept. 1995.
[10] M.K. Yeung, J. Tegner, and J.J. Collins, “Reverse Engineering Gene Networks Using Singular Value Decomposition and Robust Regression,” Proc. Nat'l Academy of Sciences USA, vol. 99, pp. 6163-6168, Apr. 2002.
[11] O. Alter, P.O. Brown, and D. Botstein, “Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling,” Proc. Nat'l Academy of Sciences USA, vol. 97, pp. 10101-10106, Aug. 2000.
[12] “Control Systems/State-Space Equations,” http://en.wikibooks. org/wiki/Control_Systems State-Space_Equations, 2011.
[13] E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen, LAPACK User's Guide, , third ed. SIAM, 1999.
[14] E.W. Weisstein, “Ordinary Differential Equation System with Constant Coefficients,” OrdinaryDifferentialEquationSystemwithConstantCoefficients. html , 2011.
[15] C. Penland, P.D. Sardeshmukh, “The Optimal Growth of Tropical Sea Surface Temperature Anomalies,” J. Climate, vol. 8, pp. 1999-2024, 1995.
[16] I. Lee, Z. Li, and E.M. Marcotte, “An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces Cerevisiae,” PLoS ONE, vol. 2, no. 10, pp. e988, 2007.
[17] P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iyer, K. Anders, M.B. Eisen, P.O. Brown, D. Botstein, and B. Futcher, “Comprehensive Identification of Cell Cycle-Regulated Genes of the Yeast Saccharomyces Cerevisiae by Microarray Hybridization,” Molecular Biology of the Cell, vol. 9, pp. 3273-3297, Dec. 1998.
[18] O. Alter and G.H. Golub, “Reconstructing the Pathways of a Cellular System from Genome-Scale Signals by Using Matrix and Tensor Computations,” Proc Nat'l Academy of Sciences USA, vol. 102, pp. 17559-17564, Dec. 2005.
[19] T. Pramila, W. Wu, W.S. Noble, and L. Breeden, “Periodic Genes of the Yeast Saccharomyces Cerevisiae: A Combined Analysis of Five Cell Cycle Data Sets,” cellcycle /, 2007.
[20] M. Bansal, G.D. Gatta, and D. di Bernardo, “Inference of Gene Regulatory Networks and Compound Mode of Action from Time Course Gene Expression Profiles,” Bioinformatics, vol. 22, pp. 815-822, 2006.
[21] M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein, “Cluster Analysis and Display of Genome-wide Expression Patterns,” Proc. Nat'l Academy of Sciences USA, vol. 95, pp. 14863-14868, Dec. 1998.
[22] T.I. Lee, N.J. Rinaldi, F. Robert, D.T. Odom, Z. Bar-Joseph, G.K. Gerber, N.M. Hannett, C.T. Harbison, C.M. Thompson, I. Simon, J. Zeitlinger, E.G. Jennings, H.L. Murray, D.B. Gordon, B. Ren, J.J. Wyrick, J.B. Tagne, T.L. Volkert, E. Fraenkel, D.K. Gifford, and R.A. Young, “Transcriptional Regulatory Networks in Saccharomyces Cerevisiae,” Science, vol. 298, pp. 799-804, Oct. 2002.
[23] K.D. MacIsaac, T. Wang, D.B. Gordon, D.K. Gifford, and G.D. Stormo, “An Improved Map of Conserved Regulatory Sites for Saccharomyces Cerevisiae,” BMC Bioinformatics, vol. 7, pp. 113-125, Mar. 2006.
92 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool