The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - April-June (2009 vol.6)
pp: 310-320
ABSTRACT
A more complete understanding of the alterations in cellular regulatory and control mechanisms that occur in the various forms of cancer has been one of the central targets of the genomic and proteomic methods that allow surveys of the abundance and/or state of cellular macromolecules. This preference is driven both by the intractability of cancer to generic therapies, assumed to be due to the highly varied molecular etiologies observed in cancer, and by the opportunity to discern and dissect the regulatory and control interactions presented by the highly diverse assortment of perturbations of regulation and control that arise in cancer. Exploiting the opportunities for inference on the regulatory and control connections offered by these revealing system perturbations is fraught with the practical problems that arise from the way biological systems operate. Two classes of regulatory action in biological systems are particularly inimical to inference, convergent regulation, where a variety of regulatory actions result in a common set of control responses (crosstalk), and divergent regulation, where a single regulatory action produces entirely different sets of control responses, depending on cellular context (conditioning). We have constructed a coarse mathematical model of the propagation of regulatory influence in such distributed, context-sensitive regulatory networks that allows a quantitative estimation of the amount of crosstalk and conditioning associated with a candidate regulatory gene taken from a set of genes that have been profiled over a series of samples where the candidate's activity varies.
INDEX TERMS
Microarray, regulatory networks.
CITATION
Marcel Brun, Jeffrey M. Trent, Edward R. Dougherty, "Conditioning-Based Modeling of Contextual Genomic Regulation", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.6, no. 2, pp. 310-320, April-June 2009, doi:10.1109/TCBB.2007.70247
REFERENCES
[1] A. Pardee, F. Jacob, and J. Monod, “The Role of the Inducible Alleles and the Constructive Alleles in the Synthesis of Beta-Galactosidase in Zygotes of Escherichia coli,” Competes Rendus Hebdomadaires des Seances de l'Academie des Sciences, vol. 246, pp.3125-3132, 1958.
[2] C. Yanofsky and V. Horn, “Role of Regulatory Features of the TRP Operon of Escherichia coli in Mediating a Response to a Nutritional Shift,” J. Bacteriology, vol. 176, pp. 6245-6254, 1994.
[3] M. Mandal, M. Lee, J.E. Barrick, Z. Weinberg, G.M. Emilsson, W.L. Ruzzo, and R.R. Breaker, “A Glycine-Dependent Riboswitch That Uses Cooperative Binding to Control Gene Expression,” Science, vol. 306, no. 5694, pp. 275-279, 2004.
[4] J. Yates and M. Nomura, “E. coli Ribosomal Protein L4 Is a Feedback Regulatory Protein,” Cell, vol. 21, pp. 517-522, 1980.
[5] M. Nomura, J. Yates, D. Dean, and L. Post, “Feedback Regulation of Ribosomal Protein Gene Expression in Escherichia coli: Structural Homology of Ribosomal RNA and Ribosomal Protein MRNA,” Proc. Nat'l Academy Sciences USA, vol. 77, pp. 7084-7088, 1980.
[6] J. Monod, Chance and Necessity. Collins, 1972.
[7] A.M. Kringstein, F.M. Rossi, A. Hofmann, and H.M. Blau, “Graded Transcriptional Response to Different Concentrations of a Single Transactivator,” Proc. Nat'l Academy Sciences USA, vol. 95, pp. 13670-13675, 1998.
[8] T.S. Gardner, C.R. Cantor, and J.J. Collins, “Construction of a Genetic Toggle Switch in Escherichia coli,” Nature, vol. 403, pp. 339-342, 2000.
[9] A. Lwoff, L. Siminovitch, and N. Kjeldgaard, “Induction of the Production of Bacteriophages in Lysogenic Bacteria,” Annales de l'Institut Pasteur, vol. 79, pp. 815-859, 1950.
[10] H. McAdams and L. Shapiro, “Circuit Simulation of Genetic Networks,” Science, vol. 269, pp. 650-656, 1995.
[11] A. Fornace Jr., I. Alamo Jr., and M. Hollander, “DNA Damage-Inducible Transcripts in Mammalian Cells,” Proc. Nat'l Academy Sciences USA, vol. 85, pp. 8800-8804, 1988.
[12] S. Amundson, T. Myers, and A. Fornace Jr., “Roles for p53 in Growth Arrest and Apoptosis: Putting on the Brakes after Genotoxic Stress,” Oncogene, vol. 17, pp. 3287-3299, 1998.
[13] K. Ryder, L. Lau, and D. Nathans, “A Gene Activated by Growth Factors Is Related to the Oncogene V-Jun,” Proc. Nat'l Academy Sciences USA, vol. 85, pp. 1487-1491, 1988.
[14] Y. Yarden, “The EGFR Family and Its Ligands in Human Cancer: Signalling Mechanisms and Therapeutic Opportunities,” European J. Cancer, vol. 37, no. 4, pp. S3-S8, 2001.
[15] W. Driever and C. Nusslein-Volhard, “A Gradient of Bicoid Protein in Drosophila Embryos,” Cell, vol. 54, pp. 83-93, 1988.
[16] W. Driever and C. Nusslein-Volhard, “The Bicoid Protein Determines Position in the Drosophila Embryo in a Concentration-Dependent Manner,” Cell, vol. 54, pp. 95-104, 1988.
[17] J. Slack, From Egg to Embryo: Regional Specification in Early Development, Developmental and Cell Biology Series, Cambridge Univ. Press, 1991.
[18] S. Biggar and G. Crabtree, “Cell Signaling Can Direct Either Binary or Graded Transcriptional Responses,” EMBO J., vol. 20, pp. 3167-3176, 2001.
[19] G. Evan and T. Littlewood, “A Matter of Life and Cell Death,” Science, vol. 281, pp. 1317-1322, 1988.
[20] S. Pelengaris, M. Khan, and G. Evan, “C-MYC: More Than Just a Matter of Life and Death,” Nature Rev. Cancer, vol. 2, pp. 764-776, 2002.
[21] E.R. Dougherty and U. Braga-Neto, “Epistemology of Computational Biology: Mathematical Models and Experimental Prediction as the Basis of their Validity,” Biological Systems, vol. 14, no. 1, pp.65-90, 2006.
[22] E.R. Dougherty, M.L. Bittner, Y. Chen, S. Kim, K. Sivakumar, J. Barrera, P.S. Meltzer, and J.M. Trent, “Nonlinear Filters in Genomic Control,” Proc. IEEE-EURASIP Workshop Nonlinear Signal and Image Processing (NSIP '99), pp. 10-15, June 1999.
[23] E.R. Dougherty, S. Kim, and Y. Chen, “Coefficient of Determination in Nonlinear Signal Processing,” Signal Processing, vol. 80, no. 10, pp. 2219-2235, Oct. 2000.
[24] S. Kim, E.R. Dougherty, M.L. Bittner, Y. Chen, K. Sivakumar, P.S. Meltzer, and J.M. Trent, “A General Nonlinear Framework for the Analysis of Gene Interaction via Multivariate Expression Array,” Biomedical Optics, vol. 5, no. 4, pp. 411-424, Oct. 2000.
[25] U.M. Braga-Neto and E.R. Dougherty, “Is Cross-Validation Valid for Small-Sample Microarray Classification,” Bioinformatics, vol. 20, no. 3, pp. 374-380, 2004.
[26] U. Braga-Neto and E.R. Dougherty, “Exact Performance of Error Estimators for Discrete Classifiers,” Pattern Recognition, vol. 38, no. 11, pp. 1799-1814, 2005.
[27] Y. Jiang, J. Lueders, A. Glatfelter, C. Gooden, and M. Bittner, Profiling Human Gene Expression with cDNA Microarrays. John Wiley & Sons, 2001.
[28] O. Troyanskaya, M. Cantor, G. Sherlock, P.O. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R.B. Altman, “Missing Value Estimation Methods for DNA Microarrays,” Bioinformatics, vol. 17, no. 6, pp. 520-525, 2001.
[29] Y. Chen, E.R. Dougherty, and M.L. Bittner, “Ratio-Based Decisions and the Quantitative Analysis of cDNA Microarray Images,” Biomedical Optics, vol. 2, no. 4, pp. 364-374, 1997.
[30] P. O'Connor, J. Jackman, I. Bae, T. Myers, S. Fan, M. Mutoh, D. Scudiero, A. Monks, E. Sausville, J. Weinstein, S. Friend, A.F. Jr., and K. Kohn, “Characterization of the p53 Tumor Suppressor Pathway in Cell Lines of the National Cancer Institute Anticancer Drug Screen and Correlations with the Growth-Inhibitory Potency of 123 Anticancer Agents,” Cancer Research, vol. 57, pp. 4285-4300, 1997.
[31] K.H. Vousden and X. Lu, “Live or Let Die: The Cell's Response to p53,” Nature Rev. Cancer, vol. 2, no. 8, pp. 594-604, 2002.
26 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool