High-Throughput Biological Data Analysis: A step toward understanding cellular regulation

IEEE Control Systems

Volumn 30, Issue 6, 2010, Pages 81-100

  Elvitigala, Thanura R ^a   Polpitiya, Ashoka D ^a   Wang, Wenxue ^a   Stöckel, Jana ^a   Khandelwal, Abha ^a   Quatrano, Ralph S ^a   Pakrasi, Himadri B ^a   Ghosh, Bijoy K ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

BASIC PRINCIPLES; BIOLOGICAL DATA; BIOLOGICAL PROCESS; CELLULAR REGULATION; COMPLEX DYNAMIC SYSTEMS; DRUG DISCOVERY; ENVIRONMENTAL CONDITIONS; HIGH-THROUGHPUT; HIGHER YIELD; LIVING CELL; MEDICAL SCIENCE; REGULATORY MECHANISM;

DATA REDUCTION; FOSSIL FUELS; GLOBAL WARMING;

LAWS AND LEGISLATION;

EID: 78649500866     PISSN: 1066033X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCS.2010.938100     Document Type: Article

References (83)

1. F. Crick, “Central dogma of molecular biology,” Nature, vol. 8, pp. 561–563,1970.
2. B. Alberts, D. Bray, J. Lewis, M. Raff, K. Roberts, and J. D. Watson, Molecular Biology of the Cell, 4th ed. New York: Garland Science, 2002.
3. A. Shapiro, “Revisiting the central dogma in the 21st century,” Ann. NY Acad. Sci., vol. 1178, pp. 6–28, 2009.
4. M. B. Gerstein, C. Bruce, J. S. Rozowsky, D. Zheng, J. Du, J. O. Korbel, O. Emanuelsson, Z. D. Zhang, S. Weissman, and M. Snyder, “What is a gene, post-ENCODE? History and updated definition,” Genome Res., vol. 17, pp. 669–681,2007.
5. M. R. Wilkins, J. C. Sanchez, A. A. Gooley, R. D. Appel, I. Humphery-Smith, D. F. Hochstrasser, and K. L. Williams, “Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it,” Biotechnol. Genet. Eng. Rev., vol. 13, pp. 19–50, 1996.
6. I. Neverova and J. E. Van-Eyk, “Role of chromatographic techniques in proteomic analysis,” J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., vol. 815, pp. 51–63, 2005.
7. A. J. Link, J. Eng, D. M. Schieltz, E. Carmack, G. J. Mize, D. R. Morris, B. M. Garvik, and J. R. Yates, III, “Direct analysis of protein complexes using mass spectrometry,” Nat. Biotechnol., vol. 17, pp. 676–682, 1999.
8. R. Craig and R. C. Beavis, “TANDEM: Matching proteins with tandem mass spectra,” Bioinformatics, vol. 12, pp. 1466–1467, 2004.
9. A. I. Nesvizhskii, A. Keller, E. Kolker, and R. Aebersold, “A statistical model for identifying proteins by tandem mass spectrometry,” Anal. Chem., vol. 75, pp. 4646–4658, 2003.
10. J. D. Storey, “A direct approach to false discovery rates,” J. R. Stat. Soc. Series B, R. Statist. Soc., vol. 64, pp. 479–498, 2002.
11. J. Wolberg, Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments. New York: Springer-Verlag, 2005.
12. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Statist. Soc. Ser. B (Methodological), vol. 39, pp. 1–3, 1977.
13. A. C. Davison and D. Hinkley, Bootstrap Methods and Their Applications. Cambridge, U.K.: Cambridge Univ. Press, 1997.
14. J. D. Storey, W. Xiao, J. T. Leek, R. G. Tompkins, and R. W. Davis, “Significance analysis of time course microarray experiments,” Proc. Nat. Acad. Sci., vol. 102, pp. 12,837–12,842, 2005.
15. M. E. Futschik and H. Herzel, “Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis,” Bioinformatics, vol. 24, pp. 1063–1069, 2008.
16. G. D. Bergland, “A guided tour of the Fast Fourier transform,” IEEE Spectr., vol. 6, pp. 41–52, 1969.
17. G. Z. Hertz and G. D. Stormo, “Identifying DNA and protein patterns with statistically significant alignments of multiple sequences,” Bioinformatics, vol. 15, pp. 563–577,1999.
18. D. MacLean, J. D. G. Jones, and D. J. Studholme, “Application of ‘next-generation’ sequencing technologies to microbial genetics,” Nat. Rev. Microbiol, vol. 7, pp. 287–296, 2009.
19. B. Snel, M. A. Huynen, and B. E. Dutilh, “Genome trees and the nature of genome evolution,” Annu. Rev. Microbiol., vol. 59, pp. 191–209, 2005.
20. L. Stein, “Genome annotation: From sequence to biology,” Nat. Rev. Genet., vol. 2, pp. 493–503, 2001.
21. A. Schulze and J. Downward, “Navigating gene expression using microarrays—A technology review,” Nat. Cell. Biol., vol. 3, pp. E190–E195, 2001.
22. M. Bantscheff, M. Schirle, G. Sweetman, J. Rick, and B. Kuster, “Quantitative mass spectrometry in proteomics: A critical review,” Anal. Bioanal. Chem., vol. 389, pp. 1017–1031, 2007.
23. M. J. Buck and J. D. Lieb, “ChIP-chip: Considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments,” Genomics, vol. 83, pp. 349–360, 2004.
24. G. A. Churchill, “Fundamentals of experimental design for cDNA microarrays,” Nat. Genet., vol. 32, pp. 490–495,2002.
25. T. Wilkes, H. Laux, and C. A. Foy, “Microarray data quality: Review of current developments,” OMICS, vol. 11, pp. 1–13, 2007.
26. R. D. Wolfinger, G. Gibson, E. D. Wolfinger, L. Bennett, H. Hamadeh, P. Bushel, C. Afshari, and R. S. Paules, “Assessing gene significance from cDNA microarray expression data via mixed models,” J. Comput. Biol., vol. 8, 625–637, 2001.
27. Y. Zhang, Z. Wen, M. P. Washburn, and L. Florens, “Effect of dynamic exclusion duration on spectral count based quantitative proteomics,” Anal. Chem., vol. 81, pp. 6317–6326, 2009.
28. A. D. Polpitiya, W. J. Qian, N. Jaitly, V. A. Petyuk, J. N. Adkins, D. J. Camp, II, G. A. Anderson, and R. D. Smith, “DAnTE: A statistical tool for quantitative analysis of omics data,” Bioinformatics, vol. 24, pp. 1556–1558, 2008.
29. S. J. Callister, R. C. Barry, J. N. Adkins, E. T. Johnson, W. Qian, B. M. Webb-Robertson, R. D. Smith, and M. S. Lipton, “Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics,” J. Proteome Res., vol. 5, pp. 277–286, 2006.
30. Y. Karpievitch, J. Stanley, T. Taverner, J. Huang, J. N. Adkins, C. Ansong, F. Heffron, T. O. Metz, W. J. Qian, H. Yoon, R. D. Smith, and A. R. Dabney, “A statistical framework for protein quantitation in bottom-up MS-based proteomics,” Bioinformatics, vol. 15, pp. 2028–2034,2009.
31. X. Du, S. J. Callister, N. P. Manes, J. N. Adkins, R. A. Alexandridis, X. Zeng, J. H. Roh, W. E. Smith, T. J. Donohue, S. Kaplan, R. D. Smith, and M. S. Lipton, “A computational strategy to analyze label-free temporal bottom-up proteomics data,” J. Proteome Res., vol. 7, pp. 2595–2604, 2008.
32. O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. B. Altman, “Missing value estimation methods for DNA microarrays,” Bioinformatics, vol. 17, pp. 520–525, 2001.
33. P. Baldi and A. D. Long, “A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes,” Bioinformatics, vol. 17, pp. 509–519, 2001.
34. G. K. Smyth, “Linear models and empirical Bayes methods for assessing differential expression in microarray experiments,” Stat. Appl. Genet. Mol. Biol., vol. 3, no. 1, article 3, 2004.
35. S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed, “Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments,” Stat. Sin., vol. 12, pp. 111–139, 2002.
36. D. Curran-Everett, “Multiple comparisons: Philosophies and illustrations,” Amer. J. Physiol. Regul. Integr. Comp. Physiol., vol. 279, pp. R1–R8, 2000.
37. X. Cui and G. A. Churchill, “Statistical tests for differential expression in cDNA microarray experiments,” Genome Biol., vol. 4, p. 210, 2003.
38. J. T. Leek, E. Monsen, A. R. Dabney, and J. D. Storey, “EDGE: Extraction and analysis of differential gene expression,” Bioinformatics, vol. 22, pp. 507–508, 2006.
39. U. de-Lichtenberg, J. Jensen, A. Fausboll, T. S. Jensen, P. Bork, and S. Brunak, “Comparison of computational methods for the identification of cell cycle-regulated genes,” Bioinformatics, vol. 21, pp. 1164–1171, 2005.
40. T. R. Elvitigala, J. Stockel, B. K. Ghosh, and H. B. Pakrasi, “Effect of continuous light on diurnal rhythms in Cyanothece sp. ATCC 51142,” BMC Genomics, vol. 10, p. 226,2009.
41. J. Stockel, E. A. Welsh, M. Liberton, R. Kunnvakkam, R. Aurora, and H. B. Pakrasi, “Global transcriptomic analysis of Cyanothece 51142 reveals robust diurnal oscillation of central metabolic processes,” Proc. Nat. Acad. Sci., vol. 105, pp. 6156–6161, 2008.
42. J. C. Alwine, D. J. Kemp, and G. R. Stark, “Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes,” Proc. Nat. Acad. Sci., vol. 74, pp. 5350–5354,1977.
43. S. A. Bustin, “Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays,” J. Mol. Endocrinol., vol. 25, pp. 169–193, 2000.
44. D. Jiang, C. Tang, and A. Zhang, “Cluster analysis for gene-expression data: A survey,” IEEE Trans. Knowledge Data Eng., vol. 16, pp. 1370–1386, 2004.
45. L. F. Wu, T. R. Hughes, A. P. Davierwala, M. D. Robinson, R. Stoughton, and S. J. Altschuler, “Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters,” Nat. Genet., vol. 31, pp. 255–265, 2002.
46. M. P. S. Brown, W. N. Grundy, D. Lin, N. Cristianini, C. W. Sugnet, T. S. Furey, M. Ares, Jr., and D. Haussler, “Knowledge-based analysis of microarray gene-expression data by using support vector machines,” Proc. Nat. Acad. Sci., vol. 97, pp. 262–267, 2000.
47. R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Networks, vol. 16, pp. 645–678,2005.
48. J. B. McQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967, vol. 1, pp. 281–297.
49. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. New York: Wiley, 1990.
50. S. Haykin, Neural Networks—A Comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.
51. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proc. 14th Int. Joint Conf. Artificial Intelligence, 1995, vol. 2, pp. 1137–1143.
52. T. R. Elvitigala, H. B. Pakrasi, and B. K. Ghosh, “Dynamic network modeling of diurnal genes in Cyanobacteria,” in Emergent Problems in Nonlinear Systems and Control, B. K. Ghosh, C. F. Martin, and Y. Zhou, E ds. vol. 393. New York: Springer-Verlag, 2009, pp. 21–41.
53. H. D. Jong, “Modeling and simulation of genetic regulatory systems: A literature review,” J. Comput. Biol., vol. 9, pp. 67–103, 2002.
54. P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker, “Cytoscape: A software environment for integrated models of biomolecular interaction networks,” Genome Res., vol. 13, pp. 2498–2504, 2003.
55. A. Khandelwal, T. R. Elvitigala, B. K. Ghosh, and R. S. Quatrano, “Arabidopsis transcriptome reveals control circuits regulating redox homeostasis and the role of an AP2 transcription factor,” Plant Physiol., vol. 148, pp. 2050–2058, 2008.
56. A. Uri, An Introduction to Systems Biology: Design Principles of Biological Circuits. London, U.K.: Chapman & Hall, 2006.
57. D. Heckerman, “A tutorial on learning with Bayesian networks,” in Learning in Graphical Models, M. I. Jorden, Ed. Dordrecht, The Netherlands: Kluwer, 1998.
58. F. V. Jensen and T. D. Nielsen, Bayesian Networks and Decision Graphs. New York: Springer-Verlag, 2007.
59. N. Friedman, M. Linial, I. Nachman, and D. Pe'er, “Using Bayesian networks to analyze expression data,” J. Comput. Biol., vol. 7, pp. 601–620, 2000.
60. A. K. Singh, T. Elvitigala, J. C. Cameron, B. K. Ghosh, M. Bhattacharyya-Pakrasi, and H. B. Pakrasi, “Integrative analysis of large scale expression profiles reveals core transcriptional response and coordination between multiple cellular processes in a cyanobacterium,” BMC Syst. Biol., vol. 4, no. 105, 2010.
61. D. T. Gillespie, “A rigorous derivation of the chemical master equation,” Physica A, vol. 188, pp. 404–425,1992.
62. H. H. McAdams and A. Arkin, “Stochastic mechanisms in gene expression,” Proc. Nat. Acad. Sci., vol. 94, pp. 814–819,1997.
63. P. Smolen, D. A. Baxter, and J. H. Byrne, “Modeling transcriptional control in gene networks—Methods, recent results and future directions,” Bull. Math. Biol., vol. 62, pp. 247–292, 2000.
64. I. Shmulevich, E. R. Dougherty, and W. Zhang, “From Boolean to probabilistic Boolean networks as models of genetic regulatory networks,” Proc. IEEE, vol. 90, pp. 1778–1792, 2002.
65. R. Bonneau, M. T. Facciotti, D. J. Reiss, A. K. Schmid, M. Pan, A. Kaur, V. Thorsson, P. Shannon, M. H. Johnson, J. C. Bare, W. Longabaugh, M. Vuthoori, K. Whitehead, A. Madar, L. Suzuki, T. Mori, D. Chang, J. DiRuggiero, C. H. Johnson, L. Hood, and N. S. Baliga, “A predictive model for transcriptional control of physiology in a free living cell,” Cell, vol. 131, pp. 1354–1365, 2007.
66. W. Dubitzky, M. Granzow, and D. P. Berrar, “Fundamentals of data mining in genomics and proteomics.” New York: Springer-Verlag, 2007.
67. A. F. Neuwald, J. S. Liu, and C. E. Lawrence, “Gibbs motif sampling: Detection of bacterial outer membrane protein repeats,” Protein Sci., vol. 4, pp. 1618–1632,1995.
68. G. D. Stormo and G. W. Hartzell, III, “Identifying protein-binding sites from unaligned DNA fragments,” Proc. Nat. Acad. Sci., vol. 86, pp. 1183–1187, 1989.
69. G. Wang, T. Yu, and W. Zhang, “WordSpy: Identifying transcription factor binding motifs by building a dictionary and learning a grammar,” Nucl. Acids Res., vol. 33, pp. 412–416, 2005.
70. C. Sabatti and K. Lange, “Genomewide motif identification using a dictionary model,” Proc. IEEE, vol. 90, pp. 1803–1810, 2002.
71. J. Stockel, T. R. Elvitigala, M. Liberton, J. Jacobs, E. Welsh, B. K. Ghosh, and H. B. Pakrasi, “Diurnal rhythms result in significant changes in the cellular protein complement in the cyanobacterium Cyanothece sp. ATCC 51142,” Mol. Cell. Proteomics, to be published.
72. M. L. Bulyk, “Protein binding microarrays for the characterization of DNA-protein interactions,” Adv. Biochem. Eng. Biotechnol., vol. 104, pp. 65–85, 2007.
73. M. J. Hessner, X. Wang, K. Hulse, L. Meyer, Y. Wu, S. Nye, S. Guo, and S. Ghosh, “Three color cDNA microarrays: Quantitative assessment through the use of fluorescein-labeled probes,” Nucl. Acids Res., vol. 31, p. e14, 2003.
74. B. J. Cheek, A. B. Steel, M. P. Torres, Y. Yu, and H. Yang, “Chemiluminescence detection for hybridization assays on the flow-thru chip, a three-dimensional microchannel biochip,” Anal. Chem., vol. 73, pp. 5777–5783, 2001.
75. J. C. Wright and S. J. Hubbard, “Recent developments in proteome informatics for mass spectrometry analysis,” Comb. Chem. High Throughput Screen., vol. 2, pp. 194–202, 2009.
76. W. Weckwerth, “Metabolomics in systems biology,” Annu. Rev. Plant Biol., vol. 54, pp. 669–689, 2003.
77. H. Kitano, “Systems biology: A brief overview,” Science, vol. 295, pp. 1662–1664, 2002.
78. S. Henikoff, “Beyond the central dogma,” Bioinformatics, vol. 18, pp. 223–225, 2002.
79. A. Razin and H. Cedar, “DNA methylation and gene expression,” Microbiol. Mol. Biol. Rev., vol. 55, pp. 451–458,1991.
80. J. S. Edwards, M. Covert, and B. Palsson, “Metabolic modelling of microbes: The flux-balance approach,” Environ. Microbiol., vol. 4, pp. 133–140, 2002.
81. 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.
82. H. H. Wang, F. J. Isaacs, P. A. Carr, Z. Z. Sun, G. Xu, C. R. Forest, and G. M. Church, “Programming cells by multiplex genome engineering and accelerated evolution,” Nature, vol. 460, pp. 894–898, 2009.
83. H. P. Mirsky, A. C. Liu, D. K. Welsh, S. A. Kay, and F. J. Doyle, III, “A model of the cell-autonomous mammalian circadian clock,” Proc. Nat. Acad. Sci., vol. 106, pp. 11,107–11,112, 2009.