Stochastic dynamic modeling of short gene expression time-series data

IEEE Transactions on Nanobioscience

Volumn 7, Issue 1, 2008, Pages 44-55

  Wang, Zidong ^a   Yang, Fuwen ^a   Ho, D W C ^b   Swift, S ^a   Tucker, A ^a   Liu, Xiaohui ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Clustering; DNA microarray technology; Expectation maximization (EM) algorithm; Gene expression; Modeling; Time series data

Indexed keywords

CLUSTERING; DNA MICROARRAY TECHNOLOGY; EXPECTATION MAXIMIZATION (EM) ALGORITHM; MODELING; TIME-SERIES DATA;

ALGORITHMS; BIOACTIVITY; DNA; DYNAMIC MODELS; GENE EXPRESSION; GENES; IMAGE SEGMENTATION; LAWS AND LEGISLATION; MAXIMUM PRINCIPLE; NUCLEIC ACIDS; OPTIMIZATION; ORGANIC ACIDS; RANDOM PROCESSES; STOCHASTIC MODELS; TIME SERIES;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; DNA MICROARRAY; GENE EXPRESSION PROFILING; METHODOLOGY; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; STOCHASTIC PROCESSES;

EID: 43749095009     PISSN: 15361241     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNB.2008.2000149     Document Type: Article

References (42)

1. T. Akutsu, S. Miyano, and S. Kuhara, "Identification of genetic networks from a small number of gene expression patterns under the Boolean network model," in Proc. Pacific Symp. Biocomput., 1999, vol. 4, pp. 17-28.
2. B. D. O. Anderson and J. B. Moore, Optimal Filtering. Englewood Cliffs, NJ: Prentice-Hall, 1979.
3. L. R. Baugh, A. A. Hill, J. M. Claggett, K. Hill-Harfe, J. C. Wen, D. K. Slonim, E. L. Brown, and C. P. Hunter, "The homeodomain protein PAL-1 specifies a lineage-specific regulatory network in the C. elegans embryo," Development, vol. 132, pp. 1843-1854, 2005.
4. M. J. Beal, F. Falciani, Z. Ghahramani, C. Rangel, and D. L. Wild, "A Bayesian approach to reconstructing genetic regulatory networks with hidden factors," Bioinformatics, vol. 21, no. 3, pp. 349-356, Oct. 2004.
5. Z. Bozdech, M. Llinas, B. L. Pulliam, E. D. Wong, and J. Zhu, "The transcriptome of the intraerythrocytic developmental cycle of plasmodium falciparum," PLoS Biol., vol. 1, no. 1, pp. 85-100, 2003.
6. C. Chatfield, The Analysis of Time Series: An Introduction, 6th ed. London, U.K.: Chapman and Hall, 2004.
7. T. Chen, H. L. He, and G. M. Church, "Modeling gene expression with differential equations," in Proc. Pacific Symp. Biocomput., 1999, vol. 4, pp. 29-40.
8. D. L. Cook, A. N. Gerber, and S. J. Tapscott, "Modeling stochastic gene expression: Implications for haploinsufficiency," in Proc. Nat. Acad. Sci., USA, 1998, vol. 95, pp. 15641-15646.
9. M. J. de Hoon, S. Imoto, K. Kobayashi, N. Ogasawara, and S. Miyano, "Inferring gene regulatory networks from time-ordered gene expression data of bacillus subtilis using differential equations," in Proc. Pacific Symp. Biocomput., 2003, pp. 17-28.
10. H. de Jong, "Modeling and simulation of genetic regulatory systems: A literature review," J. Comput. Biol., vol. 9, no. 1, pp. 67-103, 2002.
11. P. D' haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, "Linear modeling of mRNA expression levels during CNS development and injury," in Proc. Pacific Symp. Biocomput., 1999, pp. 41-52.
12. P. Diggle, Time Series: A Biostatistical Introduction, Oxford Statistical Science Series 5, 1990.
13. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, "Cluster analysis and display of genome-wide expression patterns," in Proc. Nat. Acad. Sci., USA, 1998, vol. 95, pp. 14863-14868.
14. F. Gers, N. Schraudolph, and J. Schmidhuber, "Learning precise timing with LSTM recurrent networks," J. Mach. Learning Res., vol. 3, pp. 115-143, 2002.
15. Z. Ghahramani and G. E. Hinton, "Parameter estimation for linear dynamical systems," Univ. Toronto, Toronto, Canada, Tech. Rep., CRG-TR-96-2, 1996.
16. Z. Ghahramani, "Learning dynamic Bayesian networks," in Adaptive Processing of Sequences and Data Structures, C. L. Giles and M. Gori, Eds. Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, pp. 168-197.
17. N. S. Holter, A. Maritan, M. Cieplak, N. V. Fedoroff, and J. R. Banavar, "Dynamic modeling of gene expression data," in Proc. Nat. Acad. Sci., USA, 2001, vol. 98, pp. 1693-1698.
18. S. Huang, "Gene expression profiling, genetic networks, and cellular states: An integrating concept for tumorigenesis and drug discovery," J. Molecular Med., vol. 77, pp. 469-480, 1999.
19. A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: A review," ACM Comput. Surveys, vol. 31, no. 3, pp. 264-323, 1999.
20. P. Kellam, X. Liu, N. Martin, C. Orengo, S. Swift, and A. Tucker, "A framework for modelling virus gene expression data," Intell. Data Anal., vol. 6, pp. 265-279, 2002.
21. T. B. Kepler and T. C. Elston, "Stochasticity in transcriptional regulation: Origins, consequences, and mathematical representations," Biophys. J., vol. 81, no. 6, pp. 3116-3136, 2001.
22. S. Liang, S. Fuhrman, and R. Somogyi, "REVEAL: A general reverse engineering algorithm for inference of genetic network architectures," in Proc. Pacific Symp. Biocomput., 1998, vol. 3, pp. 18-29.
23. T. Liu, W. Sung, and A. Mittal, "Model gene network by semi-fixed Bayesian network," Expert Syst. Appl., vol. 30, no. 1, pp. 42-49, 2006.
24. L. Ljung, System Identification - Theory for the User, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999.
25. H. M. McAdams and A. Arkin, "Stochastic mechanisms in gene expression," in Proc. Nat. Acad. Sci., USA, 1997, vol. 94, pp. 814-819.
26. M. F. Maduro and J. H. Rothman, "Makingworm guts: The gene regulatory network of the Caenorhabditis elegans endoderm," Dev. Biol., vol. 246, pp. 68-85, 2002.
27. K. Murphy and S. Mian, "Modelling gene expression data using dynamic Bayesian networks", Univ. California, Berkeley, CA, Tech. Rep., 1999.
28. M. F. Ramoni, P. Sebastiani, and I. S. Kohane, "Cluster analysis of gene expression dynamics," in Proc. Nat. Acad. Sci., USA, 2002, vol. 99, pp. 9121-9126.
29. C. Rangel, J. Angus, Z. Ghahramani, M. Lioumi, E. A. Sotheran, A. Gaiba, D. L. Wild, and F. Falciani, "Modeling T-cell activation using gene expression profiling and state space models," Bioinformatics, vol. 20, no. 9, pp. 1361-1372, 2004.
30. R. H. Shumway and D. S. Stoffer, "An approach to time series smoothing and forecasting using the EM algorithm," J. Time Series Anal., vol. 3, pp. 253-264, 1982.
31. P. Smolen, D. A. Baxter, and J. H. Byrne, "Mathematical modeling of gene networks review," Neuron, vol. 26, no. 3, pp. 567-580, 2000.
32. R. Somogyi and C. A. Sniegoski, "Modeling the complexity of genetic networks: Understanding multigenic and pleiotropic regulation," Complexity, vol. 1, no. 6, pp. 45-63, 1996.
33. S. Swift and X. Liu, "Predicting glaucomatous visual field deterioration through short multivariate time series modelling," Artif. Intell. Med., vol. 24, pp. 5-24, 2002.
34. P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E. S. Lander, and T. R. Golub, "Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation," in Proc. Nat. Acad. Sci., USA, 1999, vol. 96, pp. 2907-2912.
35. S. Tavazoie, J. D. Hughes, M. J. Campbell, R. J. Cho, and G. M. Church, "Systematic determination of genetic network architecture," Nature Genetics, vol. 22, no. 3, pp. 281-285, 1999.
36. T. Thattai and A. van Oudenaarden, "Stochastic gene expression in fluctuating environments," in Proc. Genetics Soc. Am., 2004, pp. 523-530.
37. T. Tian and K. Burrage, "Stochastic neural network models for gene regulatory networks," in Proc. 2003 IEEE Congr. Evol. Comput., pp. 162-169.
38. E. Weinstein, A. V. Oppenheim, M. Feder, and J. R. Buck, "Iterative and sequential algorithms for multisensor signal enhancement," IEEE Trans. Signal Process., vol. 42, no. 4, pp. 846-859, Apr. 1994.
39. L. F. Wessels, E. P. van Someren, and M. J. Reinders, "A comparison of genetic network models," in Proc. Pacific Symp. Biocomput., 2001, pp. 508-519.
40. F. Wu, W. Zhang, and A. J. Kusalik, "Modeling gene expression from microarray expression data with state-space equations," in Proc. Pacific Symp. Biocomput., Hawaii Island, HI, 2004, pp. 581-592.
41. K. Y. Yeung, C. Fraley, A. Murua, A. E. Raftery, and W. L. Ruzzo, "Model-based clustering and data transformations for gene expression data," Bioinformatics, vol. 17, no. 10, pp. 977-987, 2001.
42. I. Ziskind and D. Hertz, "Maximum-likelihood localization of narrow-band autoregressive sources via the EM algorithm," IEEE Trans. Signal Process., vol. 41, no. 8, pp. 2719-2724, Aug. 1993.