Reconstructing gene regulatory networks from time-series microarray data

Physica A: Statistical Mechanics and its Applications

Volumn 350, Issue 1, 2005, Pages 63-69

  Li, S P ^a   Tseng, J J ^a   Wang, S C ^a  

^a INSTITUTE OF PHYSICS   (Taiwan)

Author keywords

Bioinformatics; Genetic regulatory networks; Simulation

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; GENETIC ENGINEERING; OPTIMIZATION; PROBABILITY; YEAST;

BIOINFORMATICS; GENE TRANSCRIPTION; GENETIC REGULATORY NETWORKS; MICROARRAYS;

GENES;

EID: 14644401169     PISSN: 03784371     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physa.2004.11.032     Document Type: Conference Paper

References (24)

1. M. Schena, D. Shalon, R.W. Davis, and P.O. Brown Quantitative monitoring of gene expression patterns with a complementary DNA microarray Science 270 1995 467 470
2. D.J. Lockhart, H. Dong, M.C. Byrne, M.T. Follettie, M.V. Gallo, M.S. Chee, M. Mittmann, C. Wang, M. Kobayashi, H. Horton, and E.L. Brown Expression monitoring by hybridization to high-density oligonucleotidearrays Nature Biotechnol. 14 1996 1675 1680
3. M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein Cluster analysis and display of genome-wide expression patterns Proc. Natl. Acad. Sci. USA 95 1998 14863 14868
4. L.F.A. Wessels, E.P. van Someren, and M.J.T. Reinders A comparison of genetic network models Pac. Symp. Biocomput. 6 2001 508 519
5. H. De Jong Modeling and simulation of genetic regulatory systems a literature review J. Comput. Biol. 9 2002 67 103
6. H. McAdams, and A. Arkin Stochastic mechanisms in gene expression Proc. Natl. Acad. Sci. USA 94 1997 814 819
7. D. Heckerman, A tutorial on learning with Bayesian networks, Technical Report MSR-TR-95-06, Microsoft Research, 1995.
8. Z. Ghahramani, Learning dynamic Bayesian networks, in: C.L. Giles, M. Gori (Eds.), Adaptive Processing of Sequences and Data Structures, Lecture Notes in Artificial Intelligence, Springer, Berlin, 1998, pp. 168-197.
9. K. Murphy, S. Mian, Modelling gene expression data using dynamic Bayesian networks, Technical Report, Computer Science Division, University of California Berkeley, CA, 1999.
10. N. Friedman, M. Linial, I. Nachman, and D. Pe'er Using Bayesian networks to analyze expression data J. Comput. Biol. 7 2000 601 620
11. I.M. Ong, J.D. Glasner, and D. Page Modelling regulatory pathways in E. coli from time series expression profiles Bioinformatics 18 2002 241S 248S
12. M.A. Savageau Biochemical systems analysis I & II J. Theor. Biol. 25 1969 365 379
13. M.A. Savageau Rules for the evolution of gene circuitry Pac. Sym. Biocomput. 3 1998 54 65
14. S.Y. Kim, S. Imoto, and S. Miyano Dynamic Bayesian network and nonparametric regression model for inferring gene networks Genome Informatics 13 2002 371 372
15. Z. Michalewicz Genetic Algorithms + Data Structures = Evolution Programs 1996 Springer New York
16. S. Kirkpatrick, C.D. Gelatt Jr., and M.P. Vecchi Science 220 1983 671; N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller J. Chem. Phys. 21 1953 1087
17. S. Kirkpatrick, C.D. Gelatt Jr., and M.P. Vecchi Science 220 1983 671; N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller J. Chem. Phys. 21 1953 1087
18. C.I. Chou, R.S. Han, T.K. Lee, and S.P. Li Guided simulated annealing method for optimization problems Phys. Rev. E 67 2003 066704
19. G. Schwartz Estimating the dimension of a model Ann. Statist. 6 1978 461 464
20. H. Akaike A new look at the statistical model identification IEEE Trans. Automat. Contr. AC-19 1974 716 723
21. N. Sugiura Further analysis of the data by Akaike's information criterion and the finite corrections Commun. Statist. Theor. Meth. A7 1978 13 26
22. M. De Hoon, S. Imoto, S. Miyano, Inferring gene regulatory networks from time-ordered gene expression data using differential equations, Fifth International Conference on Discovery Science, 2002, pp. 267-274.
23. S.C. Wang, S.P. Li, Learning Bayesian networks by Lamarckian genetic algorithm and its application to yeast cell-cycle gene network reconstruction from time-series microarray data, presented in Bio-ADIT, 2004.
24. 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 Mol. Biol. Cell 9 1998 3273 3297