Figure 9. Transcription factor network for E. coli. A) Power-law plot of information reported in reference 37 for genes and opérons (several, coregulated genes). The fit was done the same as in Figure 8. B) Power law fits for the core TF network, the "in" (red) and "out" (blue) networks. Combining the "in" and "out" networks to get a more statistically meaningful exponent gives a value of 1.8 (yellow). C) Conceptual diagram showing how the "core" network and the "effector" network are related. A color version of this figure is available online at


1. DeRisi J, Iyer A, Brown P. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997; 278:680-686.

2. Spellman PT, Sherlock G, Zhang MQ et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998; 9:3273-3297.

3. Boldrick JC, Alizadeh AA, Diehn M et al. Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci USA 2002; 99:972-977.

4. Bechtel M, Wu X, Dewey TG. Analysis of time series expression data reveals cooperation of signaling pathways in chondrocytes. Submitted 2003.

5. Reymond P, Weber H, Damond M et al. Differential gene expression in response to mechanical wounding and insect feeding in Arabidopsis. Plant Cell 2000; 12:707-719.

6. Dewey TG, Galas DJ. Dynamic models of gene expression and classification. Func Integr Genomics 2001; 1:269-278.

7. Bhan A, Galas DJ, Dewey TG. A duplication growth model of gene expression networks. Bioinformatics 2002; 18:1486-1493.

8. Chen T, He HL, Church GE. Modeling gene expression with differential equations. Pacific Symp Biocomputing 1999; 4:29-40.

9. Holter NS, Maritan A, Cieplak M et al. Dynamic modeling of gene expression data. Proc Natl Acad Sci USA 2001; 98:1693-1698.

10. Heyer LJ, Kruglyak S, Yooseph S. Exploring expression data: Identification and analysis of coexpressed genes. Genome Res 1999; 9:1106-1115.

11. D'Haeseleer P, Wen X, Fuhrman S et al. Linear modeling of mRNA expression levels during CNS development and injury. Pac Symp Biocomput 1999; 41-52.

12. D'Haeseleer P, Liang S, Somogyi R. Genetic network inference: From coexpression clustering to reverse engineering. Bioinformatics 2000; 16:707-26.

13. Kim S, Imoto SS, Miyano S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. In: International Workshop on Computational Methods in Systems Biology (CMSB2003), Lecture Notes in Computer Science. Springer-Verlag, 2003: 2602:104-113.

14. de Hoon MJ, Imoto S, Kobayashi K et al. Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pac Symp Biocomput 2003; 17-28.

15. Yeung MK, Tegner J, Collins JJ. Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci USA 2002; 99:6163-6168.

16. Wu X, Dewey TG. Cluster analysis of dynamic parameters of gene expression. J Bioinf Comp Biol 2003; 1:447-458.

17. Simon I, Barnett J, Hannett N et al. Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 2001; 106:697-708.

18. Jeong H, Tombor B, Albert R et al. The large-scale organization of metabolic networks. Nature 2000; 407:651-654.

19. Barabisi A-L, Albert R. Emergence of scaling in random networks. Science 1999; 286:509-512.

20. Albert R, Barabisi A-L. Statistical mechanics of complex networks. Rev Mod Phys 2002; 74:47-97.

21. Strogatz S. Exploring complex networks. Nature 2001; 410:268-276.

22. Amaral LAN, Scala A, Barthélémy M et al. Classes of small-world networks. Proc Natl Acad Sci USA 2000; 97:11149-11152.

23. Cohen JE. Threshold phenomena in random structures. Discr Appl Math 1988; 19:113-128.

24. Kauffman S. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 1969; 22:437-467.

25. Watts DJ. Small worlds-the dynamics of networks between order and randomness. Princeton University Press, 1999.

26. Krapivsky PL, Redner S, Leyvraz F. Connectivity of growing random networks. Phys Rev Lett 2000; 85:4629-4632.

27. Dorogovtsev SN, Mendes JFF. Scaling properties of scale-free evolving networks: Continuous approach. Phys Rev E 2001; 63:056125-1-056125-19.

28. Wagner A, Fell D. The small world inside large metabolic networks. Proc Roy Soc London Ser B 2001; in press.

29. Uetz P, Giot L, Cagney G et al. A comprehensive analysis of protein-protein interactions in Sac-charomyces cerevisiae. Nature 2000; 403:623-627.

30. Ito T, Chiba T, Ozawa R et al. A comprehensive rwo-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sei USA 2001; 98:4569-4574.

31. Seioghe C, Wolfe K. Updated map of duplicated regions in the yeast genome. Gene 1999; 238:253-261.

32. Lander E et al. Initial sequencing and analysis of the human genome. Nature 2001; 409:860-921.

33. Ohno S. Evolution by Gene Duplication. Springer-Verlag, 1970.

34. Wagner A. The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Mol Biol Evol 2001; 18:1283-1292.

35. Rzhetsky A, Gomez SM. Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome. Bioinformatics 2001; 17:988-996.

36. Lee TI, Rinaldi NJ, Robert F et al. Transcriptional regulatory networks in Saccharomyces cerivisiae. Science 2002; 298:799-804.

37. Babu MM, Teichman SA. Evolution of transcription factors and the gene regulatory network in Escherichia coli. Nucl Acids Res 2003; 31:1234-1244.

38. Shen-Orr SS, Milo R, Mangan S et al. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet 2002; 31:64-68.

Was this article helpful?

0 0

Post a comment