Artificial gene networks for objective comparison of analysis algorithms

Bioinformatics

Volumn 19, Issue SUPPL. 2, 2003, Pages

  Mendes, Pedro ^a   Sha, Wei ^a   Ye, Keying ^b  

^a VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANALYTIC METHOD; COMPUTER MODEL; CONFERENCE PAPER; DNA MICROARRAY; GENE EXPRESSION; GENETIC ENGINEERING; GENETIC MODEL; GENETIC REGULATION; GENETIC TRANSCRIPTION; KINETICS; MATHEMATICAL MODEL; PRIORITY JOURNAL;

EID: 2942694772     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btg1069     Document Type: Conference Paper

References (31)

1. Albert, R. and Barabasi, A.L. (2000) Topology of evolving networks: local events and universality. Phys. Rev. Lett., 85, 5234-5237.
2. Barabasi, A.L. and Albert, R. (1999) Emergence of scaling in random networks. Science, 286, 509-512.
3. Batagelj, V. and Mrvar, A. (1998) Pajek - program for large network analysis. Connections, 21, 47-57.
4. Brazhnik, P., de la Fuente, A. and Mendes, P. (2002) Gene networks: how to put the function in genomics. Trends Biotechnol., 20, 467-472.
5. Calvert, K.L., Doar, M.B. and Zegura, E.W. (1997) Modeling Internet topology. IEEE Communications Magazine, 35, 160-163.
6. Eisen, M.B., Spellman, P.T., Brown, P.O. and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA, 95, 14863-14868.
7. Erdös, P. and Rényi, A. (1959) On random graphs. Publ. Math. Debrecen, 6, 290-297.
8. Featherstone, D.E. and Broadie, K. (2002) Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network. Bioessays, 24, 267-274.
9. Gansner, E.R. and North, S.C. (1999) An open graph visualization system and its applications to software engineering. Software-Practice and Experience, 30, 1203-1233.
10. Hill, A.V. (1910) The possible effect of the aggregation of the molecules of hasmoglobin. J. Physiol., 40, iv-vii.
11. Hofmeyr, J.-H.S. and Cornish-Bowden, A. (1997) The reversible Hill equation: how to incorporate cooperative enzymes into metabolic models. Comput. Appl. Biosci., 13, 377-385.
12. Hucka, M., Finney, A., Sauro, H.M., Bolouri, H., Doyle, J.C., Kitano, H., Arkin, A.P., Bornstein, B.J., Bray , D. Cornish-Bowden, A. et al. (2002) The Systems Biology Markup Language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19, 5245-31.
13. Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N. and Barabási, A.-L. (2000) The large-scale organization of metabolic networks. Nature, 407, 651-654.
14. Jin, C., Chen, Q. and Jamin, S. (2000) Inet: Internet Topology Generator. Technical Report CSE-TR443-00. Department of EECS, University of Michigan, Ann Arbor.
15. Kauffman, S. (1969a) Homeostasis and differentiation in random genetic control networks. Nature, 224, 177-178.
16. Kauffman, S. (1969b) Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theoret. Biol., 22, 437-467.
17. Kauffman, S. (1974) The large scale structure and dynamics of gene control circuits: an ensemble approach. J. Theoret. Biol., 44, 167-190.
18. Lockhart, D.J., Dong, H.L., Byrne, M.C., Folliette, M.T., Gallo, M.V., Chee, M.S., Mittmann, M., Wang, C.W., Kobayashi, M. Horton, H. et al., (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnol., 14, 1675-1680.
19. Medina, A., Lakhina, A., Matta, I. and Byers, J. (2001) BRITE: an approach to universal topology generation. In Proceedings of MASCOTS '01. Cincinnati, OH.
20. Mendes, P. (1993) GEPASI: a software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput. Appl. Biosci., 9, 563-571.
21. Mendes, P. (1997) Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem. Sci., 22, 361-363.
22. Mendes, P. (1999) Metabolic simulation as an aid in understanding gene expression data. In Proceedings of the Workshop on computation of Biochemical Pathways and Genetic Networks, EML. Heidelberg, pp. 27-34.
23. Paxson, V. and Floyd, S. (1997) Why we don't know how to simulate the Internet. In Proceedings of the Winter Simulation Conference. Atlanta, GA, pp. 1037-1044.
24. Quackenbush, J. (2001) Computational analysis of microarray data. Nat. Rev. Genet., 2, 418-427.
25. Savageau, M.A. (1976) Biochemical Systems Analysis. Addison-Wesley, Reading, MA.
26. Savageau, M.A. (1977) Design of molecular control mechanisms and the demand for gene expression. Proc. Natl Acad. Sci. USA, 74, 5647-5651.
27. Schena, M., Shalon, D., Davis, R.W. and Brown, P.O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467-470.
28. Selinger, D.W., Saxena, R.M., Cheung, K.J., Church, G.M. and Rosenow, C. (2003) Global RNA half-life analysis in Escherichia coli reveals positional patterns of transcript degradation. Genome Res., 13, 216-223.
29. Slonim, D.K. (2002) From patterns to pathways: gene expression data analysis comes of age. Nat. Genet., 32 (Suppl.), 502-508.
30. Wagner, A. and Fell, D.A. (2001) The small world inside large metabolic networks. Proc. R. Soc. London B, 268, 1803-1810.
31. Watts, D.J. and Strogatz, S.H. (1998) Collective dynamics of 'small-world' networks. Nature, 393, 440-442.