A computational algebra approach to the reverse engineering of gene regulatory networks

Journal of Theoretical Biology

Volumn 229, Issue 4, 2004, Pages 523-537

  Laubenbacher, Reinhard ^a   Stigler, Brandilyn ^a  

^a VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

Author keywords

Computational algebra; Discrete modeling; Gene regulatory networks; Reverse engineering

Indexed keywords

GENE;

ALGORITHM; ARTICLE; DATA ANALYSIS; DROSOPHILA MELANOGASTER; GENETIC REGULATORY NETWORK; INFORMATION PROCESSING; INFORMATION SCIENCE; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL;

ALGORITHMS; ANIMALS; COMPUTATIONAL BIOLOGY; DROSOPHILA MELANOGASTER; GENE EXPRESSION REGULATION; GENES, INSECT; MODELS, GENETIC;

DROSOPHILA MELANOGASTER; MELANOGASTER;

EID: 3042799574     PISSN: 00225193     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jtbi.2004.04.037     Document Type: Article

References (46)

1. Abbott J., Bigatti A., Kreuzer M., Robbiano L. Computing ideals of points. J. Symbolic Comp. 30:2000;341-356
2. Akutsu, T., Miyano, S., Kuhara, S., 1999. Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Altman, R.B., Lauderdale, K., Dunker, A.K., Hunter, L., Klein, T.E. (Eds.), Proceedings of the Pacific Symposium Biocomputation, Vol. 4, World Scientific, Singapore, pp. 17-28.
3. Albert R., Othmer H. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J. Theor. Biol. 223:2003;1-18
4. Berman D. The number of generators of a colength. N ideal in a power series ring J. Algebra. 73:1981;156-166
5. Bower J., Bolouri H. Computational Modeling of Genetic and Biochemical Networks. 2001;The MIT Press, Cambridge, MA
6. Brazma, A., Schlitt, T., 2003. Reverse engineering of gene regulatory networks: a finite state linear model. Preprint. Available at http://genomebiology.com/2003/4/6/P5.
7. Cadigan K., Grossniklaus U., Gehring W. Localized expression of sloppy paired protein maintains the polarity of Drosophila parasegments. Genes Dev. 8:1994;899-913
8. Cho R., Campbell M., Winzeler E., Steinmetz L., Conway A., Wodicka L., Wolfsberg T., Gabrielan A., Landsman D., Lockhart D., Davis R. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell. 2:1998;65-73
9. CoCoATeam CoCoA: a system for doing Computations in Commutative Algebra. Available at http://cocoa.dima.unige.it.
10. Cox D., Little J., O'Shea D. Ideals, Varieties, and Algorithms. 1997;Springer, New York
11. de Jong H. Modeling and simulation of genetic regulatory systems. a literature review J. Comput. Biol. 9:2002;67-103
12. de la Fuente A., Brazhnik P., Mendes P. Linking the genes. inferring quantitative gene networks from microarray data Trends Genet. 18:2002;395-398
13. Dougherty, J., Kohavi, R., Sahami, M., 1995. Supervised and unsupervised discretization of continuous features. In: Prieditis, A., Russell, S. (Eds.), Machine Learning: Proceedings of the 12th International Conference, Morgan Kauffman, San Francisco, CA.
14. Dubé, T., Mishra, B., Yap, C., 1986. Admissible orderings and bounds on Gröbner normal form algorithm. it NYU Computer Science Technical Report.
15. Filkov V., Istrail S. Inferring gene transcription networks. the Davidson model Genome Informatics. 13:2002;236-239
16. Filkov V., Skiena S., Zhi J. Analysis techniques for microarray time-series data. J. Comput. Biol. 9:2002;317-330
17. Friedman, N., Goldszmidt, M., 1996. Discretization of continuous attributes while learning Bayesian networks. In: Saitta, L. (Ed.), Proceedings of the 13th International Conference on Machine Learning, Morgan Kauffman, San Francisco, CA, pp. 157-165.
18. Friedman N., Linia M., Nachman I., Pe'er D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7:2000;601-620
19. Gardner T.S., di Bernardo D., Lorenz D., Collins J. Inferring genetic networks and compound mode of action via expression profiling. Science. 301:2003;102-105
20. GEPASI,. Available at http://www.gepasi.org.
21. Grayson, D., Stillman, M. Macaulay 2, a software system for research in algebraic geometry. Available at http://www.math.uiuc.edu/Macaulay2/.
22. Green, E., 2003. On polynomial solutions to reverse-engineering problems. Preprint.
23. Hartemink, A., Gifford, D., Jaakkola, S., Young, R., 2001. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pacific Symposium on Biocomputation, World Scientific, Singapore.
24. Ingham P. Transducing hedgehog. the story so far EMBO J. 17:1998;3505-3511
25. Ingham P., McMahon A. Hedgehog signaling in animal development. paradigms and principles Genes Dev. 15:2001;3059-3087
26. Kauffman S. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22:1969;437-467
27. Kitano H. Systems biology. a brief overview Science. 295:2002;1662-1664
28. Lang S. Algebra. 1971;Addison-Wesley, Reading, MA
29. Lewis J.E., Glass L. Steady states, limit cycles, and chaos in models of complex biological networks. Int. J. Bifurcation Chaos. 1:1991;477-483
30. Liang, S., Fuhrman, S., Somogyi, R., 1998. REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symposium Biocomputation, Vol. 3, World Scientific, Singapore, pp. 18-29.
31. Lidl, R., Niederreiter, H., 1997. Finite Fields, Encyclopedia of Mathematics and its Applications 20, 2nd Edition. Cambridge University Press, Cambridge.
32. Marchand, H., Le Borgne, M., 1998. Partial order control of discrete event systems modeled as polynomial dynamical systems. In: IEEE International Conference on Control Applications, Trieste, Italy.
33. Mendes P. Biochemistry by numbers. simulation of biochemical pathways with Gepasi 3 Trends Biochem. Sci. 22:1997;361-363
34. Mendes P., Sha W., Ye K. Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics. 19:2003;122-129
35. Mendoza L., Thieffry D., Alvarez-Buylla E. Genetic control of flower morphogenesis in Arabidopsis thaliana. a logical analysis Bioinformatics. 15:1999;593-606
36. Muraille E., Thieffry D., Leo O., Kaufman M. Toxicity and neuroendocrine regulation of the immune response. a model analysis J. Theor. Biol. 183:1996;285-305
37. Pe'er D., Regev A., Elidan G., Fridman N. Inferring subnetworks from expression profiles. Bioinformatics. 17:2001;215-224
38. Repsilber D., Liljenström H., Andersson S. Reverse engineering of regulatory networks. simulation studies on a genetic algorithm approach for ranking hypotheses Biosystems. 66:2002;31-41
39. Robbiano L. Gröbner bases and statistics. Buchberger B., Winkler F. Gröbner Bases and Applications. 1998;Cambridge University Press, New York
40. Schmulevich I., Dougherty E., Kim S., Zhang W. Probabilistic Boolean networks. a rule-based uncertainty model for gene regulatory networks Bioinformatics. 18:2002;261-274
41. Soinov L.A., Krestyaninova M.A., Brazma A. Towards reconstruction of gene networks from expression data by supervised learning. Genome Biol. 4:2003;R6
42. Spellman P., Sherlock G., Zhang M., Iyer V., Anders K., Eisen M., Brown P., Botstein D., Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9:1998;3273-3297
43. Thieffry, D., Thomas, R., 1998. Qualitative analysis of gene networks. Proceedings of the Pacific Symposium on Biocomputing, World Scientific, Singapore, pp. 77-88.
44. Thieffry D., Thomas R., Kaufman M. Dynamical behaviour of biological regulatory networks - I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bull. Math. Biol. 57:1995;247-276
45. Thomas R. Regulatory networks seen as asynchronous automata. a logical description J. Theor. Biol. 153:1991;1-23
46. Yeung M., Tegnér J., Collins J. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl Acad. Sci. 99:2002;6163-6168