Comparison of reverse-engineering methods using an in Silico network

Annals of the New York Academy of Sciences

Volumn 1115, Issue , 2007, Pages 73-89

  Camacho, Diogo ^a   Licona, Paola Vera ^b   Mendes, Pedro ^c,d   Laubenbacher, Reinhard ^c  

^a Boston University   (United States)

^b RUTGERS UNIVERSITY   (United States)

^c VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

^d UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Modeling; Reverse engineering; Simulation; Systems biology

Indexed keywords

ALGORITHM; BAYES THEOREM; BIOENGINEERING; COMPARATIVE STUDY; COMPUTER MODEL; CONFERENCE PAPER; GENETIC ENGINEERING; MULTIPLE REGRESSION; QUALITY CONTROL;

EID: 36248953897     PISSN: 00778923     EISSN: 17496632     Source Type: Book Series    
DOI: 10.1196/annals.1407.006     Document Type: Conference Paper

References (29)

1. ARKIN, A. & J. ROSS. 1995. Statistical construction of chemical reaction mechanisms from measured time-series. J. Phys. Chem. 99: 970-979.
2. DE LA FUENTE, A. et al. 2004. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20: 3565-3574.
3. WAHDE, M. & J. HERTZ. 2000. Coarse-grained reverse engineering of genetic regulatory networks. BioSystems 55: 129-136.
4. HARTEMINK, A. et al. 2002. Bayesian methods for elucidating genetic regulatory networks. IEEE Intel. Syst. 17: 37-43.
5. NACHMAN, I., A. REGEV & N. FRIEDMAN. 2004. Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20(Suppl 1): I248-I256.
6. BASSO, K. et al. 2005. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37: 382-390.
7. YEUNG, M.K., J. TEGNER & J.J. COLLINS. 2002. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA 99: 6163-6168.
8. SONTAG, E., A. KIYATKIN & B.N. KHOLODENKO. 2004. Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics 20: 1877-1886.
9. BRUGGEMAN, F.J. & B.N. KHOLODENKO. 2002. Modular interaction strengths in regulatory networks: an example. Mol. Biol. Rep. 29: 57-61.
10. DE LA FUENTE, A., P. BRAZHNIK & P. MENDES. 2002. Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet. 18: 395-398.
11. DE LA FUENTE, A. & P. MENDES. 2002. Quantifying gene networks with regulatory strengths. Mol. Biol. Rep. 29: 73-77.
12. KHOLODENKO, B.N. et al. 2002. Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. Proc. Natl. Acad. Sci. USA 99: 12841-12846.
13. LAUBENBACHER, R. & B. STIGLER. 2004. A computational algebra approach to the reverse engineering of gene regulatory networks. J. Theor. Biol. 229: 523-537.
14. D'HAESELEER, P., S. LIANG & R. SOMOGYI. 2000. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16: 707-726.
15. DE JONG, H. 2002. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9: 67-103.
16. MENDES, P., W. SHA & K. YE. 2003. Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19(Suppl 2): II122-II129.
17. GARDNER, T.S. et al. 2003. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301: 102-105.
18. YU, J. et al. 2004. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20: 3594-3603.
19. MENDOZA, L., D. THIEFFRY & E.R. ALVAREZ-BUYLLA. 1999. Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis. Bioinformatics 15: 593-606.
20. MARGOLIN, A.A. et al. 2006. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(Suppl 1): S7.
21. BERNARD, A. & A. HARTEMINK. 2005. Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Pac. Symp. Biocomput. 459-470.
22. WRIGHT, S. 1934. The method of path coefficients. Ann. Math. Stat. 5: 161-215.
23. KENDALL, M.G. 1942. Partial rank correlation. Biometrika 32: 277-283.
24. STEUER, R. et al. 2003. Observing and interpreting correlations in metabolomic networks. Bioinformatics 19: 1019-1026.
25. CAMACHO, D., A. DE LA FUENTE & P. MENDES. 2005. The origins of correlations in metabolomics data. Metabolomics 1: 53-63.
26. FRIEDMAN, N., K. MURPHY & S. RUSSELL. 1998. Learning the structure of dynamic proabilist networks. In 14th Conference on Uncertainty in Artificial Intelligence.
27. HOOPS, S. et al. 2006. COPASI - a COmplex PAthway SImulator. Bioinformatics 22: 3067-3074.
28. MARTINS, A.M. et al. 2004. A systems biology study of two distinct phases of Saccharomyces cerevisae cultures. Curr. Genomics 5: 649-663.
29. BANSAL, M. et al. 2007. How to infer gene networks from expression profiles. Mol. Syst. Biol. 3: 78.