Measuring structural robustness of metabolic networks under a boolean model using integer programming and feedback vertex sets

Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009

Volumn , Issue , 2009, Pages 819-824

  Tamura, Takeyuki ^a   Takemoto, Kazuhiro ^b   Akutsu, Tatsuya ^b  

^a KYOTO UNIVERSITY   (Japan)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BOOLEAN MODEL; CANCER CELLS; COMPUTATION TIME; E. COLI; ENVIRONMENTAL CHANGE; FEEDBACK VERTEX SET; FORMALIZATION METHOD; INTEGER PROGRAM; LIVING ORGANISMS; METABOLIC NETWORK; OBJECTIVE FUNCTIONS; OPTIMAL SETS; OPTIMAL SOLUTIONS; PENTOSE PHOSPHATE PATHWAY; REVERSIBLE REACTION; STRUCTURAL ROBUSTNESS; TARGET COMPOUND;

COMPUTER SOFTWARE; DYNAMIC PROGRAMMING; ESCHERICHIA COLI; METABOLISM; SIGNAL TRANSDUCTION;

INTEGER PROGRAMMING;

EID: 70349745315     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISIS.2009.49     Document Type: Conference Paper

References (23)

1. Kitano, H.: Cancer as a robust system: implications for anticancer therapy. Nature Reviews Cancer, 4, 227-235 (2004).
2. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., and Yamanishi, Y.: KEGG for linking genomes to life and the environment, Nucleic Acids Research, 36, D480-D484 (2008).
3. Karp, P.D., Keseler, I.M., Shearer, A., Latendresse, M., Krummenacker, M., Paley, S.M., Paulsen, I., Collado-Vides, J., Gama-Castro, S., Peralta-Gil, M., Santos-Zavaleta, A., Penaloza-Spinola, M.I., Bonavides- Martinez, C., Ingraham, J.: Multidimensional annotation of the Escherichia coli K-12 genome, Nucleic Acids Research, 35, 7577-7590 (2007).
4. Jeong, H., Tombor, B., Albert, R., Oltval, Z. N., Barabási, A. L.: The large-scale organization of metabolic networks, Nature, 407, 651-654 (2000).
5. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks, Science, 298, 824-827 (2002).
6. Wagner, A., Fell, D.: The small world inside large metabolic networks, Proceedings of the Royal Society of London B, 268, 1803-1810 (2001).
7. Guimerá, R., Sales-Pardo, M., Amaral, L. A. N.: A network-based method for target selection in metabolic networks, Bioinformatics, 23(13), 1616-1622 (2007).
8. Papin, J. A., Price, N. D., Wiback, S. J., Fell, D. A., Palsson, B. O.: Metabolic pathways in the post-genome era, Trends in Biochemical Sciences, 28, 250-258 (2003).
9. Haus, U. U., Klamt, S., Stephen, T.: Computing knock-out strategies in metabolic networks, Journal of Computational Biology. 15(3):259-268 (2008).
10. Behre, J., Wilhelm, T., Kamp, A., Ruppin, E., Schuster, S.: Structural robustness of metabolic networks with respect to multiple knockouts, Journal of Theoretical Biology, 252, 433-441 (2008).
11. Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S., Gilles, E. D.: Metabolic network structure determines key aspects of functionality and regulation. Nature, 420, 190-193 (2002).
12. Klamt, S., Gilles, E. D.: Minimal cut sets in biochemical reaction networks, Bioinformatics, 20(2), 226-234 (2004).
13. Schuster, S., Dandekar, T., Fell, D. A.: Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering, Trends in Biotechnology, 17, 53-60 (1999).
14. Klamt, S., Stelling, J.: Combinatorial complexity of pathway analysis in metabolic networks. Molecular Biology Reports, 29, 233-236 (2006).
15. Acuña, V., Chierichetti, F., Lacroix, V., Marchetti-Spaccamela, A., Sagot, M. F., Stougie, L., Modes and cuts in metabolic networks: Complexity and algorithms, Biosystems, doi:10.1016/j.biosystems.2008.06.015 (2008).
16. Burgard, A. P., Pharkya, P., Maranas, D.: OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering, 84. 647-657 (2003).
17. Dashika, M. S., Burgard, A., Maranas, D.: A computational framework for the topological analysis and targeted disruption of signal transduction networks. Biophysical Journal, 91, 382-398 (2006).
18. Ruths, D. A., Nakhleh, L., Iyengar, M. S., Reddy, S. A. G., Ram, P. T.: Hypothesis generation in signaling networks, Journal of Computational Biology, 13, 1546-1557 (2006).
19. Handorf, T., Ebenhöh, O., Heinrich, R.: Expanding metabolic networks: scopes of compounds, robustness, and evolution. Journal of Molecular Evolution, 61, 498-512 (2005).
20. Handorf, T., Christian, N., Ebenhöh, O., Kahn, D.: An environmental perspective on metabolism, Journal of Theoretical Biology, in press.
21. Bar-Yehuda, R., Geiger, D., Naor, J., Roth, R. M.: Approximation algorithms for the feedback vertex set problem with applications to constraint satisfaction and Bayesian inference. SIAM Journal on Computing, 27, 942-959 (1998).
22. Even, G., Naor, J., Schieber, B., Sudan, M.: Approximating minimum feedback sets and multicuts in directed graphs. Algorithmica, 20, 151- 174 (1998).
23. Tamura, T., Takemoto, K., Akutsu, T.: On the complexity of measuring structural robustness of metabolic networks (manuscript).