Robustness analysis of EGFR signaling network with a multi-objective evolutionary algorithm

BioSystems

Volumn 91, Issue 1, 2008, Pages 245-261

  Zou, Xiufen ^a   Liu, Minzhong ^a   Pan, Zishu ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

EGFR network; Multi objective evolutionary algorithm; Robustness; Signal transduction

Indexed keywords

EPIDERMAL GROWTH FACTOR RECEPTOR;

ALGORITHM; EVOLUTION; GROWTH; SIGNALING;

ALGORITHM; ARTICLE; EVALUATION; MATHEMATICAL MODEL; MULTI OBJECTIVE EVOLUTIONARY ALGORITHM; PROTEIN ANALYSIS; QUANTITATIVE ANALYSIS; SIGNAL TRANSDUCTION; SIMULATION;

EID: 37349076540     PISSN: 03032647     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystems.2007.10.001     Document Type: Article

References (22)

1. Araujo R.P., Petricoin E.F., and Liottaa L.A. A mathematical model of combination therapy using the EGFR signaling network. BioSystem 80 (2005) 57-69
2. Barkai N., and Leibler S. Robustness in simple biochemical networks. Nature 387 (1997) 913-917
3. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA II. IEEE Trans. Evol. Comput. 6 (2002) 182-197
4. Downward J. Ras signalling and apoptosis. Curr. Opin. Genet. Dev 8 (1998) 49-54
5. Fonseca C.M., and Fleming P.J. Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In: Forrest S. (Ed). Proceedings of the Fifth International Conference on Genetic Algorithms (1993), IEEE Press, Piscataway, New Jersey 416-423
6. Guo T., and Kang L.S. A new evolutionary algorithm for function optimisation. Wuhan Univ. J. Nat. Sci. 4 (1999) 409-414
7. Heinrich R., Neel B.G., and Rapoport T.A. Mathematical models of protein kinase signal transduction. Mol. Cell 9 (2002) 957-970
8. Hughes E.J. Evolutionary many-objective optimization: many once or once many?. In: Corne D. (Ed). Congress on Evolutionary Computation (CEC 2005) (2005), IEEE Service Center, Piscataway 460-465
9. Kholodenko B.N., Demin O.V., Moehren G., and Hoek J.B. Quantification of short term signaling by the epidermal growth factor receptor. J. Biol. Chem. 274 (1999) 30169-30181
10. Liu M.Z., Zou X.F., Cai Z.H., and Kang L.S. A fast dynamical evolutionary algorithm for multi-objective mechanical component design. Prog. Intell. Comput. Appl. (ISICA' 2005). (2005) 285-289
11. Ma L., and Iglesias P.A. Quantifying robustness of biochemical network models. BMC Bioinform. 3 (2002) 38
12. Marshall C.J. Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation. Cell (1995) 179-185
13. Moles C.G., Mendes P., and Banga J.R. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13 11 (2003) 2467-2474
14. Morohashi M., Winn A.E., Borisuk M.T., Bolouri H., Doyle J., and Kitano H. Robustness as a measure of plausibility in models of biochemical networks. J. Theor. Biol. 216 (2002) 19-30
15. Purshouse R.C., and Fleming P.J. Evolutionary many-objective optimisation: An exploratory analysis. The 2003 Congress on Evolutionary Computation. IEEE Press, Canberra, Australia (2003) 2066-2073
16. Schlessinger J., and Ullrich A. Growth factor signalling by receptor tyrosine kinases. Neuron 9 (1992) 383-391
17. Schoeberl B., Eichler-Jonsson C., Gilles E.D., and Muller G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat. Biotechnol. 20 4 (2002) 370-375
18. Stelling J., Sauer U., Szallasi Z., Doyle F.J., and Doyle J. Robustness of cellular functions. Cell 118 (2004) 675-685
19. Suresh Babu C.V., Yoon S., Nam H.S., and Yoo Y.S. Simulation and sensitivity analysis of phosphorylation of EGFR signal transduction pathway in PC12 cell model. J. Syst. Biol. 1 (2004) 213-221
20. Voit E.O., Marino S., and Lall R. Challenges for the identification of biological systems from in vivo time series data. Silico. Biol. 5 2 (2005) 83-92
21. Zi Z.K., and Sun Z.R. Robustness analysis of the IFN-γ induced JAK-STAT signaling pathway. J. Comput. Sci. Technol. 20 (2005) 491-495
22. Zou X.F., Liu M.Z., Kang L.S., and He J. A high performance multi-objective evolutionary algorithm based on the principle of thermodynamics. In: Yao X., et al. (Ed). Proceeding of Parallel Problem Solving from Nature. Lecture Notes in Computer Science, Vol. 3242, Springer (2004) 922-931