Systematic calibration of a cell signaling network model

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Kim, Kyoung Ae ^a   Spencer, Sabrina L ^b,c   Albeck, John G ^b,c   Burke, John M ^b,c   Sorger, Peter K ^b,c   Gaudet, Suzanne ^b,c   Kim, Do Hyun ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Massachusetts Institute of Technology Cambridge   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX BIOLOGICAL SYSTEMS; COMPLEX NETWORK SYSTEMS; GLOBAL SENSITIVITY ANALYSIS; QUALITATIVE FEATURES; RATE-LIMITING STEPS; SYSTEMATIC METHODOLOGY; TUMOR NECROSIS FACTORS; VARIANCE-BASED SENSITIVITY INDICES;

CALIBRATION; CELL DEATH; COMPLEX NETWORKS; EXPERIMENTS; GLOBAL OPTIMIZATION; OPTIMIZATION; SENSITIVITY ANALYSIS; SIGNALING;

BIOLOGICAL SYSTEMS;

TUMOR NECROSIS FACTOR RELATED APOPTOSIS INDUCING LIGAND;

ARTICLE; BIOLOGICAL MODEL; BIOLOGY; CELL DEATH; METABOLISM; METHODOLOGY; SIGNAL TRANSDUCTION;

CELL DEATH; COMPUTATIONAL BIOLOGY; MODELS, BIOLOGICAL; SIGNAL TRANSDUCTION; TNF-RELATED APOPTOSIS-INDUCING LIGAND;

EID: 77951142621     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-202     Document Type: Article

References (24)

1. Palsson B. The challenges of in silico biology. Nat Biotech 2000, 18:1147-1150.
2. Wolkenhauer O, Ullah M, Wellstead P, Cho K-H. The dynamic systems approach to control and regulation of intracellular networks. FEBS Lett 2005, 579:1846-1853. 10.1016/j.febslet.2005.02.008, 15763562.
3. Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK. Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006, 8:1195-1203. 10.1038/ncb1497, 17060902.
4. Polynikis A, Hogan SJ, di Bernardo M. Comparing different ode modelling approaches for gene regulatory networks. J Theor Biol 2009, 261:511-530. 10.1016/j.jtbi.2009.07.040, 19665034.
5. Asthagiri AR, Lauffenburger DA. Bioengineering models of cell signaling. Annu Rev Biomed Eng 2000, 2:31-53. 10.1146/annurev.bioeng.2.1.31, 11701506.
6. Tyson JJ, Chen KC, Novak B. Sniffers, buzzers, toggles and blinkers: Dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 2003, 15:221-231. 10.1016/S0955-0674(03)00017-6, 12648679.
7. Albeck JG, Burke JM, Spencer SL, Lauffenburger DA, Sorger PK. Modeling a snap-action, variable-delay switch controlling extrinsic cell death. PLoS Biol 2008, 6:2831-2852. 10.1371/journal.pbio.0060299, 2592357,2592357, 19053173.
8. Yang K, Ma W, Liang H, Ouyang Q, Tang C, Lai L. Dynamic simulations on the arachidonic acid metabolic network. PLoS Comput Biol 2007, 3:523-530.
9. Frey HC, Patil SR. Identification and review of sensitivity analysis methods. Risk Anal 2002, 22:553-578. 10.1111/0272-4332.00039, 12088234.
10. Saltelli A, Tarantola S, Chan KP-S. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 1999, 41:39-56.
11. Sobol IM. Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates. Math Comput Simulat 2001, 55:271-280.
12. Zheng Y, Rundell A. Comparative study of parameter sensitivity analyses of the tcr-activated erk-mapk signalling pathway. IEE P Syst Biol 2006, 153:201-211.
13. Cho K-H, Shin S-Y, Kolch W, Wolkenhauer O. Experimental design in systems biology, based on parameter sensitivity analysis using a monte carlo method: A case study for the tnf{alpha}-mediated nf-{kappa} b signal transduction pathway. Simulation 2003, 79:726-739.
14. Zi Z, Cho K-H, Sung M-H, Xia X, Zheng J, Sun Z. In silico identification of the key components and steps in ifn-[gamma] induced jak-stat signaling pathway. FEBS Lett 2005, 579:1101-1108. 10.1016/j.febslet.2005.01.009, 15710397.
15. Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: A comparison of global optimization methods. Genome Res 2003, 13:2467-2474. 10.1101/gr.1262503, 403766, 14559783.
16. Mendes P, Kell D. Non-linear optimization of biochemical pathways: Applications to metabolic engineering and parameter estimation. Bioinformatics 1998, 14:869-883. 10.1093/bioinformatics/14.10.869, 9927716.
17. Bentele M, Lavrik I, Ulrich M, Stosser S, Heermann DW, Kalthoff H, Krammer PH, Eils R. Mathematical modeling reveals threshold mechanism in cd95-induced apoptosis. J Cell Biol 2004, 166:839-851. 10.1083/jcb.200404158, 2172102, 15364960.
18. Deveraux QL, Takahashi R, Salvesen GS, Reed JC. X-linked iap is a direct inhibitor of cell-death proteases. Nature 1997, 388:300-304. 10.1038/40901, 9230442.
19. Spencer SL, Gaudet S, Albeck JG, Burke JM, Sorger PK. Non-genetic origins of cell-to-cell variability in trail-induced apoptosis. Nature 2009, 459:428-432. 10.1038/nature08012, 2858974, 19363473.
20. Stein M. Large sample properties of simulations using latin hypercube sampling. Technometrics 1987, 29:143-151.
21. Edgar TF, Himmelblau DM, Lasdon LS. Optimization of chemical processes 2001, Singapore: McGraw-Hill, second.
22. Runarsson TP, Xin Y. Stochastic ranking for constrained evolutionary optimization. IEEE T Evolut Comput 2000, 4:284-294.
23. Stochastic ranking with evolution strategy for matlab. , http://www3.hi.is/~tpr/index.php?page=software/sres/sres
24. Rodriguez-Fernandez M, Mendes P, Banga JR. A hybrid approach for efficient and robust parameter estimation in biochemical pathways. Biosystems 2006, 83:248-265. 10.1016/j.biosystems.2005.06.016, 16236429.