Maximin and bayesian robust experimental design for measurement set selection in modelling biochemical regulatory systems

International Journal of Robust and Nonlinear Control

Volumn 20, Issue 9, 2010, Pages 1059-1078

  He, Fei ^a,b   Brown, Martin ^a   Yue, Hong ^b  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

Bayesian design; Biochemical network modelling; Maximin design; Parameter estimation; Robust experimental design

Indexed keywords

BAYESIAN DESIGN; BIOCHEMICAL NETWORK; DESIGN PARAMETERS; EXPERIMENTAL DESIGN; MAXIMIN;

BAYESIAN NETWORKS; BIOLOGY; METAL ANALYSIS; PRODUCT DESIGN; STATISTICS; UNCERTAINTY ANALYSIS;

PARAMETER ESTIMATION;

EID: 77953017679     PISSN: 10498923     EISSN: 10991239     Source Type: Journal    
DOI: 10.1002/rnc.1558     Document Type: Article

References (39)

1. Robert DP. Development of kinetic models in the nonlinear world of molecular cell biology. Metabolism 1997; 46: 1489-1495.
2. Anand RA, Douglas AL. Bioengineering models of cell signaling. Annual Review of Biomedical Engineering 2000; 2:31-53.
3. Robert DP, Tom M. Kinetic modeling approaches to in vivo imaging. Nature Reviews Molecular Cell Biology 2001; 2: 898-907.
4. Tyson JJ, Kathy C, Bela N. Network dynamics and cell physiology. Nature Reviews Molecular Cell Biology 2: 908-916.
5. Conrad ED, Tyson JJ. Modeling molecular interaction networks with nonlinear ordinary differential equations. In Systems Modeling in Cellular Biology: from Concept to Nuts and Bolts, Szallasi Z, Stelling J, Periwal V (eds). MIT Press: Cambridge, 2006.
6. Voit EO. Computational Analysis of Biochemical Systems. Cambridge University Press: Cambridge, U.K., 2000.
7. Montomery DC. Design and Analysis of Experiments (5th edn). Wiley: New York, 2001.
8. Box PE, Hunter JS, Hunter WG. Statistics for Experimenters: Design, Innovation, and Discovery (2nd edn). Wiley Insterscience: New Jersey, 2005.
9. Atkinson AC, Donev AN. Optimum Experimental Design. Oxford Science Publications: Oxford, 1996.
10. van Riel NAW. Dynamic modeling and analysis of biochemical networks: mechanism-based models and model- based experiments. Briefing in Bioinformatics 2006; 7(4): 364-374.
11. Franceschini G, Macchietto S. Model-based design of experiments for parameter precision: state of the art. Chemical Engineering Science 2008; 63:4846-4872.
12. 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 TNFa-Mediated NF-kB signal transduction pathway. Simulation 2003; 79(12):726-739.
13. Faller D, Klingmuller U, Timmer J. Simulation methods for optimal experimental design in systems biology. Simulation 2003; 79(12):717-725.
14. Kutalik Z, Cho K-H, Wolkenhauer O. Optimal sampling time selection for parameter estimation in dynamic pathway modeling. Biosystems 2004; 75(1-3):43-55.
15. Balsa-Canto E, Alonso AA, Banga JR. Computational procedures for optimal experimental design in biological systems. IET Systems Biology 2008; 2(4):163-172.
16. Rodriguez-Fernandez M, Mendes P, Banga JR. A hybrid approach for efficient and robust parameter estimation in biochemical pathways. Biosystems 2006; 83:248-265.
17. Yue H, Brown M, Kell DB, Knowles J, Wang H, Broomhead D. Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kB signalling pathway. Molecular BioSystems 2006; 2(12):640-649.
18. Korkel S, Bauer I, Bock HG, Schloder JP. A sequential approach for nonlinear optimum experimental design in DAE system. Proceedings of International Workshop on Scientific Computing in Chemical Engineering, vol.II. TU Hamburg- Harburg: Germany, 1999.
19. Baltes M, Schneider R, Sturm C, Reuss M. Optimal experimental design for parameter estimation in unstructured growth models. Biotechnolology Progress 1994; 10: 480-488.
20. Korkel S, Hans EK, Bock HG, Schloder JP. Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes. Optimization Methods and Software 2004; 19(3-4): 327-338.
21. Silvey SD. Optimal Design. Chapman & Hall: London, 1980.
22. Boyd S, Vandenberghe L. Convex Optimization. Cambridge University Press: Cambridge, 2004.
23. Yue H, Brown M, He F, Jia J, Kell DB. Sensitivity analysis and robust experimental design of a signal transduction pathway system. International Journal of Chemical Kinetics 2008; 40(11):730-741.
24. Banga JR, Balsa-Canto E. Parameter estimation and optimal experimental design. Essays in Biochemistry 2008; 45: 195-209.
25. Brown M, He F, Yeung LF. Robust measurement selection for biochemical pathway experimental design. Proceedings of First International Symposium on Optimization and Systems Biology (OSB'07), Beijing, China, 8-10 August 2007, Lecture Notes in Operations Research, vol.7, 2007; 259-266.
26. Walter E, Pronzato L. Robust experiment design: between qualitative and quantitative identifiabilities. In Identifiability ofParametric Models, Walter E (ed.). Pergamon: Oxford, 1987.
27. Asprey SP, Macchietto S. Designing robust optimal dynamic experiments. Journal of Process Control 2002; 12: 545-556.
28. Dette H, Melas VB, Pepelyshev A, Strigul N. Robust and efficient design of experiments for the Monod model. Journal of Theoretical Biology 2005; 34:537-550.
29. Korkel S, Hans EK, Bock HG, Schloder JP. Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes. Optimization Methods and Software 2004; 19(3-4): 327-338.
30. Flaherty P, Jordan MI, Arkin AP. Robust Design of Biological Experiments. Proceedings of the Neural Information Processing Symposium (NIPS), Vancouver, Canada, 2006.
31. Lindley DV. Bayesian Statistics-A Seview.SIAM: Philadelphia, 1972; 19-20.
32. Chaloner K, Verdinelli I. Bayesian experimental design: a review. Statistical Science 1995; 10(3):273-304.
33. Merlé Y, Mentré F. Bayesian design criteria: computation, comparison, and application to a pharmacokinetic and a pharmacodynamic model. Journal of Pharmacokinetics and Bioph armaceutics 1995; 23(1):101-125.
34. Asprey SP, Macchietto S. Designing robust optimal dynamic experiments. Journal ofProcess Control 2002; 12: 545-556.
35. Hoffmann A, Levchenko A, Scott ML, Baltimore D. The IkB-NF-kB signaling module: temporal control and selective gene activation. Science 2002; 298:1241-1245.
36. Nelson DE, Ihekwaba AEC, Elliott M, Johnson J, Gibney CA, Foreman BE, Nelson G, See V, Horton CA, Spiller DG, Edwards SW, McDowell HP, Unitt JF, Sullivan E, Grimley R, Benson N, Broomhead D, Kell DB, White MRH. Oscillations in NF-kB signaling control the dynamics of gene expression. Science 2004; 306:704-708. (Pubitemid 39440922)
37. Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M. Mathematical model of NF-kB regulatory module. Journal of Theoretical Biology 2004; 228(2):195-215.
38. Jin Y, Yue H, Brown M, Liang Y, Kell DB. Improving data fitting of a signal transduction model by global sensitivity analysis. American Control Conference, New York, U.S.A., 2007; 2708-2713.
39. Leskovac V. Comprehensive Enzyme Kinetics. Springer: Berlin, 2003.