Robust measurement selection for biochemical pathway experimental design.
As a general lack of quantitative measurement data for pathway modelling and parameter identification process, time-series experimental design is particularly important in current systems biology research. This paper mainly investigates state measurement/observer selection problem when parametric uncertainties are considered. Based on the extension of optimal design criteria, two robust experimental design strategies are investigated, one is the regularisation-based design method, and the other is Taguchi-based design approach. By implementing to a simplified IkappaBalpha - NF - kappaB signalling pathway system, two design approaches are comparatively studied. When large parametric uncertainty is present, by assuming that different parametric uncertainties are identical in scale, two methods tend to provide a similar uniform design result.
Brown
Martin
M
Control Systems Centre, School of Electrical and Electronic Engineering, The University of Manchester, M60 1QD, Manchester, UK. martin.brown@manchester.ac.uk
He
Fei
F
Yeung
Lam Fat
LF
D001671:Biochemistry / Q000706:statistics & numerical data*; D019295:Computational Biology; D020672:I-kappa B Proteins / Q000378:metabolism; D008954:Models, Biological; D000072000:NF-KappaB Inhibitor alpha; D016328:NF-kappa B / Q000378:metabolism; D017711:Nonlinear Dynamics; D015398:Signal Transduction; D049490:Systems Biology / Q000706:statistics & numerical data*
D020672:I-kappa B Proteins; D016328:NF-kappa B; D000072000:NF-KappaB Inhibitor alpha