Probabilistic sensitivity analysis of biochemical reaction systems.
Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of biochemical reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of biochemical reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for biochemical reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of biochemical reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical.
Zhang
Hong-Xuan
HX
The Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Dempsey
William P
WP
Goutsias
John
J
D000704:Analysis of Variance; D016022:Case-Control Studies; D019411:Clinical Laboratory Techniques; D019295:Computational Biology; D003198:Computer Simulation; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism; D008954:Models, Biological; D009010:Monte Carlo Method; D012106:Research; D012680:Sensitivity and Specificity; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology; D014861:Washington
D020928:Mitogen-Activated Protein Kinases