Sloppy models, parameter uncertainty, and the role of experimental design.
Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected from them.
Apgar
Joshua F
JF
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Witmer
David K
DK
White
Forest M
FM
Tidor
Bruce
B
D004815:Epidermal Growth Factor / Q000235:genetics / Q000378:metabolism; D055785:Gene Knockdown Techniques; D008962:Models, Theoretical; D020932:Nerve Growth Factor / Q000235:genetics / Q000378:metabolism; D012107:Research Design; D015398:Signal Transduction; D035501:Uncertainty
D004815:Epidermal Growth Factor; D020932:Nerve Growth Factor