Cellular signaling identifiability analysis: a case study.
Two primary purposes for mathematical modeling in cell biology are (1) simulation for making predictions of experimental outcomes and (2) parameter estimation for drawing inferences from experimental data about unobserved aspects of biological systems. While the former purpose has become common in the biological sciences, the latter is less common, particularly when studying cellular and subcellular phenomena such as signaling-the focus of the current study. Data are difficult to obtain at this level. Therefore, even models of only modest complexity can contain parameters for which the available data are insufficient for estimation. In the present study, we use a set of published cellular signaling models to address issues related to global parameter identifiability. That is, we address the following question: assuming known time courses for some model variables, which parameters is it theoretically impossible to estimate, even with continuous, noise-free data? Following an introduction to this problem and its relevance, we perform a full identifiability analysis on a set of cellular signaling models using DAISY (Differential Algebra for the Identifiability of SYstems). We use our analysis to bring to light important issues related to parameter identifiability in ordinary differential equation (ODE) models. We contend that this is, as of yet, an under-appreciated issue in biological modeling and, more particularly, cell biology.
Roper
Ryan T
RT
Resource Facility for Population Kinetics, Department of Bioengineering Box 355061, University of Washington, Seattle, WA 98195-5061, United States. rtroper@u.washington.edu
Pia Saccomani
Maria
M
Vicini
Paolo
P
D000465:Algorithms; D002468:Cell Physiological Phenomena / Q000502:physiology*; D003198:Computer Simulation; D007700:Kinetics; D008954:Models, Biological; D017711:Nonlinear Dynamics; D015398:Signal Transduction / Q000502:physiology*