Systematic calibration of a cell signaling network model.
BACKGROUND
Mathematical modeling is being applied to increasingly complex biological systems and datasets; however, the process of analyzing and calibrating against experimental data is often challenging and a rate limiting step in model development. To address this problem, we developed a systematic methodology for calibrating quantitative models of dynamic biological processes and illustrate its utility by validating a model of TRAIL (Tumor necrosis factor Related Apoptosis-Inducing Ligand)-induced cell death.


RESULTS
We propose a serial framework integrating analysis and calibration modules and we compare various methods for global sensitivity analysis and global parameter estimation. First, adequacy of the network structure is checked by global sensitivity analysis to changes in concentrations of molecular species, validating that the model can reproduce qualitative features of the system behavior derived from experiments or literature surveys. Second, rate parameters are ranked by importance using gradient-based and variance-based sensitivity indices, and we systematically determine the optimal number of parameters to include in model calibration. Third, deterministic, stochastic and hybrid algorithms for global optimization are applied to estimate the values of the most important parameters by fitting to time series data. We compare the performance of these three optimization algorithms.


CONCLUSIONS
Our proposed framework covers the entire process from validating a proto-model to establishing a realistic model for in silico experiments and thereby provides a generalized workflow for the construction of predictive models of complex network systems.
Kim
Kyoung Ae
KA
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
Spencer
Sabrina L
SL
Albeck
John G
JG
Burke
John M
JM
Sorger
Peter K
PK
Gaudet
Suzanne
S
Kim
Do Hyun
DH
D016923:Cell Death; D019295:Computational Biology / Q000379:methods*; D008954:Models, Biological; D015398:Signal Transduction; D053221:TNF-Related Apoptosis-Inducing Ligand / Q000378:metabolism
D053221:TNF-Related Apoptosis-Inducing Ligand