Open Access Open Badges Methodology article

Systematic calibration of a cell signaling network model

Kyoung Ae Kim1, Sabrina L Spencer23, John G Albeck23, John M Burke23, Peter K Sorger23, Suzanne Gaudet23 and Do Hyun Kim1*

Author Affiliations

1 Department of Chemical and Biomolecular Engineering(BK21 Program) and Center for Ultramicrochemical Process Systems, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea

2 Center for Cell Decision Process, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA

3 Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, 02115, USA

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BMC Bioinformatics 2010, 11:202  doi:10.1186/1471-2105-11-202

Published: 23 April 2010



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.


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.


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.