Systematic calibration of a cell signaling network model
Author(s)
Kim, Kyoung A.E.; Spencer, Sabrina L.; Albeck, John G.; Burke, John M.; Sorger, Peter K.; Gaudet, Suzanne; Kim, Do Hyun; ... Show more Show less
Download1471-2105-11-202.pdf (1.045Mb)
PUBLISHER_CC
Publisher with Creative Commons License
Creative Commons Attribution
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2010-04Department
Massachusetts Institute of Technology. Department of Biological EngineeringJournal
BMC Bioinformatics
Publisher
BioMed Central Ltd
Citation
BMC Bioinformatics. 2010 Apr 23;11(1):202
Version: Final published version
ISSN
1471-2105