Systematic calibration of a cell signaling network model

BMC Bioinformatics

Methodology Article

Suzanne Gaudet¹  John M Burke¹  John G Albeck¹  Peter K Sorger¹  Sabrina L Spencer¹  Kyoung Ae Kim²  Do Hyun Kim² 

[1] Center for Cell Decision Process, Department of Biological Engineering, Massachusetts Institute of Technology, 02139, Cambridge, Massachusetts, USA;Department of Systems Biology, Harvard Medical School, 02115, Boston, Massachusetts, USA;Department of Chemical and Biomolecular Engineering(BK21 Program) and Center for Ultramicrochemical Process Systems, Korea Advanced Institute of Science and Technology, 335 Gwahak-, 305-701, Yuseong-gu, Daejeon, Republic of Korea;

关键词: PARP Cleavage;    Sensitivity Analysis Method;    Local Sensitivity;    Global Sensitivity Analysis;    Reaction Rate Constant;   

DOI  :  10.1186/1471-2105-11-202

 received in 2009-06-12, accepted in 2010-04-23,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMathematical 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.ResultsWe 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.ConclusionsOur 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.

【 授权许可】

CC BY   
© Kim et al; licensee BioMed Central Ltd. 2010