Systematic calibration of a cell signaling network model

Kyoung Ae Kim, Sabrina L. Spencer, John Albeck, John M. Burke, Peter K. Sorger, Suzanne Gaudet, Do Hyun Kim

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number202
JournalBMC Bioinformatics
Volume11
DOIs
StatePublished - Apr 23 2010
Externally publishedYes

Fingerprint

Cell signaling
Calibration
Network Model
Biological Phenomena
Workflow
Cell
Computer Simulation
Cell Death
Tumor Necrosis Factor-alpha
Global Analysis
Apoptosis
Ligands
Sensitivity Analysis
Cell death
Model
Sensitivity analysis
Background Modeling
Tumor Necrosis Factor
Model Calibration
Stochastic Algorithms

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Kim, K. A., Spencer, S. L., Albeck, J., Burke, J. M., Sorger, P. K., Gaudet, S., & Kim, D. H. (2010). Systematic calibration of a cell signaling network model. BMC Bioinformatics, 11, [202]. https://doi.org/10.1186/1471-2105-11-202

Systematic calibration of a cell signaling network model. / Kim, Kyoung Ae; Spencer, Sabrina L.; Albeck, John; Burke, John M.; Sorger, Peter K.; Gaudet, Suzanne; Kim, Do Hyun.

In: BMC Bioinformatics, Vol. 11, 202, 23.04.2010.

Research output: Contribution to journalArticle

Kim, KA, Spencer, SL, Albeck, J, Burke, JM, Sorger, PK, Gaudet, S & Kim, DH 2010, 'Systematic calibration of a cell signaling network model', BMC Bioinformatics, vol. 11, 202. https://doi.org/10.1186/1471-2105-11-202
Kim, Kyoung Ae ; Spencer, Sabrina L. ; Albeck, John ; Burke, John M. ; Sorger, Peter K. ; Gaudet, Suzanne ; Kim, Do Hyun. / Systematic calibration of a cell signaling network model. In: BMC Bioinformatics. 2010 ; Vol. 11.
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