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Systematic calibration of a cell signaling network model
Kyoung Ae Kim1, Sabrina L Spencer, John G Albeck
1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
BMC Bioinformatics
|April 27, 2010
Summary
We developed a systematic method to calibrate complex biological models. This framework validates models and aids in creating predictive computational models for biological systems.
Area of Science:
- Systems biology
- Computational biology
- Mathematical modeling
Background:
- Mathematical modeling of biological systems is complex.
- Model calibration against experimental data is a bottleneck.
- A systematic methodology is needed for quantitative model calibration.
Purpose of the Study:
- To develop a systematic methodology for calibrating quantitative models of dynamic biological processes.
- To validate a model of Tumor necrosis factor Related Apoptosis-Inducing Ligand (TRAIL)-induced cell death.
Main Methods:
- A serial framework integrating analysis and calibration modules.
- Global sensitivity analysis to check network structure adequacy.
- Parameter ranking using sensitivity indices.
- Global optimization algorithms (deterministic, stochastic, hybrid) for parameter estimation.
Main Results:
- Validated model structure using sensitivity analysis.
- Identified and ranked important rate parameters.
- Estimated parameters by fitting to time series data.
- Compared performance of different optimization algorithms.
Conclusions:
- The proposed framework enables model validation and calibration.
- It provides a generalized workflow for constructing predictive models.
- Facilitates in silico experiments for complex biological networks.
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