Systemic Calibration of a Cell Signaling Network Model

DSpace/Manakin Repository

Systemic Calibration of a Cell Signaling Network Model

Citable link to this page

 

 
Title: Systemic Calibration of a Cell Signaling Network Model
Author: Spencer, Sabrina Leigh; Albeck, John Gerald; Burke, John M.; Sorger, Peter Karl; Gaudet, Suzanne; Kim, Kyoung Ae; Kim, Do Hyun

Note: Order does not necessarily reflect citation order of authors.

Citation: Kim, Kyoung A., Sabrina L. Spencer, John G. Albeck, John M. Burke, Peter K. Sorger, Suzanne Gaudet, and Do H. Kim. 2010. Systematic calibration of a cell signaling network model. BMC Bioinformatics 11:202.
Full Text & Related Files:
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.
Published Version: doi: 10.1186/1471-2105-11-202
Other Sources: http://www.biomedcentral.com/1471-2105/11/202
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10139270
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters