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SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool.

Zi Z, Zheng Y, Rundell AE, Klipp E - BMC Bioinformatics (2008)

Bottom Line: However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models.This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. zhike_zi@molgen.mpg.de

ABSTRACT

Background: It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.

Results: This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.

Conclusion: SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.

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Simulation of different types of models in SBML-SAT. (A) Simulation result of the fission yeast cell cycle model (events included, BioModels ID: BIOMD0000000111), identical to Fig. 4 of [44]. (B) Simulation result of a NF-κB signalling pathway model (BioModels ID: BIOMD0000000140), identifical to Fig. 2F of [1]. (C) Simulation result of a T cell gene expression model (BioModels ID: BIOMD0000000122), identical to Fig. 4a of [45]. (D) Simulation result of a metabolic model (BioModels ID: BIOMD0000000106), identical to Fig. 2A of [46].
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Figure 3: Simulation of different types of models in SBML-SAT. (A) Simulation result of the fission yeast cell cycle model (events included, BioModels ID: BIOMD0000000111), identical to Fig. 4 of [44]. (B) Simulation result of a NF-κB signalling pathway model (BioModels ID: BIOMD0000000140), identifical to Fig. 2F of [1]. (C) Simulation result of a T cell gene expression model (BioModels ID: BIOMD0000000122), identical to Fig. 4a of [45]. (D) Simulation result of a metabolic model (BioModels ID: BIOMD0000000106), identical to Fig. 2A of [46].

Mentions: SBML-SAT provides an easy way to run a simulation and visualize the simulation results for SBML models. The output screen for SBML-SAT model simulation is shown in Figure 2. In order to test the wide applicability of SBML-SAT, we ran simulations for a variety of models from the BioModels Database, which include biophysical models, signaling pathways, gene expression and metabolic networks. The results shown in Figure 3 demonstrates that SBML-SAT appropriately simulates both continuous SBML models (signaling pathway, gene expression and metabolic models), as well as those with discontinuous events (cell cycle model) with different degrees of complexity and nonlinearity.


SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool.

Zi Z, Zheng Y, Rundell AE, Klipp E - BMC Bioinformatics (2008)

Simulation of different types of models in SBML-SAT. (A) Simulation result of the fission yeast cell cycle model (events included, BioModels ID: BIOMD0000000111), identical to Fig. 4 of [44]. (B) Simulation result of a NF-κB signalling pathway model (BioModels ID: BIOMD0000000140), identifical to Fig. 2F of [1]. (C) Simulation result of a T cell gene expression model (BioModels ID: BIOMD0000000122), identical to Fig. 4a of [45]. (D) Simulation result of a metabolic model (BioModels ID: BIOMD0000000106), identical to Fig. 2A of [46].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2529325&req=5

Figure 3: Simulation of different types of models in SBML-SAT. (A) Simulation result of the fission yeast cell cycle model (events included, BioModels ID: BIOMD0000000111), identical to Fig. 4 of [44]. (B) Simulation result of a NF-κB signalling pathway model (BioModels ID: BIOMD0000000140), identifical to Fig. 2F of [1]. (C) Simulation result of a T cell gene expression model (BioModels ID: BIOMD0000000122), identical to Fig. 4a of [45]. (D) Simulation result of a metabolic model (BioModels ID: BIOMD0000000106), identical to Fig. 2A of [46].
Mentions: SBML-SAT provides an easy way to run a simulation and visualize the simulation results for SBML models. The output screen for SBML-SAT model simulation is shown in Figure 2. In order to test the wide applicability of SBML-SAT, we ran simulations for a variety of models from the BioModels Database, which include biophysical models, signaling pathways, gene expression and metabolic networks. The results shown in Figure 3 demonstrates that SBML-SAT appropriately simulates both continuous SBML models (signaling pathway, gene expression and metabolic models), as well as those with discontinuous events (cell cycle model) with different degrees of complexity and nonlinearity.

Bottom Line: However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models.This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. zhike_zi@molgen.mpg.de

ABSTRACT

Background: It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.

Results: This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.

Conclusion: SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.

Show MeSH