## SBML-SAT

** Category** Cross-Omics>Pathway Analysis/Tools

** Abstract** SBML-SAT is a Systems Biology Markup Language (SBML) based Sensitivity Analysis Tool (SAT).

This tool is designed to implement a variety of simulation, sensitivity analysis, steady state analysis and robustness analysis for ordinary differential equations (ODE) based biological models including biophysical models, signaling pathways, gene regulation networks and metabolic pathways.

SBML-SAT supports the import of models in the System Biology Mark- up Language (SBML) format.

The following is a summary of the features/capabilities of SBML-SAT:

Support for SBML import and export --

Current release of SBML-SAT supports:

(1) SBML Level 1 Version 1 and 2.

(2) SBML Level 2 Versions 1, 2 and 3.

The import of a model to SBML-SAT should be an SBML Extensible Markup Language (XML) file. SBML-SAT will automatically generate the necessary ODE models and run the simulation and various analyses.

Support of events and rules -- SBML-SAT supports rules (except algebraic rules) and events very well including different event(s) triggers (complicated logical triggers are supported) and assignments.

Local sensitivity analysis -- SBML-SAT provides traditional local sensitivity analysis for the SBML models.

Local sensitivity analysis is the study of the changes in the model outputs with respect to parameter (factor) variations around a local point in the parameter space, which are quantified by the sensitivity coefficients.

Mathematically, the sensitivity coefficients are the first order derivatives of model outputs with respect to the model parameters.

Global sensitivity analysis -- SBML-SAT can run four (4) different global sensitivity analysis algorithms for the SBML-Models, which are:

1) Multi-Parametric Sensitivity Analysis (MPSA) - MPSA can be used to study the relative importance of the parameters with respect to the model output.

The basic idea of MPSA is to map the uncertainty of the parameters into the model output by randomly generating parameter values from predefined distributions (without prior knowledge, uniform distributions are assumed).

2) Partial Rank Correlation Coefficient Analysis (PRCC) - The PRCC method is a rank transformed linear regression analysis that is routinely used for analysis of systems with a nonlinear and monotonic relationship between the system inputs and outputs. Linear regression analysis methods best fit a straight line to input and output values.

When nonlinear, monotonic relationships exist between system input and outputs, poor linear regression fits can be alleviated by performing the linear regression analysis on a rank ordered list of the model output and input values.

PRCC calculates the sensitivity indices from the Pearson correlation coefficients between the model output and input parameters as well as each pair of parameters after rank transformation.

(3) SOBOL's Method - is a variance based method that makes No assumptions on the relationship between the system inputs and outputs. It is computationally expensive since it utilizes a large number of model simulations with parameter values sampled from the parameter space by the winding stair algorithm.

The variance of the numerous model outputs is estimated by Monte Carlo integrations. The model output variance is apportioned into summands of partial variances from combinations of input parameters with increasing dimensionality.

The total effects sensitivity indices quantify all of the effects that a parameter, in combination with any other parameter(s), has on the model output.

They are defined as the ratio of the sum of the related partial variances to the overall variance of the model output. The larger the fraction, the higher is the corresponding sensitivity. SBML-SAT calculates the 'total effect sensitivity' indices.

(4) Weighted Average of Local Sensitivities - In this approach, local sensitivity indices are calculated at multiple random points within the parameter space; a weighted average of the local sensitivity indices serves to provide some approximation of the global parameter sensitivities.

Steady state analysis -- SBML-SAT runs steady state analysis for the stable system. The steady state of the model can be detected only when all the components (state variables) of the model don’t change at a certain time.

Robustness analysis -- Robustness is one of the fundamental properties of biological systems, which allows the system to maintain its behavior against random perturbations.

SBML-SAT implements robustness analysis for a variety of model output by simultaneous variations of the selected parameters/initial conditions.

SBML model editor module -- Currently, a SBML model editor module is Not available in SBML-SAT. Fortunately, many existing free software packages such as CellDesigner (see G6G Abstract Number 20159), SBMLeditor and COPASI, share a common functionality for constructing and editing SBML models.

The users can generate their models with these free software packages and then run a variety of analyses in SBML-SAT by importing the model in SBML format.

Although SBML-SAT doesn't provide a SBML editor for model construction, it provides a convenient track for modifying the initial conditions of the state variables and parameter values in the model.

*System Requirements*

Runs on Windows, Linux and Mac with MATLAB.

*Note: MATLAB is a high-level language and interactive environment
that enables you to perform computationally intensive tasks faster than
with traditional programming languages such as C, C++, and FORTRAN*

*Manufacturer*

- Max Planck Institute for Molecular Genetics
- Ihnestr. 73
- 14195 Berlin, Germany

** Manufacturer Web Site**
SBML-SAT

** Price** Contact manufacturer.

** G6G Abstract Number** 20258

** G6G Manufacturer Number** 101736