We recommed that you use the development version at github. It has lots of improvements over the last official release (1.6.0, 2005). It works with libSBML versions 3.4.1 - 5.x.x. A branch using the latest Sundials exists..

Support via the sourceforge mailing list is still available, but you can also contact us in private ... always happy to help.

SOSlib (SBML ODE Solver Library)

SOSlib is both a programming library and a command-line application for symbolic and numerical analysis of a system of ordinary differential equations (ODEs) derived from a chemical reaction network encoded in the Systems Biology Markup Language (SBML). It is written in ANSI/ISO C and distributed under the terms of the GNU Lesser General Public License (LGPL). The package employs libSBML's AST (Abstract Syntax Tree) for formula representation to construct ODE systems, their Jacobian matrix and other derivatives. CVODES, the sensitivity-enabled ODE solver in the SUNDIALS package is used for numerical integration and sensitivity analysis of stiff and non-stiff ODE systems.

The native API provides fine-grained interfaces to all internal data structures, symbolic operations and numerical routines, enabling the construction of powerful and efficient analytic applications, hybrid solvers or multi-scale models with interfaces to non SBML data sources. Optional modules based on Graphviz and XMGrace allow a quick inspection of a model's structure and dynamics. All functionalities are accessible directly via a command-line application and several example programs.

SBML PIT (SBML Parameter Identification Toolkit)

The capabilities of SOSlib for sensitivity analysis allow the implementation of efficient algorithms for parameter identification. The identification of model parameters and initial conditions from noisy experimental data is a typical ill-posed inverse problem and can be formulated in a stable way as a minimization problem with a data mismatch and a regularization term. In a parameter identification software based on SOSlib, the local (gradient based) search is performed with the interior point optimizer


using the capabilities of SOSlib for adjoint sensitivity analysis to efficiently compute the gradient of the data mismatch. To stabilize the solutions with respect to noise in the measurements several regularization techniques have been implemented.
The parameter identification software requires the following input: The output consists in a list of the identified values for the unknown parameters and initial conditions together with the corresponding confidence intervals. The latter can be estimated using the so-called Fisher Information Matrix, which is computed using the capabilities of SOSlib for forward sensitivity analysis.

Availability Precompiled binaries of SBML PIT and example input files are available upon request from Stefan Müller


The work on SOSlib and SBML PIT was supported by the WWTF, project number MA04-005.


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Last modified: 2008-12-28 18:33:05 raim raim