ODEion- a software module for structural identification of ordinary differential equations
Journal article, 2014

In the systems biology field, algorithms for structural identification of ordinary differential equations (ODEs) have mainly focused on fixed model spaces like S-systems and/or on methods that require sufficiently good data so that derivatives can be accurately estimated. There is therefore a lack of methods and software that can handle more general models and realistic data. We present ODEion, a software module for structural identification of ODEs. Main characteristic features of the software are: • The model space is defined by arbitrary user-defined functions that can be nonlinear in both variables and parameters, such as for example chemical rate reactions. • ODEion implements computationally efficient algorithms that have been shown to efficiently handle sparse and noisy data. It can run a range of realistic problems that previously required a supercomputer. • ODEion is easy to use and provides SBML output. We describe the mathematical problem, the ODEion system itself, and provide several examples of how the system can be used. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219720013500157

Author

Peter Gennemark

Chalmers, Mathematical Sciences

University of Gothenburg

Dag Wedelin

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Journal of Bioinformatics and Computational Biology

0219-7200 (ISSN)

Vol. 12 1 Art. no. 1350015-

Areas of Advance

Information and Communication Technology

Transport

Life Science Engineering (2010-2018)

Subject Categories

Computational Mathematics

Bioinformatics (Computational Biology)

Computer Science

Roots

Basic sciences

DOI

10.1142/S0219720013500157

More information

Created

10/8/2017