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ODEion - A Software Module For Structural Identification Of Ordinary Differential Equations
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics. Univ Gothenburg, Gothenburg, Sweden..
Chalmers, Dept Comp Sci & Engn, Gothenburg, Sweden..
2014 (English)In: Journal of Bioinformatics and Computational Biology, ISSN 0219-7200, E-ISSN 1757-6334, Vol. 12, no 1, 1350015Article in journal (Refereed) Published
Abstract [en]

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 de fined by arbitrary user-de fined 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. Available at: www.odeidentification.org.

Place, publisher, year, edition, pages
2014. Vol. 12, no 1, 1350015
Keyword [en]
System identification, ordinary differential equations
National Category
Mathematics
Identifiers
URN: urn:nbn:se:uu:diva-310537DOI: 10.1142/S0219720013500157ISI: 000348346200003OAI: oai:DiVA.org:uu-310537DiVA: diva2:1057530
Available from: 2016-12-19 Created: 2016-12-16 Last updated: 2016-12-19Bibliographically approved

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