ODEion- a software module for structural identification of ordinary differential equations
Artikel i vetenskaplig tidskrift, 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


Peter Gennemark

Chalmers, Matematiska vetenskaper

Göteborgs universitet

Dag Wedelin

Chalmers, Data- och informationsteknik, Datavetenskap

Journal of Bioinformatics and Computational Biology

0219-7200 (ISSN)

Vol. 12 1 Art. no. 1350015- 1350015


Informations- och kommunikationsteknik


Livsvetenskaper och teknik (2010-2018)



Bioinformatik (beräkningsbiologi)

Datavetenskap (datalogi)


Grundläggande vetenskaper



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