Publication details

Esther: Introducing an Online Platform for Parameter Identification of Boolean Networks



Year of publication 2013
Type Article in Proceedings
Conference Computational Methods in Systems Biology 11th International Conference, CMSB 2013, Klosterneuburg, Austria, September 22-24, 2013, Proceedings
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords systems biology; boolean networks; model checking
Description When trying to uncover the nature of gene regulatory and signaling networks, the modelers currently have a zoo of inference algorithms at their disposal. These algorithms are very useful for converting raw experimental data into mathematical models of various nature – from pure differential equations to very abstract, logical causal networks. However, the algorithms rarely permit refitting of the network based on new data, and further enhancements are commonly done by hand, with quality of the process dependent on the experience of the modeler. We are therefore focusing on development of an environment that works with high level causal models and allows for automated comparison of the properties of the model and the behavior measured or observed in the modeled system. Since we expect the models to have wide range of possible kinetic parameters, we also provide means of manipulating with sets of parametrizations of the model, e.g. their ranking w.r.t. dynamic properties, visualizations, automated filtering, set operations etc.
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