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Parasim GUI

tomvej edited this page Dec 10, 2012 · 21 revisions

Parasim provides an alternate interface to experiments management (via file system and text editor) and analysis (see Parasim CLI) in the form of swing application. This application has one very important feature: Experiments created using its project manager are fully compatible with parasim CLI, i.e. it is possible to create experiments using parasim GUI and execute them in batch using parasim CLI (note: reverse may not apply unless certain naming and placement conventions are maintained).

On unix-like operating systems parasim may be executed using the parasim-gui.sh script (parasim has to be built).

Projects

For user convenience, experiments are aggregated into projects. A project comprises a single model and a collection of experiments, properties and configurations associated with this model. Parasim GUI may be used to manage and edit all project components, with two exceptions: * **model** -- model file is asthe sumed to be the basis project and as such is presumed not to change during model analysis, therefore, it cannot be edited or managed by parasim GUI. * **properties** -- properties (STL formulae) are too complex to be edited by this simple GUI, therefore, it is used only to manage (i.e. add, rename and delete) properties associated with parasim project.

Note: Parasim is not designed to edit model files, as there are several usable SBML editors. So far, model file may be edited (for its location see naming and placement conventions), however, parasim GUI does not adjust configuration after the model change, which may result in loss of project integrity when the number of variables (or parameters) is altered.

Simulation configuration

Simulation configuration comprises settings which pertain to numeric simulation of the model, such as precision configuration. Generally, default settings should satisfy most needs, however, some tweaking may be necessary to obtain desired outcome.

Perturbation configuration

Perturbation configuration is directly connected to robustness analysis. It specifies which variables/parameters should be perturbed, in which intervals and how may samples should be used for initial iteration.

Experiment