MonaLisa
MonaLisa
MonaLisa
MonaLisa
MonaLisa
MonaLisa
MonaLisa
MonaLisa

MonaLisa

0 Reviews

FREE

App specs:

License

PAID

Version

MonaLisa 5.100

LatestUpdate

Last updated

OS

Windows All

Language

EN

MonaLisa is an advanced Petri net-based application specialized in analyzing biochemical networks. You can check models for structural faults and identify biological modules, as well as visualize the computed results.

The portable running mode ensures your Windows registry does not get bloated with extra entries. You may get rid of the utility by simply deleting the files that you have downloaded from the Internet.

Copying it on USB flash drives or other devices is also possible. You can run the program by opening the executable file. This is a Java-based tool so you need to previously install the working environment on your computer.

The application works with various analysis techniques which are related to network decomposition and knockout analysis, and offers support for the computation of elementary modes (T-invariants), P-invariants, maximal common transition sets (MCT-sets), T-clusters, minimal cut sets (MCS), degree distribution, and cluster coefficient distribution.

The GUI can be described as practical. You can work with three main panes: ones is used for project management, other for visualizing the PN model, while the last one provides access to a set of configuration settings.

The program offers you the possibility to import data from various systems biology and graph-theoretic formats, such as SBML, KGML, PNT, APNN, MetaTool, and PNML.

MonaLisa allows you to save projects to a file so you can easily import data in the future for making adjustments, view a list with recently used projects, set up the approaches, define the settings, and export the results and/or PN model to one of the file formats used for importing data.

The application lets you view the representation of the PN model of the carbon metabolism. The metabolites are displayed with the aid of circle while the reactions (transitions) are revealed using rectangles. Gray-filled circles indicate clone markers, and you may save the current representation to PNG or SVG file format, and alter the colors used for analysis, knockout analysis and background.

When it comes to managing all modelling and visualization settings, you can alter the highlighting of elementary models and place invariants and maximal common transition sets. You may divide the elementary elements into several categories.

In conclusion, MonaLisa comes with an advanced suite of features that can be used for visualization and analysis of functional modules in biochemical networks, and is suitable especially for professional users.

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