ByoDyn 5.3

ByoDyn software is an integrative tool for Systems Biology.

Software package for the analysis of paratemer landscapes in biological networks.

In systems biology it is becoming a routine task to build models of increasing complexity on a given biochemical network or pathway of interest. One of the main problems in building such models is the determination of the parameters underlying each modelled process. ByoDyn has been designed to provide an easily extendable computational framework to estimate and analyze parameters in highly uncharacterized models.

ByoDyn includes a set of tools to 1) integrate ordinary differential equations (ODEs), including systems with events, rules (differential algebraic equations, DAE) and delays built from a given biological model; 2) globally optimize the parameters that fit the provided experimental information and evaluate the sensitivity of the model with respect to the different parameters; and 3) include the sensitivity of the parameters in an optimal experimental design pipeline. The program makes use of external software, providing a Python binding schema that allows the user to easily implement new software in the desired calculation protocol. The program benefits from its interface with the SBML library, which ensures communication with other existing tools in the field.

ByoDyn (released under the GPL) is a Python based program built on top of several open source libraries, PORT FORTRAN, SciPy and libSBML, and it provides easy binders to OpenModelica and Octave. The program has been tested in Linux Fedora and in Mac OS X platforms, having been parallelized in a Platform LSF grid environment and on a Slurm+MOAB scheduling system. ByoDyn code and complete documentation is available at http://cbbl.imim.es/ByoDyn.


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What's New in This Release: [ read full changelog ]

· The table about requirements and capabilities relation (table 1) in the installationGuide had some mistakes.
· Fixed a bug at the create documentation script, references were not taken into account.
· Matplotlib figures show the legend out of the frame for clarity.
· Matplotlib figures resolution was set to 1200 dpi which made them too large. Default value is 80 dpi which will be used instead from now on.
· A new functionality called "rankTargets" implemented in ByoDyn. It evaluates the amount the identifiability associated to each target point.
· Figures for documentation are in png format now to reduce the size the directory.
· New implementation for figures of optimal experimental design. The information of "addNewPoint" is plotted as spectra.
· Optimal experimental design results are shown in logarithmic scale.
· Scale as colour boxes added to matrix plots.
· Fixed bug about holding graphs after saving the matplotlib figures.
· Adding color scale for rank target optimal experimental design graphs.
· Fixed bug when it was saving the SBML with the optimisation results.