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Welcome to StochPy 2.3 (August 2015)

The most important improvements of StochPy 2.3 are listed here:

• improved sampling method to calculate extant cell species AND volumes (new)
• we allow plotting of mother and baby cell species AND volume at birth
• we use a different function for plotting distributions (gives the user more flexibility)
• Silence Mode
• support of events in cell division simulations
• bug resolved for an event that changes the value of parameters
• the cell division module works now also with the tau leap method
• we added DNA replication to the cell division module
• fixed a bug in the interface to StochKit for models with customized reactions
• with the next reaction method (and the delayed one), it was not possible to change parameters. This is now possible

Since StochPy 2.1 we support also Python 3. For those interested in both interface and plug-ins, make sure to install the latest version

StochPy is a versatile modeling package for stochastic simulation of molecular control networks inside living cells. Its integration with Python’s scientific libraries and PySCeS makes it an easily extensible and an user-friendly simulator. The high-level statistical and plotting functions of StochPy allow for quick and interactive model interrogation at the command-line. Python’s scripting capabilities allow for more complicated and in-depth analysis of stochastic models.

Because of the flexible design of StochPy, we decided to offer users of StochPy both the Cain and StochKit stochastic solvers in StochPy. As a result, the user can benefit from the speed advantage of these fixed-interval solvers in the interactive modeling environment of StochPy. StochPy's solvers return the original stochastic simulation output which makes it slower than these fixed-interval solvers. However, the original data allows us to store all the data of the simulation such that we can analyze the information about the time between events (waiting times).

For fixed-interval simulations note that the number of fixed-interval chosen by the user determines the accuracy of the simulation results. As an example, creating accurate probability density functions requires a number of fixed-intervals that is in the same order of the number of time steps. On the other hand, frame-based simulations can be useful to get an indication of the behavior of your model, or to get the species means and standard deviations in a faster manner.

This version of StochPy allows users to specify which species have to be tracked over time, which can result in a major reduction of stored data for models with many species. Alternatively, StochPy now offers an option to store only the last time point of a simulation.

Source: README.txt, updated 2015-08-10

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