MOTIVATION: Gene regulation is inherently stochastic. In many applications 
concerning Systems and Synthetic Biology such as the reverse engineering and the 
de novo design of genetic circuits, stochastic effects (yet potentially crucial) 
are often neglected due to the high computational cost of stochastic 
simulations. With advances in these fields there is an increasing need of tools 
providing accurate approximations of the stochastic dynamics of gene regulatory 
networks (GRNs) with reduced computational effort.
RESULTS: This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a 
software toolbox for the simulation of stochastic multidimensional gene 
regulatory networks. SELANSI exploits intrinsic structural properties of gene 
regulatory networks to accurately approximate the corresponding Chemical Master 
Equation with a partial integral differential equation that is solved by a 
semi-lagrangian method with high efficiency. Networks under consideration might 
involve multiple genes with self and cross regulations, in which genes can be 
regulated by different transcription factors. Moreover, the validity of the 
method is not restricted to a particular type of kinetics. The tool offers total 
flexibility regarding network topology, kinetics and parameterization, as well 
as simulation options.
AVAILABILITY AND IMPLEMENTATION: SELANSI runs under the MATLAB environment, and 
is available under GPLv3 license at https://sites.google.com/view/selansi.
CONTACT: antonio@iim.csic.es.