  Background: Species abundance distributions in chemical reaction network
models cannot usually be computed analytically. Instead, stochas- tic
simulation algorithms allow sample from the the system configuration. Although
many algorithms have been described, no fast implementation has been provided
for {\tau}-leaping which i) is Matlab-compatible, ii) adap- tively alternates
between SSA, implicit and explicit {\tau}-leaping, and iii) provides summary
statistics necessary for Bayesian inference. Results: We provide a
Matlab-compatible implementation of the adap- tive explicit-implicit
{\tau}-leaping algorithm to address the above-mentioned deficits. matLeap
provides equal or substantially faster results compared to two widely used
simulation packages while maintaining accuracy. Lastly, matLeap yields summary
statistics of the stochastic process unavailable with other methods, which are
indispensable for Bayesian inference. Conclusions: matLeap addresses
shortcomings in existing Matlab-compatible stochastic simulation software,
providing significant speedups and sum- mary statistics that are especially
useful for researchers utilizing particle- filter based methods for Bayesian
inference. Code is available for download at
https://github.com/claassengroup/matLeap. Contact:
<EMAIL_ADDRESS>