matLeap: A fast adaptive Matlab-ready tau-leaping implementation   suitable for Bayesian inference
  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: justin.feigelman@imsb.biol.ethz.ch 
Justin Feigelman, Stefan Ganscha and Manfred Claassen
q-bio.QM