2016/08/25 by Justin Feigelman, Feigelman, Justin, Stefan Ganscha +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bacterial Genetics and Biotechnology #Bayesian Modeling and Causal Inference #Bioinformatics and Genomic Networks #FOS: Biological sciences #Gene Regulatory Network Analysis #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM) #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.1608.07058
openalex publication_date 2016/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Background: Species abundance distributions in chemical reaction network\nmodels cannot usually be computed analytically. Instead, stochas- tic\nsimulation algorithms allow sample from the the system configuration. Although\nmany algorithms have been described, no fast implementation has been provided\nfor \τ-leaping which i) is Matlab-compatible, ii) adap- tively alternates\nbetween SSA, implicit and explicit \τ-leaping, and iii) provides summary\nstatistics necessary for Bayesian inference. Results: We provide a\nMatlab-compatible implementation of the adap- tive explicit-implicit\n\τ-leaping algorithm to address the above-mentioned deficits. matLeap\nprovides equal or substantially faster results compared to two widely used\nsimulation packages while maintaining accuracy. Lastly, matLeap yields summary\nstatistics of the stochastic process unavailable with other methods, which are\nindispensable for Bayesian inference. Conclusions: matLeap addresses\nshortcomings in existing Matlab-compatible stochastic simulation software,\nproviding significant speedups and sum- mary statistics that are especially\nuseful for researchers utilizing particle- filter based methods for Bayesian\ninference. Code is available for download at\nhttps://github.com/claassengroup/matLeap. Contact:\[email protected]\n