PyDREAM: high-dimensional parameter inference for biological models in python.

Shockley EM, Vrugt JA, Lopez CF
Bioinformatics. 2018 34 (4): 695-697

PMID: 29028896 · PMCID: PMC5860607 · DOI:10.1093/bioinformatics/btx626

Summary - Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.

Availability and implementation - PyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM.

Contact - c.lopez@vanderbilt.edu.

Supplementary information - Supplementary data are available at Bioinformatics online.

© The Author(s) 2017. Published by Oxford University Press.

MeSH Terms (8)

Algorithms Calibration Computational Biology Markov Chains Models, Biological Monte Carlo Method Software Uncertainty

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