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

• Published in: Bioinformatics Vol. 34; no. 4; pp. 695 - 697
• Main Authors: Shockley, Erin M, Vrugt, Jasper A, Lopez, Carlos F
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.02.2018
• Subjects: Algorithms; Applications Notes; BASIC BIOLOGICAL SCIENCES; Calibration; Computational Biology - methods; Markov Chains; MATHEMATICS AND COMPUTING; Models, Biological; Monte Carlo Method; Software; Uncertainty
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btx626

Abstract 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. Supplementary information Supplementary data are available at Bioinformatics online.