AI Article Synopsis
- Biological models often have challenging parameters that need calibration using experimental data, and traditional MCMC methods may struggle with high-dimensional spaces.
- The text introduces PyDREAM, a Python tool based on the DREAM(ZS) algorithm, which improves performance in estimating parameters for complex models and utilizes distributed computing for better efficiency.
- PyDREAM is open-source, available under the GNU GPLv3 license on GitHub, with supplementary information accessible online.
Article 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.
Contact: c.lopez@vanderbilt.edu.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860607 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btx626 | DOI Listing |
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