Journal
BIOINFORMATICS
Volume 34, Issue 4, Pages 695-697Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx626
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Funding
- National Science Foundation [MCB-1411482]
- NIH [5T32GM065086]
- Office of Science of the US Department of Energy [DE-AC05-00OR22725]
- Div Of Molecular and Cellular Bioscience
- Direct For Biological Sciences [1411482] Funding Source: National Science Foundation
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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.
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