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

Abstract

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.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]
  1. Vanderbilt Univ., Nashville, TN (United States)
  2. Univ. of California, Irvine, CA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF); National Institutes of Health (NIH)
OSTI Identifier:
1565685
Grant/Contract Number:  
[AC05-00OR22725]
Resource Type:
Accepted Manuscript
Journal Name:
Bioinformatics
Additional Journal Information:
[ Journal Volume: 34; Journal Issue: 4]; Journal ID: ISSN 1367-4803
Publisher:
Oxford University Press
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Shockley, Erin M., Vrugt, Jasper A., and Lopez, Carlos F. PyDREAM: high-dimensional parameter inference for biological models in python. United States: N. p., 2017. Web. doi:10.1093/bioinformatics/btx626.
Shockley, Erin M., Vrugt, Jasper A., & Lopez, Carlos F. PyDREAM: high-dimensional parameter inference for biological models in python. United States. doi:10.1093/bioinformatics/btx626.
Shockley, Erin M., Vrugt, Jasper A., and Lopez, Carlos F. Wed . "PyDREAM: high-dimensional parameter inference for biological models in python". United States. doi:10.1093/bioinformatics/btx626. https://www.osti.gov/servlets/purl/1565685.
@article{osti_1565685,
title = {PyDREAM: high-dimensional parameter inference for biological models in python},
author = {Shockley, Erin M. and Vrugt, Jasper A. and Lopez, Carlos F.},
abstractNote = {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.},
doi = {10.1093/bioinformatics/btx626},
journal = {Bioinformatics},
number = [4],
volume = [34],
place = {United States},
year = {2017},
month = {10}
}

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Cited by: 6 works
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    Works referencing / citing this record:

    Intrinsic limits of information transmission in biochemical signalling motifs
    journal, October 2018