PyDREAM: high-dimensional parameter inference for biological models in python
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 distribut...
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| Published in | Bioinformatics Vol. 34; no. 4; pp. 695 - 697 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
15.02.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI | 10.1093/bioinformatics/btx626 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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.
PyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM.
c.lopez@vanderbilt.edu.
Supplementary data are available at Bioinformatics online. 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. 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. 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.SummaryBiological 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.PyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM.Availability and implementationPyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM.c.lopez@vanderbilt.edu.Contactc.lopez@vanderbilt.edu.Supplementary data are available at Bioinformatics online.Supplementary informationSupplementary data are available at Bioinformatics online. |
| Author | Vrugt, Jasper A Shockley, Erin M Lopez, Carlos F |
| AuthorAffiliation | 2 Department of Civil and Environmental Engineering, University of California Irvine, 4130 Engineering Gateway, Irvine, CA, USA 3 Department of Earth System Science, University of California Irvine, 3200 Croul Hall St, Irvine, CA, USA 1 Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN, USA |
| AuthorAffiliation_xml | – name: 1 Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN, USA – name: 2 Department of Civil and Environmental Engineering, University of California Irvine, 4130 Engineering Gateway, Irvine, CA, USA – name: 3 Department of Earth System Science, University of California Irvine, 3200 Croul Hall St, Irvine, CA, USA |
| Author_xml | – sequence: 1 givenname: Erin M orcidid: 0000-0001-8114-8098 surname: Shockley fullname: Shockley, Erin M organization: Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN, USA – sequence: 2 givenname: Jasper A surname: Vrugt fullname: Vrugt, Jasper A organization: Department of Civil and Environmental Engineering, University of California Irvine, 4130 Engineering Gateway, Irvine, CA, USA – sequence: 3 givenname: Carlos F orcidid: 0000-0003-3668-7468 surname: Lopez fullname: Lopez, Carlos F email: c.lopez@vanderbilt.edu organization: Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29028896$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1565685$$D View this record in Osti.gov |
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Summary
Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration... Biological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against... |
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| SubjectTerms | Algorithms Applications Notes BASIC BIOLOGICAL SCIENCES Calibration Computational Biology - methods Markov Chains MATHEMATICS AND COMPUTING Models, Biological Monte Carlo Method Software Uncertainty |
| Title | PyDREAM: high-dimensional parameter inference for biological models in python |
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