Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN 37212, USA.
Department of Civil and Environmental Engineering, University of California Irvine, 4130 Engineering Gateway, Irvine, CA 92697-2175, USA.
Bioinformatics. 2018 Feb 15;34(4):695-697. doi: 10.1093/bioinformatics/btx626.
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.
Supplementary data are available at Bioinformatics online.
生物模型包含许多参数，这些参数的值很难通过实验直接测量，因此需要根据实验数据进行校准。马尔可夫链蒙特卡罗（MCMC）方法适合估计多元后验模型参数分布，但这些方法在高维搜索空间中可能表现出缓慢或过早的收敛。在这里，我们介绍了 PyDREAM，这是由 Vrugt 和 ter Braak（2008 年）以及 Laloy 和 Vrugt（2012 年）开发的（多次尝试）微分进化自适应 metropolis [DREAM（ZS）]算法的 Python 实现。PyDREAM 为复杂、参数丰富的模型提供了出色的性能，并充分利用了分布式计算资源，方便了 CPU 密集型生物模型的参数推断和不确定性估计。
PyDREAM 根据 GNU GPLv3 许可证免费提供，可以从 Lopez 实验室的 GitHub 存储库 http://github.com/LoLab-VU/PyDREAM 获得。
补充数据可在生物信息学在线获得。
