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

Erin M Shockley1, Jasper A Vrugt2,3, Carlos F Lopez1

  • 1Department of Biochemistry, Vanderbilt University, 2215 Garland Avenue, Nashville, TN 37212, USA.

Bioinformatics (Oxford, England)
|October 14, 2017
PubMed
Summary
This summary is machine-generated.

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PyDREAM is a new Python tool that improves parameter estimation for complex biological models. It uses Differential Evolution Adaptive Metropolis (DREAM) to efficiently calibrate model parameters against experimental data.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Biological models often have parameters difficult to measure directly.
  • Parameter calibration against experimental data is crucial for model accuracy.
  • Markov chain Monte Carlo (MCMC) methods can be slow for high-dimensional biological models.

Purpose of the Study:

  • Introduce PyDREAM, a Python implementation of the DREAM(ZS) algorithm.
  • Improve parameter inference and uncertainty estimation for complex biological models.
  • Facilitate the use of distributed computing for CPU-intensive biological modeling.

Main Methods:

  • Implemented the Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm in Python.
  • Utilized MCMC methods for multivariate posterior model parameter distribution estimation.

Related Experiment Videos

  • Leveraged distributed computing for enhanced performance.

    • Main Results:

    • PyDREAM demonstrates excellent performance for parameter-rich biological models.
    • The tool facilitates efficient parameter inference and uncertainty estimation.
    • Achieved improved convergence in high-dimensional search spaces compared to traditional MCMC.

    Conclusions:

    • PyDREAM offers an efficient solution for calibrating complex biological models.
    • The software enhances the ability to estimate parameters and quantify uncertainty.
    • PyDREAM is a valuable tool for computational and systems biology research.