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Combining Experiments and Simulations Using the Maximum Entropy Principle

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Listed:
  • Wouter Boomsma
  • Jesper Ferkinghoff-Borg
  • Kresten Lindorff-Larsen

Abstract

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.

Suggested Citation

  • Wouter Boomsma & Jesper Ferkinghoff-Borg & Kresten Lindorff-Larsen, 2014. "Combining Experiments and Simulations Using the Maximum Entropy Principle," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-9, February.
  • Handle: RePEc:plo:pcbi00:1003406
    DOI: 10.1371/journal.pcbi.1003406
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    References listed on IDEAS

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    1. Kresten Lindorff-Larsen & Jesper Ferkinghoff-Borg, 2009. "Similarity Measures for Protein Ensembles," PLOS ONE, Public Library of Science, vol. 4(1), pages 1-13, January.
    2. Marlin U. Thomas, 1979. "Technical Note—A Generalized Maximum Entropy Principle," Operations Research, INFORMS, vol. 27(6), pages 1188-1196, December.
    3. Kresten Lindorff-Larsen & Robert B. Best & Mark A. DePristo & Christopher M. Dobson & Michele Vendruscolo, 2005. "Simultaneous determination of protein structure and dynamics," Nature, Nature, vol. 433(7022), pages 128-132, January.
    4. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    5. Einat Granot-Atedgi & Gašper Tkačik & Ronen Segev & Elad Schneidman, 2013. "Stimulus-dependent Maximum Entropy Models of Neural Population Codes," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-14, March.
    6. Simon Olsson & Jes Frellsen & Wouter Boomsma & Kanti V Mardia & Thomas Hamelryck, 2013. "Inference of Structure Ensembles of Flexible Biomolecules from Sparse, Averaged Data," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-7, November.
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    1. Matteo Tiberti & Elena Papaleo & Tone Bengtsen & Wouter Boomsma & Kresten Lindorff-Larsen, 2015. "ENCORE: Software for Quantitative Ensemble Comparison," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-16, October.

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