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Journal article

Metainference : a Bayesian inference method for heterogeneous systems

  • Bonomi, Massimiliano Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
  • Camilloni, Carlo Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
  • Cavalli, Andrea Institute for Research in Biomedicine (IRB), Faculty of Biomedical Sciences, Università della Svizzera italiana, Switzerland
  • Vendruscolo, Michele Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
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    22.01.2016
Published in:
  • Science advances. - 2016, vol. 2, no. 1, p. e1501177
Statistical inference
Structural biology
Maximum entropy principle
English Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called “metainference,” that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.
Language
  • English
Classification
Medicine
License
CC BY-NC
Open access status
gold
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Persistent URL
https://n2t.net/ark:/12658/srd1319103
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