How to learn from inconsistencies: Integrating molecular simulations with experimental data

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Molecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modeling tool to interpret experimental measurements, and to use experimental data to refine our biophysical models. Thus, explicit integration and synergy between molecular simulations and experiments is fundamental for furthering our understanding of biological processes. This is especially true in the case where discrepancies between measured and simulated observables emerge. In this chapter, we provide an overview of some of the core ideas behind methods that were developed to improve the consistency between experimental information and numerical predictions. We distinguish between situations where experiments are used to refine our understanding and models of specific systems, and situations where experiments are used more generally to refine transferable models. We discuss different philosophies and attempt to unify them in a single framework. Until now, such integration between experiments and simulations have mostly been applied to equilibrium data, and we discuss more recent developments aimed to analyze time-dependent or time-resolved data.

Original languageEnglish
Title of host publicationComputational Approaches for Understanding Dynamical Systems : Protein Folding and Assembly
EditorsBirgit Strodel, Bogdan Barz
Number of pages54
PublisherAcademic Press
Publication date2020
Pages123-176
Chapter3
ISBN (Print)978-0-12-821135-9
DOIs
Publication statusPublished - 2020
SeriesProgress in Molecular Biology and Translational Science
Volume170
ISSN1877-1173

    Research areas

  • Bayesian methods, Force fields, Integration with experiments, Maximum entropy, Molecular simulations, Time-dependent, Time-resolved

ID: 237999356