How to learn from inconsistencies: Integrating molecular simulations with experimental data
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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 language | English |
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Title of host publication | Computational Approaches for Understanding Dynamical Systems : Protein Folding and Assembly |
Editors | Birgit Strodel, Bogdan Barz |
Number of pages | 54 |
Publisher | Academic Press |
Publication date | 2020 |
Pages | 123-176 |
Chapter | 3 |
ISBN (Print) | 978-0-12-821135-9 |
DOIs | |
Publication status | Published - 2020 |
Series | Progress in Molecular Biology and Translational Science |
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Volume | 170 |
ISSN | 1877-1173 |
- Bayesian methods, Force fields, Integration with experiments, Maximum entropy, Molecular simulations, Time-dependent, Time-resolved
Research areas
ID: 237999356