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

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Standard

How to learn from inconsistencies : Integrating molecular simulations with experimental data. / Orioli, Simone; Larsen, Andreas Haahr; Bottaro, Sandro; Lindorff-Larsen, Kresten.

Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly. ed. / Birgit Strodel; Bogdan Barz. Academic Press, 2020. p. 123-176 (Progress in Molecular Biology and Translational Science, Vol. 170).

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Orioli, S, Larsen, AH, Bottaro, S & Lindorff-Larsen, K 2020, How to learn from inconsistencies: Integrating molecular simulations with experimental data. in B Strodel & B Barz (eds), Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly. Academic Press, Progress in Molecular Biology and Translational Science, vol. 170, pp. 123-176. https://doi.org/10.1016/bs.pmbts.2019.12.006

APA

Orioli, S., Larsen, A. H., Bottaro, S., & Lindorff-Larsen, K. (2020). How to learn from inconsistencies: Integrating molecular simulations with experimental data. In B. Strodel, & B. Barz (Eds.), Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly (pp. 123-176). Academic Press. Progress in Molecular Biology and Translational Science Vol. 170 https://doi.org/10.1016/bs.pmbts.2019.12.006

Vancouver

Orioli S, Larsen AH, Bottaro S, Lindorff-Larsen K. How to learn from inconsistencies: Integrating molecular simulations with experimental data. In Strodel B, Barz B, editors, Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly. Academic Press. 2020. p. 123-176. (Progress in Molecular Biology and Translational Science, Vol. 170). https://doi.org/10.1016/bs.pmbts.2019.12.006

Author

Orioli, Simone ; Larsen, Andreas Haahr ; Bottaro, Sandro ; Lindorff-Larsen, Kresten. / How to learn from inconsistencies : Integrating molecular simulations with experimental data. Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly. editor / Birgit Strodel ; Bogdan Barz. Academic Press, 2020. pp. 123-176 (Progress in Molecular Biology and Translational Science, Vol. 170).

Bibtex

@inbook{b5c561e4bd594483896e6912e4384e67,
title = "How to learn from inconsistencies: Integrating molecular simulations with experimental data",
abstract = "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.",
keywords = "Bayesian methods, Force fields, Integration with experiments, Maximum entropy, Molecular simulations, Time-dependent, Time-resolved",
author = "Simone Orioli and Larsen, {Andreas Haahr} and Sandro Bottaro and Kresten Lindorff-Larsen",
year = "2020",
doi = "10.1016/bs.pmbts.2019.12.006",
language = "English",
isbn = "978-0-12-821135-9",
series = "Progress in Molecular Biology and Translational Science",
publisher = "Academic Press",
pages = "123--176",
editor = "Birgit Strodel and Bogdan Barz",
booktitle = "Computational Approaches for Understanding Dynamical Systems",
address = "United States",

}

RIS

TY - CHAP

T1 - How to learn from inconsistencies

T2 - Integrating molecular simulations with experimental data

AU - Orioli, Simone

AU - Larsen, Andreas Haahr

AU - Bottaro, Sandro

AU - Lindorff-Larsen, Kresten

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Bayesian methods

KW - Force fields

KW - Integration with experiments

KW - Maximum entropy

KW - Molecular simulations

KW - Time-dependent

KW - Time-resolved

U2 - 10.1016/bs.pmbts.2019.12.006

DO - 10.1016/bs.pmbts.2019.12.006

M3 - Book chapter

C2 - 32145944

AN - SCOPUS:85078773649

SN - 978-0-12-821135-9

T3 - Progress in Molecular Biology and Translational Science

SP - 123

EP - 176

BT - Computational Approaches for Understanding Dynamical Systems

A2 - Strodel, Birgit

A2 - Barz, Bogdan

PB - Academic Press

ER -

ID: 237999356