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

TL;DR

This paper reviews methods for integrating molecular simulations with experimental data to improve biological models, especially addressing discrepancies and extending to time-resolved data for dynamic processes.

Contribution

It unifies various philosophies of combining simulations and experiments into a single framework, highlighting recent advances in analyzing time-dependent data.

Findings

Methods improve consistency between simulations and experiments.

Framework unifies different approaches to data-model integration.

Recent developments extend analysis to time-resolved 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 modelling 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 analyse time-dependent or time-resolved data.