Uncertainty Quantification for Multiscale Fusion Plasma Simulations with VECMA Toolkit
Ontology highlight
ABSTRACT:
SUBMITTER: Krzhizhanovskaya V
PROVIDER: S-EPMC7304739 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that "consumers" of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some 'enhanced' sampling approaches. We do not discuss systematic errors arising, e.g., from inaccuracy in the chosen model or force field.
| S-EPMC6286151 | biostudies-literature
Project description:In this article we review the key modeling tools available for simulating biomolecular systems. We consider recent developments and representative applications of mixed quantum mechanics/molecular mechanics (QM/MM), elastic network models (ENMs), coarse-grained molecular dynamics, and grid-based tools for calculating interactions between essentially rigid protein assemblies. We consider how the different length scales can be coupled, both in a sequential fashion (e.g. a coarse-grained or grid model using parameterization from MD simulations), and via concurrent approaches, where the calculations are performed together and together control the progression of the simulation. We suggest how the concurrent coupling approach familiar in the context of QM/MM calculations can be generalized, and describe how this has been done in the CHARMM macromolecular simulation package.
| S-EPMC6407689 | biostudies-literature
Project description:Multiscale simulations model phenomena across natural scales using monolithic or component-based code, running on local or distributed resources. In this work, we investigate the performance of distributed multiscale computing of component-based models, guided by six multiscale applications with different characteristics and from several disciplines. Three modes of distributed multiscale computing are identified: supplementing local dependencies with large-scale resources, load distribution over multiple resources, and load balancing of small- and large-scale resources. We find that the first mode has the apparent benefit of increasing simulation speed, and the second mode can increase simulation speed if local resources are limited. Depending on resource reservation and model coupling topology, the third mode may result in a reduction of resource consumption.
| S-EPMC4084531 | biostudies-literature
Project description:Smoldyn is a software package for stochastic modelling of spatial biochemical networks and intracellular systems. It was originally developed with an accurate off-lattice particle-based model at its core. This has recently been enhanced with the addition of a computationally efficient on-lattice model, which can be run stand-alone or coupled together for multiscale simulations using both models in regions where they are most required, increasing the applicability of Smoldyn to larger molecule numbers and spatial domains. Simulations can switch between models with only small additions to their configuration file, enabling users with existing Smoldyn configuration files to run the new on-lattice model with any reaction, species or surface descriptions they might already have.Source code and binaries freely available for download at www.smoldyn.org, implemented in C/C++ and supported on Linux, Mac OSX and MS Windows.
| S-EPMC4495299 | biostudies-other
Project description:Collateral circulation in the circle of Willis (CoW), closely associated with disease mechanisms and treatment outcomes, can be effectively investigated using one-dimensional-zero-dimensional hemodynamic simulations. As the entire cardiovascular system is considered in the simulation, it captures the systemic effects of local arterial changes, thus reproducing collateral circulation that reflects biological phenomena. The simulation facilitates rapid assessment of clinically relevant hemodynamic quantities under patient-specific conditions by incorporating clinical data. During patient-specific simulations, the impact of clinical data uncertainty on the simulated quantities should be quantified to obtain reliable results. However, as uncertainty quantification (UQ) is time-consuming and computationally expensive, its implementation in time-sensitive clinical applications is considered impractical. Therefore, we constructed a surrogate model based on machine learning using simulation data. The model accurately predicts the flow rate and pressure in the CoW in a few milliseconds. This reduced computation time enables the UQ execution with 100 000 predictions in a few minutes on a single CPU core and in less than a minute on a GPU. We performed UQ to predict the risk of cerebral hyperperfusion (CH), a life-threatening condition that can occur after carotid artery stenosis surgery if collateral circulation fails to function appropriately. We predicted the statistics of the postoperative flow rate increase in the CoW, which is a measure of CH, considering the uncertainties of arterial diameters, stenosis parameters, and flow rates measured using the patients' clinical data. A sensitivity analysis was performed to clarify the impact of each uncertain parameter on the flow rate increase. Results indicated that CH occurred when two conditions were satisfied simultaneously: severe stenosis and when arteries of small diameter serve as the collateral pathway to the cerebral artery on the stenosis side. These findings elucidate the biological aspects of cerebral circulation in terms of the relationship between collateral flow and CH.
| S-EPMC9307280 | biostudies-literature
Project description:According to Mayr, polar organic synthesis can be rationalized by a simple empirical relationship linking bimolecular rate constants to as few as three reactivity parameters. Here, we propose an extension to Mayr's reactivity method that is rooted in uncertainty quantification and transforms the reactivity parameters into probability distributions. Through uncertainty propagation, these distributions can be transformed into uncertainty estimates for bimolecular rate constants. Chemists can exploit these virtual error bars to enhance synthesis planning and to decrease the ambiguity of conclusions drawn from experimental data. We demonstrate the above at the example of the reference data set released by Mayr and co-workers [J. Am. Chem. Soc. 2001, 123, 9500; J. Am. Chem. Soc. 2012, 134, 13902]. As by-product of the new approach, we obtain revised reactivity parameters for 36 π-nucleophiles and 32 benzhydrylium ions.
| S-EPMC9314972 | biostudies-literature
Project description:Protein functions require specific structures frequently coupled with conformational changes. The scale of the structural dynamics of proteins spans from the atomic to the molecular level. Theoretically, all-atom molecular dynamics (MD) simulation is a powerful tool to investigate protein dynamics because the MD simulation is capable of capturing conformational changes obeying the intrinsically structural features. However, to study long-timescale dynamics, efficient sampling techniques and coarse-grained (CG) approaches coupled with all-atom MD simulations, termed multiscale MD simulations, are required to overcome the timescale limitation in all-atom MD simulations. Here, we review two examples of rotary motor proteins examined using free energy landscape (FEL) analysis and CG-MD simulations. In the FEL analysis, FEL is calculated as a function of reaction coordinates, and the long-timescale dynamics corresponding to conformational changes is described as transitions on the FEL surface. Another approach is the utilization of the CG model, in which the CG parameters are tuned using the fluctuation matching methodology with all-atom MD simulations. The long-timespan dynamics is then elucidated straightforwardly by using CG-MD simulations.
| S-EPMC5899732 | biostudies-literature
Project description:The catalytic promiscuity and fidelity of cytochrome P450 enzymes are widespread in the skeletal modification of terpenoid natural products and have attracted much attention. CYP76AH1 is involved in key modification reactions in the biosynthetic pathway of tanshinone, a well-known medicinal norditerpenoid. In this work, classical molecular dynamic simulations, metadynamics, and DFT calculations were performed to investigate the protein conformational dynamics, ligand binding poses, and catalytic reaction mechanism in wide-type and mutant CYP76AH1. Our results not only reveal a plausible enzymatic mechanism for mutant CYP76AH1 leading to various products but also provide valuable guidance for rational protein engineering of the CYP76 family.
| S-EPMC8207200 | biostudies-literature
Project description:When photoactive molecules interact strongly with confined light modes in optical cavities, new hybrid light-matter states form. They are known as polaritons and correspond to coherent superpositions of excitations of the molecules and of the cavity photon. The polariton energies and thus potential energy surfaces are changed with respect to the bare molecules, such that polariton formation is considered a promising paradigm for controlling photochemical reactions. To effectively manipulate photochemistry with confined light, the molecules need to remain in the polaritonic state long enough for the reaction on the modified potential energy surface to take place. To understand what determines this lifetime, we have performed atomistic molecular dynamics simulations of room-temperature ensembles of rhodamine chromophores strongly coupled to a single confined light mode with a 15 fs lifetime. We investigated three popular experimental scenarios and followed the relaxation after optically pumping (i) the lower polariton, (ii) the upper polariton, or (iii) uncoupled molecular states. The results of the simulations suggest that the lifetimes of the optically accessible lower and upper polaritons are limited by (i) ultrafast photoemission due to the low cavity lifetime and (ii) reversible population transfer into the "dark" state manifold. Dark states are superpositions of molecular excitations but with much smaller contributions from the cavity photon, decreasing their emission rates and hence increasing their lifetimes. We find that population transfer between polaritonic modes and dark states is determined by the overlap between the polaritonic and molecular absorption spectra. Importantly, excitation can also be transferred "upward" from the lower polariton into the dark-state reservoir due to the broad absorption spectra of the chromophores, contrary to the common conception of these processes as a "one-way" relaxation from the dark states down to the lower polariton. Our results thus suggest that polaritonic chemistry relying on modified dynamics taking place within the lower polariton manifold requires cavities with sufficiently long lifetimes and, at the same time, strong light-matter coupling strengths to prevent the back-transfer of excitation into the dark states.
| S-EPMC6914212 | biostudies-literature
Project description:High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.
| S-EPMC6059398 | biostudies-other