Single-molecule studies can be used to study biological processes directly and in real-time. In particular, the fluorescence energy transfer between reporter dye molecules attached to specific sites on macromolecular complexes can be used to infer distance information. When several measurements are combined, the information can be used to determine the position and conformation of certain domains with respect to the complex. However, data analysis schemes that include all experimental uncertainties are highly complex, and the outcome depends on assumptions about the state of the dye molecules. Here, we present a new analysis algorithm using Bayesian parameter estimation based on Markov Chain Monte Carlo sampling and parallel tempering termed Fast-NPS that can analyse large smFRET networks in a relatively short time and yields the position of the dye molecules together with their respective uncertainties. Moreover, we show what effects different assumptions about the dye molecules have on the outcome. We discuss the possibilities and pitfalls in structure determination based on smFRET using experimental data for an archaeal transcription pre-initiation complex, whose architecture has recently been unravelled by smFRET measurements.
The quality of the inverse approach in electroencephalography (EEG) source analysis is -among other things -depending on the accuracy of the forward modeling approach, i.e., the simulation of the electric potential for a known dipole source in the brain. Here, we use multilayer sphere modeling scenarios to investigate the performance of three different finite element method (FEM) based EEG forward approachessubtraction, Venant and partial integration -in the presence of tissue conductivity anisotropy in the source space. In our studies, the effect of anisotropy on the potential is related to model errors when ignoring anisotropy and to numerical errors, convergence behavior and computational speed of the different FEM approaches. Three different source space anisotropy models that best represent adult, child and premature baby volume conduction scenarios, are used. Major findings of the study include (1) source space conductivity anisotropy has a significant effect on electric potential computation: The effect increases with increasing anisotropy ratio; (2) with numerical errors far below anisotropy effects, all three FEM approaches are able to model source space anisotropy accordingly, with the Venant approach offering the best compromise between accuracy and computational speed;(3) FE meshes have to be fine enough in the subdomain between the source and the sensors that capture its main activity. We conclude that, especially for the analysis of cortical development, but also for more general applications using EEG source analysis techniques, source space conductivity anisotropy should be modeled and the FEM Venant approach is an appropriate method.
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