Simulated single molecule microscopy with SMeagol

Abstract

Summary: SMeagol is a software tool to simulate highly realistic microscopy data based on spatial systems biology models, in order to facilitate development, validation and optimization of advanced analysis methods for live cell single molecule microscopy data. Availability and implementation: SMeagol runs on Matlab R2014 and later, and uses compiled binaries in C for reaction–diffusion simulations. Documentation, source code and binaries for Mac OS, Windows and Ubuntu Linux can be downloaded from http://smeagol.sourceforge.net. Contact: johan.elf@icm.uu.se Supplementary information: Supplementary data are available at Bioinformatics online.

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

“…24,88–93 For instance, it is now becoming more widely appreciated that neglecting measurement noise in single particle tracking (SPT) can be mistakenly interpreted as a signature of anomalous diffusion. 22 …”

Variational algorithms for analyzing noisy multi-state diffusion trajectories

“…To evaluate the capabilities of our new trajectory analysis only, we seek test data with known ground truth and sufficient realism to be experimentally relevant. We use simulated video microscopy [9] to produce realistic test data, run spot detection and localization using our standard methods (see Methods), but use our knowledge of the simulated ground truth to produce trajectories free from false positives and linking errors which may lead to bad performance that do not reflect the intrinsic quality of the trajectory analysis. We allow at most 3 consecutive missing positions before starting an new trajectory.…”

“…The ribosome-bound states further display spatial structure in the form of nucleoid exclusion [25], as well as non-exponential waiting times [26], since tRNA goes through several reaction steps before dissociating from the ribosome [27]. We constructed a simplified kinetic and spatial model incorporating these features and generated synthetic fluorescent microscopy data with 200 Hz frame rate [9], as shown in Fig. 4.…”

“…A fundamental unknown in many live cell SPT studies is the number of underlying molecular states, which may differ in, e.g., diffusion constant. Counting diffusive states in SPT data presents a statistical model selection problem that has so far only been solved with simplified noise models [2], which may be inappropriate in many live cell applications [7,9].…”

A model for the spatio-temporal design of gene regulatory circuits

“…of spatio-temporal gene regulation may turn decisive to elucidated these details. Although existing tools such as SmolDyn(54 ), eGFRD(55 ), SMeagol(56 ) and others (57 ) simulate spatial dynamics to various extents, they do not focus on gene regulatory circuits, which is the goal of our study.Our model builds on a previous definition of facilitated diffusion (36 ) that include the classification of transcription factors (TF) into two groups: of local and global. This is a crucial feature of the model.…”