Analysis tools

Published projects:

Single molecule tracking and analysis framework including theory-predicted parameter settings

Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

Timo Kuhn, Johannes Hettich, J. Christof M. Gebhardt

Computer aided interactive gene network simulations including stochastic molecular kinetics and noise

Recent next generation sequencing and single molecule methodologies provide functional insights into gene regulatory networks beyond Boolean interactions, including the number, binding modes and kinetic rates of transcription factors and the kinetics of gene bursting. We report CaiNet, a fast computer aided interactive network simulation environment to set up and simulate arbitrary gene regulatory networks. CaiNet automatically compiles a network laid out in a graphical user interface into a fast hybrid stochastic-deterministic simulation framework without further mathematical knowledge or input by the user. Stochastic noise can optionally be omitted for simplified deterministic solutions. We validate CaiNet by comparison with Gillespie simulations using an auto feedback motive. We apply CaiNet to the circadian clock and find that temporally modulated external input signals allow regulating the periodicity of oscillations in a nested network topology. We further use CaiNet to simulate the temporal behavior of the pluripotency network, using published kinetic parameters where possible, as it transits from the naive state to germ layer lineages upon changes in signal inputs.

Johannes Hettich, View ORCID ProfileJ. Christof M. Gebhardt
https://doi.org/10.1101/872374

Inferring quantity and qualities of superimposed reaction rates in single molecule survival time distributions

Actions of molecular species, for example binding of transcription factors to chromatin, are intrinsically stochastic and may comprise several mutually exclusive pathways. Inverse Laplace transformation in principle resolves the rate constants and frequencies of superimposed reaction processes, however current approaches are challenged by single molecule fluorescence time series prone to photobleaching. Here, we present a genuine rate identification method (GRID) that infers the quantity, rates and frequencies of dissociation processes from single molecule fluorescence survival time distributions using a dense grid of possible decay rates. In particular, GRID is able to resolve broad clusters of rate constants not accessible to common models of one to three exponential decay rates. We validate GRID by simulations and apply it to the problem of in-vivo TF-DNA dissociation, which recently gained interest due to novel single molecule imaging technologies. We consider dissociation of the transcription factor CDX2 from chromatin. GRID resolves distinct, decay rates and identifies residence time classes overlooked by other methods. We confirm that such sparsely distributed decay rates are compatible with common models of TF sliding on DNA.

https://www.biorxiv.org/content/10.1101/679258v1

Software https://gitlab.com/GebhardtLab/GRID