RDNets

Online high-throughput mathematical analysis of reaction-diffusion systems

RDNets performs an automated linear stability analysis to screen for reaction-diffusion network topologies that can form self-organizing spatial patterns. The software is optimized to analyze reaction-diffusion signaling networks with cell-autonomous factors. The analysis can be constrained with qualitative and quantitative data.

The online software can be accessed at:

www.rdnets.com

Original publication:
Marcon L, Diego X, Sharpe J, Müller P. High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals. Elife. 2016 Apr 8;5. pii: e14022. doi: 10.7554/eLife.14022.

PyFDAP

Python software to analyze Fluorescence Decay After Photoconversion (FDAP) data sets

Download the PyFDAP software, user guide, and a test data set:

http://people.tuebingen.mpg.de/mueller-lab/

Original publication:
Bläßle A, Müller P. PyFDAP: automated analysis of fluorescence decay after photoconversion (FDAP) experiments. Bioinformatics. 2015 Mar 15;31(6):972-4. doi: 10.1093/bioinformatics/btu735. Epub 2014 Nov 6.

PyFDAP on GitHub:

https://github.com/mueller-lab/PyFDAP

PyFRAP

Python software to analyze Fluorescence Recovery After Photobleaching (FRAP) data sets

Download the PyFRAP software, user guide, and a test data set:
https://mueller-lab.github.io/PyFRAP

Original publication:

Bläßle A, Soh G, Braun T, Mörsdorf D, Preiß H, Jordan BM, Müller P (2018). Quantitative diffusion measurements using the open-source software PyFRAP. Nature Communications, doi: 10.1038/s41467-018-03975-6.