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