A novel aspect of DataRail is its focus on data integrity through the use of a flexible metadata structure. As data is processed in the toolbox, DataRail keeps track of data origins and creates an internal copy of functions and parameters that are used. Thus, a DataRail Project is self-contained and can serve as a way to exchange both data and models.

The toolbox comprises the data-handling component of a larger effort in the Cell Decision Processes Center at MIT and Harvard Medical School to create Systems Biology pipeline software (SBPipeline) for managing data, protocols, assumptions, and models.

DataRail was developed by members of the Sorger Lab, Harvard Medical School Department of Systems Biology and of the Lauffenburger Lab, MIT Biological Engineering. The primary developers of DataRail are Julio Saez-Rodriguez and Arthur Goldsipe; Jeremy Muhlich and Bjorn Millard also assist in the development of DataRail and are developing additional components of SBPipeline.

Data can be exported in a minimal information standard (MIDAS-Minimal Information for Data Analysis in Systems Biology) as well as in specific formats for PottersWheel, CellNetAnalyzer and CellNetOptimizer.

is a MATLAB toolbox for creating logic-based models signaling networks and train them agains high-throughput biochemical data.

You can find more details about DataRail in the following article:

J. Saez-Rodriguez, A. Goldsipe, J. Muhlich, L. Alexopoulos, B. Millard, D. A. Lauffenburger, P. K. Sorger Flexible Informatics for Linking Experimental Data to Mathematical Models via DataRail. Bioinformatics, 24:6, 840-847 (2008). doi:10.1093/bioinformatics/btn018 JSR and AG contributed equally to this work.

Click here to download DataRail and some additional materials such as slides of a presentation or a tutorial.

05/22/09 - New version 1.1 - Click here to download

New features:

- Simplified data input format

- Extended normalizations routines

- Bugs fixed

- Coming soon: Coupling to logic-based pathway calibration (CellNetOptimizer) and Bayesian inference