Fig. 1.

Process diagram for model-centric information management in DataRail. Measurements generated using one or more methods (left side of diagram) are processed to output new knowledge (right); hypothesis testing links modeling and measurement in an iterative cycle. Processes and entities within the red box have been implemented; those outside the box remain to be completed; dotted lines denote external processes that have been linked to DataRail. Experimental measurements are first converted into a MIDAS format using one or more routines (pink lozenges; see text for details) and then used to assemble a multi-dimensional primary data array (green). Alternatively, an empty MIDAS-compliant spreadsheet is generated using a Java utility and experimental values then entered. Algorithms for normalization, scaling, discretization, etc. transform the data to create new data arrays (orange) that can then be modeled using internal or external routines. Finally, analysis and visualization assist in knowledge generation. The calibration of kinetic and Boolean models is not shown explicitly, although it constitutes a critical and complicated step in the overall workflow of systems biology that is as-yet external to DataRail.