cdf data sets. The repeated execution of the FiatFlux computation steps defined in the workflow parts A-C of Figure 3 can be realized without further programming, since the standard library of SIBs that is provided with the jABC software

contains a number of functions for often recurring tasks, for example for file management and processing of data collections. In FiatFlux, all experimental data have to be entered manually by the user at different steps of the analysis procedure and at different parts in the GUI. For Flux-P, we defined a simple table structure that provides the experimental Inhibitors,research,lifescience,medical parameters for numerous data sets in a single file. Each line of the table represents one data set and contains a number of defined entries that specify all data required for the analysis. The table has to be stored in a comma-separated format (.csv file). This format can be exported from all common spreadsheet programs, thus researchers can continue

to document Inhibitors,research,lifescience,medical their experiments within MS Excel, OpenOffice Calc or other. Extension of the workflow in Figure 3 (boxes A-C) with box D enables the processing of several data sets: The user has to specify the working directory, the MS specific data file and the .csv file. The latter is read and split into its lines using a regular expression. Each line (containing the information for one data set) is split into its separate entries (again via a regular expression), which are used Inhibitors,research,lifescience,medical as parameters for the Flux-P functions in the current Inhibitors,research,lifescience,medical iteration. All these actions are called by the SIB ‘process csv file’. As user input is only required once at the beginning, this workflow

is able to process very large sets of input data autonomously, speeding up the analysis procedure significantly. The modular structure of FiatFux [5] allows the calculation of flux distributions using flux ratios of complementary 13C labeling experiments. Such a combined analysis is shown in Figure 4A: Metabolic flux ratios of two 13C data files are calculated and used EGFR inhibitors cancer together for the subsequent netFlux analysis. Figure 4B shows another workflow variant. Here, instead of using Inhibitors,research,lifescience,medical one of the preconfigured networks, a custom metabolic network is uploaded by the user and processed via a special SIB, which translates those the content of the text file into the Flux-P model structure. The subsequent analysis is identical to the process described above. Further custom process models are conceivable and can be defined with the same ease as in the illustrated examples. Of course, these workflows variants can also be run in a batch-processing manner as depicted in Figure 3D. Figure 4 Alternative Flux-P workflows enabling the combined analysis of complementary 13C data sets (A) and the use of custom network models (B). 2.8. Evaluation of Flux-P To assess the performance of Flux-P and the reliability of the calculated flux ratios and fluxes, the tool was tested with 13C data from labeling experiments with E.