After this data preprocessing steps, metabolic flux ratios are de

After this data preprocessing steps, metabolic flux ratios are determined. Based on the known atom transitions occurring during the amino acid biosynthesis from central carbon metabolites, the MDVs of these amino acid precursors (MDVM) are inferred from the amino acid labeling patterns (MDVAA). This is accomplished within FiatFlux by a least square fitting procedure. Metabolic flux ratios are then estimated from these MDVs via probabilistic species specific equations. Figure 1 Workflow

of 13C MFA of FiatFlux [5]. The automatic tasks are listed on the left, the manual user interactions on the right side. Net fluxes in the central carbon metabolism network are computed by MFA. Under steady Inhibitors,research,lifescience,medical state conditions, the mass balances of the metabolites of a stoichiometric model of an appropriate reaction network form a linear equation system, which is made mathematically accessible by transformation to matrix

notation. The system is further constrained by experimentally determined reaction rates and the calculated flux ratios, Inhibitors,research,lifescience,medical which are translated into linear equations. Generally, this constrained equation system is fully or even overly determined and a flux distribution Inhibitors,research,lifescience,medical is computed by solving the system by a least square optimization. Fluxes are given with confidence intervals estimated from the error of the extracellular fluxes and the flux ratios. 2. Results Inhibitors,research,lifescience,medical and Discussion In order to obtain a standardized analysis process and to increase the amount of data sets that can be analyzed in a certain amount of time, the interactive flux analysis procedure has to be automated. FiatFlux [5], for which we exemplify the automation procedure, is a MATLAB-based software, freely available for academic purposes. The developed automated version of this software has been made Inhibitors,research,lifescience,medical learn more remotely accessible by integrating

it as services in the Bio-jETI framework [22,23], which has also been used to build the actual analysis workflows. It is based on the jETI electronic tool integration platform [24] and the jABC workflow modeling framework [25]. The name Bio-jETI refers to the distributed nature of Idoxuridine many bioinformatics applications, which profit from the easy provisioning and integration of remote services via the jETI technology. We call this process-based 13C-flux analysis software, which in its current version allows the user to work with the FiatFlux functionality in a highly flexible and automated manner, Flux-P. In order to make the FiatFlux functionality available for workflow integration, we first developed a “headless” variant of the software, which provides programmatic access to its functions. Afterwards, it was a straightforward process to integrate the required pieces of FiatFlux functionality into the Bio-jETI platform and to use the new services for the definition of the data processing workflows.

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