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Background  Steady-state  13 C-based metabolic flux analysis ( 13 C-MFA) is the most powerful method available for the quantification of intracellular fluxes. These analyses include concertedly linked experimental and computational stages: ( i ) assuming the metabolic model and optimizing the experimental design; ( ii ) feeding the investigated organism using a chosen  13 C-labeled substrate (tracer); ( iii ) measuring the extracellular effluxes and detecting the  13 C-patterns of intracellular metabolites; and ( iv ) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages,  13 C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based  13 C-MFA is an important factor for PLE implementation.    Results  The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2,  in silico  simulation confirmed that flux precision is improved when  13 C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes ( i ) the optimization of the experimental design, ( ii ) the computation of the flux parameters from LEs data, ( iii ) goodness-of-fit testing of the model’s adequacy, ( iv ) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and ( v ) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method.    Conclusions  The developed  open-source  OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state  13 C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.    Electronic supplementary material  The online version of this article (doi:10.1186/s12934-014-0152-x) contains supplementary material, which is available to authorized users.

OpenFLUX2: 13C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments