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Recently we proposed an algorithm for the fast reconstruction of compactcontext-specific metabolic networks (FASTCORE) that allowed dropping thereconstruction time to the time order of seconds (Vlassis et al.,2014). Thisextremely low computational demand opens new possibilities for improving thequality of the models. Several rounds of model reconstruction, testing of themodel's predictions against real experimental data, curation steps of the inputmodel and the set of core reactions as well as cross-validations assays arerequired to reconstruct high-quality models. These semi-automated modelcurations steps are in such extend not possible with competing algorithms dueto their high computational demands. To adapt FASTCORE for the integration ofmicroarray data, we therefore propose a new workflow: FASTCORMICS. FASTCORMICSrequires as input microarray data and a Genome-scale reconstruction.FASTCORMICS is devoid of heuristic parameter settings and has a lowcomputational demand with overall building times in the order of a few minutes.FASTCORMICS preprocesses the microarrays data with the discretization toolBarcode (Zillox et al, 2007). Barcode uses prior knowledge on the intensitydistribution of each probe set for a given microarray platform to segregatebetween expressed genes and non-expressed genes. This preprocessing step allowscircumventing the need of setting a heuristic expression threshold, which iscritical for the output models as in response to this threshold alternativepathways or subsystems might be included or excluded, thereby heavily changingthe functionalities of the model.  In general, FASTCORMICS outperforms competing algorithms and allows obtaininghigh-quality, robust models in a high-throughput manner. This will allow theuse of metabolic modelling as routine process for the analysis of microarraydata e.g. in the field of personalized medicine.

Fast reconstruction of compact context-specific metabolic networks via integration of microarray data