Systems biology approaches to the study of a cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstructionof such models from high-throughput data can routinely involve large numbers of simulations under different conditions, expensive cross-validation tests,and threshold tuning by trial and error, thereby necessitating the development of fast algorithms. We propose here different versions of FASTCORE thatallow for the very fast building of context-specific models. FASTCORE takes as input a core set of reactions that are known to be active in the context ofinterest (e.g., cell or tissue or disease), and completes this set by adding a minimal number of reactions from a generic metabolic model to build astoichiometrically consistent network model.The originality of this algorithm resides on his computational speed (less than 1 minute against weeks for his direct concurrent) and the search strategybased on an iterative expansion of the core set via a carefully crafted objective function and convex programming, resulting in fast and compactreconstructions. Experiments on liver and other data sets demonstrate speed-ups of several orders of magnitude over competing methods.Given its simplicity and its excellent performance, FASTCORE allows for the first time, to take fully advantage of the existing high-throughput technologies(microrarray and GC-MS platforms) also established in Luxembourg, to build in serie metabolic models for different of cells, diseases and patients.At least two publications in high – medium ranking journals for the different versions of FASTCORE and their application to the integrated study ofmonocyte differentiation are a realistic goal for this project.