  Background: Nowadays, the reconstruction of genome scale metabolic models is
a non-automatized and interactive process based on decision taking. This
lengthy process usually requires a full year of one person's work in order to
satisfactory collect, analyze and validate the list of all metabolic reactions
present in a specific organism. In order to write this list, one manually has
to go through a huge amount of genomic, metabolomic and physiological
information. Currently, there is no optimal algorithm that allows one to
automatically go through all this information and generate the models taking
into account probabilistic criteria of unicity and completeness that a
biologist would consider. Results: This work presents the automation of a
methodology for the reconstruction of genome scale metabolic models for any
organism. The methodology that follows is the automatized version of the steps
implemented manually for the reconstruction of the genome scale metabolic model
of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for
the reconstruction are implemented in a computational platform (COPABI) that
generates the models from the probabilistic algorithms that have been
developed. Conclusions: For validation of the developed algorithm robustness,
the metabolic models of several organisms generated by the platform have been
studied together with published models that have been manually curated. Network
properties of the models like connectivity and average shortest mean path of
the different models have been compared and analyzed.
