The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. 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. 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, 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. 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.

The temporal evolution of the electrical signal generated by Pinus halepensis was measured in a sample of 15 trees. Weekly experiments were carried out during a long-term campaign lasting over a year, while trials with a high frequency of measurements were also performed during several days. In the latter case, day-night oscillations of the electrical magnitudes were observed. Additionally, punctual meteorological events such as rainfall and electrical storms affect the electrical signal as well. The measured electrical intensity grows exponentially with the voltage. In fact, no electrical intensity that exceeds the threshold of 0.01 μA is gathered when voltage values are lower than 0.6 V. In general, higher electrical signals were gathered during the rainy seasons with moderate temperatures; while very low signals, including few measures of zero intensity, were obtained during the most stressful periods over the year, mainly by mid-summer. There is a strong correlation between the rainfall and the electrical signal. The rain-intensity correlation, together with sustained intensity values during the reproductive period in spring, suggests that this electrical magnitude could be an indicator of the physiological state of the tree and thus used for in situ and minimally invasive forest monitoring.