The Bioinformatics and Systems Biology team develops computational tools for metabolic model construction, simulation and optimization, and automatic retrieval of relevant information laying in the literature (text mining). An important topic deals with the development of Evolutionary methods for strain optimization. Other topics relate to database integration, gene expression and metabolomics data analysis and mining.
The main applications include the identification of targets for metabolic engineering, aiming at the construction of improved cellular factories for the production of succinic, fumaric or amino acids, and the model-driven physiological characterization of pathogens (such as H. pylori or S. faecalis) and the identification for drug targets for unmet health concerns.
@@Note: workbench for biomedical text mining.
Development efforts are focused on:
Development of bioinformatics techniques for the semi-automated reconstruction of metabolic networks of microorganisms. Those techniques are focused on the tasks of genome re-annotation, database integration and model validation. Application to industrial organisms for performing metabolic engineering design (E. coli and K. marxianus) and to pathogenic organisms for drug target identification and virulence studies (E. faecalis, S. pneumonia, and H. pylori)
merlin: metabolic models reconstruction using genome-scale information. merlin is a user-friendly Java application that performs the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs several steps of the reconstruction process, including the functional genomic annotations of the whole genome. Moreover, merlin includes tools for the identification and annotation of transport proteins encoding genes, as well as the generation of transport reactions for such carriers. Also, merlin includes tools for the compartmentation of the model that predict the localisation of the proteins encoded in the genome, and thus the localisation of the metabolites involved in the reactions induced by such proteins. Finally, merlin expedites the transition from genome-scale data to SBML metabolic models, allowing the user to have a preliminary view of the biochemical network.
Simulation of metabolic networks and optimization of bacterial strains to attain industrial aims.
OptFlux: A user-friendly and open-source software platform for in silico Metabolic Engineering. This application has been developed by our group and allows the user to take a genome-scale model of a given organism and to simulate the phenotype of wild type and mutants strains. This can be performed by using a number of approaches (e.g. Flux-Balance Analysis, Minimization of Metabolic Adjustment or Regulatory On/Off Minimization of metabolic fluxes) that allow the set of fluxes in the organism's metabolism to be determined, given a set of environmental constraints. The software also includes a number of optimization methods (e.g. Evolutionary Algorithms or Simulated Annealing) to reach the best set of gene deletions given an objective function, typically related with a given industrial goal. It also integrates visualization tools from the BioVisualizer application.
The derivation of strategies for increasing the productivity of recombinant protein production processes by applying a Systems Biology perspective is being pursued.
The main purpose is to gain fundamental insight into the molecular mechanisms governing the main metabolic bottlenecks observed during the production of heterologous protein with E. coli and use this information to identify strategies for how these processes can be eliminated. This approach involves the use of genome-scale analysis of the transcriptome, proteome, and fluxome.