Metabolomic studies allow the investigation and optimization of the main metabolic fluxes in a cell. They also allow reconstruction of the cell metabolism from a sequenced genome as well as the design of totally novel pathways/cells “in silico” which can then be constructed experimentally using genetic engineering tools. In the last years we have been extremely successful in investigating recombinant and non-recombinant yeast and E.coli cells as well as for the first time reconstructing bacteria active in microbial leaching processes. Presently we are incorporating new pathways into E.coli and yeast, firstly “in silico” using metabolic flux analysis (MFA), metabolic flux balances (MFB) and metabolic control analysis (MCA) in order to design potential pathways with appropriate energy (ATP), reducing power and H+, C and O supply to utilize novel carbon sources. We are also very active in an international collaboration on isolation of microorganisms (Streptomyces and others) with unique properties from the Atacama Desert (e.g. novel antibiotics and anticancer drugs). After genome sequencing our group has developed the metabolic reconstruction of these strains and has built a genome scale metabolic model (GSM) of Streptomyces leeuwenhoeki. Similarly we have constructed several genome scale models for Salinispora which are the first GSMs for this marine microorganism. Salinispora tropica is a marine actinomycete that produces diverse secondary metabolites, including many that posess pharmaceutical properties such as Salinosporamide A, a potent anticancer agent, and sporolides, candidates for antiviral compounds. Our latest development is a Salinispora core model that contains the genes shared by all 93 sequenced strains and a few non-conserved genes associated with essential reactions. Models will allow in silico metabolism studies of Salinispora strains and help researchers to guide and increase production of specialized metabolites. Also models can be used as templates to build Genome Scale Models of closely related microorganisms with high biotechnology potential.
We have also built a metabolic network model of the green microalga Chlamydomonas reinhardtii which was used to characterize photoautotrophic and heterotrophic growth under light and dark, respectively. The metabolic network comprised more than 2000 reactions distributed among nine intracellular compartments and the extracellular space. Although extensive experimental work exists on culture and physiology of microalgae, it does not allow quantitative predictions of the influence of dark metabolism on the productivity of metabolites. This limitation is overcome using the present model. A metabolic network model of Chlamydomonas reinhardtii is shown to simulate growth and synthesis of energy-rich compounds (triacylglycerols and starch) under light. The same model also simulates dark growth and maintenance through consumption of the stored energy-rich compounds.