The ultimate goal in studying a biological system is to unravel the underlying mechanisms that link a specific genotype to a correspondent phenotype: i.e. how the genetic information (genotype) yields a specific set of properties (phenotype) under given environmental conditions. This field, which studies the functional expression of the genomic information of an organism using genome-wide or system-wide experimental techniques and computational/statistical approaches, is referred to as func tional genomics (FG) (Hieter, P.; Boguski, M. Science. 1997, 278, 601-602). Clearly, FG encompasses many processes where genes, RNAs, proteins, and metabolites interact with each other. Several genome-wide analysis techniques in the areas of transcriptomics, proteomics, and metabolomics are available. We are currently using transcriptomics and proteomics tools in conjunction with fluxomics tools to elucidate the biological function of individual genes at cellular levels. A quantitative global approach to the study of cell functioning that includes all of the areas mentioned above (?mics), and their interactions, will definitely increase the potential of each individual technology and will result in findings very relevant for FG and cell physiology. This is the type of approach advocated in the field of Systems Biology (SB) (Ideker, T.; Galitski, T.; Hood, L. Annu. Rev. Genomics Hum. Genet. 2001, 2, 343-372) . As with any emerging field, many definitions currently exist for the field of SB (Henry, C. M. Systems biology: integrative approach to drug discovery. Chem. Eng. News. 2003, 81, 45-55). Although a systemic approach to study biological systems has existed for some time (e.g. cellular physiology), it became a reality only after the arrival of genome-wide techniques for quantification of cellular species. No doubt there are two factors that will determine the success of SB: first, the availability and/or improvement of analytical techniques to quantify cellular species on the genomic or system scale, and second, the development of sound mathematical/statistical tools that allow the analysis and integration of the data produced by these studies. Clearly, the distinctive part of a systemic approach is its integrative component that includes solid mathematical and computational basis.
We are using this global and integrative approach to understand complex metabolic and regulatory networks in bacterial systems, which are the basis for understanding similar processes in more complex organisms. Our ultimate goal is the design of specific genotypes based on the desired phenotype. We are currently focusing our efforts on studying the anaerobic fermentation of glycerol and the efficient metabolism of 5- and 6-C sugar mixtures. We are making use of state-of-the-art techniques in the areas of transcriptomics (DNA microarrays), proteomics (2DE-MS), and fluxomics (Metabolic Flux Analysis) and have developed a number of useful tools in this area. The latter includes: (1) a PCA-based method for the identification of assay-specific signatures in functional genomic studies , (2) a novel tool (Elementary Network Decomposition, END) to help elucidate the network topology of regulatory systems , (3) an “in silico hybridization” method as a new paradigm in the design and validation of oligonucleotides for DNA microarrays , and (4) a superior method for estimating metabolic fluxes using 13C labeling experiments.