TECHNOLOGY FOCUS - BIOTECHNOLOGY - Preferred Media: At least one reactant in SC reactants or at least one reaction in SC reactions is annotated with an assignment to a subsystem or compartment. Several reactions comprises at least one reaction from a peripheral metabolic pathway such as amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes. The data structure comprises set of linear algebraic equations or matrix, and the commands comprise an optimization problem or a linear program. A first substrate or product in SC reactions is assigned to a first compartment and a second substrate or products in SC reactions is assigned to a second compartment. Several SC reactions are annotated to indicate several associated genes and the gene database comprises information characterizing several associated genes.Preferred Method: In (II), a reaction in the data structure is identified from an annotated genome in a gene database. The method further comprises annotating a reaction in the data structure. The annotation is chosen from assignment of a gene, assignment of a protein, assignment of a subsystem, assignment of a confidence rating, reference to genome annotation information and reference to a publication. Relating the reactants to reactions in a data structure, further comprises identifying an unbalanced reaction in the data structure and adding a reaction to the data structure, thus changing the unbalanced reaction to a balanced reaction. Adding a reaction comprises adding a reaction chosen from intra-system reaction, an exchange reaction, a reaction from a peripheral metabolic pathway, reaction from a central metabolic pathway, a gene associated reaction and a non-gene associated reaction. EXAMPLE - This example shows how the Saccharomyces cerevisiae metabolic model can be used to determine the effect of deletions of individual reactions in the network. Gene deletions were performed in silico by constraining the flux(es) corresponding to a specific gene to zero. The impact of single gene deletions on growth was analyzed by simulating growth on a synthetic complete medium containing glucose, amino acids, as well as purines and pyrimidines.    In silico results were compared to experimental results as supplied by the Saccharomyces genome database (SGD) (Cherry et al., Nucleic Acid Research 26(1):73-79 (1998)) and by the comprehensive yeast genome database (Mewes et al., Nucleic Acid Research 30(1):31-34 (2002)). In 85.6% of all considered cases (499 out of 583 cases), the in silico prediction was in qualitative agreement with experimental results. For central metabolism, growth was predicted under various experimental conditions and 81.5% (93 out of 114 cases) of the in silico predictions were in vivo phenotypes. Thus, this example demonstrated that the in silico model can be used to uncover essential genes to augment or circumvent traditional genetic studies.