Analysis of stoichiometry in metabolic networks

Cellular metabolism is comprised of a complex network of reactions. The kinetics are understood for only some of these reactions; the stoichiometry, however, is well-known and is similar among many different organisms. The stoichiometry has been useful for calculating fluxes in networks with more compounds than fluxes, and to quantify maximum product yields in networks with more fluxes than compounds. A thorough analysis of stoichiometry and its applications for studying cell metabolism was developed here. Linear optimization in conjunction with the stoichiometric matrix provide a systematic analysis of the capabilities of the cell. The reduced costs and shadow prices allow interpretation of flux measurements that are not possible by other means. The extent to which demands such as antibody synthesis and maintenance energy drain the cell's resources was evaluated. The stoichiometry and linear optimization can also be used to estimate the fluxes within the network when it is impractical to perform detailed experiments. An algorithm was derived based on the condition number of the stoichiometric matrix, for the optimal selection of fluxes to be measured when detailed measurements of fluxes are needed. This analysis was applied to E. coli, hybridoma cell, and the red blood cell metabolism. The measurement of the best set of fluxes in the hybridoma cell result in sensitivity that is 1/1000 of that obtained using the worst set of fluxes. When the concept of stoichiometric sensitivity was applied to the study of metabolic design of the red blood cell, it was determined that hexokinase and ATP dissipation are the most influential fluxes. The methods of analyzing stoichiometry, whether through linear optimization or condition number calculations, can be applied to any metabolic network which has fewer mass balances than fluxes. Many aspects of metabolism, such as metabolite concentrations and detailed thermodynamics, cannot be considered. Stoichiometric analysis was found to be useful for defining the metabolic capabilities of the cell, interpreting flux measurements, improving experimental design, and studying metabolic design.