Flux balance analysis (FBA) of a genome-scale metabolic model allows calculation of intracellular fluxes by optimizing an objective function, such as maximization of cell growth, under given constraints, and has found numerous applications in the field of systems biology and biotechnology. Due to the underdetermined nature of the system, however, it has limitations such as inaccurate prediction of fluxes and existence of multiple solutions for an optimal objective value. Here, we report a strategy for accurate prediction of metabolic fluxes by FBA combined with systematic and conditionindependent constraints that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses. Analyses of three types of genomic contexts, conserved genomic neighborhood, gene fusion events, and co-occurrence of genes across multiple organisms, were performed to suggest a group of fluxes that are likely on or off simultaneously. The flux ranges of these grouped reactions were constrained by flux-converging pattern analysis. FBA of the Escherichia coli genome-scale metabolic model was carried out under several different genotypic (pykF, zwf, ppc, and sucA mutants) and environmental (altered carbon source) conditions by applying these constraints, which resulted in flux values that were in good agreement with the experimentally measured 13 C-based fluxes. Thus, this strategy will be useful for accurately predicting the intracellular fluxes of large metabolic networks when their experimental determination is difficult.Escherichia coli | flux balance analysis | grouping reaction constraints | 13 C-based flux | genome-scale metabolic model T he accumulation of omics data, including genome, transcriptome, proteome, metabolome, and fluxome, provides an opportunity to understand cellular physiology and characteristics at multiple levels (1, 2). Among them, the fluxome profiling allows quantification of metabolic fluxes, which collectively represent the cellular metabolic characteristics. For successful metabolic engineering, it is important to understand and predict the changes of metabolic fluxes after modifying interesting genes, pathways, and regulatory circuits. On the basis of systems-level understanding of cellular status through fluxome profiling, rational and systematic development of an improved strain becomes possible (3-6).One of the popular methods that has been used to examine cellular metabolic fluxes and predict their changes in perturbed conditions is flux balance analysis (FBA) (7-12). To calculate the optimal flux distribution in the multidimensional flux solution space of metabolic network, FBA optimizes an objective function, such as maximizing cell growth rate, under pseudosteady state with mass balance and other constraints (13). Given the fact that the information used to reconstruct the metabolic networks is incomplete, multiple equivalent solutions can be computed for the states of these networks (14). One method that has been presented to overcome the problem of mul...