Computational metabolic flux modeling has been a great aid for both understanding and manipulating microbial metabolism. A previously developed metabolic flux model for Aspergillus niger, an economically important biotechnology fungus known for protein and organic acid production, is comprised of 1190 biochemically unique reactions that are associated with 871 open reading frames. Through a systematic in silico deletion of single metabolic reactions using this model, several essential metabolic pathways were identified for A. niger. A total of 138 reactions were identified as being essential biochemical reactions during growth on a minimal glucose medium. The majority of the reactions grouped into essential biochemical pathways covering cell wall biosynthesis, amino acid biosynthesis, energy metabolism and purine and pyrimidine metabolism. Based on the A. niger open reading frames associated with the reactions, we identified orthologous candidate essential genes in Aspergillus fumigatus. Our predictions are validated in part by the modes of action for some antifungal drugs and by molecular genetic studies of essential genes in A. fumigatus and other fungi. The use of metabolic models to predict essential reactions and pathways in Aspergillus spp. has promise to inform reverse genetic studies of gene essentiality and identify potential targets for antifungal development.