Motivation: Gap filling for the reconstruction of metabolic networks is to restore the connectivity of metabolites via finding high-confidence reactions that could be missed in target organism. Current methods for gap filling either fall into the network topology or have limited capability in finding missing reactions that are indirectly related to dead-end metabolites but of biological importance to the target model. Results: We present an automated dead-end filling (DEF) approach, which is derived from the wisdom of endosymbiosis theory, to fill gaps by finding the most efficient dead-end utilization paths in a constructed quasi-endosymbiosis model. The recalls of reactions and dead ends of DEF reach around 73% and 86%, respectively. This method is capable of finding indirectly dead-end-related reactions with biological importance for the target organism and is applicable to any given metabolic model. In the E. coli iJR904 model, for instance, about 42% of the dead-end metabolites were fixed by our proposed method. Availabilty and Implementaion: DEF is publicly available at http://bis.zju.edu.cn/DEF/. Contact: [email protected] Supplimentary Information: Supplementary data are available at Bioinformatics online.