As Rule-Based System (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Increased availability of specialized hardware and software platforms and environments to develop these systems and greater incidence of successful commercial application have served to generate enthusiasm for the technology. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In general, the procedures for domain experts to accumulate knowledge and expertise is incremental and intuitive, thus, a rule base needs to be constructed incrementally and experienced multiple times of refinement. Besides, an expert system is usually constructed by consulting with numerous experts, and experts may have conflict expertise. Therefore, it is not surprising that many rules in a rule base may have structural errors. Four typical types of structural errors include inconsistency (conflict rules), incompleteness (missing rules), redundancy (redundant rules), and circularity (circular depending rules). Many different techniques have been developed to detect the above errors in rule based systems. Earlier work mainly focused on detecting structural errors by checking rules pair-wisely. Recent work aimed at detecting structural errors caused from applying multiple rules in longer inference chains. The majority of recent verification techniques involve using some graphical notation such as graphs, and Petri nets. DNA computing, in which parallel computing is an inherent characteristics, can be used to solve large problems. By solving a Hamiltonian path problem (HPP) for a directed graph with seven nodes, for the first time, Adleman demonstrated the efficiency of using molecules in a solution to solve computational problems. Subsequently, by solving Satisfiability problem (SAT), Lipton demonstrated the advantage of using the massive parallelism inherent in DNA-based computing. In this study algorithms mainly based on Adleman's operations, which are able to detect structural errors effectively, in any form that they can arise in rule base, are presented. Key Words: 1- Rule-Based system 2- Strucrural error 3- Rule verificatio 4- DNA computation