A system level view of cellular processes for human and several organisms can be cap- tured by analyzing molecular interaction networks. A molecular interaction network formed of differentially expressed genes and their interactions helps to understand key players behind disease development. So, if the functions of these genes are blocked by altering their interactions, it would have a great impact in controlling the disease. Due to this promising consequence, the problem of inferring disease causing genes and their pathways has attained a crucial position in computational biology research. However, considering the huge size of interaction networks, executing computations can be costly. Review of literatures shows that the methods proposed for finding the set of disease causing genes could be assessed in terms of their accuracy which a perfect algorithm would find. Along with accuracy, the time complexity of the method is also important, as high time complexities would limit the number of pathways that could be found within a pragmatic time interval.