Identification of Toosendanin steady states of BRNs is crucial in several applications such as the treatment of various human cancers and genetic engineering. Additionally, the steady state analysis has proven to be successful to explain the flower morphogenesis of Arabidopsis thaliana, the differentiation process of T-helper cells, the mechanism of T cell receptor signaling and the cell cycles of yeast types. We use Boolean values for the states of the genes since it is successfully used in the literature for BRNs. Timosaponin-BII Recently, several methods have used categorical values for gene states in their model. The steady states extracted by these methods showed high parallelism with the ones found using Boolean models. The naive approach to steady state identification in Boolean networks is to exhaustively search the state space. However, the number of possible states of a BRN is exponential in the number of its genes. Therefore, exhaustive methods are computationally infeasible for even moderately sized BRNs. To address this problem, some existing methods use finitestate Markov chains, binary decision diagrams, constraint programming, probabilistic Boolean networks, linear programming, relational programming and module networks. Orthogonal to the selection of the computational method, there are two commonly used alternatives for modeling the state transitions. These are synchronous and asynchronous models and both are used in the literature. Synchronous models assume that the activity levels of all the genes change simultaneously. Hence, the next state is deterministically decided by the current state. On the other hand, asynchronous models consider time in small intervals, such that only one gene can change its state at an interval and state change is equally likely for all genes. For an n gene BRN, the state space of synchronous model has 2n states and 2n state transitions. For asynchronous model, the number of states is still 2n but the number of possible transitions can go up to n2n. The advantages/disadvantages of these models together with their effect on running time of steady state identification algorithms are discussed in the literature.