This research aims at advancing probabilistic verification techniques for the rigorous design of dependable systems in synthetic biology and nanotechnology. Major goals of the project include the following. First, scale up stochastic model checking with efficient and accurate state space truncation techniques. Secondly, investigate practical stochastic counterexample generation techniques and utilize them to improve the accuracy of the state reductions. Thirdly, derive automated guidance mechanisms learned from stochastic counterexamples to improve the quality and efficiency of rare-event stochastic simulations. Lastly, integrate our proposed framework within existing state-of-the-art stochastic model checking tools, PRISM and STORM; and evaluate the proposed methodology on a wide range of case studies derived from synthetic biology and nanotechnology applications. The combination of these methods into this new methodology is being explored for the first time. Altogether, this research will improve the accuracy of analysis of infinite state stochastic systems with rare-event properties.