Reasoning on Stochastic Models in Systems Biology Under Uncertainty

System models of biological processes are stochastic. The probability distributions in the models of biological system are computed from imprecise and incomplete data. The environment plays an important role in the computation of model distributions. In this work, we create a formalism that is based on interval Markov chains with open intervals to address uncertainty in the environment. An automated abstraction of the interval Markov chain based model is described. Model checking is performed on the formalism and computational feasibility of the model is evaluated on Galactose utilization pathway in Yeast.