A simulation-based systems engineering framework is defined to design, optimize and simulate complex, large scale systems under uncertainty through integrated models encompassing multiple disciplines such as, for example, structural-thermal-optical. A model's input parameter uncertainties are rigorously quantified upstream of the model through literature reviews, experiments or elicitation from subject matter experts and then propagated through the model to determine their influence on specific quantities of interest requested in output. A variance-based global sensitivity analysis is used to identify and rank the critical system parameters, based on their contribution to the variance of the quantities of interest. These parameters can then be targeted by additional research through optimal parameter inference experiments in order to reduce their variability. By so doing, one incorporates uncertainty in the model and updates the model iteratively as new parameter information becomes available. This process increases one's knowledge about the system, its subcomponents and all of their mutual interactions, and represents a crucial commodity when important design decisions are to be made. When applied early in a project's life-cycle, it can potentially reduce mission costs related to resources (e.g., mass or power) and processes (e.g., design, verification and validation). As a case study, this paper presents results from the application of this framework to the integrated model of the James Webb Space Telescope, used to ultimately revise the model uncertainty factors applied to nominal temperature predictions for the benchmark hot-to-cold slew thermal analysis case.