One of the key features of systems engineering is the integration of knowledge and expertise across large and diverse expert teams. Theories and models of knowledge transfer in multidisciplinary teams indicate that the existence of multiple sources of expertise in a team have the potential to vastly improve decision-making, especially when the team is faced with novel tasks. However, knowledge is not always easily transferred in expert teams. Research suggests that expertise can actually impede performance through mechanisms like groupthink, the hidden profile effect, and the competency trap. In this paper, we explore the role of expertise and shared mental models in systems engineering, an interdisciplinary context that is characterized by large and highly interdependent teams with a wide array of distinct but overlapping expertise. Based on two prior studies of expert teams in different U.S. government agencies, we propose the development of a rigorous set of approaches for applying network analysis and its associated metrics to the study of expertise, shared mental models, and learning in large and heterogeneous teams. We then discuss ongoing and future research addressing the role of expertise and shared mental models in systems engineering.