Complex Networks are pervasive in our society. Realistic biological, information, social and technical networks share a number of unique features that distinguish them from physical networks. Examples of such features include: irregularity, time-varying structure, heterogeneity among individual components and selfish/cooperative game-like behavior by individual components. Furthermore, the network structure, the dynamical process on the network and the behavior of constituent agents co-evolve over time. The size and heterogeneity of these networks, their co-evolving nature and the technical difficulties in applying dimension reduction techniques commonly used to analyze physical systems makes reasoning, prediction and controlling of these networks even more challenging. Recent quantitative changes in high performance and wireless computing capability have created new opportunities for collecting, integrating, analyzing and accessing information related to such large complex networks. The advances in network and information science that build on this new capability provide entirely new ways for reasoning and controlling these networks. Together, they enhance our ability to formulate, analyze and realize novel public policies pertaining to these complex networks. Over the last 15 years, our group has established a theory based program for modeling, simulation and associated decision support tools for understanding such large socio-technical systems. Complementing this modeling environment is a scalable service delivery framework that provides policy analysts and scientists seamless access to the modeling environment. After a brief overview, I will describe our approach within the context of a specific application: development of modeling and decision support environments to study epidemics in co-evolving social and wireless networks.