Respiratory syncytial virus (RSV) is an important pathogen affecting all age groups and has been implicated in the inception of asthma in a subpopulation of children. Despite considerable research, the mechanisms of acute RSV bronchiolitis and post-bronchiolitis wheezing/ asthma are poorly understood and this has hampered efforts for defining risk stratification of patients and development of improved regimens in therapy and prevention. Progress has been made into the identification of molecular pathways involved in RSV pathogenesis, typically by using a traditional “reductionist” approach that focuses on examination of pre-selected targets of interest. By contrast, use of a “systems biology” approach, in which the expression levels and inter-relationships of numerous components of a complex system are interrogated in an unbiased manner, can provide a more global perspective for the identification of novel molecules and pathways for subsequent validation and translation into improved diagnostics and interventions for predictive and personalized medicine. We describe a novel method, consisting of a combination of high-throughput immunoblotting followed by data analysis using GoMiner pathway modeling software, that can screen for RSV-associated alterations in protein expression in multiple cellular pathways, as an example of both the potential and current limitations of using systems biology approaches in the study of RSV infections.