Inflammation has been known to be the underlying cause of many diseases, including cancer, autoimmune conditions, atherosclerosis and infections, and is a major factor in aging. A well-known inflammation-associated disease in the lung is bronchoconstriction, which is commonly observed in asthma. Inflammatory diseases pose substantial global health and economic burden. Therefore, many experimental and computational studies have shed light on their dynamics and mechanisms. To further the understanding of inflammation dynamics, new methods are needed to complement experiments. The goal of this research is to develop informatics methods, including network and agent-based models, -to investigate inflammation dynamics and lung bronchoconstriction. Specific objectives of this dissertation include development of an informatics model of bronchoconstriction and agent-based inflammation, and development of new metrics to further analyze the underlying responses. The results of this study are consistent with the findings in the literature. Analysis of the results from information metrics indicated that bronchoconstriction is an adaptive network process and the network metrics can be used to identify its progression. Further, the course of inflammation and process of wound healing are related to the complexity of cell-to-cell interactions. These findings may help to further address the cellular level processes in various diseases and other applications.