Inflammation is a process driven by underlying cell-cell communication and many other factors. In this study, a model of cell-cell communications was proposed to study factors driving the inflammation time course. Analyses of inflammations that are driven by the combined effects of strain (mechanical stimuli) and/or pathogens are considered in this paper. An agent-based model was employed to simulate inflammation where macrophages and fibroblasts influence each other through cell signaling cytokines that diffuse and spread in the tissue space. The communication network of macrophages and fibroblasts was then inferred and its network model (termed TE network) was generated and analyzed. The results suggest that factors driving inflammation time course can be discriminated by the characteristics of TE networks. Inflammation driven only by pathogens has certain TE network characteristics indicating slower and lower information exchange among cells. Multiple stimuli can help to maintain sufficient information exchange among cells, which is beneficial for inflammation resolution. The TE network captures the unfolding of the innate immune system over time, and the history of pathogens invasion. The resulting network leads to an improved understanding of the resilience of the system to future pathogen invasion.

Respiratory diseases such as asthma and acute respiratory distress syndrome as well as acute lung injury involve inflammation at the cellular level. The inflammation process is very complex and is characterized by the emergence of cytokines along with other changes in cellular processes. Due to the complexity of the various constituents that makes up the inflammation dynamics, it is necessary to develop models that can complement experiments to fully understand inflammatory diseases. In this study, we developed a discrete informatics model based on cellular automata (CA) approach to investigate the influence of elastic field (stretch/strain) on the dynamics of inflammation and account for probabilistic adaptation based on statistical interpretation of existing experimental data. Our simulation model investigated the effects of low, medium, and high strain conditions on inflammation dynamics. Results suggest that the model is able to indicate the threshold of innate healing of tissue as a response to strain experienced by the tissue. When strain is under the threshold, the tissue is still capable of adapting its structure to heal the damaged part. However, there exists a strain threshold where healing capability breaks down. The results obtained demonstrate that the developed discrete informatics based CA model is capable of modeling and giving insights into inflammation dynamics parameters under various mechanical strain/stretch environments.

Patients with acute lung injury, airway and other pulmonary diseases often require Mechanical Ventilation (MV). Knowledge of the stress/strain environment in lung airway tissues is very important in order to avoid lung injuries for patients undergoing MV. Airway tissue strains responsible for stressing the lung’s fiber network and rupturing the lung due to compliant airways are very difficult to measure experimentally. Multi-level modeling is adopted to investigate the transient mechanical response of the tissue under MV. First, airflow through a lung airway bifurcation (Generation 4–6) is modeled using Computational Fluid Dynamics (CFD) to obtain air pressure during 2 seconds of MV breathing. Next, the transient air pressure was used in structural analysis to obtain mechanical strain experienced by the airway tissue wall. Structural analysis showed that airway tissue from Generation 5 in one bifurcation can stretch eight times that of airway tissue of the same generation number but with different bifurcation. The results suggest sensitivity of load to geometrical features. Furthermore, the results of strain levels obtained from the tissue analysis are very important because these strains at the cellular-level can create inflammatory responses, thus damaging the airway tissues.