Abstract
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.
Highlights
Inflammation is seen as the cause of many diseases, such as cancer [1,2], autoimmune conditions, atherosclerosis and infections [3], and is a major factor in aging [4]
(at time = 8000 in Figure 8), fibroblast domination declined and shifted to macrophages dominating the collective dynamics as indicated by the low fibroblast node’s centrality (Fc)/macrophage nodes (Mc) ratio. These results indicate that the inflammation driven by pathogens and strains can be distinguished based on their transfer entropy (TE) networks
Inflammation is a process driven by many factors as well as underlying cell-cell communication
Summary
Inflammation is seen as the cause of many diseases, such as cancer [1,2], autoimmune conditions, atherosclerosis and infections [3], and is a major factor in aging [4]. Developing mathematical analyses of inflammation dynamics is important in order to advance understanding of its underlying mechanisms. Inflammation is an example of collective dynamics and is a non-linear process, where many cells and events interact after perturbation to reach equilibrium. There are many factors driving the inflammation time course. It is important to be able to distinguish the dominant factor(s) influencing its time course in order to improve diagnosis
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