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A Conceptual Cellular Interaction Model of Left Ventricular Remodeling: Dynamic Network with Exit‐Entry Evolution Strategy
Author(s) -
Wang Yunji,
Han Haichao,
Yang Jack,
Lindsey Merry,
Jin Yufang
Publication year - 2010
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.24.1_supplement.1060.4
Subject(s) - population , cell , microbiology and biotechnology , ventricular remodeling , ventricle , biology , cell type , neuroscience , myocardial infarction , computational biology , medicine , genetics , cardiology , environmental health
Progressive remodeling of the left ventricle (LV) following myocardial infarction involves spatial‐temporal interactions among multiple cell types, including myocytes, neutrophils, macrophages, endothelial cells, and fibroblasts. Interactions associated with cellular functional adaption determine cell populations and remodeling outcomes. However, little is known about the relationship among cell populations, interaction strength, and network structure. The objective of this study was to illustrate the effect of cell‐cell interactions on the evolution of cell population in LV remodeling. This research proposed a conceptual cellular interaction model based on a recent established graphic network. Stability analysis was performed to investigate the effects of the interaction strengths on the cellular population in the dynamic network consisting of two cell‐types. Our numerical simulations demonstrated that the interaction strength between two types of cells, number of interaction links, and initial cell population ratio, all together, determine the evolution profiles of the cell population in the network. Our model predicted three potential scenarios of cell population evolution. The results on stability analysis can be used as a useful tool to predict the spatial‐temporal changes of cell populations. The authors acknowledge grant support from NSF 0644646, 0602834 (to HH), NIH R01 HL75360 and AHA GIA 0855119F (to MLL), NSF 0649172 and NIH 1SC2 HL101430 (to YJ).