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Description
Traumatic brain injury (TBI) is a leading cause of injury related death in the United States. The complexity of the injury environment that follows TBI creates an incomplete understanding of all the mechanisms in place to regulate chemotactic responses to

Traumatic brain injury (TBI) is a leading cause of injury related death in the United States. The complexity of the injury environment that follows TBI creates an incomplete understanding of all the mechanisms in place to regulate chemotactic responses to TBI. The goal of this project was to develop a predictive in silco model using diffusion and autocrine/paracrine signaling specific to stromal cell derived factor-1α (SDF-1α) gradient formation after TBI and compare this model with in vivo experimental data. A COMSOL model using Fickian diffusion and autocrine/paracrine reaction terms was generated to predict the gradient formation observed in vivo at three physiologically relevant time points (1, 3, and 7 days). In vivo data was gathered and analyzed via immunohistochemistry and MATLAB. The spatial distribution of SDF-1α concentration in vivo more consistently demonstrated patterns similar to the in silico model dependent on both diffusion and autocrine/paracrine reaction terms rather than diffusion alone. The temporal distribution of these same results demonstrated degradation of SDF-1α at too rapid a rate, compared to the in vivo results. To account for differences in behavior observed in vivo, reaction terms and constants of 1st-order reaction rates must be modulated to better reflect the results observed in vivo. These results from both the in silico model and in vivo data support the hypothesis that SDF-1α gradient formation after TBI depends on more than diffusion alone. Future work will focus on improving the model with constants that are specific to SDF-1α as well as testing methods to better control the degradation of SDF-1α.
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Details

Title
  • Modeling SDF-1α Chemotactic Gradient Formation After Traumatic Brain Injury
Contributors
Date Created
2016-05
Resource Type
  • Text
  • Machine-readable links