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Latest revision as of 23:41, 17 October 2014
Methods for Simulation
We set out to model the inflammation on the skin in the context of a cathelicidin antimicrobial peptide treatment first identifying key interactions as pictured in Figure 3. System diagrams were made using Microsoft PowerPoint, and interaction tables were made using literature searches and Microsoft Excel.
Figure 3. System diagram of Boolean network model to represent interactions of cathelicidin in the skin.
Next, we made the following set of key assumptions to guide the behavior of the model:
1) Epithelial cells, keratinocytes and neutrophils are the only significant sources of natural cathelicidin in the skin.
2) Topical application of cathelicidin is equivalent to increased output of pro-cathelicidin by epithelial cells.
3) Inflammation due to injury versus immune response is roughly equivalent.
Boolean network rules were tabulated by hand and simulated using the Python library BooleanNet (1.2.7). Under “Normal Conditions,” the Epithelial Cell node was set to Random, and to simulate “Topical Cathelicidin,” the Epithelial Cell node was set to True. For both conditions, 1,000 trials were simulated across 50 time points with all nodes initialized to False. After simulation, individual nodes were averaged across trials and analyzed using a custom Python script and Microsoft Excel. See below for documentation:
Cathelicidin Model DIAGRAM.ppt
Interaction Tables REV 4.xls
Cathelicidin Model Rules REV 4.txt
Cathelicidin Model Simulation Rev 1.txt
Cathelicidin Model Output REV 4.xls
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