Team:Pitt/cathelicidinModels/Results

Results

All the results are split between two sets of data: Normal Conditions, where cathelicidin production by epithelial cells is random, and Topical Cathelicidin, where cathelicidin production is pseudo-applied by over-stimulating epithelial cells.

When analyzing Boolean networks, relative values are far more important than literal values, because no units were ever associated to the values. Despite the absence of real units, relative interactions between nodes can still carry important information. Given the simplicity of the model, we expected Topical Cathelicidin led to an increased immune system response relative to Normal Conditions, as shown in Table 1. Furthermore, Topical Cathelicidin showed a decrease in P. acnes and in Injury, which is to be expected. These results are graphically shown in Figure 1 where more cathelicidin causes a relatively significant drop in the average value of P. acnes.

Node	Normal Conditions	Topical Cathelicidin
Cathelicidin	23%	35%
Probionibacterium acnes	44%	37%
Immune Resposne (Inflammation)	21%	28%
Injury (Inflammation)	52%	42%
Average Inflammation (Immune + Injury)	37%	35%

Table 1 – Average percent time activation of key nodes after 1000 simulations of 50-rounds cycles.

Figure 1 – Average percent time activation of Cathelcidin and P. acnes nodes under Normal and Topical Cathelicidin conditions.

Figure 2 – Ratio of inflammation due to the immune system over inflammation due to injury. A higher ratio indicates a strong immune system presence opposed to just injury signals.

Turning towards relative activation of key nodes, Figure 2 shows a higher ratio of immune system inflammation to injury inflammation with topical cathelicidin present. While Normal Conditions show a 50/50 split between inflammation due to the immune system versus injury, cathelicidin drives up this ratio to a 60/40 split, where the immune system is now the dominant source of inflammation. Interestingly, Table 1 shows how the average amount of inflammation stays constant between the two conditions. Therefore, while immune system inflammation may increase, net inflammation remains unchanged.

Figure 3 – Average value of injury node over time.

In addition, the magnitude of oscillations for Injury seem to increase with Topical Cathelicidin, as seen in Figure 3. Logically, the higher amount of immune-system-induced inflammation allows for more healing to occur in between cycles of P. acnes growth and damage.

Conclusions

Even with a long list of assumption, the questions raised over skin treatment with cathelicidin can be easily answered with a very simple Boolean network. Initially, concern was raised whether topical application of cathelicidin will even affect the growth of P. acnes, but the results in Figure 1 show a clear inverse relationship between cathelicidin and the activity of P. acnes. A secondary concern is whether an excess of cathelicidin would cause unwanted inflammation due to over-recruitment of immune cells and other inflammatory signals. According to the model, however, the relative overall inflammation remained constant with additional cathelicidin. Instead of increasing inflammation, Figure 2 shows a rebalancing of inflammation, where distress signals are presumably re-routed through immune pathways, as opposed to injury pathways.

Interestingly enough, the oscillations of injury seen in Figure 3 bear resemblance towards cyclic inflammation seen empirically with acne. Most importantly, the extra boost to the immune system helps clear overgrowth of P. acnes faster, allowing skin to heal more in between outbreaks, as seen in Figure 2. In conclusion, we have shown not only the viability of cathelicidin as a topical treatment of acne, but within the context of iGEM, we have proven how even a rudimentary Boolean network can answer important scientific questions.

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