LL-37 tetR mRFP and BMP2 STATH GFP Production Simulation

  Because E. coli itself would also be affected by LL37, in order to test whether this will further affect the concentration of the target product, we then used the analysis above to predict the concentration of these products over time.
  The total amount of AHL was composed of the initial AHL from the quorum sensing model. The AHL-LuxR complex would activate the Plux promoter , which could lead to the production of LL37, tetR and mRFP.

  When the concentration of bacteria was low, DenTeeth would start to produce BMP2, STATH and GFP. Thus, we wanted to predict the production of these proteins. Considering the Quorum Sensing Model, we constructed the simulation of BMP2 and STATH.

  In order to observe the switching between sterilization and restoration of DenTeeth, we added RFP after the sterilization sequence and GFP after the restoration sequence. Next, we simulated the relative fluorescence intensity of RFP and GFP to know the actual operation of DenTeeth. The result is shown in the figure below. :

  These models calculate and simulate the experiment result.

Model Validation

  In order to ensure that our model’s predictions match the real situation, we used experimental data to fitting the model. After the experiment, we found that it was necessary to consider the dead E.coli because it influenced the O.D. value. The following picture(Fig.7) is the adjusted growth curve of E. coli.

  As you can see, the red line is the prediction growth curve of dead E. coli [deadE. coli(prediction)]. The blue line is the E. coli which is still alive [liveE. coli(prediction)]. And the black line is all the E. coli include living and dead, which is the prediction O.D. value [totalE. coli(predict)].

  Next, we considered the expression of RFP and fit the model with experimental data as before. We found that the environment of the Erlenmeyer flask was different from the paper. The degradation of RFP was lower than expected. Thus, we lowered the degradation rate and verified it with the experimental results again. The following picture(Fig.8) is the result.

  Then, we fit the data of GFP. We found that the expression of GFP exceeded expectations. So, we raised the translation rate of GFP and lowered the degradation rate. Although we did the same experiment for 24 hours, since the GFP expression had exceeded the detection range of the machine, the measured values were maintained at the maximum. Therefore, we only took the first 10 hours for validation. The result is shown below. (Fig.9)

  After the validation, we compared the expression of GFP and RFP. As you can see in the picture (Fig.10), because E. coli concentration was low at the beginning of the experiment, DenTeeth expressed GFP first. As E. coli continued to grow over time, it started to sterilize and the RFP was expressed. Then, the concentration of E. coli decreased due to the sterilization. DenTeeth turned to express GFP and started the restoration function. According to this experiment, we confirmed that the Quorum Sensing System of DenTeeth worked successfully.

  After finishing the whole validation, we predicted the LL37, BMP2, and STATH expression again (Fig.11~Fig.12).

  And we also predicted the new growth curve of E. coli and P.gingivalis with DenTeeth. As you can see in the picture (Fig.13), compared to the growth of P.gingivalis without DenTeeth (Fig.1), the final O.D. value of P.gingivalis decreased from 0.7 to 0.08, which showed that our DenTeeth could effectively kill 88% of the pathogenic bacteria in dogs' oral cavities.

DenTeeth can kill up to 88% of the pathogenic bacteria in dogs' oral cavities.

Protein Functional Test

  Because E. coli itself would also be affected by LL-37, in order to test whether this will further affect the concentration of the target product, we then used the analysis above to predict the concentration of these products over time.
  The total amount of AHL was composed of the initial AHL from the quorum sensing model. The AHL-LuxR complex would activate the Plux promoter , which could lead to the production of LL-37, tetR and mRFP.
  The prediction formula of LL-37 tetR RFP are shown below(Eq.4) _[6]:

$$\frac{d[mLL-37]}{dt}= K_{mLuxI}·β·[(A-R)_{2}]-deg_{mLL-37}[mLL-37]$$

$$\frac{d[mtetR]}{dt}= K_{mLuxI}·β·[(A-R)_{2}]-deg_{mtetR}[mtetR]$$

$$\frac{d[mRFP]}{dt}= K_{mLuxI}·β·[(A-R)_{2}]-deg_{mRFP}[mRFP]$$

$$\frac{d[LL-37]}{dt}= k_{LL-37}·[mLL-37]-deg_{LL-37}[LL-37]$$

$$\frac{d[tetR]}{dt}= k_{tetR}·[tetR]-deg_{tetR}[tetR]$$

$$\frac{d[RFP]}{dt}= k_{RFP}·[RFP]-deg_{RFP}[RFP]$$

$$β=\frac{k_{a}+α[LuxR-AHL_{in}]_{2}}{k_{a}+[LuxR-AHL_{in}]_{2}}$$

And the parameters (Tab.5) can be seen below _[6]:

Efficiency Optimization Model

  When the concentration of bacteria was low, DenTeeth would start to produce BMP2, STATH and GFP. Thus, we wanted to predict the production of these proteins. Considering the Quorum Sensing Model, we could write down the formula(Eq.5) _[7]:

$$\frac{d[BMP2]}{dt}= C_{ptet} ·({l_{ptet}+\frac{1-l_{ptet}}{1+(\frac{[tet]}{k_{tet}})^{n_{tet}} } })-(d_{BMP2} ·[BMP2])$$

$$\frac{d[STATH]}{dt}= C_{ptet} ·({l_{ptet}+\frac{1-l_{ptet}}{1+(\frac{[tet]}{k_{tet}})^{n_{tet}} } })-(d_{STATH} ·[STATH])$$

$$\frac{d[GFP]}{dt}= C_{ptet} ·({l_{ptet}+\frac{1-l_{ptet}}{1+(\frac{[tet]}{k_{tet}})^{n_{tet}} } })-(d_{GFP} ·[GFP])$$

And the parameters (Tab.5) can be seen below _[7]:

  In order to observe the switching between inhibition and restoration of DenTeeth, we added RFP after the inhibition sequence and GFP after the restoration sequence. Next, we simulated the relative fluorescence intensity of RFP and GFP to know the actual operation of DenTeeth. The result is shown in the figure below. (Fig.6):

DenTeeth-bone

Concept

  DenTeeth can produce antimicrobial peptides, LL-37 when the concentration of bacteria in the mouth is higher. After the growth of bacteria is inhibited, STATH and BMP2 will express, maintaining a high calcium level in saliva, and repairing soft tissues in the oral cavity. Therefore, oral problems, especially periodontal disease can be successfully prevented.

How do we prove it?

  We proved our concept with a meticulous process which can be roughly divided into three parts: Model, Lab Work, and Device design. Combining modeling results and predictions with our lab work, we enable to make DenTeeth work as we imagined. We could further prove that DenTeeth can be implemented in the real world for daily usages.