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. :

  This model calculates and simulates the experiment result.

Model Validation

  Considered the previous growth model plus the killing formula of LL-37. We could write down the growth model of E. coli and P.gingivalis under the inhibition action of DenTeeth(Eq.3):

$$\frac{d[E. coli]}{dt}= g_{E. coli}(1-\frac{[E. coli]}{[E. coli_{Max}]})-k_{k}[B][LL-37]$$

$$\frac{d[P]}{dt}= g_{P}[P](1-\frac{[P]}{P_{Max}})-N⋅k_{k} [B][LL-37] $$

  As we can see above, the concentration of P.gingivalis and E. coli are reduced. And finally they will achieve dynamic balance.

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.