Introduction

To fully appreciate the mechanism of our biosensor and its behavior in an ideal situation, we explored the structure and dynamics of our system by creating a mathematical model of the reaction kinetics. We integrated the work done on Kdp system mechanism and information on the expression of GFP generator into this modeling and obtained a graph of green fluorescence intensity per cell against [K⁺].

Prediction model

Model's Assumption

The effect of the endogenous Kdp system of E. coli was neglected.

In our engineered E. coli, it contains the inserted plasmid of P_KdpF plus green fluorescence protein generator (BBa_E0240) in a pSB3K3 backbone as well as the endogenous kdp operon, and both operons have the same promoter, P_KdpF. As a matter of fact, titration by the endogenous kdp operon of the transcription inducer, phosphorylated KdpE which binds to P_KdpF was expected initially. And the titration of phosphorylated KdpE is anticipated to lower the expression of GFP. However, when the DNA copy number of both endogenous and the inserted operons were determined, it was found that the number of endogenous kdp operon is 10 times smaller than that of the inserted one:

This implied that the number of endogenous promoter P_KdpF is ten times smaller than that of the inserted operon and accounts only 8.33% of the total number of promoter P_KdpF in the engineered E. coli. Therefore, the titration effect of phosphorylated KdpE becomes insignificant. As a result, the effect of endogenous Kdp system was negligible.

Level of KdpD, KdpE and KdpF were assumed to be constant.

Equations of the Model:

Phosphorylation of KdpD:

Phosphyl-group Transfer:

Binding of KdpE to promoter PkdpF:

Transcription:

Translation:

Green Fluorescent Protein maturation:

Data point fitting to the model:

The experimental data from FACS were used to fit in the prediction model; then we adapt the unit conversion from [Caitlin C, Jeniffer B 13] to convert GFP per cell fluorescence intensity to concentration of GFP per cell:

Parameters and Variables list

Table 1. Parameters list 1.

Table 2. Parameters list 2.

Table 3. Variables list.

References

Heermann R, Zigann K, Gayer S, Rodriguez-Fernandez M, Banga JR, et al. (2014) Dynamics of an Interactive Network Composed of a Bacterial Two- Component System, a Transporter and K+ as Mediator. PLoS ONE 9(2): e89671. doi:10.1371/journal.pone.0089671

Brewster RC, Jones DL, Phillips R (2012) Tuning Promoter Strength through RNA Polymerase Binding Site Design in Escherichia coli. PLoS Comput Biol 8(12): e1002811. doi:10.1371/journal.pcbi.1002811

Modeling, Simulation and Identification of the Dynamics of K Uptake in E. coli. (2014). Universitatsbibliothek der TU Munchen.

Kelly, Jason et al. “Measuring the activity of BioBrick promoters using an in vivo reference standard.” Journal of Biological Engineering 3.1 (2009): 4.

J. Gayer, Stefan. "Modeling, Simulation and Identification of the Dynamics of K Uptake in E. Coli." Technische Universitat Munchen Fachgebiet Fur Systembiotechnologie (2013). Print.

Kremling, A., Heermann, R., Centler, F., & Gilles, E. (2004). Analysis of two-component signal transduction by mathematical modeling using the KdpD/KdpE system of Escherichia coli.

Conboy, C., & Braff, J. (2013, May 29). Molecules of Equivalent GFP. Retrieved from http://openwetware.org/wiki/MEG