Team:HKUST-Rice/Potassium Sensor Modelling

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Modelling

Potassium sensor

Phosphate sensor

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 PKdpF plus green fluorescence protein generator (BBa_E0240) in pSB3K3 backbone as well as the endogenous Kdp operon, and both operons have the same promoter, PKdpF. As a matter of fact, titration by the endogenous kdp operon of the transcription inducer, phosphorylated KdpE which binds to PKdpF 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 PKdpF is ten times smaller than that of the inserted operon and accounts only 8.33% of the total number of promoter PKdpF 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

In accordance to [Kremling A. 04], in the potassium ions concentration range which we were studying, from 0mM to 0.02 mM, the fluctuation of the concentration KdpF as well as KdpD and KdpE was only within 10 µM and 3 µM respectively. Due to the small fluctuation range compared to the gene expression of GFP reporter, it was reasonable to assume the concentration of KdpD, KdpE and KdpF to be constant in the model.

It was assumed that the initial concentration of mRNA for GFP, immature GFP and mature GFP equal to zero.

It was assumed that all reactions below were in steady state such that:

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:

Prediction model:

Reference:
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). Universitätsbibliothek der TU München.

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 Universität München Fachgebiet Für 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