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Model

Overview

Based on the assumption that the growth of Gluconacetobacter hansenii (G. hansenii) is only affected by environmental and nutritional factors, we measured the OD600 values of wild type G. hansenii ATCC53582 and the engineered G. hansenii containing the gshF expression system, and constructed a growth model of G. hansenii based on the Logistic equation. In addition, our project has also constructed the simulation of the lysis and safety module. The above modeling helps us to predict the growth stage of G. hansenii from a macroscopic perspective, verify the feasibility of the lysis and safety module.

G. hansenii Growth Model

Based on the assumption that the growth of G. hansenii is only affected by environmental and nutritional factors, we used the Logistic growth model to describe the growth of G. hansenii.

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By solving the initial value, that is, the integral function of the fermentation time at t=0, it can be obtained that the integral form of the Logistic growth model is:

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Results

We used Origin software to simulate the following growth curve of wild type G. hansenii ATCC53582 and the engineered G. hansenii containing the gshF expression system.

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Figure 1: Growth fitting curve of the engineered G. hansenii containing the gshF expression system
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Figure 2: Growth fitting curve of wild type G. hansenii ATCC53582

Figure1 and Figure2 show that the cell growth curves of G. hansenii are very consistent with the actual experimental measurements, in that the fitting correlation coefficients R2 are both 0.99.

By measuring the OD600 value of wild type G. hansenii ATCC53582 and the engineered G. hansenii containing the gshF expression system and using the Levenberg-Marquardt method to find the optimal value of rm in equation 2, the parameter values were solved as followed.

Parameters Meaning Value Source
N0 Initial OD600 value of the engineered G. hansenii containing the gshF expression system 0.0945 Experimental data
N0 Initial OD600 value of wild type G. hansenii ATCC53582 0.1654 Experimental data
Nm Maximum OD600 value of the engineered G. hansenii containing the gshF expression system 2.450 Experimental data
Nm Maximum OD600 value of wild type G. hansenii ATCC53582 2.630 Experimental data
rm Maximum specific growth rate of the engineered G. hansenii containing the gshF expression system 0.0314 Derivation from experimental data
rm Maximum specific growth rate of wild type G. hansenii ATCC53582 0.0333 Derivation from experimental data

Conclusions

Simulation of Lysis and Safety Module

We use the blue light responsive system (pDawn) to control the expression of the lysis protein X174E, for the release of the intracellular glutathione (GSH) and bacterial lysate, as well as biocontaiment. In order to accurately describe this process and speed up the engineering cycle, we use modeling to simulate the response of kill switch to blue light, namely the changes in the level of the lysis protein X174E with blue light intensity.

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Figure 3: Blue light responsive system

Reaction Equations

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Mathematical Equations

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Results

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Figure 4: Blue light intensity profile
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Figure 5: Profile of the level lysis protein X174E upon blue light illumination

According to the modeling results, blue light illumination has a significant effect on the concentration of the lysis protein X174E, which is determined by the expression of the gene X174E and the degradation of X174E. Due to the expression leakage of the pDawn system, the X174E level is low without blue light. However, the concentration of X174E increases rapidly upon blue light illumination. Thus, from a theoretical point of view, we can use blue light to control bacterial lysis.

References

[1] https://2021.igem.org/Team:SZPT-CHINA/Model
[2] https://2021.igem.org/Team:BIT-China/Model
[3] Zwietering, M. H., Jongenburger, I., Rombouts, F. M. & van 't Riet, K. Modeling of the bacterial growth curve. Appl. Environ. Microbiol. 56, 1875-81 (1990).
[4] Kahm, M., Hasenbrink, G., Lichtenberg-Frate, H., Ludwig, J. & Kschischo, M. grofit: Fitting Biological Growth Curves with R. J. Stat. Softw. 33, 1-21 (2010).
[5] Milo, R. & Phillips, R. Cell Biology by the numbers. (Garland Science, 2015). doi:10.1201/9780429258770.
[6] Jin, X. & Riedel-Kruse, I. H. Biofilm Lithography enables high-resolution cell patterning via optogenetic adhesin expression. Proc. Natl. Acad. Sci. U. S. A. 115, 3698-3703 (2018).