Not gate-based kill switch

For the food producing systems, we designed a vanillic acid-controlled suicide switch. It can be used in other food-related projects.

Vanillic acid is a FDA-approved food addictive. Based on our design, expression of toxin is inhibited by TetR, and its expression is upstreamly inhibited by VanR. During the production process, vanillic acid will be added. It can release the inhibition of VanR to PvanCC, therefore expression of TetR will be strong, and it can inhibit the expression of toxin, and cell will not be kill. If engineer strain was released to the environment accidentally, without the presence of Vanillic acid, the VanR will express the expression of tetR, and expression of toxin will be strong, so strain will be kill.

We expect to prove how molecular feedback can trigger the effect of a suicide switch via constructing a theoretical mathematical model. To be concise, this suicide switch contains three negative feedback loops: Vanllic acid molecules will inhibit VanR, while VanR will inhibit the transcription of tetR, and tetR continuously inhibits the transcription of the lethal protein S_0766 (it's from cyanobacteria). How the expression of these three negative feedback processes can be demonstrated using Hill equations. In addition to that, our priority is molecular feedback interactions, in order to simplify the model to outline the key processes, specifically by idealising the translation processes of VanR and S_0766 lethal protein molecules, and expressing the translation processes via linear equations. Based on those hypotheses, we formulated the following mathematical model:

In the above model, A represents the concentration of vanillic acid molecules, B represents the concentration of VanR molecules, C represents the concentration of tetR molecules, D represents the mRNA concentration of the killing protein S_0766, and P represents the concentration of the killing protein. In the cultivation environment, an excess of vanillic acid is added, so A is considered a constant in the calculations. Other parameters are detailed in the parameter table. We assign a standard value to each parameter and set A to 0 to calculate the expression of the killing protein over time, as shown in Figure 1. The killing protein is expressed in large quantities, which can lead to a rapid lethal effect in practice. By gradually increasing the value of A, as depicted in the figure, the expression level of the killing protein shows a significant decline. Notably, when A equals 1, there is a precipitous drop in expression compared to when A is absent. This result partially demonstrates the rationality of our suicide switch mechanism.

From the figure, it can be seen that this suicide switch exhibits a time delay, meaning there is a small amount of killer protein expression at the initial moment. This is due to the setting of the initial value of tetR to zero during the model calculation process to highlight the feedback effect. In actual circumstances, the initial value of such molecules would not be zero, so this time delay can be considered negligible.

In addition, through multiple upregulations or downregulations of the gene expression rate kkk of the molecules, we found that when the ratio k1:k2:k3=1:3:1k_1:k_2:k_3 = 1:3:1k1:k2:k3=1:3:1, the entire suicide system becomes highly sensitive. The promoter of the entire suicide system is fixed and unchangeable, so when designing it, we can choose specific ribosome binding sites (RBS) to enhance the sensitivity of the suicide switch.

Hill equation model

Description: This model is based on the Hill equation and is used to describe the cooperativity between an inducer and a regulatory factor, from presence to absence. It is used when multiple inducer molecules act together to activate or repress gene expression.

In this model, n is the Hill coefficient, which indicates the degree of cooperativity. If n>1n > 1n>1, it represents positive cooperativity.

Two-Step Induction Model

Description: This model considers that the inducer first activates a regulatory protein, which then activates the expression of the target gene. It is suitable for two-step or multi-step regulatory processes.

In this model, regulator refers to the concentration of the regulatory protein, while kact and ksyn are the activation and synthesis rate constants, respectively.

Module environment sensing kill switch (BBa_K5308000)

For more information, please check BBa_K5308000 in registry.

While it depends on the cooperation of terminator, promoter and RBS, we needed to design it carefully by analyzing the expression of KillRed (NOTE: Also named as KillerRed) .

Here, we used de novo DNA to predict both the transcription rate and translation rate of killRed, and to design the stage changer.

We first set RBS (BBa_B0034) between the downstream loxp and KillRed (Figure 4A), and after recombining by inducible Cre, the DNA sequence will change (Figure 4B).

We predicted the potential transcription site in Figure 4B. To our surprise, we found there is a high transcription value, reaching 2416 (Figure 5A). We noticed that sequence in loxp comprises potential -10 box and -35 box in predicted promoter in yellow (Figure 5B).

So next we redesigned this cluster. Firstly, we move RBS (BBa_B0034) upstream to loxp, and removed the 'ATG' in Killred. Then, we set a 'ATG' unpstream loxp, and designed a linker between peptide translated from loxp region and killred region (Figure 6A). After recombining by inducible Cre, bothe RBS and start codon 'ATG' will be inverted, so killred will not be expressed (Figure 6B). There is also no 'TTG' and 'GTG' for potential weak translation of killRed (Figure 6B).

These designs make it robust.

DNA storage

In our model united nations, there is a great deal of enthusiasm for the preservation of civilizations on Earth, and there is even a consensus that we recognize the need to preserve civilizations, and which civilizations should be preserved. This inspired us to store information in DNA.

We design a method to turn information into DNA.

References

[1]https://en.wikipedia.org/wiki/Hill_equation_(biochemistry)
[2]Meyer, Adam J., et al. "Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors." Nature chemical biology 15.2 (2019): 196-204.
[3]Datta, Debika, et al. "Phenotypically complex living materials containing engineered cyanobacteria." Nature Communications 14.1 (2023): 4742.
[4]Chen, Ye, et al. "Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors." Nature Communications 9.1 (2018): 64.
[5]https://www.denovodna.com/software
[6]LaFleur, Travis L., Ayaan Hossain, and Howard M. Salis. "Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria." Nature communications 13.1 (2022): 5159.
[7]Yang, Xu, et al. "Towards Chinese text and DNA shift encoding scheme based on biomass plasmid storage." Frontiers in Bioinformatics 3 (2023): 1276934.