In our experiment, a brief dtRNA gene segment was introduced before the RBS, which successfully increases the efficiency of gene expression without placing an undue amount of growth pressure on the bacteria. In most cases, replacing a promoter with a higher expression rate and speeding up transcription is the most direct technique to raise the rate of gene expression. However, this approach will put a lot of pressure on bacterial development, preventing the concentration of bacteria from rising. We have created pertinent data models to explicitly illustrate the aforementioned ideas from a data perspective to boost the project's persuasiveness.
This model, which describes the link between bacterial growth, gene expression, and other relevant variables across time, is a gene expression model in and of itself. Then, by modifying the mRNA degradation parameter, we model the impact of dtRNA. With the help of this technique, we can confirm and clarify the design concept for our project and make assumptions about how dtRNA functions. This model serves as a foundation for future scholars in addition to being helpful to our mission.
Escherichia coli serves as our chassis. Figure 1 depicts the typical gene pathway using a pSB1A2 plasmid. As a result, if the gene expression is excessively powerful, the resources in the culture medium will drop rapidly, which will directly cause the slowing down of bacterial fission. In our opinion, the pressure on bacterial growth is caused by the reduction of living resources in the environment. This procedure is what we are simulating. We make certain fair assumptions to streamline the model.
Figure.1 Scheme for gene expression and degradation
Assumptions:
1. Assume that the environment of the culture medium has R0 resources for bacterial formation.
2. We keep putting resources into the culture medium to ensure that they are not detrimental.
3. The only processes that use resources are bacterial division and intracellular gene expression.
4. Ignore how other gene expressions may affect the system.
Building Model
The Monod equation describes a growth mode of substrate inhibition in which the development of bacteria is constrained by the availability of living resources as shown in formula (2.1).
Although gene transcription and translation are thought to function as a negative feedback system, the transcription and translation rate is thought to be constant. An example of a negative feedback regulator for mRNA is (1/1+Mn), where n denotes the strength of the regulation and P is the same as above.
The results of the literature indicate that the rate of resources consumed by bacterial division at any given moment is approximately 10–7 mg, but transcription and translation should consume significantly fewer resources than this amount. An mRNA molecule produced by transcription is thought to consume more than 10-10 mg, and a protein molecule produced by translation is thought to use roughly 10-10 mg.
We can locate some trustworthy parameters by reviewing pertinent data, as indicated in Table 1. Estimation shows that the negative feedback intensity of transcription is around 0.15, while that of translation is approximately 0.25.
We modified the mRNA degradation rate, used MATLAB to solve the equation, and computed the GFP concentration change curve and the bacterial concentration change curve that correspond to the transcription rate [100,200,400,600]. As depicted in Figure 2,
Figure 2. Changes in bacterial concentration and GFP concentration at different transcription rates
The chart clearly shows that after increasing the transcription rate, the gene expression rate has improved greatly, but the bacterial survival pressure has also increased dramatically and the maximum concentration of bacteria has decreased significantly. This demonstrates that although the expression of a single bacterium is faster following the replacement of the stronger promoter, bacterial proliferation has been suppressed.
When the mRNA degradation rate is [0,0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], we set the transcription rate to a fixed value and compute the variation diagram of bacterial concentration and GFP concentration-time curve in Figure 3:
change in bacterial concentration with different mRNA degradation rates
Figure 3. change in GFP concentration with different mRNA degradation rates
The image shows that the change in mRNA degradation rate has little effect on bacterial growth and that the rate at which GFP is expressed has also greatly increased. We use the bacterial concentration at time t=12 as a measure of the size of the bacterial survival pressure to illustrate the issue more. The concentration of GFP is employed as a measure of the intensity of gene expression; the higher the concentration, the lower the pressure. The higher the concentration, the greater the expression intensity. We made scatter plots of the changes in survival pressure and expression intensity with the degradation rate of mRNA, as shown in Figure 4.
Pressure change scatter chart
Figure 4. Expression Intensity Scatter Chart
Table:
Table 1: values of certain parameters