Overview

In this section, we have developed a software tool for the toxic gene expression model, aiming to precisely control and observe how changes in experimental data impact the results. This tool allows for accurate analysis of experimental data variations and their effects on the outcomes, presented graphically for better visualization. By inputting data and scientific parameters, we transform them into two distinct models: one depicting the interaction between methyltransferase concentration (Met) and unmethylated modified promoter concentration (Pro) over time, and another illustrating the interaction model between protein levels of toxic genes and cell counts over time. These models provide a detailed insight into the dynamics of the experimental system.

The specific code for the software is elaborately explained in the Gitlab.

Program for Input Controls

The theme of this program revolves around control combinations and two blank images. Upon initialization, all controls start with a default value of 0.

Figure 1

Taking the first control ‘a’ as an example, there are two specific methods for numerical input:

(1)Users can directly input numerical values in the field labeled '1', with a maximum precision of up to three decimal places.

(2)Alternatively, users can slide the input using the slider in field '2'. By placing the mouse pointer over the inverted arrow and moving it left or right, corresponding numerical values can be input. However, this input method is relatively coarse and does not allow precise control over values.

Figure 2

Method for Precision Adjustment

The default maximum precision for all controls is set to three decimal places. If necessary, the precision of individual controls can be adjusted separately. Taking the ‘K’ control as an example:

(1)Clicking at the midpoint between the letter and the box enables the selection of the entire 'edit field (numeric)'.

(2)Locate ‘ValueFormat’, then click ‘’ in the direction indicated by the arrow in the diagram. Next, in the second-row position, users can specify the precision of the numerical value to the nth decimal place (n is user-defined). Additionally, users can modify the representation style of the number, such as converting it to an integer or scientific notation.

Figure 3

Parameter Range Adjustment Method

Using the ‘K’ control as an example:

(1)Click on the slider below the letter 'K' to select the entire ‘slider’.

Locate 'Limits', click ‘’ in the direction indicated by the arrow , and then input the minimum and maximum values for adjusting the parameter range in the 'min' and 'max' columns.

Figure 4

Program for Image Rendering

Using the left blank image as an example (controlled by two models on the left, and connected by four models on the right):

The left-side image is controlled by two models: the methyltransferase-induced expression model and the methyltransferase-modified promoter model within Escherichia coli. The specific forms of these two models are detailed in the Model page.

By inputting the control parameters associated with the two models, namely a, b, c, d, S, n, K, α, k2(Controls the value of k1) and Ptotal, one can obtain an interaction model describing the variation of methyltransferase concentration (Met) and unmethylated modified promoter concentration (Pro) over time.

Figure 5. The trend of Met and Pro concentration over time. E.coli.

The operation method for the right-side image is similar, however, when entering specific numerical values for control parameters, it is essential to input all parameter values. This is because the creation of the right-side image requires the collective involvement of all models.

Results

Our software program supports a wide range of research scenarios related to cell growth and death, including induced expression, epigenetic modifications, and the interplay between gene expression and cell survival and death. Its applicability is extensive.

Furthermore, our software can seamlessly integrate into new workflow processes. By adjusting the physical meaning and numerical ranges of the controls, along with the corresponding differential equations, and inputting appropriate values, you can obtain results tailored to specific projects. Consequently, it can easily be incorporated into various workflow environments.

Our source code has been put on Gitlab, hoping to provide you with more ideas.