Modeling for Farming

Modeling for the incoherent feed forward loop

1. Introduction

In order to achieve the natural plants’ temporal pattern for producing plant hormones in E. coli, we introduced an incoherent feed forward loop, including new hybrid promoter part (BBa_K1139150).

The system of our designed incoherent feed forward loop is shown in Fig. 4-2-1.. We newly developed PRM/lac hybrid promoter, which is activated by CI and repressed by LacI (Fig. 4-2-2.). We plan to ligate a hormone synthase part downstream of this hybrid promoter. While PRM/lac activation by CI is a single-step reaction, repression by LacI is a two-step reaction. Thus, the activation of PRM/lac is faster than its repression. This time lag between activation and repression is important for generating a temporal pattern of plant hormone production.

Fig. 4-2-1.. Our designed incoherent feed forward loop

Fig. 4-2-2.. Our new designed hybrid promoter

By following mathematical model, we predicted that we can achieve pulse-generation of cytokinin as output, using nitric acid (NO3-) as input. In addition, we analyzed the relation between the waveform and the expression level of LacI.

2. Dynamic behavior of our circuit

2-1. Building a mathematical model

Our model is described by the following differential equations based on Hill equation.

Fig. 4-2-3.. Our Equations for our modeling

We will explain each equation.

As for NO3-, we only take the degradation into consideration, assuming that NO3- act directly on nitrate promoter.

Fig. 4-2-4.. Equation of NO3-

CI is activated by NO3-, LacI is activated by CI. Behaviors of CI and LacI are described by equation (2) and (3).

Fig. 4-2-5.. Equations of CI and LacI

Finally, behavior of PRM/lac hybrid promoter is shown as multiplication of activation by CI and repression by LacI. Therefore, the dynamics of cytokinin is described by equation (4).

Fig. 4-2-6.. Equation of cytokinin

We set the parameters as follows:

Fig. 4-2-7.. Descriptions of the parameters

The values of parameters are set as follows:

Fig. 4-2-8.. Values of the parameters

2-2. Simulation results of incoherent feed forward loop

Fig. 4-2-9. shows the simulation results about the relation between input (NO3-) and output (cytokinin) in our circuit. The red line represent the concentration of NO3-, the green line represents the concentration of cytokinin.

Fig. 4-2-9.. Time-dependent change of the concentrations of NO3- and cytokinin

As shown in Fig. 4-2-9. we verified the pulse-generation of cytokinin as the output with an input of nitric acid NO3-. We will discuss the process of the pulse-generation by looking at the expression level of each substance.

First, NO3- as an input activates CI. CI continues to increase until the input is completely degraded because the expression level of CI depends on the volume of the input (Fig. 4-2-10.).

Fig. 4-2-10.. Time-dependent change of the concentrations of NO3- and CI

Next, Fig. 4-2-11. shows that LacI and cytokinin are activated by CI. Also, in Fig. 4-2-12., which shows the enlarged view of Fig. 4-2-11. around 0min, LacI is expressed after the expression of CI.

Fig. 4-2-11.. Time-dependent change of the concentrations of CI, LacI, cytokinin

File:Titech2013 IFFL Fig 4-2-12 Enlarged view of Fig. 4-2-11..png

Fig. 4-2-12.. Enlarged view of Fig. 4-2-11.

Finally, Fig. 4-2-11. also shows that cytokinin is repressed by LacI after it is activated by CI. Cytokinin is expressed during this time lag between the activation and the repression.

2-3. Relation between the waveform and the maximum level of LacI expression

In 2-2, we confirmed that the expression of LacI is deeply concerned with the expression of cytokinin. Here, we analyze the waveform of cytokinin output by changing the maximum level of LacI expression (α2). Fig. 4-2-13. shows the change in the waveforms of cytokinin depending on the maximum level of LacI expression.

Fig. 4-2-13.. Waveforms of cytokinin depending on the maximum level of LacI expression

We see that the waveform of cytokinin output changes depending on the maximum level of LacI expression. In considering the application of this system, we predict that we can adapt the output waveforms to the circumstances of plants.

2-4. Relation between the output waveform and IPTG

Here, we will predict the activity of LacI by considering IPTG, which represses LacI. Decreasing of LacI activity is described by equation (5).

Fig. 4-2-14.. Equation of decreasing LacI activity

Substituting equation (5) for equation (4), we obtain equation (6).

Fig. 4-2-15.. Equation of cytokinin led from equation (4) and (5)

Shown in equation (6), pulse width of the cytokinin expression can be controlled by external IPTG. In simulation by changing the value of γ, we obtained the result shown in Fig. 4-2-16..

Fig. 4-2-16.. Changes of cytokinin expression time controlled by IPTG

Therefore, we confirmed that the pulse width of the cytokinin expression can be controlled by the volume of IPTG.