Team:NAU-CHINA/Model
Model
This year, we design a yeast system to produce antifungl peptide which inhibits the activity of Fusarium graminearum, the pathogen of wheat scab. To choose the suitable antifungl peptide, we establish an evaluation model and want to know which antimicrobial peptide gets the highest score.
We also design a complex system to sense and degrade the toxin produced by Fusarium graminearum. Sensing and degrading are dynamic processes so dynamic model is a good choice to represent this part. Since the expression of degradation enzymes are controlled by engineered photoactivatable switch, we consider photochemical reaction kinetics and it performs well. In the same system, we put in a kill switch into the yeast. Yeast will die without galactose induction so the safety of the system is ensured. You can see how the yeast die by our model.
Antifungl Peptide Part
We intend to use yeast system to produce a large number of antifungl peptides to kill Fusarium graminearum which causes the wheat scab. Therefore, the selection of antifungl peptide is very important.
We searched for some antimicrobial peptide databases like Peptaibol Database, Defensin Knowledgebase, MiniCOPE, APD and so on. Finally, we found 20 antimicrobial peptides that inhibit the activity of Fusarium graminearum.
The ideal antifungl peptide should have the following characteristics:
(1) Being harmless to host cells.
(2) Being harmless to the human body.
(3) Having little damage on microbes in the soil.
Therefore, we set effective antifungl concentration, Activity object, half-life as three indexs, and established an evaluation model to evaluate if the peptide is suitable for our project.
Figure 1 : The scores of different antifungl peptides
Finally, we choose PDC1 and cecropin A to kill Fusarium graminearum. They can inhibit the growth of Fusarium graminearum in a very low concentration(1.5μM、3.6μM), PDC1 is specific for Fusarium graminearum.
Sensing Part
We intend to sense toxins through the yeast system. In the sensing part, estrogen receptor is secreted by constitutive promoter, and then binding to the membrane. ZEN comes into the cell via the receptor-mediated endocytosis process, the endocytosis complex will activate ERE(estrogen response element) which incorporated with a yeast promoter and the promoter begin to express fluorescent protein.
Figure 2 : Schematic diagram of the sensing part
In order to observe the fluorescence intensity in different concentrations of ZEN and the corresponding reaction time. We set a dynamic model by some differential equations:
Simulation
In different ZEN concentrations, the system will express different intensities of fluorescent protein. We use MATLAB GUI to simulate the process.
Figure3: Simulation result
You can adjust the ZEN concentration and observe the change of protein concentration. According to the Chinese National Standards For Food Safety, ZEN’s concentration should not high than 0.015 mg/L. If the concentration of ZEN is higher than the level, the device will sense the fluorescence, turn on the photoactivatable switch and degrade the toxin.
Variable
| ER | Estrogen receptor |
| ERcp | Complex between estrogen receptor and ZEN |
| FLU | Fluorescent protein |
| ERm | Estrogen receptor mRNA |
| FLUm | Fluorescent protein mRNA |
| ∂ | The concentration of ZEN |
Parameters
| Parameters | Description | Value |
| β1 | Maximun transcription rate of constitutive promoter | 5 |
| β2 | The maximum transcription rate of ERE promoter | 6 |
| α | Translation rate per animoacid | 1020 |
| k1 | Endocytosis rate | 4 |
| k2 | Dssociation rate of complex ERcp | 1 |
| d1 | Degradation rate of ER | 0.8 |
| d2 | Degradation rate of ERcp | 0.6 |
| d3 | Degradation rate of FLU | 0.8 |
| dmrna | Degradation rate of mRNA | 0.231 |
| sER | Length of Estrongen receptor | 596 |
| sFLU | Length of Fluorescent protein | 240 |
| ξ | Leakage factor of the ERE promoter | 0.02 |
| k | The concentration of ERcpwhen the ERE promoter reaches half of the maximum generation rate | 200 |
| m | Hills coefficient | 1 |
Degrading Part
We intend to degrade toxins through E.coli system. When the fluorescence intensity is higher than threshold, our device will sense it and turn on the LED light. In our E.coli system, N564 Fragment and C565 Fragment are expressed by the constitutive promoter. Induced by LED light, N564 Fragment and C565 Fragment will complex with each other. pT7 promoter will be induced by the complex and then expresses ZHD101 and TRI101 which respectively modifies ZEN and DON toxin, modification will reduce their toxicity.
Figure 4 : Schematic diagram of the degrading part
The first part is a kind of photochemical reaction, N564 Fragment and C565 Fragment are activated by light and react with each other.
The reaction process can be described as following:
We can get some differential equations which describes the process:
Simulation
We use Matlab to simulate the process.
Figure 5 : Simulation result
According to our simulation result, after about 36s, the photoactivatable switch will fully open, the complex reaching the maximum number of molecules, 220 molecules.
The following part is a dynamic model, the complex activate pT7 promoter and pT7 expresses ZHD101 and TRI101. The reaction process can be described as following:
Simulation
We use Matlab to simulate the process.
Figure 6 : Simulation result
According to our simulation result, after 35 minutes, ZHD101 is fully produced and reaches the maximum number of molecules, 600 molecules. At the same time, 30 minutes later, TRI101 reaches the max, 500 molecules.
Sensitivity analysis
We briefly evaluate the sensitivity of the model by perturbing the value of parameters. Changes of ±10% in β, COM, ξ, d1, d2, sZHD, sTRI and k have different effects on the variable of ZEN and TRI, as they are showed in Figure. We can see that changes of ±10% in β, d1 and sZHD have a greater influence for the variable ZEN, respectively, while the changes of d2 and sTRI affect the variable TRI greatly. As indicated in Figure, the parameter of COM has no effect on the results.
Figure 7 : Simulation result
Variables
| N567 | N567 Fragment |
| C565 | C565 Fragment |
| N564 | N564 Fragment activated by light |
| C565 | C565 Fragment activated by light |
| Complex | Complex between N564 Fragment and C565 Fragment |
| ZHDm | ZHD101 mRNA |
| TRIm | TRI101 mRNA |
Parameters
| Parameters | Description | Value |
| k1 | The 1st light reaction rate | 1 |
| k2 | The 2nd light reaction rate | 1 |
| k3 | The 3rd light reaction rate | 0.5 |
| k4 | The 4th light reaction rate | 0.1 |
| k5 | The 5th light reation rate | 0.1 |
| k | The concentration of product when pT7 promoter reaches half of the maximum generation rate | 40 |
| Ia | Incident light intensity | 0.5 |
| α | Translation rate per aminoacid | 1020 |
| β | Maximum transcription rate of pT7 promoter | 24 |
| ξ | Leakage factor of pT7 promoter | 0.24 |
| d1 | Degradation rate of ZHD | 0.4 |
| d2 | Degradation rate of TRI | 0.8 |
| dmrna | Degradation rate of mRNA | 0.2 |
| sZHD | Length of ZHD | 517 |
| sTRI | Length of TRI | 174 |
| m | Hills coefficient | 1 |
Kill Switch Part
In the final part, we design a kill switch to control our system’s hazards to the environment. Without galactose induction to the system, TFs will not been expressed and BI-1 will not been induced. So the concentration of Bax is much higher than BI-1, the yeast will die.
Figure 8 : Schematic diagram of Kill Switch
Since Bax is expressed by a constitutive promoter, we can describe it as following:
Simulation
We use Matlab to simulate the process.
Figure 9 : Simulation result
According to our simulation result, produced by a constitutive promoter, Bax is fully expressed after 80 minuutes and it’s highest molecule number is 380.
BI-1 is induced by TFs which is induced by galactose, we can describe it as following:
Simulation
In different Galactose concentrations, the system will express different concentrations of BI-1. We use MATLAB GUI to simulate the process.
Figure 10 : Simulation result
You can adjust the Galactose concentration and observe the change of BI-1’s concentration. If BI-1’s concentration is higher than Bax which simulated before, the yeast will survive. From the simulation result, we know if galactose’s concentration is lower than 0.02g/ml, the yeast will die.
Variables
| Baxm | Bax protein mRNA |
| TFsm | Plat-derived TFs protein mRNA |
| Bl1m | BI-1 protein mRNA |
| Bax | Bax protein |
| TFs | Plant-derived TFs protein |
| Bl1 | Bl-1 protein |
| gal | Galactose |
Parameters
| α | Translation rate per aminoacid | 1020 |
| β | Maximum transcription rate of constitutive promoter | 1 |
| β1 | Maximum transcription rate of TFs promoter | 4 |
| β2 | Maximum transcription rate of BL1 promoter | 6 |
| ξ1 | Leakage factor of TFs promoter | 0.02 |
| ξ2 | Leakage factor of BL-1 promoter | 0.02 |
| k1 | The concentration of galactose when TFs promoter reaches half of the maximum generation rate | 0.02 |
| k2 | The concentration of TFs when Bl-1 promoter reaches half of the maximum generation rate | 200 |
| d | Degradation rate of Bax | 0.06 |
| d1 | Degradation rate of TFs | 0.8 |
| d2 | Degradation rate of Bl-1 | 1 |
| dmrna | Degradation rate of mRNA | 0.231 |
| sBax | Length of Bax protein | 191 |
| sTFs | Length of TFs protein | 280 |
| dBl1 | Length of BL-1 protein | 138 |
Reference
[1] Pragya Kant, Wen-Zhe Liu, K. Peter Pauls. PDC1, a corn defensin peptide expressed in Escherichia coli and Pichia pastoris inhibits growth of Fusarium graminearum. Peptides 30 (2009) 1593–1599.
[2] ANTHONY J. DELUCCA, JOHN M. BLAND, THOMAS J. JACKS, CASEY GRIMM, THOMAS E. CLEVELAND, AND THOMAS J. WALSH. Fungicidal Activity of Cecropin A. ANTIMICROBIAL AGENTS AND CHEMOTHERAPY,Feb. 1997, p. 481–483.
[3] Tiyun Han, Quan Chen, and Haiyan Liu. Engineered Photo- activatable Genetic Switches Based on the Bacterium Phage T7 RNA Polymerase. DOI: 10.1021/acssynbio.6b00248. ACS Synth. Biol. 2017, 6, 357−366
[4] Gita Naseri, Salma Balazadeh, Fabian Machens, Iman Kam- ranfar, Katrin Messerschmidt, and Bernd Mueller-Roeber. Plant Derived Transcription Factors for Orthologous Regulation of Gene Expression in the Yeast Saccharomyces cerevisiae.
DOI: 10.1021/acssynbio.7b00094. ACS Synth. Biol. XXXX, XXX, XXX−XXX
[5] https://2016.igem.org/Team:NAU-CHINA/Model/DM
[6] https://2013.igem.org/Team:TU-Delft/Timer-Sumo-KillSwitch
[7] https://2012.igem.org/Team:HIT-Harbin/project/model
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Orz
- Antifungl Peptide Part
- Sensing Part
- Simulation
- Variable
- Parameters
- Degrading Part
- Simulation
- Simulation
- Sensitivity analysis
- Variables
- Parameters
- Kill Switch Part
- Simulation
- Simulation
- Variables
- Parameters
- Reference
