Team:NEFU China/fit
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Modeling1
MODELING
Mathematical modeling is a very powerful tool for biological research. And we have used it to assist our project in three aspects.
We described and solved the following questions:
（1）Detection efficiency of the products for different pathogenic bacteria
（2）Using mathematical methods to clearly express the process of AI-2 transport in cells
（3）The expression of the report gene was used to reflect the results of the experiment
(1) The sensitivity of this approach to different pathogens
@Introduction
Since
itself doesn’t produce AI-2, we set as a piece of negative control in our experiment. It can produce some material with absorbance, the data of Bacillus subtilis was similar to the blank control. Through the experiment, we can find that the E.coli is the most suitable for our product.
(2) The change of blue pigment in response to AI-2.
@Introduction
We can use the obtained experimental data to draw the curve directly. According to the curve, AI-2 produced by pathogen changes with time. We use a specific instrument to quantitatively determine the intensity of blue pigment. The deeper the color, the greater the intensity, the more the number of reported gene expression. The graph on the right can indicate the relationship between AI-2 and bacterium intensity.
@ Figure
(3) The dynamic change of AI-2.
@Introduction
We can express the transport process of AI-2 in the cell by mathematical modeling. So we can find out the relationship between the components. The model is based on the establishment of the biological dynamics, which indicates the transport routes of AI-2 in the cell. Each equation here represents one of the pathways.
@Equation
(1).Intracellular synthesis of proteins related to the equation ：
[1]LsrACBD synthesis:
[2]LsrR and LsrK synthesis: Equation
[1]
[2]
(2)The equations for the AI-2 in the cell membrane ：
[3]Absorption:
[4]Secretion:
[3]
[4]
(3)Ultimate expression of AI-2 cells equation:
[5] Generating content of AI-2 in the cells:
[6] Secretion levels of AI-2 cells:
[5] - [6] The total content of AI-2 in the cells:
[7] AI-2 phosphorylation of the equation: Equation
[5]
[6]
[7]
[Constant]
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Constant |
legend |
Value |
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Maximum transcription rate of lsrACBD |
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Maximum transcription rate of lsrRK |
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Affinity constant between lsrR and lsrACBD |
1m |
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Affinity constant between lsrR and lsrKR |
2m |
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Inhibition constant between lsrR and lsrACBD |
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LsrR inhibition constant on lsrR |
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Degradation of mRNA lsrACBD and lsrKR |
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Degradation of lsrACBD |
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Degradation of lsrR,lsrFG，and lsrK |
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Translation rate of mRNA lsrACBDFG |
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Translation of lsrRK |
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Binding between LsrR and phosphorylated AI-2 |
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Binding between LsrFG and phosphorylated AI-2 |
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Binding between LsrK and AI-2 |
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Binding between LsrACBD and extracellular AI-2 |
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AI-2 synthesis rate |
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Excretion constant |
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Dimerization rate（active LsrR |
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inactive LsrR）
[Definition of Variables]
|
Variable |
Description |
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mRNA lsrACBDFG |
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Mrna lsrKR |
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LsrACBD |
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lsrR |
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Intracellular AI-2 |
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Extracellular AI-2 |
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Phosphorylated AI-2 |
