Team:ZJUT-China/Model

Team:ZJUT-China - 2018.igem.org

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Team:ZJUT-China

Abstract

In order to ensure that our system will be used better by the researchers who want to clean the antibiotic resistance gene(arg) after experiment, we developed a model to calculate when all args will be cut off and cause bacteria auto-lysis. We get some data of our parts and built some rate equations. We then used those rate equations to simulate how our system work and find a better ratio of the vector’s copy number.

System modeling Ver1.0

Our ODE model of the light control arg cutting system ver1.0. We assume the express rate proportional to the copy number of the vector, vm means the mixture rate of one vector express the cas9, [light signal] we use is a dimensionless parameter here, and the cut off rate of cas9 conform Michaelis-Menten equation.

Result

This is the result we got by the ODE model of the light-controlled ARG cutting system ver1.0

System modeling Ver1.1

This model is built to predict the growth curve. And to test whether our system model ver1.0 can fit the data of our wet lab. This model concern more about the growth of cell, and the death

Result

When time=5h we put in different concentrations of Arabinose in the medium.
The points are the data get tested by wet lab and the lines are the data simulated by computer.
The first 3 equations are built to show the how the E.coli with our system ver1.0 growth, and the last one is the growth equation of control group.

System modeling Ver2.0

This presents an issue for researchers and factors who wish to make use of our system of the arg killing parts in a more complex environment. In order to address this issue, we decided to develop a mathematical model of how light signal effects influence the time of cut off all args and suicide of bacterial by our arg killing system. In the light control arg cutting system ver2.0 we want add 3 repressors, one more sgRNA and a lysin gene to let our cells can cut off all the args and suicide by the time order we designed.

Our ODE model of the light control arg cutting system ver2.0. In this model we considered the express and degrade rate of the repressors and the effect of the copy number of 2vectors.

Through change the copy number of two vectors in our system model, we can get a ratio of the copy number let our system do nothing before we give the light signal, while has a faster react rate when we want it work.

Result

Figure 1:when Time>=1t the strength of light signal=0.1,it use 9t to cut off all antibiotic resistance gene.
Figure 2:when Time>=1t the strength of light signal=1,it use 8t to cut off all antibiotic resistance gene.
Figure 3:when Time>=1t the strength of light signal=1, and shutdown when Time=2, it use 11t to cut off.
Figure 4:when Time>=1t the strength of light signal=0.01, and shutdown when Time=2, it use 11t to cut off.

It’s obverse that final system will react faster when under a very strength light signal. However, just a very weak plased light signal is enough to start our system. So our system is very safe even if some bacteria equipped with our system we don’t treat it seriously, it will finally cut off all args it has and lysis automaticly.

Discussion

Our modeling and analysis was focused to achieve a better theoretical grounding of forecasting how our system work after we give the light signal.

From the test of our ODE model of the light control arg cutting system ver2.0, we find give a short pulse light signal can make almost same effect as we constant light signal, so we can find a plan to just give a short pulse light signal but make our system till response as fast as it, and save energy used to give the light signal.

[1]Quantitative approaches to the study of bistability in the lac operon of Escherichia coli J R Soc Interface. 2008 Aug 6; 5(Suppl 1): S29–S39. Published online 2008 Apr 15. doi:  10.1098/rsif.2008.0086.focus
[2]Combinatorial transcriptional control of the lactose operon of Escherichia coli Thomas Kuhlman, Zhongge Zhang, Milton H. Saier, Jr., Terence Hwa Proc Natl Acad Sci U S A. 2007 Apr 3; 104(14): 6043–6048. Published online 2007 Mar 21. doi: 10.1073/pnas.0606717104