Team:SUSTC-Shenzhen-A/Project

Abstract

There are many applications of the game theory in some aspects of our life. Each individual has two kinds of choices--to betray or stay silent, and the choice you make would determine your fate. To betray the other side, you may risk being revenged. While staying silent, companion's betrayal may hurt you deeply. As for our project, we work out a new way to imitate the game theory by constructing a community of two E. Coli bacteria. Here we use the growth rate of each species to represent its fate. The effect of one's silent or betrayal on the other species' fate is acted through intercellular signal molecules of two quorum sensing systems. Each signal molecule regulates the expression of toxic genes in the other species and reduces its growth rate. We characterize the consequence of each strategy by quantitatively measure the growth rates of each species in the community.

Background

We find that most of the researches in synthetic biology aim to study the interaction between parts of a single system. But few focus on the interaction between systems with multi-elements. However, in the real world, there are complex competition and cooperation among the various creatures. And they can work together to achieve a certain function, for example, different kinds of yeast in brewing beer. There is no lack of the ideas of game theory in these processes. So we chose a classical method—prisoner's dilemma as a model to design our project.

Our project can simulate the interaction among multiple systems in reality and the final state of them. Through a well-known issue of game theory, we can give synthetic biology and iGem greater publicity from many perspectives. Furthermore, after a slight modification, the project can provide us with an interesting solution to security.

Project Details

our design

Parts:

Communication: quorum sensing

LuxI produces 6HSL, 6HSL cross membrane, 6HSL binds to LuxR, LuxR activate promoter Plux

LasI produces 12HSL, 12HSL cross membrane, 12HSL binds to LasR, LasR activate promoter Plas

Poissonness genes:

tetA: import Nickel into cell, which kills cell

Zeo-r: Zeocin damage DNA and kills cell, Zeo-r binds and neutralize Zeocin

Truth Table

Tuning gene expression: repressor and inducer

Cell A: Constant expressions:

LuxR(with C6HSL, activate pLux, express Zeo-r): promoter J23100 and rbs B0034

Pcon: promoter J23100 to J23107 and rbs B0034

RFP (red fluorescent protein labeled cell A): promoter pCon, rbs

Adjustable expressions:

Zeo-r, adjusted by C6HSL, produced by LuxI from cell B, depends on cell B density and IPTG concentration

LasI: adjusted by arabinose, LasI produces C12HSL, control cell B

tetA: adjusted by arabinose

Zeocin, Nickel concentration are controlled by you

C12SHL concentration: controlled by cell A concentration and arabinose

Nickel either kill cell or slow cell growth: tetA increase sensitivity of cell B to Nickel:

zeocin either kill cell or slow cell growth:Zeo-r decreases sensitivity of cell B to zeocin

-asv: amino acid sequence that increase response time of LuxI, tetA and Zeo-r to regulation

pMB1 and p15A: control plasmid replication in E coli</p>

Kan and Cam(r): with antibiotics Kanamycin and chloramphenicol, prevent lost of plasmids

Experiment process

Please refer to our notebook.

Results

project results

What we have done are the plasmid construction and the early experiments ,because of the time limite. Here are some results of our project

modeling results

Biobricks checking

results

We chose four biobricks to detect after search. They are xylose,KNO₃,glucose and 12HSL.

BBa_K733018: xylose

First we used the microplate to detect the fluorescence of this part, because we could not see the transition point and we were wondering what it would be look like. The data we got is not so good, for we didn’t see the transition point obviously, and also has some differences comparing to the data from HKUST. According to the detecting from HKUST, fluorescence produced in about 5% xylose is more than that in 10% xylose, however what we got is fluorescence produced in 5% xylose than that in 10% xylose.

Since we didn’t get what we really want using microplate, we tried FCM this time. In 0% xylose culture, we almost got no fluorescence produced and in 0.003% xylose culture we saw obvious fluorescence came out. There was an obvious tendency that the efficiency of the promoter increased as the xylose concentration gathered up. However, with the increase of xylose concentration, the fluorescence increased choppily. What was amazing was that the fluorescence we got was 10 times brighter than the result the HKT got, which appeared to be great.

Another interesting thing is most of the cell we detected had no fluorescence produced. Then we guess that may because the xylose molecules taken in the different cell are different, or may because the copy number in each cell is different. Maybe it has some relationship with epigenetics.

BBa_K774007: KNO₃

In this part we got a similar beginning, but have a different ending. With the increasing of nitrate of potash, the fluorescence produced by single cell increases slowly at the beginning but rapidly later. However, according to the data from NRP-UEA-Norwich, the fluorescence starts to decrease at the 20mM nitrate of potash.

BBa_K741002: glucose

Compared with testing result from USTC_China, we’ve got a better data. According to the data from USTC_China, after adding glucose, the fluorescence reduces only about 10% in the most obvious one. However, we got a clear reduce in the production of fluorescence. In about 10-2.4M glucose, the fluorescence has left only a half in one cell. With the increasing of glucose, the fluorescence/OD continuously decreases, and at about 10-1M glucose, the fluorescence/OD remains very low.

references

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