2014.igem.org talk:HUST-China

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Team

Team Roaster

Instructors

zhanyee	Yi Zhan
HUST_XueYu	Yu Xue
Sissi	Qian Ge

HUST_Yanyunjun	Yunjun Yan
xieqian	Qian Xie

Student Members

wangjinjing12	Jinjing Wang
hustcaiwang	Wang Cai
HUST_XueZeng	Xue Zeng
HUST_YuanxinWang	Yuanxin Wang
HUST_YanAn	Yan An
Gin	Ruihao Li
griffy	Jiajun Tan
HUST_XiaofengZhang	Xiaofeng Zhang
Lei_Yin	Lei Yin
ShuyanTang	Shuyan Tang

WendyZhangQing	Zhang Qing
HUST_Dona	Danyang Wang
AllenZhang	Ziang Zhang
HUST_HuabinWang	Huabin Wang
HUST_WangLu	Lu Wang
sudo	Peng Su
xiaojiajing1992	jiajing Xiao
JoKercHOw	Shiji Chow
yusuku	Yiwen Ping
228870057@qq.com	Yuqing Wang

Modeling

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Modeling of a synthetic biological wastewater treatment

engineering system

Yuanxin Wang, Jianjie Zhao, Ruihao Li

Overview

Our project mainly focus on designing gene circuits to gather copper ions, degrading cyanide, detoxifying fluoride and suggesting whether the water is safety for further use. With these giant goals, the first thing we needed to do is using computational method to simulate the biological process and figure out whether our design is feasible. We established DDEs (delay differential equations) to see whether our instructors are trustable and give some further information for the detective part of our toolkit. Then we tested the robustness and sensitivity to get a broader insight of biological system both in single cell level and multicellular level. By doing this, we can get their properties for better application.

Single cell level

DDEs simulation

There are two kinds of E.Coli in the project—workers and instructors. The former ones produce some proteins binding with copper ions in the polluted water and the latter ones tell us whether the water is safe enough for further use. Since the thing we care about most is the safety of the water and the workers will be dedicated to remove the ions in the water before we decided to let them flow to the following pool, we established some equations to simulate the biological process of instructors. Considering about it will take some time for the transcription and translation process before a protein can bind with some certain promoters, we use DDEs instead of ODEs to make our simulation closer to the reality. And here are the equations:

parameter

description

value

reference

copynum

copy number of pACYDuet-1 plasmid

18~22

[1]

trc₁

transcription rate of mCII

w/o inducing: 0

inducing:

[2]

deg₁

transcription rate of mCII

0.12

[3]

deg₂

degradation rate of CII

0.1

[4]

deg₃

degradation rate of mCI

0.12

[3]

deg₄

degradation rate of CI

0.042

[4]

deg₅

degradation rate of mGFP

0.13

[5]

deg₆

degradation rate of GFP

0.017

[6]

deg₇

degradation rate of mRFP

0.13

[5]

deg₈

degradation rate of RFP

0.017

[6]

trl₁

translation rate of CII

0.12

[4]

trl₂

translation rate of CI

0.09

[3]

trl₃

translation rate of GFP

5.4

[7]

trl₄

translation rate of RFP

5.4

[7]

V_max₁

maximum transcription rate when induced by CII protein

0.9

[4]

V_max₂

maximum transcription rate when induced by CI₂ protein

0.66

[4]

τ₁

time for CII transcription, translation and folding

0.24min

estimated the same as CI₂

τ₂

time for CI2 transcription, translation and folding

0.24min

[3]

k_m₁

apparent association constant for CII binding with pRE promoter

0.398

[8]

k_m₂

apparent association constant for CI₂ binding with pR promoter

1.58*10^-3<>

[3]

k₁

reaction constant for CI forming CI₂

3

[4]

k₂

reaction constant for CI₂ disassociating to CI

30

[4]

[1]

[2] Copper-inducible transcriptional regulation at two promoters in the Escherichia coli copper resistance determinant pco D. A. Rouch and N. L. Brown Microbiology (1997), 143, 1191-1202

[3] K噬菌体操纵基因和调控蛋白相互作用网络及溶原态/裂解态转变特性的动力学研究丁辉,et al. Journ al of Inn er Mongolia University Sep. 2007 Vol. 38 No. 5

[4] Stochastic Kinetic Analysis of Developmental Pathway Bifurcation in Phage l-Infected Escherichiacoli Cells Adam Arkin ,et al. Genetics 149: 1633–1648(August 1998)

[5] Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays, Jonathan A. Bernstein, et al. PNAS July23, 2002, vol.99 no.15, 9697–9702

[6] <a href="http://bionumbers.hms.harvard.edu/">http://bionumbers.hms.harvard.edu/</a>

[7] <a href="https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters">https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters</a>

[8] Kinetic analysis of mutations affecting the cII activation site at the PRE promoter of bacteriophage λ, MING-CHE SHIH, et al. Proc. Natl. Acad. Sipi. USA, Vol. 81, pp. 6432-6436, October 1984, Genetics

The results of simulation are shown in the graphs below:

As you can notice in the picture, the expression level of fluorescent protein is changed a lot between polluted and non-polluted water. Thus, by detecting the fluorescence intensity of each protein, we can gain the information about whether the water is safe for further use. Considering about the severe consequences about taking in too much copper ions, we should make sure that our data is credible and the information we get from it is accurate.

We simulated the whole process of the water-dealing procedure. In the view of that the transcription rate of the copper sensitive promoter is related to the concentration of copper in the water, we divided the dealing process into several parts with different transcriptional rate and combine all the data eventually to make our simulation closer to the reality. The result showed below indicates that detecting one of the fluorescent intensity only is enough to get the information we want. But to detect the other fluorescent intensity redundantly can make the conclusion more trustable.