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| <li><a href="#humanpractice">Human Practice</a></li> | | <li><a href="#humanpractice">Human Practice</a></li> |
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− | <li><a href="#collabration">Collabration</a></li> | + | <li><a href="#collaboration">Collaboration</a></li> |
| <!--<li><a href="#notebook">Notebook</a></li>--> | | <!--<li><a href="#notebook">Notebook</a></li>--> |
| <li><a href="#team">Team&Attribution</a></li> | | <li><a href="#team">Team&Attribution</a></li> |
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− | <option value="#collabration">Collabration</option> | + | <option value="#collaboration">Collaboration</option> |
| <!--<option value="#notebook">Notebook</option>--> | | <!--<option value="#notebook">Notebook</option>--> |
| <option value="#team">Team&Attribution</option> | | <option value="#team">Team&Attribution</option> |
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| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <p class="page-main" style="text-indent:1em"><span>Part 1: T7 promoter + toehold triggered by miR144 + GFP + T7 terminator</span></p> | | <p class="page-main" style="text-indent:1em"><span>Part 1: T7 promoter + toehold triggered by miR144 + GFP + T7 terminator</span></p> |
− | <p class="page-main" style="text-indent:1em">For part 1, we want to add the toehold triggered by miR-144 to the upstream region of the GFP coding sequence. We used gene2oligo to design many short single-strand DNA and then conducted LCR and second PCR to synthesis our part 1.</p> | + | <p class="page-main" style="text-indent:1em">For part 1, we want to add the toehold triggered by miR-144 to the upstream region of the GFP coding sequence. We used gene2oligo to design many short single-strand DNA and then conducted LCR and second PCR to synthesis our part 1.<a href="http://parts.igem.org/Part:BBa_K1311000:Design"><span> (http://parts.igem.org/Part:BBa_K1700000:Design)</span></a></p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <p class="page-main" style="text-indent:1em"><span>Part 2: T7 promoter + toehold triggered by GFP mRNA + T3 RNA polymerase + T7 terminator</span></p> | | <p class="page-main" style="text-indent:1em"><span>Part 2: T7 promoter + toehold triggered by GFP mRNA + T3 RNA polymerase + T7 terminator</span></p> |
− | <p class="page-main" style="text-indent:1em">We can obtained T3 RNAP sequence from BBa_K346000 in iGEM distribution. This part was much larger than part 1, so LCR could be inconvenient and expensive. So we used assembly PCR method to add T7 promotor, toehold triggered by GFP mRNA and T7 terminator to the gene.</p> | + | <p class="page-main" style="text-indent:1em">We can obtained T3 RNAP sequence from BBa_K346000 in iGEM distribution. This part was much larger than part 1, so LCR could be inconvenient and expensive. So we used assembly PCR method to add T7 promotor, toehold triggered by GFP mRNA and T7 terminator to the gene.<a href="http://parts.igem.org/Part:BBa_K1311001:Design"><span> (http://parts.igem.org/Part:BBa_K1700001:Design)</span></a></p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <p class="page-main" style="text-indent:1em"><span>Part 3: T3 promoter + RBS + GFP + terminator</span></p> | | <p class="page-main" style="text-indent:1em"><span>Part 3: T3 promoter + RBS + GFP + terminator</span></p> |
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| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| </div> | | </div> |
| + | |
| + | <div class="twelve columns page-content"> |
| + | <h1 class="page-title">Reference</h1> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main"><span>1. Pardee K, Green A A, Ferrante T, et al. Paper-based synthetic gene networks.[J]. Cell, 2014, 159.</span></p> |
| + | <p class="page-main"><span>2. Green A, Silver P, Collins J, et al. Toehold Switches: De-Novo-Designed Regulators of Gene Expression[J]. Cell, 2014, 159:925–939.</span></p> |
| + | <p class="page-main"><span>3. Rouillard J M, Lee W, Truan G, et al. Gene2Oligo: oligonucleotide design for in vitro gene synthesis[J]. Nucleic acids research, 2004, 32(suppl 2): W176-W180.</span></p> |
| + | <p class="page-main"><span>4. Shimizu Y, Inoue A, Tomari Y, et al. Cell-free translation reconstituted with purified components[J]. Nature biotechnology, 2001, 19(8): 751-755.</span></p> |
| + | <p class="page-main"><span>5. Gibson D G, Young L, Chuang R Y, et al. Enzymatic assembly of DNA molecules up to several hundred kilobases[J]. Nature methods, 2009, 6(5): 343-345.</span></p> |
| + | <p class="page-main"><span>6. http://www.snapgene.com/resources/plasmid_files/your_time_is_valuable/</span></p> |
| + | <p class="page-main"><span>7. http://www.addgene.org/plasmid-protocols/gibson-assembly/</span></p> |
| + | <p class="page-main"><span>8. http://www.snapgene.com/resources/gibson_assembly/</span></p> |
| + | <p class="page-main"><span>9. Lin X, Lo H C, Wong D T, et al. Noncoding RNAs in Human Saliva as Potential Disease Biomarkers[J]. Frontiers in Genetics, 2015, 6.</span></p> |
| + | <p class="page-main"><span>10. Zijun, Xie, Gang, Chen, Xuchao, Zhang, et al. Salivary MicroRNAs as Promising Biomarkers for Detection of Esophageal Cancer[J]. Plos One, 2013, 8(4):e57502.</span></p> |
| + | <p class="page-main"><span>11. Minhua Y E, Penghui Y E, Zhang W, et al. [Diagnostic values of salivary versus and plasma microRNA-21 for early esophageal cancer].[J]. Journal of Southern Medical University, 2014, 34(6):885-889.</span></p> |
| + | <p class="page-main"><span> 12. http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi</span></p> |
| + | <p class="page-main"><span> 13. http://www.nupack.org</span></p> |
| + | <p class="page-main"><span> 14. http://helixweb.nih.gov/dnaworks</span></p> |
| + | <p class="page-main"><span> 15. http://berry.engin.umich.edu/gene2oligo</span></p> |
| + | |
| + | </div> |
| + | |
| + | <div class="twelve columns page-content"> |
| + | <h1 class="page-title">Acknowledgement</h1> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main"><span>Team Peking provided us pET-28a vector. (Although we didn’t use it at last.)</span></p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main"><span>Tsinghua Team once gave us competent cells when we needed to do transformation but there was no competent cells left in our lab. And we used their machine to dry our part for part submission.</span></p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main"><span>Xu Yingqi from Art School, Tsinghua University helps us with painting the picture. We appreciate her help sincerelly.</span></p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <img src="https://static.igem.org/mediawiki/2015/0/0b/TsinghuaA_Acknow_1.png" width="100%"> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | |
| + | </div> |
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| <div class="twelve columns page-content"> | | <div class="twelve columns page-content"> |
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| <div class="row"> | | <div class="row"> |
| + | <div class="twelve columns page-content"> |
| + | <h1 class="page-title">Overview</h1> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">Our project hopes to judge whether a man has esophagus cancer or the tendency of having it (set as y∈{0,1}, 0 stands for not having it or no tendency and 1 otherwise) through the expression level of a few kinds of miRNA (set as x∈Rn, n denotes the kinds of the miRNA being selected) in human saliva. This problem can be decomposed into two sub-problems, which is 1) what kind of mathematical relationship does the expression level of these certain kinds of miRNA satisfies so that we can make the prediction of whether or not having the cancer and 2) how to construct a biological circuit to express this kind of mathematical relationship and the conclusion of the judgment.</p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">Sub-problem 1 can be summarized as a pattern recognition problem. We can use some statistical learning (supervised learning) methods to obtain the pattern of the way that the concentrations of these kinds of miRNAs are combined, concretely, a set of parameters that the classifier possesses, to make prediction of whether or not having cancer.</p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">If sub-problem 1 is an applied mathematical problem, then sub-problem 2 is more related to biology, because a biological system is needed to physically realize the mathematical model obtained from sub-problem 1. We can analyze this problem from a systematic view. The input of this system is the concentrations of n kinds of miRNA, which is quite trivial. And how to use a biological system to express “ill or not”? The common method is to use some chemical to be the signal molecule, and our project follows the same routine. So the output of the system is the expression level of a certain kind of chemical (such as GFP).</p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">If sub-problem 1 uses combinatory logic to characterize the relationship between y and x, first the thresholds should be set. Secondly, gene circuits needs to be designed to characterize the relationship of “AND” “OR” “NOT”. Usually this can be realized through the combination of different kinds of promoter or repressor and also cascade reactions and so on. To characterize the threshold, the binding site of the trigger and switch needs to be designed and so does the binding strength of the promoter and repressor. This part involves more analysis of biochemistry and biophysics.</p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">If sub-problem 1 utilizes linear combination to characterize the relationship between y and x, the analysis of the problem is similar to the above.</p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | </div> |
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| <div class="twelve columns page-content"> | | <div class="twelve columns page-content"> |
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| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <h2 class="page-subtitle2"><span>Aim</span></h2> | | <h2 class="page-subtitle2"><span>Aim</span></h2> |
− | <div class="spacespace twelve columns page-content"></div>
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| <p class="page-main" style="text-indent:1em">To find out the relationship between whether or not have the cancer with the combinatory concentration of 4 or 3 kinds of miRNAs in the saliva.</p> | | <p class="page-main" style="text-indent:1em">To find out the relationship between whether or not have the cancer with the combinatory concentration of 4 or 3 kinds of miRNAs in the saliva.</p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <h2 class="page-subtitle2"><span>Idea</span></h2> | | <h2 class="page-subtitle2"><span>Idea</span></h2> |
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| <p class="page-main" style="text-indent:1em">This problem can be summarized as a classification problem. Concretely, we can use variable y, which only takes the values 1 or 0, to indicate with or without the cancer, and a 4 or 3 dimensional vector x, each element of which takes a real value, to represent the concentration of 4 or 3 kinds of miRNAs in the saliva. Then y can be modeled as a function of x:y=g(x)</p> | | <p class="page-main" style="text-indent:1em">This problem can be summarized as a classification problem. Concretely, we can use variable y, which only takes the values 1 or 0, to indicate with or without the cancer, and a 4 or 3 dimensional vector x, each element of which takes a real value, to represent the concentration of 4 or 3 kinds of miRNAs in the saliva. Then y can be modeled as a function of x:y=g(x)</p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
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| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <h2 class="page-subtitle2"><span>Method and Results</span></h2> | | <h2 class="page-subtitle2"><span>Method and Results</span></h2> |
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| <p class="page-main" style="text-indent:1em">We use the experimental data from Salivary MicroRNAs as Promising Biomarkers for Detection of Esophageal Cancer, Zijun Xie et. al. Altogether, there are 58 samples, 46 of which are patients and the rest of which are healthy control.</p> | | <p class="page-main" style="text-indent:1em">We use the experimental data from Salivary MicroRNAs as Promising Biomarkers for Detection of Esophageal Cancer, Zijun Xie et. al. Altogether, there are 58 samples, 46 of which are patients and the rest of which are healthy control.</p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
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| <p class="page-main"><span>Logistic Regression and SVM with linear kernel:</span></p> | | <p class="page-main"><span>Logistic Regression and SVM with linear kernel:</span></p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
− | <p class="page-main" style="text-indent:1em">Logistic Regression and SVM are 2 kinds of commonly used classifiers which belong to the domain of machine learning. For this problem, considering the scale of the dataset and the number of features (reference), it is most appropriate to use SVM with Gaussian kernel. This method was tried and the outcome was good. However, we decide not to choose this method. The reason is that Gaussian kernel or other more complex kernels transform the original features to another set of features, which is difficult to realize in in vivo biological systems, and the parameters obtained are hard to be transformed back to those of miRNA concentration.</p> | + | <p class="page-main" style="text-indent:1em">Logistic Regression and SVM are 2 kinds of commonly used classifiers which belong to the domain of machine learning. For this problem, considering the scale of the dataset and the number of features (see Machine Learning Lecture 12 on Coursera), it is most appropriate to use SVM with Gaussian kernel. This method was tried and the outcome was good. However, we decide not to choose this method. The reason is that Gaussian kernel or other more complex kernels transform the original features to another set of features, which is difficult to realize in in vivo biological systems, and the parameters obtained are hard to be transformed back to those of miRNA concentration.</p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <p class="page-main" style="text-indent:1em"><span>Tuning the Parameters:</span></p> | | <p class="page-main" style="text-indent:1em"><span>Tuning the Parameters:</span></p> |
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| <p class="page-main" style="text-indent:2em">3) mean and variance: mean = 0.145, variance = 1 * 10-4</p> | | <p class="page-main" style="text-indent:2em">3) mean and variance: mean = 0.145, variance = 1 * 10-4</p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| + | <img src="https://static.igem.org/mediawiki/2015/b/be/TsinghuaA_Model1_6.png" width="70%"> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em"><span>Matlab Source Code:</span></p> |
| + | <p class="page-main" style="text-indent:1em"><a href="https://2015.igem.org/File:TsinghuaA_source_code_for_model1.zip">Download link(Click here)</span></a></p> |
| </div> | | </div> |
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| <div class="twelve columns page-content"> | | <div class="twelve columns page-content"> |
| <h1 class="page-title">Model2</h1> | | <h1 class="page-title">Model2</h1> |
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| + | <div class="spacespace twelve columns page-content"></div> |
− | </div>
| + | <img src="https://static.igem.org/mediawiki/2015/d/da/TsinghuaA_Model2_1.png" width="70%"> |
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| + | <img src="https://static.igem.org/mediawiki/2015/8/8a/TsinghuaA_Model2_2.png" width="70%"> |
− | <div class="twelve columns page-content">
| + | <img src="https://static.igem.org/mediawiki/2015/a/a4/TsinghuaA_Model2_3.png" width="70%"> |
− | <h1 class="page-title">Model3</h1>
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| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <h1 class="page-subtitle2"><span>Using CV Technology to Quantitative Forecasting</span></h1> | | <h1 class="page-subtitle2"><span>Using CV Technology to Quantitative Forecasting</span></h1> |
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| </div> | | </div> |
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− | <!--Collabration Page--> | + | <!--Collaboration Page--> |
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| <!-- 分割5 --> | | <!-- 分割5 --> |
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| <div class="row"> | | <div class="row"> |
| <div class="twelve columns parallax-container"> | | <div class="twelve columns parallax-container"> |
− | <h1 class="parallax-title">COLLABRATION</h1> | + | <h1 class="parallax-title">COLLABORATION</h1> |
| <div class="parallax-divider"> | | <div class="parallax-divider"> |
| <img src="https://static.igem.org/mediawiki/2015/7/77/TsinghuaA_Homepage-text-top-icon.png" alt="iGEM"> | | <img src="https://static.igem.org/mediawiki/2015/7/77/TsinghuaA_Homepage-text-top-icon.png" alt="iGEM"> |
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| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| <img src="https://static.igem.org/mediawiki/2015/7/73/NYU_Shanghai_Day_47.jpg" width="80%"> | | <img src="https://static.igem.org/mediawiki/2015/7/73/NYU_Shanghai_Day_47.jpg" width="80%"> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <h2 class="page-subtitle2"><span>Try out Team Tsinghua's Prototype</span></h2> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">This is our feedback after trying out their product</p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">Tsinghua Team has made a very interesting device. It looks a mysterious, with two boxes, a 96-well plate and many wires. And it needs a computer when it works. After a short introduction I learned how to use it. Their software has a funny flash at the beginning and a friendly interface. There are two main functions their system can achieve. </p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">The first function of their device is very imaginative – their device can store information in bacteria. I can choose a small-sized file and then the program will encode the information into the bacteria in the 96-well plate. Since this process would take a relatively long time, I just loaded the file but didn’t wait for the cells to grow up. </p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">The second function allowed me to encode the information by myself. I can choose the time and the color of the light and change these parameters at different time. And for every well it can receive a different pattern of light input. Then, the 96-well plate will receive a set of light input, which may transfer the information to the bacteria. Besides, I think this light change can be used as a light inducer which may activate gene expression or change the state of a photosensitive protein. That is, we can conduct many different experiments in one 96-well plate. So, their device can be a useful tool not only in information encoding but also in many other fields, like synthetic biology or optogenetics. </p> |
| + | <div class="spacespace twelve columns page-content"></div> |
| + | <p class="page-main" style="text-indent:1em">Although their device is an experimental model by now, I believe it is not hard to bring it into real utility. </p> |
| <div class="spacespace twelve columns page-content"></div> | | <div class="spacespace twelve columns page-content"></div> |
| </div> | | </div> |
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| <div class="three columns padding-four-columns team-member mobile-two"> | | <div class="three columns padding-four-columns team-member mobile-two"> |
| <img src="https://static.igem.org/mediawiki/2015/6/6c/TsinghuaA_Team_Mx.png" alt="Our Team"> | | <img src="https://static.igem.org/mediawiki/2015/6/6c/TsinghuaA_Team_Mx.png" alt="Our Team"> |
− | <h3 class="our-team-title">Meixi Li</h3> | + | <h3 class="our-team-title">Meixi Liu</h3> |
| <h3 class="our-team-subtitle">Biological Science</h3> | | <h3 class="our-team-subtitle">Biological Science</h3> |
| <div class="our-team-divider"></div> | | <div class="our-team-divider"></div> |