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| − | <!-- Mobile Specific Metas================================================== --> | + | <div id="fullpage" class="fullpage-default"> |
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| + | <div class="section animated-row" data-section="slide02"> |
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| | + | <div class="about-section"> |
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| | + | <div class="about-contentbox"> |
| | + | <div> |
| | + | <h2>Contribution</h2> |
| | + | <p style="font-size=36px">Through the comprehensive use of life science and information science knowledge, we have obtained the results of this experiment. In the whole process, we encountered many difficulties and challenges, but after careful thinking and practice, we finally successfully overcome these. In addition, we have also summarized some information that may be helpful to other teams, hoping to make some contributions to the iGEM community. </p> |
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| + | <p style="font-size=36px">1. We propose molecular computation models for arithmetic operations in artificial neural networks as well as relevant reaction kinetic analysis. </p> |
| − | <link rel="stylesheet" href="https://2019.igem.org/Template:SEU/CSS?action=raw&ctype=text/css"/>
| + | < center><a href= "https://2019.igem.org/Team:SEU/ Model" class=" buttonContri"> Model</a></ center> |
| − | <link rel="stylesheet" href="https://2019.igem.org/Template:SEU/STYLE?action=raw&ctype=text/css"/>
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| − | | + | <p style="font-size=36px">2. Based on these models, we implement artificial neurons, which is the basic element of neural networks, with DNA reactions. Also, we achieve backpropagation training process for DNA-based neural networks.</p> |
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| + | < center><a href= "https://2019.igem.org/Team:SEU/Demonstrate " class=" buttonContri"> Demonstrate</a></ center> |
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| − | <ul>
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| − | <li class='has-sub'><a href= 'https://2019.igem.org/Team:SEU '><span>Home</ span></a></li> | + | |
| − | <li class="active"><a href='https://2019.igem.org/Team:SEU/Team'><span>TEAM</span></a>
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| − | <ul>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Team'><span>Team Members</span></a>
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| − | <li class="active"><a href='https://2019.igem.org/Team:SEU/Collaborations'><span>Collaborations</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Description'><span>PROJECT</span></a>
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| − | <ul>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Description'><span>Description</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Design'><span>Design</span></a>
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| − | <li class='has-sub'><a href= 'https://2019.igem.org/Team:SEU/Experiments'><span>Experiment</span></a> | + | |
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Notebook'><span>Notebook</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Contribution'><span>Contribution</span></a>
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| − | </li>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Results'><span>Results</span></a>
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| − | </li>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Demonstrate'><span>Demonstrate</span></a>
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| − | </li>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Improve'><span>Improve</span></a>
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| − | </li>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Attributions'><span>Attributions</span></a>
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| − | </ul>
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| − | <li class=' has-sub'><a href='https://2019.igem.org/Team:SEU/Parts'><span>PARTS</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Parts'><span>Parts Overview</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Basic_Part'><span>Basic Parts</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Part_Collection'><span>Part Collection</span></a>
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| − | <li><a href='https://2019.igem.org/Team:SEU/Safety'><span>SAFETY</span></a></li>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Human_Practices'><span>HUMAN PRACTICES</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Human_Practices'><span>Human Practices</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Public_Engagement'><span>Education & Engagement</span></a>
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| − | <li class=' has-sub'><a href='https://2019.igem.org/Team:SEU/Entrepreneurship'><span>AWARDS</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Entrepreneurship'><span>Entrepreneurship</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Model'><span>Model</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Plant'><span>Plant</span></a>
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| − | <li class='has-sub'><a href='https://2019.igem.org/Team:SEU/Software'><span>Software</span></a>
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| − | <li><a href="https://igem.org/2019_Judging_Form?team=SEU"><span>JUDGING FORM</span></a>
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| − | <h1 class="entry-title">Collaboration</h1>
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| | | | |
| − | <div class="excerpt"><p>Through the comprehensive use of life science and information science knowledge, we have obtained the results of this experiment. In the whole process, we encountered many difficulties and challenges, but after careful thinking and practice, we finally successfully overcome these. In addition, we have also summarized some information that may be helpful to other teams, hoping to make some contributions to the iGEM community.</p></div>
| + | <p style="font-size=36px">3. To help with experiment, we develop a software tool which can generate DNA reactions and relevant DNA sequences accroding to the input size of the neural networks. Researchers can directly use this tool to abtain their exepected DNA-based neural networks and conduct experiments.</p> |
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| + | <center><a href="https://2019.igem.org/Team:SEU/Software" class="buttonContri">Software</a></center> |
| − | <blockquote><p> New method of using DNA</p></blockquote>
| + | <p style="font-size=36px"> 4. We also conduct DNA experiments and try to validate our theory. qRT-PCR results and PAGE results are obtained, analysis of experiment data is also provided.</p> |
| − | <p>In the experiment, we take the complementary matching principle of single stranded DNA bases as the basis of experiment and theory, which is similar to the function of BioBricks, but different.</p>
| + | <center><a href="https://2019.igem.org/Team:SEU/Experiments" class="buttonContri">Experiments</a></center> |
| − | <p>BioBricks uses DNA fragments to build blocks to systematically assemble new organisms and other biologically related components. BioBricks uses DNA as a genetic information base to construct targeted organisms.</p>
| + | |
| − | <p>In this experiment, we also use DNA as the basic material, but unlike the functions of DNA mentioned above, DNA no longer plays a role of genetic material in the experiment. We take the structure and properties of DNA as the precondition of constructing neural network, destroy the hydrogen bond between DNA double strands by heating, and then reassemble the corresponding fragments through annealing operation to achieve the effect of chain-to-chain replacement.</p>
| + | </div> |
| − | <p>In the process of experiment, by controlling the reaction conditions, changing the reaction rate and environment, and according to the specificity of pairing between DNA single strands, we can realize the function of inputting a single strand of DNA and then outputting a specific single strand of DNA. We have successfully constructed a neural network and provided an effective way to customize a specific single strand of DNA. This method expands the idea of using DNA. DNA can perform more diverse tasks through the specificity of DNA base pairing.</p>
| + | </div> |
| − | <blockquote><p>Optimizing Artificial Intelligence Network.</p></blockquote>
| + | </div> |
| − | <p> It is worth mentioning that we have constructed an efficient artificial neural network, which can prepare specific DNA single strands and show great potential in biology and medical biological information processing. Different from the general artificial neural network, our artificial neural network achieves back propagation. According to the output of the neural network, we can adjust the input parameters accordingly, and then optimize the next output, and finally get more stable output in line with our expectations. This method of continuous optimization using feedback results is not involved in the previous artificial neural network, which is also the advantage of our model..</p> | + | </div> |
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