diff --git a/wiki/pages/contribution.html b/wiki/pages/contribution.html index 011c30ab5899aac5d0248255fbb2ca09db748a0c..3638a16290c44d2b969b03dbb1c6a7882cfdfcbd 100644 --- a/wiki/pages/contribution.html +++ b/wiki/pages/contribution.html @@ -1,138 +1,18 @@ {% extends "layout.html" %} -{% block title %}Contribution{% endblock %} +{% block title %}Human Practices{% endblock %} {% block page_content %} - <meta charset="UTF-8"> - - <style>body{background-color: white !important;}</style> - <style> - .imgz{ - position: relative; - left: 50%; - transform: translateX(-50%); - } - .imgl{ - left: 0; - } - .imgr{ - right: 0; - } - - - .tuzhu { - text-align: center; - } - - .tiansuohao2 { - width: 50%; - /* 设置宽度为 50% 以实现左å³å¹¶ç½® */ - float: left; - /* 使用浮动使两个 div 并排显示 */ - box-sizing: border-box; - /* 让边框和填充ä¸å¢žåŠ 宽度 */ - border: 0px solid #000; - /* æ·»åŠ è¾¹æ¡†æ ·å¼ï¼Œå¯æ ¹æ®éœ€æ±‚修改 */ - padding: 2.5%; - /* æ·»åŠ å¡«å……ï¼Œå¯æ ¹æ®éœ€æ±‚修改 */ - margin: 0; - /* æ·»åŠ å¤–è¾¹è·ï¼Œå¯æ ¹æ®éœ€æ±‚修改 */ - } - - .clear { - clear: both; 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font-size: 50px; /*æ–‡å—大å°*/ @@ -143,8 +23,11 @@ /*æ–‡å—å 用宽度*/ height: 20%; /*æ–‡å—高度*/ - } - </style> + }.bannertt2{letter-spacing:2px;font-size:16px;font:14px/1.5em "Roboto";}.dabiaotihe{padding-top:20px;font-family:'Roboto',Helvetica,Arial,sans-serif;font-weight:700;margin-bottom:10px;font-size:30px;text-align:center;}.wenbenkuang{font-family:'Poppins',sans-serif;font-size:18px;color:#3c4258;text-transform:initial;line-height:1.8;text-align:justify;}.xiaobiaoti{font-size:22px;font-family:'Roboto',Helvetica,Arial,sans-serif;font-weight:700;line-height:1.2;display:block;margin-block-start:1.33em;margin-block-end:1.33em;margin-inline-start:0px;margin-inline-end:0px;color:#5271FF;text-align:left;}.zuohe{position:absolute;width:42.5%;height:300px;margin:5%;left:0;}.youhe{position:absolute;width:42.5%;height:300px;margin:5%;right:0;}.zhouwenbenkuang{position:absolute;width:100%;text-align:center;}.xiaozhou{position:relative;width:100%;height:465px;background-image:url(https://static.igem.wiki/teams/4815/wiki/chain3.png);background-size:auto 100%;background-position:center;background-repeat:no-repeat;} .imgz{ + position: relative; + left: 50%; + transform: translateX(-50%); + }</style> <style>@keyframes zoom { 0% {background-size: 100%;} 50% {background-size: 140%;} @@ -160,11 +43,11 @@ animation: zoom 4s infinite; " > -<div style="position: relative;height: 60vh;width: 100%;background-image: url(https://static.igem.wiki/teams/4815/wiki/contribution-banner.png);background-size: 100% auto;background-position: top;"> - <div class="bannertt"> - CONTRIBUTION - <p class="bannertt2"></p> - </div> +<div style="position: relative;height: 60vh;width: 100%;background-image: url(https://static.igem.wiki/teams/4815/wiki/hpbanner.png);background-size: 100% auto;background-position: center;"> +<div class="bannertt"> +INTEGRATED HP +<p class="bannertt2">responsible and good for the world</p> +</div> </div> <div class="row"> @@ -174,51 +57,65 @@ <nav class="nav" role="navigation"> <ul class="nav__list"> <li> - <input id="group-1" type="checkbox" hidden /> - <a href="#s0"><span class="fa fa-angle-right"></span> Introduction </a> - </li> + <input id="group-1" type="checkbox" hidden /> + <a href="#s1"><span class="fa fa-angle-right"></span> Overview </a> + </li> <li> <input id="group-2" type="checkbox" hidden /> - <label for="group-2"><span class="fa fa-angle-right"></span> Pymaker </label> + <label for="group-2"><span class="fa fa-angle-right"></span> Topic Research</label> <ul class="group-list"> - <li><a href="#s1">principle</a></li> - <li><a href="#s3">Training data source</a></li> - <li><a href="#s4">Pearson coefficient</a></li> - </ul> + <li> + <li><a href="#s21">Background Research</a></li> + <li><a href="#s22">Stakeholder Analysis</a></li> + <li><a href="#s23">Public Survey</a></li> + </li></ul> </li> <li> <input id="group-3" type="checkbox" hidden /> - <label for="group-3"><span class="fa fa-angle-right"></span> Strong promoter sequence</label> + <label for="group-3"><span class="fa fa-angle-right"></span> Exploration</label> <ul class="group-list"> <li> - <li><a href="#s5">Parts</a></li> - <li><a href="#s6">Recombinant construction plasmid</a></li> - <li><a href="#s7">Flow cytometry</a></li> + <li><a href="#s31">The Interview of Prof. Ma</a></li> + <li><a href="#s32">The Interview of Prof. Ding</a></li> + <li><a href="#s33">AI Model and Paradigm</a></li> + <li><a href="#s34">Data Source</a></li> + <li><a href="#s35">Yeast Expression System</a></li> + </li></ul> + </li> + <li> + <input id="group-4" type="checkbox" hidden /> + <label for="group-4"><span class="fa fa-angle-right"></span> Application</label> + <ul class="group-list"> + <li> + <li><a href="#s41">The Visit to Nanjing Yiweisen Biotechnology Co., LTD</a></li> + <li><a href="#s42">The visit to Carbon Silicon Institute of Artificial Intelligence Biomedical Research</a></li> + <li><a href="#s43">Enhance LTB Expression</a></li> + <li><a href="#s44">Feedback</a></li> + </li></ul> + <li> + <input id="group-5" type="checkbox" hidden /> + <a href="#s5"><span class="fa fa-angle-right"></span> Ethic </a> + </li> + <li> + <input id="group-6" type="checkbox" hidden /> + <a href="#s6"><span class="fa fa-angle-right"></span> Environment </a> + </li> + <li> + <input id="group-7" type="checkbox" hidden /> + <a href="#s7"><span class="fa fa-angle-right"></span> Entrepreneurship </a> </li> </ul> </li> - - - <li> - <input id="group-4" type="checkbox" hidden /> - <a href="#s8"><span class="fa fa-angle-right"></span> LTB expression test</a> - </li> - <li> - <input id="group-5" type="checkbox" hidden /> - <a href="#s9"><span class="fa fa-angle-right"></span> Reference</a> - - </li> - </ul> - </li> + + </ul> </nav> </header> </div> - <!-- 文本内容 --> - <div class="col-9" style="padding-left: 5%;padding-right: 5%;"> - <div style=" +<div class="col-9" style="padding-left: 5%;padding-right: 5%;"> + <div style=" width: 100%; height: 100%; background-color: #fff; @@ -228,365 +125,369 @@ margin-top:60px; border-radius: 15px; "> - <div class="dahe"> - <div class="dabiaotihe" id="s0">Introduction </div> - <div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> - <div class="wenbenkuang"> - <p>From a more fundamental perspective, our project shows a practical and efficient way to utilize AI in synthetic biology, which is a pure invention and has not been proposed before as far as we know [1]. We provide a excellent performing AI model that can <a style="font-weight: bolder">generate promoter sequences with specific expression rate</a>, which will help fasten the commercialization of synthetic biology. Furthermore, we proved that through our ‘pre-train + fine-tuning’ paradigm, synthetic biologists can fully <a style="font-weight: bolder">utilize limited data generated in experiments to generate high-performing AI model</a>. Meanwhile, our paradigm is very <a style="font-weight: bolder">biologists-favored</a>, which means high professional model building skills are not requirements in our paradigm to persue a high-performing AI model [2]. </p> - <p>Trough our project, we hope we can introduce a tighter bound between synthetic biology and AI, pushing forward the boundary of AI for biology.</p> - <br> - <p>At the mean time, our artificial intelligence product Pymarker has shown its ability in tackling real challenges. Currently, significant breakthroughs have been achieved in the application of brewing yeast in the field of biotechnology. One of them is <a style="font-weight: bolder">the production of the heat-labile toxin B subunit (LTB) of Escherichia coli using brewing yeast</a>. LTB is an important <a style="font-weight: bolder">oral vaccine adjuvant</a> widely used to prevent various diseases such as cholera, traveler’s diarrhea, and E. coli infection. LTB produced by brewing yeast has good safety and immunogenicity. Compared traditional LTB production methods, such as the E. coli expression system, LTB produced by brewing yeast is <a style="font-weight: bolder">purer and does not contain residual host cell materials and endotoxins</a>. At the same time, LTB produced by brewing yeast shows <a style="font-weight: bolder">good immunogenicity</a> in oral vaccines, effectively activating the immune system and inducing specific immune responses [3].</p> - <p><b><b style="font-weight:bold">HOWEVER</b></b>, <a style="font-weight: bolder">limited expression rate of LTB in yeasts</a> have remaining a grate problem for a long time. The promoter is an important element in gene expression regulation. It determines the expression levels of genes in cells. Our Pymaker can predict the strength levels of different promoters, providing a basis for selecting suitable promoters to ensure efficient expression of LTB in brewing yeast. Ultimately, we successfully <a style="font-weight: bolder">applied the predicted promoters to express LTB in brewing yeast</a> by combining the predicted results with the gene expression system of brewing yeast. This provides new methods and tools for the efficient and controllable production of LTB. Importantly, our project offers a more feasible and efficient approach to LTB production. Traditional production methods may require a significant amount of time and resources, while our project uses artificial intelligence technology to predict promoter strength, enabling the rapid screening of suitable promoters and thereby improving the yield and purity of LTB. is of great meeting the demand for oral vaccine adjuvants.</p> - </div> - </div> - <div class="dahe"> - <div class="dabiaotihe">Pymaker</div> - <div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> - <div class="wenbenkuang"> - <p class="xiaobiaoti" id="s1">1. principle</p> - <img class="imgz" src="https://static.igem.wiki/teams/4815/wiki/contribution/code2.png" width="80%"> - - - - <p class="xiaobiaoti" id="s3">2. Training data source</p> - <div class="wenbenkuang"> - <p> - We extensively searched for raw data suitable for training artificial intelligence and finally discovered a - dataset - publicly available in a Nature article [4]. The dataset consists of a total of <a style="font-weight: bolder">30 million pairs of core promoter - sequences - and expression levels</a>. The format is shown in the diagram below, with random synthetic core promoter sequences - in the - front and high-throughput measured expression intensities in the back. Specifically, <a style="font-weight: bolder">the log2(RFP/YFP) of the - dual-fluorescent expression</a> driven by the promoter is the data. - </p> - <br> - </div> - <img class="imgz" - src="https://static.igem.wiki/teams/4815/wiki/contribution/data.png" width="80%"> - - - - - - <p class="xiaobiaoti" id="s4">3. Pearson coefficient</p> - <div class="wenbenkuang"> - <p> - Our artificial intelligence (AI) model is essentially a <a style="font-weight: bolder">regression model</a>, and as such, we selected the <a style="font-weight: bolder">Pearson - Correlation Coefficient</a> as the evaluation metric to assess goodness of fit. This coefficient is commonly used to - measure - the linear relationship between two sets of variables. It ranges from 0.0 to 1.0, where 0.8-1.0 indicates a very - strong - correlation, 0.6-0.8 indicates a strong correlation, 0.4-0.6 indicates a moderate correlation, 0.2-0.4 indicates - a weak - correlation, and 0.0-0.2 indicates a very weak or no correlation. - </p> - </div> - <br> - <img class="imgz" - src="https://static.igem.wiki/teams/4815/wiki/con/con3.png" width="50%"> - <div class="wenbenkuang"> - <p> - The figure illustrates the correlation between expression predicted by Pymaker and measured expression from the dataset. - </p> - </div> - <br> - <img class="imgz" src="https://static.igem.wiki/teams/4815/wiki/con/con4.png" width="70%"> - - - - </div> - </div> - <div class="dahe"> - <div class="dabiaotihe">Strong promoter sequence</div> - <div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> - <div class="wenbenkuang"> - <p class="xiaobiaoti" id="s5">1. Parts</p> - - <style> - table { - border-collapse: collapse; -} - -td, th { - border: 1px solid black; - padding: 8px; - text-align: center; -} - -tr:not(:last-child) { - border-bottom: 1px solid black; -} - -td:not(:last-child), th:not(:last-child) { - border-right: 1px solid black; -} - </style> - <table width="100%"> - <tr> - <th>registry No.</th> - <th>Name</th> - <th>Full Name</th> - <th>Type</th> - <th>URL Link</th> - </tr> - <tr> - <td>BBa_K4815000*</td> - <td>H1</td> - <td>PYPH1</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815000">link</td></a> - </tr> - <tr> - <td>BBa_K4815001</td> - <td>H2</td> - <td>PYPH2</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815001">link</td></a> - </tr> - <tr> - <td>BBa_K4815002</td> - <td>H3</td> - <td>PYPH3</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815002">link</td></a> - </tr> - <tr> - <td>BBa_K4815003</td> - <td>H4</td> - <td>PYPH4</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815003">link</td></a> - </tr> - <tr> - <td>BBa_K4815004</td> - <td>H5</td> - <td>PYPH5</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815004">link</td></a> - </tr> - <tr> - <td>BBa_K4815005</td> - <td>H6</td> - <td>PYPH6</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815005">link</td></a> - </tr> - <tr> - <td>BBa_K4815006</td> - <td>H7</td> - <td>PYPH7</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815006">link</td></a> - </tr> - <tr> - <td>BBa_K4815007</td> - <td>L1</td> - <td>PYPL1</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815007">link</td></a> - </tr> - <tr> - <td>BBa_K4815008</td> - <td>L2</td> - <td>PYPL2</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815008">link</td></a> - </tr> - <tr> - <td>BBa_K4815009</td> - <td>L3</td> - <td>PYPL3</td> - <td>promoter</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815009">link</td></a> - </tr> - <tr> - <td>BBa_K4815010</td> - <td>LTB-eGFP</td> - <td>LTB-eGFP</td> - <td>fusion protein</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815010">link</td></a> - </tr> - <tr> - <td>BBa_K4815011</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815011">link</td></a> - </tr> - <tr> - <td>BBa_K4815012</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815012">link</td></a> - </tr> - <tr> - <td>BBa_K4815013</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815013">link</td></a> - </tr> - <tr> - <td>BBa_K4815014</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815014">link</td></a> - </tr> - <tr> - <td>BBa_K4815015</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815015">link</td></a> - </tr> - <tr> - <td>BBa_K4815016</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815016">link</td></a> - </tr> - <tr> - <td>BBa_K4815017</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815017">link</td></a> - </tr> - <tr> - <td>BBa_K4815018</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815018">link</td></a> - </tr> - <tr> - <td>BBa_K4815019</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815019">link</td></a> - </tr> - <tr> - <td>BBa_K4815020</td> - <td>pDual</td> - <td>pDual-fluorescence <br/>reporter system</td> - <td>plasmid</td> - <td><a href="http://parts.igem.org/Part:BBa_K4815020">link</td></a> - </tr> - </table> - <br> -<p>* we use BBa_K4815000 (PYPH1) for the judging of new basic part, as it outperforms any other pats. </p> - - <p class="xiaobiaoti" id="s6">2. Recombinant construction plasmid</p> - - <div class="wenbenkuang"> - <p>As is shown in the figure, our Pymaker originated promoter PYPH/PYPLs consists of two parts: the core promoter and the scaffold. The core promoter is an 80 bp sequence and is seated at approximately -170 to -90 upstream to the codon (which is the presumed transcription start site-TSS and is where most transcription factors binding sites lie). The scaffold is a preserved sequence in all PYPH/PYPLs (‘pT’ and ‘pA’ sign in the figure) . It is a structure that we learned and utilized from previous research that can link the core promoter with the codon and provide restriction sites of BamH I and Xho I which make it possible for the plasmids with the scaffold to be inserted by various core promoter sequences at ease. Then, the whole promoter sequence is ligated into the plasmid framework, driving the expression of YeGFP(details can be found in <a href="http://parts.igem.org/Part:BBa_K4815011">Part:BBa K4815011 - parts.igem.org</a>).</p> - </div> - <br> - - <img class="imgz" src="https://static.igem.wiki/teams/4815/wiki/contribution/con5.png" width="50%"> - - - - - - - <p class="xiaobiaoti" id="s7">3. Flow cytometry</p> - <div class="wenbenkuang"> - <p> - We utilized <a style="font-weight: bolder">flow cytometry</a> to monitor the two fluorescence signals excited by different light channels and analyzed the - corresponding data. We plotted the natural logarithm of the ratio of GFP to mCherry (<a style="font-weight: bolder">ln(GFP/mCherry)</a>) as a frequency - distribution graph to showcase the relative expression strength of different promoters in yeast. - </p> - </div> - <br> - - <img class="imgz" src="https://static.igem.wiki/teams/4815/wiki/con7xin.png" - width="40%"> - - - - </div> - </div> - - - - - - - - - - <div class="dahe"> - <div class="dabiaotihe" id="s8">LTB expression test</div> - <div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> - <div class="wenbenkuang"> - <p> - The target proteins were detected with specific primary antibodies (rabbit anti-GFP) and HRP-conjugated secondary - antibodies. Western band intensities, which reflect the relative amount of target proteins in the samples, were - determined using the ImageJ software. - </p> - <p> In the figure above, the selected bands in the red box are the targeted LTB expressed in yeasts. - </p> - </div> - <br> - <div class="wenbenkuang"><img><img class="imgz" src="https://static.igem.wiki/teams/4815/wiki/contribution/con8xin.png" width="80%"> - - - - - </div> - </div> - <div class="dahe"> - <div class="dabiaotihe" id="s9">Reference</div> - <div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> - <div class="wenbenkuang" style="font-size: 15px;"> - <p>[1]Beardall, W. A. V., Stan, G. B., & Dunlop, M. J. (2022). Deep Learning Concepts and Applications for Synthetic Biology. GEN biotechnology, 1(4), 360–371. https://doi.org/10.1089/genbio.2022.0017</p> - <p>[2]Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., & Ellinor, P. T. (2023). Transfer learning enables predictions in network biology. Nature, 618(7965), 616–624. https://doi.org/10.1038/s41586-023-06139-9 </p> - <p>[3]So, K. K., Le, N. M. T., Nguyen, N. L., & Kim, D. H. (2023). Improving expression and assembly of difficult-to-express heterologous proteins in Saccharomyces cerevisiae by culturing at a sub-physiological temperature. Microb Cell Fact, 22(1),55.https://doi.org/10.1186/s12934-023-02065-7 </p> - <p>[4]Vaishnav, E. D., de Boer, C. G., Molinet, J., Yassour, M., Fan, L., Adiconis, X., Thompson, D. A., Levin, J. Z., Cubillos, F. A., & Regev, A. (2022). The evolution, evolvability and engineering of gene regulatory DNA. Nature, 603(7901), 455-463.https://doi.org/10.1038/s41586-022-04506-6 </p> - - - - </div> - </div> - - +<div class="dahe"> +<div class="dabiaotihe" id="s1">Overview</div> +<div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> +<div class="wenbenkuang"> +<div class="wenbenkuang">NJU-China devotes ourselves to creating an easy-to-use AI model and aparadigm +specifically for synthetic biology research, and then proposes new solutions to the challenge of applying +Artificial Intelligence broadly into synthetic biology. In the Integrated Human Practice page, we show how we +have carefully considered whether our project is responsible and good for the world throughout the whole +lifecycle.During the process, we address both how our project responds to such considerations and how our +proposed solution is implemented responsibly and reflectively.<a style="font-weight: bolder">The public survey</a> gave us an overall picture of +the topic we focus.Through communications with <a style="font-weight: bolder">scholars</a>, we clarified the specific problem to work on and +obtained professional guidance and opinionsfor the design and implementation. We also got inspiration from +<a style="font-weight: bolder">enterprises</a> to take application scenarios, customer needs and expert knowledge on feasibility into +consideration, and we are cheerful to see our model is successfully utilized in their production and +brings benefits. Thanks to these professionals in different fields, our project manages to open a new window +for the future of synthetic biology and gratifying progress to the development of the society.</div> +<div class="dahe"> +<div class="dabiaotihe">Topic Research</div> +<div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> +<div class="wenbenkuang"> +<p class="xiaobiaoti" id="s21">1. Background Research</p> +<p>Leveraging its immense computational power and intelligent algorithms, AI provides researchers with +unprecedented insights. AI can handle vast amounts of biological data, such as genomes and protein +interaction networks, accelerating drug development and disease diagnosis. The impact is staggering, +with AI speeding up <a style="font-weight: bolder">gene identification and analysis</a> by at least <a style="font-weight: bolder">100 times</a>. In protein folding +prediction, AI achieves an accuracy rate of 90%, greatly reducing the time and resources required +compared to traditional methods. Additionally, AI excels in medical imaging diagnostics, with an +impressive <a style="font-weight: bolder">96%</a> accuracy in breast cancer detection, surpassing human doctors' assessment +capabilities. These remarkable numbers highlight the enormous potential of AI in biology, +revolutionizing medical research and healthcare management, and make our team to think, how can AI +be used generally for synthetic biology?</p> +<p class="xiaobiaoti" id="s22">2. Stakeholder Analysis</p> +<p>We hope to identify the problem together with potential stakeholders, and screen the initial idea +with them. Therefore, our team primarily listed all the actors who could be relevant to our project +in different fields by brainstorming, and determined the order of interaction in order to plan and +design our projects <a style="font-weight: bolder">from the shallower to the deeper</a>.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp1.png" width="70%" class="imgz"> +<p></p> +<p>Based on the spiral line, we engaged with our stakeholders step by step, and gradually constructed +and improved our project design according to their suggestions and feedback. The process of the +communications and their impact on us will be shown in detail below. At the same time, as clarifying +exactly what project tends to do, we were continuously <a style="font-weight: bolder">adding and refiningour stakeholders list</a> and +manage them through a <a style="font-weight: bolder">power-interest matrix</a>, which helps to prioritize the values of the most +relevant stakeholders. All stakeholders are grouped based on Power (their ability to influence our +project and our strategy) and Interest (how interested they are in our project succeeding). For high +power, high interested group, we <a style="font-weight: bolder">fully engage with them</a> mainly, through discussing all the choices we +make and the progress we book with our project. We also consideredother stakeholdersin respective +ways, and contact them when we require expertise on a specific topic.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp2.png" width="70%" class="imgz"> +<p class="xiaobiaoti" id="s23">3. Public Survey</p> +<p>Tofully understand the real needs of society and create new value in a targeted manner, public +opinion is very essential, which will determine whether our project can actually benefit society and +what we are supposed to focus on.Therefore, before implementing new technology, we conducted +extensive public questionnaire survey and detailed analyses.In the past year, we have collected <a style="font-weight: bolder">265 +questionnaires</a> from all over the China, covering different kinds of educational background and +occupation, which ensures the universality of our investigation.</p> +<p>Given that the topic of AI for synthetic biology is highly specialized, our questionnaire was divided +into three parts.In the first part we’d like to find out <a style="font-weight: bolder">the public’s awareness and attitude towards +AI application in work and daily life.The results show that about 28% of people never use AI in +daily life and only 7% of them use AI at a high frequency. The reason why some of them <a style="font-weight: bolder">never use AI +or barely use AI</a> including the high threshold for the use of AI, concerns that AI will not meet +individual needs, invade personal privacy or provide false information or there is no need to use +AI.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp3.png" width="70%" class="imgz"> +<p class="tuzhu">Figure 1. Frequency of using AI in daily life</p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp4.png" width="70%" class="imgz"> +<p class="tuzhu">Figure 2. Reasons for never or less use of AI</p> +<p>When asking about the level of mastery of AI technology, 44% of people only use AI as a tool and 17% +of them know about the principle of AI, which shows that <a style="font-weight: bolder">public’s understanding of AI is very +shallow</a>. Based on the situation, we asked respondents about the obstacles to learning AI, about 60% +of them agreed that it is hard to find AI learning resources and tool resources, and learning AI is +difficult which we need to invest a lot of time in. </p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp5.png" width="70%" class="imgz"> +<br> +<p class="tuzhu">Figure 3. Mastery degree of artificial intelligence</p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp6.png" width="70%" class="imgz"> +<p class="tuzhu">Figure 4. Obstacles of learning AI</p> +<p>These results show that <a style="font-weight: bolder">the application of AI is still relatively limited</a>, and further research about +AI technology is needed to effectively solve problems in specific fields, and we believe this is +also true in the field of synthetic biology. In addition, <a style="font-weight: bolder">the high difficulty of professional +knowledge</a> limits people's further study of the application of AI in specific fields, which also +reminds us that it is necessary and helpful to carry out popular science and education activities +about AI for synthetic biology to better promote them to the public.</p> +<p>Then we asked users and developers of AI tools respectively that which aspect of AI they will focus +on. About 70% of them have the request of accuracy, 61% of them pursue ease operation of AI and +about 50% of them expect AI to have data privacy and fast processing speed, which shows that if AI +want to be widespread, it must both to be <a style="font-weight: bolder">accurate and easy to understand</a>, just like web page +technology. Developers concentrated most on the <a style="font-weight: bolder">efficiency and accuracy</a>, as well as their <a style="font-weight: bolder">ability to +transfer to multiple problems</a>, which provides a guidance on our AI model design.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp7.png" class="imgz" width="70%"> +<p class="tuzhu">Figure 5. Focus as a user on aspects of AI models or algorithms</p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp8.png" class="imgz" width="70%"> +<p class="tuzhu">Figure 6. Focus as a developer on aspects of AI models or algorithms</p> +<p>In terms of the function of AI, we found it widely used in various aspects, and nearly <a style="font-weight: bolder">92%</a> of people +believe AI will have <a style="font-weight: bolder">positive effect</a> on the fields they work in. However, it has relatively <a style="font-weight: bolder">few +applications in scientific research</a>, thus we believe it is our value to improve the new application +of AI technology in synthetic biology research.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp9.png" width="50%" class="imgz"> +<p class="tuzhu">Figure 7. Goals of using AI</p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp10.png" width="70%" class="imgz"> +<p class="tuzhu">Figure 8. The impact AI have on the field you work in</p> +<p>In the next part, we aimed to further explore the public perceptions of the promise of AI for +synthetic biology. We were upset to find that <a style="font-weight: bolder">half</a> the respondents have <a style="font-weight: bolder">never heard of synthetic biology</a> +before, and over <a style="font-weight: bolder">16%</a> of people who know synthetic biology still have <a style="font-weight: bolder">little knowledge about the +application of AI in the field</a>. Obviously, it is necessary for us to introduce and propagate AI for +synthetic biology to the public in a more efficient and suitable way.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp11.png" width="70%" class="imgz"> +<p class="tuzhu">Figure 9. Degree of understanding of synthetic biology</p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp12.png" width="70%" class="imgz"> +<p class="tuzhu">Figure 10. The extent to which AI is used in the field of synthetic biology</p> +<p>Above all, for those who know the application of AI in the field of synthetic biology, it’s generally +believed that <a style="font-weight: bolder">the application of artificial intelligence in the synthetic biology industry has +bright prospects</a>. As we considered about the specific convenience or revolution that AI can bring to +biology, <a style="font-weight: bolder">improving efficiency and shortening research cycle</a> won the highest score among the few +options we have listed. For the current challenges or constraints which limits the development of AI +for synthetic biology, nearly 70% of them agreed that it lacks uniform standards and specifications +(e.g. data formats, sharing platforms, etc.). About 60% of them believe that the scarce of AI +expertise and skills in biological researchers and lack of synthetic biology data with high quality +and quantity are also main problems.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp13.png" width="70%" class="imgz"> +<p class="tuzhu">Figure 11. The extent to which AI can help synthetic biology</p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp14.png" width="40%" class="imgz"> +<p class="tuzhu">Figure 12. the application prospect of AI in synthetic biologycompared with other +industries(Assuming an average score of 5) </p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp15.png" width="60%" class="imgz"> +<p class="tuzhu">Figure 13. The importance of the different changes that AI bring to synthetic biology</p> +<br> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp16.png" width="100%" class="imgz"> +<p class="tuzhu">Figure 14. Challenges of the application of AIin synthetic biology</p> +<p>After anextensive literature research, we chose to <a style="font-weight: bolder">pre-train the AI on existing models and adapt the +parameters to a specific problem</a>. We asked the public for their opinion and were stimulated to find +that most people think this approach <a style="font-weight: bolder">makes practical sense</a>.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp17.png" width="45%"> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp18.png" width="45%"> +<p class="tuzhu">Figure 15. (left)the practical value of pre-train and fine-tune AI based on an existing +model(compared with building an AI model from scratch) </p> +<p class="tuzhu">Figure 16. (right)The meaning of applying transfer learning to solvespecific problems in +synthetic biology</p> +<p>In a word, we are glad to find that the public is optimistic about the application of artificial +intelligence in the field of synthetic biology, which gives us great motivation on finding more +possibilities of artificial intelligence applied to synthetic biology on the basis of predecessors. +The survey also provides new ideas on our model design and education activities to improve public +understanding.</p> +</div> +</div> +<div class="dahe"> +<div class="dabiaotihe">Exploration</div> +<div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> +<div class="wenbenkuang"> +<p class="xiaobiaoti" id="s31">1. The Interview of Prof. Ma</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp19.png" class="tiansuohao2" style="width: 30%;"> +<p>Professor Ma Lijia is currently working at Westlake University, focusing on genomics and systems +biology, and has in-depth research on data mining and AI applications in regulatory sequences.</p> +<p>In the application of biology, what direction is the most significant problem of data limitation? +Keeping this question in mind, we went to Westlake University to have an in-depth exchange with +Professor Ma Lijia. "The regulatory sequence is critical. In fact, 90% of the human genome is +regulatory sequences, and our research group is currently working on the characterization of +regulatory sequences. In my opinion, specific regulatory sequences, such as promoter sequences, are +currently suffering the most data-scarcity, which is mainly limited by experimental techniques for +selecting and characterizing." A crucial keyword in synthetic biology is <a style="font-weight: bolder">expression</a>, which is also +closely related to <a style="font-weight: bolder">regulatory sequences</a>. Building on the important professional insights provided by +Professor Ma Lijia, we finally set our sights on regulating the most direct and ubiquitous +functional regulatory sequences—<a style="font-weight: bolder">promoters</a>, and put it as the specific direction of our project.</p> +<div> +<p class="xiaobiaoti" id="s32">2. The Interview of Prof. Ding</p> +<p>During our communication and promotion efforts with various stakeholders, we have encountered +some <a style="font-weight: bolder">skepticism</a>. Professor Bi Ding from Fudan University questioned the significance of our +project during a discussion, stating that the prevalent use of active learning</a> in the field of +biology and AI suggests that data may not be as limiting as we claim. This has prompted us to +further contemplate the significance of our project and how to convince more people. We have +further confirmed that <a style="font-weight: bolder">the availability of data is not only limited by technical development but +also constrained by costs</a>. In fact, there are instances where obtaining a sufficient amount of +high-quality data is not impossible, but the corresponding high economic and time costs cannot +be justified by the expected output. </p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp20.jpg" width="50%" class="imgz"> +<p></p> +<p class="xiaobiaoti" id="s33">3. AI Model and Paradigm</p> +<p>After determining our project direction, choosing the appropriate large-scale model is the most +important problem to consider. We went to Nanjing GenScript Biotechnology Company to conduct an +exchange interview with Sheng Xia, a senior scientist in bioinformatics. Mr. Sheng Xia has been +engaged in biological data mining and analysis for a long time, and has quite mature +professional experience in AI application. After understanding the relevant situation of our +project, Sheng Xia believes that what we need to deal with is <a style="font-weight: bolder">genetic data</a>, which is generally +applicable to language models, the most popular of which are <a style="font-weight: bolder">GPT and Bert models</a>. </p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp21.jpg" width="70%" class="imgz"> +<p></p> +<p class="xiaobiaoti" id="s34">4. Data Source</p> +<p>When conducting preliminary dry experiment training with selected 30,000,000-scale dataset, +continuous attempts and optimization of parameters could not obtain good results, and the focus +was on the characteristics of the training data through inter-team communication. The students +of the dry experiment found that the result plotting showed that there was a large number of +data in the complete data whose actual intensity deviated from the reasonable experimental +results, and after communicating with the author of the literature, that is, the data +contributor, and learning his team's method to screen the data, the effect increased +significantly. Since then, we have <a style="font-weight: bolder">maintained communication with the author team</a>, which has +played an important role in promoting the optimization and improvement of our dry lab result. +</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp22-24.png" width="70%" class="imgz"> +<p></p> +<p class="xiaobiaoti" id="s35">5. Yeast Expression System</p> +<p>After deciding to use yeast as our expression system, we conducted an exchange interview with +Professor Sheng Xia at Nanjing GenScript Biotechnology Company. Professor Sheng agreed with our +choice and mentioned that <a style="font-weight: bolder">yeast has a slower cultivation speed and it is challenging to achieve +high expression levels</a> compared to some commonly used engineered bacteria in industrial +fermentation processes. He emphasized that if our project could provide a solution to this +issue, it would be extremely helpful. Professor Sheng further advised us that if we plan to +express proteins from prokaryotes or viruses in yeast, it is advisable to optimize the yeast +source. He provided us with <a style="font-weight: bolder">a web platform from GenScript for yeast sequence optimization</a>, which +played a crucial role in facilitating our subsequent project.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp25.jpg" width="70%" class="imgz"> +<p></p> +<p>The next challenge in protein expression within yeast is how to separate, purify, and quantify +the proteins. In fact, due to the cell wall of yeast, it is relatively difficult to separate and +purify the expressed proteins. Through discussions with Dr. Yiling Hu, a postdoctoral researcher +at the School of Life Sciences, Nanjing University, who specializes in yeast cultivation, Dr. Hu +provided us with a feasible solution. It involves <a style="font-weight: bolder">dding a His-tag to the protein or fusing the +protein with a fluorescent protein</a>a to enable purification and quantification through Western +blot analysis after yeast lysis. Dr. Hu further mentioned that it is worth noting that there is +no precedent for detecting His-tag in yeast, so if our project can <a style="font-weight: bolder">detect His-tag in the yeast +lysate</a>, it would be a significant advancement.</p> +<p>In the process of further exploring our project, we also keep close contact with other iGEM +teams, and shared the progress and challenges with each other. Through these collaborations and +partnerships, we get peer support and review, enhance creativity and advanced our project. For +more information, please click the Partnership link to see how the interaction with other teams +has influenced our project.</p> +</div> +</div> +<div class="dahe"> +<div class="dabiaotihe">Application</div> +<div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> +<div class="wenbenkuang"> +<p class="xiaobiaoti" id="s41">1. The Visit to Nanjing Yiweisen Biotechnology Co., LTD</p> +<p>After gaining recognition from relevant researchers, we wanted to further explore the industrial significance of our project. We visited Nanjing Yiweisen Biotechnology Co. and had a discussion with Professor Zhongchang Wang. Professor Wang established Nanjing Yiwesen Biotechnology Co., Ltd as the main founder. The company is a high-tech enterprise based on artificial intelligence technology in the field of synthetic biology, incubated by Artificial Intelligence Biomedical Research of Nanjing University. It primarily focuses on microbial genetic modification and downstream industrial applications, researching and developing high-value natural active substances for use in sectors such as food, cosmetics, plant protection, and biopharmaceuticals. Professor Wang acknowledged the significance of our project and pointed out that the current traditional screening methods for selecting target strains from a large number of randomly mutated strains are often costly, time-consuming, and limited in scope. The application of artificial intelligence in synthetic biology has greatly improved the accuracy of mutagenic strains and reduced the cost of screening. Additionally, he provided us with some potential application directions from a business perspective.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp26.jpg" width="70%" class="imgz"> +<p></p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp27.jpg" width="70%" class="imgz"> +<p></p> +<p class="xiaobiaoti" id="s42">2. The visit to Carbon Silicon Institute of Artificial Intelligence Biomedical +Research</p> +<p>We hoped that our project could provide solutions to the most realistic and important human +health or environmental issues, demonstrating responsibility and a positive impact on the world +throughout its entire lifecycle. During our visit and discussions, a possibility that had never +been considered before caught our attention—<a style="font-weight: bolder">the mucosal vaccine</a>. </p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp28.jpg" width="70%" class="imgz"> +<p></p> +<p>Professor Chao Yan, from the institute, has extensive experience in drug development and provided +profound insights into using AI for precision drug discovery. He first acknowledged that the +pre-training + fine-tuning model we proposed can not only be applied to synthetic biology but +also to <a style="font-weight: bolder">predictive drug development</a>. He further emphasized that data limitations are not just +technical barriers but are greatly influenced by the spatiotemporal factors of <a style="font-weight: bolder">data generation</a>, +which have high heterogeneity and are difficult to utilize. As an example, he mentioned that AI +image recognition models are trained on datasets in the order of billions, while in the field of +biology, measurements are typically limited to dozens of patients at a time, with only a few +dozen data points per dimension. The disparity between the two is significant. Additionally, he +pointed out the issue of the dimensionality of biological data. Biological data often have <a style="font-weight: bolder">a high +number of dimensions but a low sample size</a>, which is not conducive to leveraging the strengths +of AI. For example, AI excels at image processing with low dimensionality and large sample +sizes, where there are data points in each dimension, resulting in better predictive models. +However, in biology, such as genomics, each gene represents a dimension, but the samples for +each gene are relatively scarce.</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp29.jpg" width="70%" class="imgz"> +<p></p> +<p>Professor Yan's interview deepened our understanding of the significance and value of our project +and made us more fully aware of the multiple aspects of data limitations. Additionally, we +received affirmation and support from him regarding our plans for mucosal vaccine production. +</p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp/ihp30.jpg" width="70%" class="imgz"> +<p></p> +<p class="xiaobiaoti" id="s43">3. Enhance LTB Expression</p> +<p>After receiving such affirmation, we needed to find suitable mucosal vaccine-related proteins as +the target product for our project. Our attention turned to LTB (Heat-Labile Enterotoxin B +subunit). Through literature review and understanding its production and functions, we +discovered that LTB plays a crucial role in mucosal vaccines and indeed faces expression +limitations. If our project can address the challenges associated with LTB expression, it would +greatly promote the production and dissemination of mucosal vaccines.</p> +<p class="xiaobiaoti" id="s44">4. Feedback</p> +<p>After formulating such a concept, we further engaged in discussions with the aforementioned +researchers. Many of them raised concerns about the <a style="font-weight: bolder">inconsistency between our project's training +data and the downstream product</a>. Specifically, we obtained data from fluorescent protein +expression for training, but intended to use the trained sequences for the production of a +different protein. As they pointed out, the strength of the promoter is highly likely to be +influenced by downstream genes. This prompted us to consider <a style="font-weight: bolder">providing feedback from wet lab +experiments</a>, specifically the expression data obtained for LTB, to the AI involved in dry lab +experiments. This feedback would facilitate the development of a more targeted model for LTB +expression.</p> +</div> +</div> +<div class="dahe"> +<div class="dabiaotihe" id="s5">Ethic</div> +<div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> +<div class="wenbenkuang"> +<p>We believe that the use and training of AI models must adhere to <a style="font-weight: bolder">ethical guidelines for data</a>, +meaning that only <a style="font-weight: bolder">publicly available datasets</a> should be used for training. It is important to +respect the owners' rights and avoid using private data without permission. When it comes to the +pre-training and fine-tuning paradigms, copyright issues pertaining to pre-trained models must +be taken into consideration. It is advisable to utilize <a style="font-weight: bolder">publicly available pre-trained models</a>. +Moreover, during the AI for Synbio Seminar, we engaged in discussions with other teams and +reached a consensus that <a style="font-weight: bolder">data transparency and sharing of AI model algorithms</a> are essential, +particularly for our iGEM team. Other teams should also ensure that they make use of publicly +available datasets when collecting data.</p> +<p>In addition, one of the primary concerns related to AI safety is that the training of AI models +should be focused on tasks that are beneficial to humans and compliant with legal regulations +within that specific domain.</p> +</div> +</div> +<div class="dahe"> +<div class="dabiaotihe" id="s6">Environment</div> +<div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> +<div class="wenbenkuang"> +<p>After achieving some experimental results, we engaged in discussions with AI pharmaceutical +companies to seek guidance on the subsequent industrialization process. They recognized the +potential utility of our model in industrial production but also highlighted certain +considerations. Firstly, for yeast production and larger-scale experiments, it is crucial to pay +special attention to <a style="font-weight: bolder">preventing biological contamination</a>. Secondly, due to the high level of +pollution associated with yeast production, <a style="font-weight: bolder">wastewater treatment</a> should be prioritized. In fact, +the production of <a style="font-weight: bolder">1 ton</a> of dry yeast can generate <a style="font-weight: bolder">150 tons</a> of wastewater. Building upon these +considerations, Zhang Xiaotong, the supervisor of the wet lab experiments, guided the innovative +development of a <a style="font-weight: bolder">biological wastewater treatment device</a> (Patent No: CN215886592U) and a +<a style="font-weight: bolder">bioreactor</a> to prevent contamination from miscellaneous bacteria (Patent No: CN216141527U). These +developments have prepared us for the industrial production phase of our project. </p> +<img src="https://static.igem.wiki/teams/4815/wiki/ihp31.png" width="50%" class="imgz"> +</div> +</div> +<div class="dahe"> + <div class="dabiaotihe" id="s7">Entrepreneurship</div> + <div style="text-align: center;"><img src="https://static.igem.wiki/teams/4815/wiki/devider.png"></div> + <div class="wenbenkuang"> + <p>Based on Pymaker, we plan to develop <a style="font-weight: bolder">a software for designing cis-regulatory elements in yeast promoter regions based on deep learning</a>. We have already reached intentions for collaboration and signed agreements with Nanjing LianDu Biological Technology Co., Ltd. and Nanjing YiWeiSen Biological Technology Co., Ltd. Through the collaboration, our research results and developed models can be used to <a style="font-weight: bolder">guide the design and optimization of yeast fermentation production lines</a>, significantly reducing the cost of industrial production screening and validation, and ultimately bringing high-quality products to the market. </p> + <img + src="https://static.igem.wiki/teams/4815/wiki/contribution/liandu1.jpg" + width="49%" + alt="" + /> + <img + src="https://static.igem.wiki/teams/4815/wiki/contribution/liandu2.jpg" + width="49%" + alt="" + /> + <img + src="https://static.igem.wiki/teams/4815/wiki/contribution/yiwei1.jpg"" + width="49%" + alt="" + /> + <img + src="https://static.igem.wiki/teams/4815/wiki/contribution/liandu2.jpg" + width="49%" + alt="" + /> +</div> +</div> +</div> +</div> +</div> </div> - </div> </div> </div> </div> - - - - - - - - - - - - - - - - - - - - - - - - - - - {% endblock %} \ No newline at end of file