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<html lang="en"><head><meta charset="utf-8"/><meta content="width=device-width,initial-scale=1" name="viewport"/><title>Engineering | UIUCiGEM</title><script src="https://2020.igem.org/common/MathJax-2.5-latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><link href="https://2021.igem.org/Template:UIUC_Illinois/css/contentCSS?action=raw&amp;ctype=text/css" rel="stylesheet"/></head><body><nav class="navbar navbar-expand-xl fixed-top"><div class="container d-flex justify-content-between"><a class="navbar-brand d-lg-inline-block" href="https://2021.igem.org/Team:UIUC_Illinois"><span>iGEM </span>UIUC</a><button aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation" class="navbar-toggler" data-target="#navbarNav" data-toggle="collapse" type="button"><span class="navbar-toggler-icon"></span></button><div class="collapse navbar-collapse" id="navbarNav"><ul class="navbar-nav ml-auto"><li class="nav-item"><a class="nav-link" 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class="far fa-moon"></i></div></div></nav><header class="d-flex justify-content-center align-items-center"><div class="container"><h1>Engineering</h1><p class="lead pl-1">Our Engineering Success page.</p><hr class="my-4"/></div></header><main><div class="container"><div class="row"><div class="sidebar col-lg-3"><div class="nav" id="contents"><h5>Contents</h5><ul></ul></div></div><div class="content col-lg-9"><article><h1>Headings</h1><h2>Level 2 Heading</h2><h3>Level 3 Heading</h3><h4>Level 4 Heading</h4><h1>Emphasis</h1><p>This is regular text.</p><p><strong>This is bold text.</strong> <strong>This is also bold text.</strong></p><p><em>This is italic text.</em> _This is also italix text.*</p><p><em><strong>This text is bold and italic both.</strong></em> <em><strong>This text is bold and italic both.</strong></em></p><p><s>Strikethrough</s></p><h1>Lists</h1><ol><li>This is item one.</li><li>This is item two.</li><li>It's okay to number every item as 1.<ul><li>This is a nested list.</li><li>Use asterisk for an unordered list,</li><li>Further nesting?</li></ul><ul><li>Plus sign also works.</li></ul><ul><li>And so does minus.</li></ul></li><li><s>Birds aren't real.</s></li></ol><h1>Links</h1><p><a href="https://google.com">Google</a></p><p><a href="https://2021.igem.org/Team:UIUC_Illinois/Engineering/Link-goes-in-parenthesis">Text goes in square brackets</a></p><h1>Blockquote</h1><blockquote><p>This is a blockquote. This continues in the same paragraph.</p><p>To change the paragraph, you have to leave a line.</p></blockquote><blockquote><p>This is another blockquote.</p><p><strong>Pranav Ballaney, 2020</strong></p></blockquote><h1>Images</h1><p>This is a regular paragraph.</p><h4>This is a level 4 heading.</h4><p><a href="google.com">This is a link</a></p><div class="image"><img alt="Caption" src="https://static.igem.org/mediawiki/2021/f/f0/T--UIUC_Illinois--img--Description--josh-withers.jpg" style="width: 100%"/><p>Figure 1: Caption</p></div><p>Back to regular text.</p><h1>Tables</h1><p>This is a regular paragraph, which precedes the table. When you want to insert a table, indent one level back and specify the plugin. Then indent inside again and start writing the table.</p><table><caption id="table1captiongoeshere">Table 1: Caption goes here.</caption><thead><tr><th>This is</th><th>the table</th><th>header row</th></tr></thead><tbody><tr><td>1</td><td>2</td><td>3</td></tr><tr><td>4</td><td>5</td><td>6</td></tr></tbody></table><p>And when you're done, go back to the regular markdown filter.</p><p><strong>Example 2:</strong></p><table><thead><tr><th style="text-align:left">Left aligned</th><th style="text-align:center">Center aligned</th><th style="text-align:right">Right aligned</th></tr></thead><tbody><tr><td style="text-align:left">1</td><td style="text-align:center">2</td><td style="text-align:right">3</td></tr><tr><td style="text-align:left">4</td><td style="text-align:center">5</td><td style="text-align:right">6</td></tr></tbody></table><p><strong>Example 3:</strong></p><table><caption id="table3captiongoeshere">Table 3: Caption goes here.</caption><thead><tr><th></th><th colspan="2" style="text-align:center">Grouping</th></tr><tr><th>First Header</th><th style="text-align:center">Second Header</th><th style="text-align:right">Third Header</th></tr></thead><tbody><tr><td>Content</td><td colspan="2" style="text-align:center"><em>Long Cell</em></td></tr><tr><td>Content</td><td style="text-align:center"><strong>Cell</strong></td><td style="text-align:right">Cell</td></tr><tr><td>New section</td><td style="text-align:center">More</td><td style="text-align:right"><a href="https://google.com">Data</a></td></tr><tr><td>And more</td><td colspan="2" style="text-align:center">With an escaped '|'</td></tr></tbody></table><h1>Definitions</h1><p><dfn>Term ~ Definition</dfn></p><p>This can come <dfn>anywhere in ~ the text</dfn>.</p><h1>MathJax</h1><p>$$\int_{0}^{\infty} e^{-x^2} dx = \frac{\sqrt{\pi}}{2}$$</p><p>$$-\frac{\hbar^2}{2m}\nabla^2\psi + V\psi = E\psi$$</p><p>$$i\hbar\gamma^\mu\partial_\mu\psi = mc\psi$$</p><p>$$y_k = \frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}x_n\omega_N^{kn}$$</p><h1>Citations</h1><p>In text citation for a research article with a DOI. <a href="#citation2">Rosano et al., 2019</a></p><p>In text citation for another research article with a DOI. <a href="#citation1">Allen &amp; Sheridan, 2015</a></p><p>In text citation for a book with no DOI. <a href="#citation3">Ingalls, 2013</a></p><p>In text citation for a website with institutional author. <a href="#citation4">TNAU Agritech Portal, n.d.</a></p><p>In text citation for a website with an author. <a href="#citation5">Author, n.d.</a></p></article><article id="references"><h1>References</h1><ol><li id="citation1"><p class="author">Allen, M. J., &amp; Sheridan, S. C. (2015).</p><cite>Mortality risks during extreme temperature events (ETEs) using a distributed lag non-linear model.</cite><p><span class="journalTitle">International Journal of Biometeorology</span> <span class="journalInfo">62(1), 57-67.</span></p><a class="in-text" href="https://doi.org/10.1007/s00484-015-1117-4" rel="noopener" target="_blank">CrossRef</a><a class="in-text" href="https://scholar.google.com/scholar?q=Mortality risks during extreme temperature events (ETEs) using a distributed lag non-linear model." rel="noopener" target="_blank">Google Scholar</a><a class="in-text" href="#intext1">Back to text</a></li><li id="citation2"><p class="author">Rosano, A., Bella, A., Gesualdo, F., Acampora, A., Pezzotti, P., Marchetti, S., ... &amp; Rizzo, C. (2019).</p><cite>Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14-2016/17 seasons).</cite><p><span class="journalTitle">International Journal of Infectious Diseases</span> <span class="journalInfo">88, 127-134.</span></p><a class="in-text" href="https://doi.org/10.1016/j.ijid.2019.08.003" rel="noopener" target="_blank">CrossRef</a><a class="in-text" href="https://scholar.google.com/scholar?q=Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14-2016/17 seasons)." rel="noopener" target="_blank">Google Scholar</a><a class="in-text" href="#intext2">Back to text</a></li><li id="citation3"><p class="author">Ingalls, B. P. (2013).</p><cite>Mathematical modeling in systems biology: An introduction.</cite><p><span class="details">MIT Press.</span></p><a class="in-text" href="https://books.google.co.in/books?id=OYr6AQAAQBAJ" rel="noopener" target="_blank">Google Books</a><a class="in-text" href="#intext3">Back to text</a></li><li id="citation4"><p class="author"></p><cite>Agriculture: Crop production: Sugarcane. TNAU Agritech Portal.</cite><p><span class="details">(March 15, 2019). Retrieved on June 22, 2020. from </span><a class="in-text" href="https://google.com" rel="noopener" target="_blank">https://google.com</a></p><a class="in-text" href="#intext4">Back to text</a></li><li id="citation5"><p class="author">Author Name. (n.d.).</p><cite>Agriculture: Crop production: Sugarcane. TNAU Agritech Portal.</cite><p><span class="details">Retrieved on June 22, 2020. from </span><a class="in-text" href="https://google.com" rel="noopener" target="_blank">https://google.com</a></p><a class="in-text" href="#intext5">Back to text</a></li></ol></article></div></div></div></main><footer><div class="container"><p>Sample template built using the iGEM Wiki Starter Pack by BITS Goa.</p><p>Code released under the MIT license.</p><p>Based on <a href="https://getbootstrap.com">Bootstrap</a> and themes <a href="https://bootswatch.com/flatly/">Flatly</a> and <a href="https://bootswatch.com/darkly/">Darkly</a> from <a href="https://bootswatch.com/">Bootswatch</a>.</p><p>Some content from the <a href="https://2020.igem.org/Team:Example">iGEM Example Wiki</a>. Images from <a href="https://unsplash.com">Unsplash</a>. 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<html lang="en"><head><meta charset="utf-8"/><meta content="width=device-width,initial-scale=1" name="viewport"/><title>Engineering Success | UIUCiGEM</title><script src="https://2020.igem.org/common/MathJax-2.5-latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><link href="https://2021.igem.org/Template:UIUC_Illinois/css/contentCSS?action=raw&amp;ctype=text/css" rel="stylesheet"/></head><body><nav class="navbar navbar-expand-xl fixed-top"><div class="container d-flex justify-content-between"><a class="navbar-brand" href="https://2021.igem.org/Team:UIUC_Illinois"><i class="navbar-logo"></i></a><button aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation" class="navbar-toggler" data-target="#navbarNav" data-toggle="collapse" type="button"><span class="navbar-toggler-icon"></span></button><div class="collapse navbar-collapse" id="navbarNav"><ul class="navbar-nav ml-auto"><li class="nav-item dropdown"><a aria-expanded="false" aria-haspopup="true" class="nav-link dropdown-toggle" data-toggle="dropdown" href="#" id="navbarTeamDropdown" role="button">Team</a><div aria-labelledby="navbarTeamDropdown" class="dropdown-menu"><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Team">Team</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Attributions">Attributions</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Collaborations">Collaborations</a></div></li><li class="nav-item dropdown"><a aria-expanded="false" aria-haspopup="true" class="nav-link dropdown-toggle" data-toggle="dropdown" href="#" id="navbarProjectDropdown" role="button">Project</a><div aria-labelledby="navbarProjectDropdown" class="dropdown-menu"><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Contribution">Contribution</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Description">Description</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Engineering">Engineering Success</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Package">Excellence in another area</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Experiments">Experiments</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Model">Model</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Notebook">Notebook</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Partnership">Partnership</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Implementation">Proposed Implementation</a><a class="dropdown-item" href="https://2021.igem.org/Team:UIUC_Illinois/Results">Results</a></div></li><li class="nav-item"><a class="nav-link" href="https://2021.igem.org/Team:UIUC_Illinois/Parts">Parts</a></li><li class="nav-item"><a class="nav-link" href="https://2021.igem.org/Team:UIUC_Illinois/Safety">Safety</a></li><li class="nav-item"><a class="nav-link" href="https://2021.igem.org/Team:UIUC_Illinois/Human_Practices">Human Practices</a></li></ul></div><div class="d-flex" id="themeSwitchWrapper"><i class="far fa-sun"></i><div id="themeSwitch"><label class="switch" for="themeSwitchInput"><input id="themeSwitchInput" type="checkbox"/><span class="slider round"></span></label></div><i class="far fa-moon"></i></div></div></nav><header class="d-flex justify-content-center align-items-center"><div class="container"><h1>Engineering Success</h1><p class="lead pl-1"></p><hr class="my-4"/></div></header><main><div class="container"><div class="row"><div class="sidebar col-lg-3"><div class="nav" id="contents"><h5>Contents</h5><ul></ul></div></div><div class="content col-lg-9"><article><div class="image"><img alt="Wet Lab and Dry Lab Design" src="https://static.igem.org/mediawiki/2021/d/dc/T--UIUC_Illinois--img--Engineering--diagram.png" style="width: 100%"/><p>Figure 1: Wet Lab and Dry Lab Design</p></div><h1>Drylab</h1><p>Our focus with drylab this year was to discover a solution to design and optimize new PETase sequences such that they have an increased rate of PET degradation. To do this, we aimed to design a machine learning algorithm that utilized a recurrent neural network (RNN) with specific filters, such that the output will display a selected number of PETase sequences, each of which are potential candidates for increased enzymatic activity.</p><p>We began the process with a literature review and research with PETase completed in the past. We saw that the iGEM Toronto team attempted this two years ago in 2019, however we did find some potential for improvement upon their pipeline. Following our research, we gathered several sequences found in literature and in other teams’ tests to include in our training dataset. We tested and reworked our pipeline with various training datasets to improve and clean up its output based on the filters created within the pipeline. Once we were able to achieve this, we generated our sequences and sent three of our candidates over to the wetlab team for validation, where they will test the mutants for enzymatic activity.</p><h1>Wetlab</h1><p>Our focus with wetlab this year was to carry out wildtype PETase protein purification and ultimately purify and gather data for the mutant PETase sequences that drylab made. To do this, we began early in the summer by doing literature review on the PET degradation process itself, from looking into key enzymes involved as well as researchers in this field that we ask questions to.</p><p>Following our research, we began to slowly design protocols from scratch, as seen in our Notebook section. We’ve made countless revisions each time to our protocols based on what we found in research papers and what past iGEM teams did. During our “build” stage, we began working with e.coli and our PET sequences where we kept track of colony growth, color changes in culture media as well as beginning to run tests and assays such as induction and SDS-PAGE.</p><p>During our “build” and “test” stages, we did a lot of troubleshooting as the results were not what we expected. For a few weeks in the summer, we went through the cycle of design, build, and test which allowed us to learn a lot about why our experimental setup was not working and what we can do differently.</p><p>Along the way, we shared what we learned with UT Austin and they did the same. Both of our teams ran into similar issues but were able to troubleshoot together. At this time point, we successfully purified our wild-type PEtase protein and have begun running a nanodrop test to look at PET film degradation. We are currently in the process of purifying our mutant PETase sequences and gathering data for it.</p></article></div></div></div></main><footer><div class="container"><div class="row justify-content-center"><div id="footerTeamLogo"></div><div id="UIUCLogo"></div><div id="CABBILogo"></div><div id="IGBLogo"></div></div><p>iGEM UIUC_Illinois</p><p>Email: <a href="mailto:igembitsgoa@gmail.com">illinoisigem@gmail.com</a></p><p>Headquarter: <a href="https://www.igb.illinois.edu/">Carl R. Woese Institute for Genomic Biology</a></p><p>1206 W Gregory Dr. Urbana IL 61801</p><br/><p>Sample template built using the iGEM Wiki Starter Pack by BITS Goa.</p><p>2021 UIUC_Illinois[Judging Form](https://igem.org/2021_Judging_Form?id=3713)</p></div></footer><script src="https://2021.igem.org/Template:UIUC_Illinois/content-bundleJS?action=raw&amp;ctype=text/javascript"></script></body></html>

Latest revision as of 02:57, 22 October 2021

Engineering Success | UIUCiGEM

Engineering Success


Wet Lab and Dry Lab Design

Figure 1: Wet Lab and Dry Lab Design

Drylab

Our focus with drylab this year was to discover a solution to design and optimize new PETase sequences such that they have an increased rate of PET degradation. To do this, we aimed to design a machine learning algorithm that utilized a recurrent neural network (RNN) with specific filters, such that the output will display a selected number of PETase sequences, each of which are potential candidates for increased enzymatic activity.

We began the process with a literature review and research with PETase completed in the past. We saw that the iGEM Toronto team attempted this two years ago in 2019, however we did find some potential for improvement upon their pipeline. Following our research, we gathered several sequences found in literature and in other teams’ tests to include in our training dataset. We tested and reworked our pipeline with various training datasets to improve and clean up its output based on the filters created within the pipeline. Once we were able to achieve this, we generated our sequences and sent three of our candidates over to the wetlab team for validation, where they will test the mutants for enzymatic activity.

Wetlab

Our focus with wetlab this year was to carry out wildtype PETase protein purification and ultimately purify and gather data for the mutant PETase sequences that drylab made. To do this, we began early in the summer by doing literature review on the PET degradation process itself, from looking into key enzymes involved as well as researchers in this field that we ask questions to.

Following our research, we began to slowly design protocols from scratch, as seen in our Notebook section. We’ve made countless revisions each time to our protocols based on what we found in research papers and what past iGEM teams did. During our “build” stage, we began working with e.coli and our PET sequences where we kept track of colony growth, color changes in culture media as well as beginning to run tests and assays such as induction and SDS-PAGE.

During our “build” and “test” stages, we did a lot of troubleshooting as the results were not what we expected. For a few weeks in the summer, we went through the cycle of design, build, and test which allowed us to learn a lot about why our experimental setup was not working and what we can do differently.

Along the way, we shared what we learned with UT Austin and they did the same. Both of our teams ran into similar issues but were able to troubleshoot together. At this time point, we successfully purified our wild-type PEtase protein and have begun running a nanodrop test to look at PET film degradation. We are currently in the process of purifying our mutant PETase sequences and gathering data for it.