Difference between revisions of "Team:EpiphanyNYC/Model"

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<div class="content_wrap"><div class="content"><article class="itemscope post_item post_item_single post_featured_center post_format_standard post-741 page type-page status-publish hentry" itemscope itemtype="http://schema.org/Article"><section class="post_content" itemprop="articleBody"><h2 style="text-align: center;"><strong>Model (Computational data model) </strong></h2>
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<h1> Modeling</h1>
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<p>&nbsp;</p>
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<p><span style="font-weight: 400;">Summary:</span></p>
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<p><span style="font-weight: 400;">To begin computational research, huntingtin mutants and their percentage of occurrence were located on various genetic databases, mainly NCBI. Our goal was to achieve toehold strand displacement of mutated HTT with a corrected strand. Sequences were run through protein-folding softwares to select viable candidates for the project. By aligning proteins, hairpin loops could be identified and targeted. Candidates had to be created for chaperone and promoter sequences, with approximately 40 CAG repeats within the sequence. The computational team was split up to test for feasible sequences. When attempting to order strands, the sequence was not practical, so it was revised.</span></p>
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<p>&nbsp;</p>
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<p><span style="font-weight: 400;">Revised what was on the website a little bit:</span></p>
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<p><img data-attachment-id="1379" data-permalink="https://hdresolutionigem.com/model/screen-shot-2017-10-10-at-9-21-47-pm/" data-orig-file="https://i1.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.21.47-PM.png?fit=658%2C152&amp;ssl=1" data-orig-size="658,152" data-comments-opened="1" data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}" data-image-title="Screen Shot 2017-10-10 at 9.21.47 PM" data-image-description="" data-medium-file="https://i1.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.21.47-PM.png?fit=300%2C69&amp;ssl=1" data-large-file="https://i1.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.21.47-PM.png?fit=658%2C152&amp;ssl=1" class=" wp-image-1379 aligncenter" src="https://i1.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.21.47-PM.png?resize=544%2C125&#038;ssl=1" alt="" srcset="https://i1.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.21.47-PM.png?resize=300%2C69&amp;ssl=1 300w, https://i1.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.21.47-PM.png?w=658&amp;ssl=1 658w" sizes="(max-width: 544px) 100vw, 544px" data-recalc-dims="1" /></p>
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<p><span style="font-weight: 400;">Our goal was to attempt a toehold strand displacement of mutated HTT with a corrected strand. We used mFold software packages to model RNA sequence folds in order to find a tractable hairpin within the 5’ UTR. However, using mFold did not provide enough information on any full sequences the  size of HTT. We switched to the Vienna package as it provided a much better model of the data. Model folding calculations/visualizations allowed prediction of the position of a usable a hairpin loop for strand displacement. </span></p>
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<p>&nbsp;</p>
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<p><img data-attachment-id="1378" data-permalink="https://hdresolutionigem.com/model/screen-shot-2017-10-10-at-9-22-32-pm/" data-orig-file="https://i2.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.22.32-PM.png?fit=414%2C617&amp;ssl=1" data-orig-size="414,617" data-comments-opened="1" data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}" data-image-title="Screen Shot 2017-10-10 at 9.22.32 PM" data-image-description="" data-medium-file="https://i2.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.22.32-PM.png?fit=201%2C300&amp;ssl=1" data-large-file="https://i2.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.22.32-PM.png?fit=414%2C617&amp;ssl=1" class=" wp-image-1378 aligncenter" src="https://i2.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.22.32-PM.png?resize=246%2C367&#038;ssl=1" alt="" srcset="https://i2.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.22.32-PM.png?resize=201%2C300&amp;ssl=1 201w, https://i2.wp.com/hdresolutionigem.com/wp-content/uploads/2017/07/Screen-Shot-2017-10-10-at-9.22.32-PM.png?w=414&amp;ssl=1 414w" sizes="(max-width: 246px) 100vw, 246px" data-recalc-dims="1" /></p>
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<p>&nbsp;</p>
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<p><span style="font-weight: 400;">After running our sequence through Vienna, it was apparent mRNA molecule was still too large. One software prediction was not enough. Many online sites with protein folding capabilities proved difficult to use or were not being maintained. Genstrip and RNAI designer were two programs provided multiples sequences for us to target. UGENE was used to view and align these sequences, allowing us to target the optimal hairpin loop and figure out exactly where to begin targeting. </span></p>
 +
<p>&nbsp;</p>
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<p><b>Computational Lab Notes:</b></p>
 +
<ul>
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<li style="font-weight: 400;"><span style="font-weight: 400;">Looked for Huntington’s mRNA in particular in NCBI</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">NM numbers, other prefixes gave types of mRNA</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">HW: Find huntington’s-related sequences in NCBI database  (assumed at least 5-10 sequences)</span></li>
 +
<li style="font-weight: 400;"><span style="font-weight: 400;">We used data we received from [ncbi.com] and used it to compare with the wild type and the infected types.</span></li>
 +
<li style="font-weight: 400;"><span style="font-weight: 400;">HW Results: Found 2 accession numbers- NM_002111.8  (mRNA) and NP_002102.4  (protein); however, only one was mRNA and none were disease form</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">We then collected the data in our shared folder</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">Looked through other databases for other mutants; found:</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">Study with “20 Huntington’s Disease and 49 neurologically normal control samples from post-mortem human subjects”</span></li>
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<li style="font-weight: 400;"><a href="http://trace.ddbj.nig.ac.jp/DRASearch/study?acc=SRP051844"><span style="font-weight: 400;">http://trace.ddbj.nig.ac.jp/DRASearch/study?acc=SRP051844</span></a></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">Results for HTT on DNA Data Bank of Japan (includes above):</span></li>
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<li style="font-weight: 400;"><a href="http://trace.ddbj.nig.ac.jp/DRASearch/query?keyword=htt&amp;show=20&amp;fq_rep_name=Homo%20sapiens"><span style="font-weight: 400;">http://trace.ddbj.nig.ac.jp/DRASearch/query?keyword=htt&amp;show=20&amp;fq_rep_name=Homo%20sapiens</span></a></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">HW: Run wild-type mRNA on mfold (Create a text document with the NM_00211.8 RNA  and run mFold on it </span><a href="http://unafold.rna.albany.edu/?q=mfold"><span style="font-weight: 400;">http://unafold.rna.albany.edu/?q=mfold</span></a><span style="font-weight: 400;">)</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">We then collected the data in our shared folder</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">HW Results: Nobody able to run mfold successfully locally or on web</span></li>
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</ul></section> <!-- </section> class="post_content" itemprop="articleBody"> --></article> <!-- </article> class="itemscope post_item post_item_single post_featured_center post_format_standard post-741 page type-page status-publish hentry" itemscope itemtype="http://schema.org/Article"> --> <section class="related_wrap related_wrap_empty"></section>
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<p>Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.</p>
 
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<h3> Gold Medal Criterion #3</h3>
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To complete for the gold medal criterion #3, please describe your work on this page and fill out the description on your <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. To achieve this medal criterion, you must convince the judges that your team has gained insight into your project from modeling. You may not convince the judges if your model does not have an effect on your project design or implementation.
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Please see the <a href="https://2017.igem.org/Judging/Medals"> 2017 Medals Page</a> for more information.
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<h3>Best Model Special Prize</h3>
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To compete for the <a href="https://2017.igem.org/Judging/Awards">Best Model prize</a>, please describe your work on this page  and also fill out the description on the <a href="https://2017.igem.org/Judging/Judging_Form">judging form</a>. Please note you can compete for both the gold medal criterion #3 and the best model prize with this page.
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You must also delete the message box on the top of this page to be eligible for the Best Model Prize.
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<h5> Inspiration </h5>
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Here are a few examples from previous teams:
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<li><a href="https://2016.igem.org/Team:Manchester/Model">Manchester 2016</a></li>
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<li><a href="https://2016.igem.org/Team:TU_Delft/Model">TU Delft 2016  </li>
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<li><a href="https://2014.igem.org/Team:ETH_Zurich/modeling/overview">ETH Zurich 2014</a></li>
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<li><a href="https://2014.igem.org/Team:Waterloo/Math_Book">Waterloo 2014</a></li>
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Revision as of 06:46, 27 October 2017

Model (Computational data model)

 

Summary:

To begin computational research, huntingtin mutants and their percentage of occurrence were located on various genetic databases, mainly NCBI. Our goal was to achieve toehold strand displacement of mutated HTT with a corrected strand. Sequences were run through protein-folding softwares to select viable candidates for the project. By aligning proteins, hairpin loops could be identified and targeted. Candidates had to be created for chaperone and promoter sequences, with approximately 40 CAG repeats within the sequence. The computational team was split up to test for feasible sequences. When attempting to order strands, the sequence was not practical, so it was revised.

 

Revised what was on the website a little bit:

Our goal was to attempt a toehold strand displacement of mutated HTT with a corrected strand. We used mFold software packages to model RNA sequence folds in order to find a tractable hairpin within the 5’ UTR. However, using mFold did not provide enough information on any full sequences the size of HTT. We switched to the Vienna package as it provided a much better model of the data. Model folding calculations/visualizations allowed prediction of the position of a usable a hairpin loop for strand displacement.

 

 

After running our sequence through Vienna, it was apparent mRNA molecule was still too large. One software prediction was not enough. Many online sites with protein folding capabilities proved difficult to use or were not being maintained. Genstrip and RNAI designer were two programs provided multiples sequences for us to target. UGENE was used to view and align these sequences, allowing us to target the optimal hairpin loop and figure out exactly where to begin targeting.

 

Computational Lab Notes:

  • Looked for Huntington’s mRNA in particular in NCBI
  • NM numbers, other prefixes gave types of mRNA
  • HW: Find huntington’s-related sequences in NCBI database (assumed at least 5-10 sequences)
  • We used data we received from [ncbi.com] and used it to compare with the wild type and the infected types.
  • HW Results: Found 2 accession numbers- NM_002111.8 (mRNA) and NP_002102.4 (protein); however, only one was mRNA and none were disease form
  • We then collected the data in our shared folder
  • Looked through other databases for other mutants; found:
  • Study with “20 Huntington’s Disease and 49 neurologically normal control samples from post-mortem human subjects”
  • http://trace.ddbj.nig.ac.jp/DRASearch/study?acc=SRP051844
  • Results for HTT on DNA Data Bank of Japan (includes above):
  • http://trace.ddbj.nig.ac.jp/DRASearch/query?keyword=htt&show=20&fq_rep_name=Homo%20sapiens
  • HW: Run wild-type mRNA on mfold (Create a text document with the NM_00211.8 RNA and run mFold on it http://unafold.rna.albany.edu/?q=mfold)
  • We then collected the data in our shared folder
  • HW Results: Nobody able to run mfold successfully locally or on web