Difference between revisions of "Team:Vilnius-Lithuania/Contribution"
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<h3 class="index-headline"> Kill-switch - VapXD system | <h3 class="index-headline"> Kill-switch - VapXD system | ||
</h3> | </h3> | ||
| − | <p> We characterised and implemented two toxin-antitoxin system parts - toxin VapD ( | + | <p> We characterised and implemented two toxin-antitoxin system parts - toxin VapD (<a href="http://parts.igem.org/Part:BBa_K3904000" target="_blank">BBa_K3904000</a>) and VapX (<a href="http://parts.igem.org/Part:BBa_K3904001" target="_blank">BBa_K3904001</a>). The whole network works best for human digestive system and for the bacteria which leaves it. None of the previous teams have used VapXD system and we hope to path the way for comparison of widely used mazEF and novel VapXD. |
</p> | </p> | ||
| − | <p> As similar toxin-antitoxin systems, toxin VapD works as endoribonuclease for cellular RNAs. It’s activity is inhibited by antitoxin VapX which is produced under bile inducible promoter ( | + | <p> As similar toxin-antitoxin systems, toxin VapD works as endoribonuclease for cellular RNAs. It’s activity is inhibited by antitoxin VapX which is produced under bile inducible promoter (<a href="http://parts.igem.org/Part:BBa_K3904002" target="_blank">BBa_K3904002</a>). We hope to introduce chassis for future iGEM teams to work with genetically engineered probiotic bacteria and design further for more applications. |
</p> | </p> | ||
<p> | <p> | ||
| − | <a href="https://2021.igem.org/Team:Vilnius-Lithuania/Design"> More about toxin-antitoxin system VapXD | + | <a href="https://2021.igem.org/Team:Vilnius-Lithuania/Design"> <b>More about toxin-antitoxin system VapXD</b> |
</a> | </a> | ||
</p> | </p> | ||
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</i> Although ours and TU Delft team’s attempts to run the Python script provided in the team’s GitHub repository were not successful. Additionally, we found out that this software tool was improved by Heidelberg iGEM team in 2017, and Athens 2019 iGEM team made a review about this program. We made an assumption that this software should be functional after the correction of disturbing errors, which motivated us to dig deeper into the code. | </i> Although ours and TU Delft team’s attempts to run the Python script provided in the team’s GitHub repository were not successful. Additionally, we found out that this software tool was improved by Heidelberg iGEM team in 2017, and Athens 2019 iGEM team made a review about this program. We made an assumption that this software should be functional after the correction of disturbing errors, which motivated us to dig deeper into the code. | ||
</p> | </p> | ||
| + | <p> | ||
| + | We can present Transformer Enhanced Aptamer (TEA) generation method that is based on transformer neural network | ||
| + | model and genetic algorithm. It has the potential to generate affine aptamer sequences to the target protein. | ||
| + | </p> | ||
| + | <p> | ||
| + | More information about the performance of the software tool can be found on <a href="https://2021.igem.org/Team:Vilnius-Lithuania/Software" target="_blank">Software</a> and <a href="https://2021.igem.org/Team:Vilnius-Lithuania/Results" target="_blank">Results</a> pages. | ||
| + | </p> | ||
<h3 class="index-headline"> 3D test kit model | <h3 class="index-headline"> 3D test kit model | ||
</h3> | </h3> | ||
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</p> | </p> | ||
<div class="figure-container"> | <div class="figure-container"> | ||
| − | <img alt="" id="Figure1" src="https://static.igem.org/mediawiki/2021/ | + | <img alt="" id="Figure1" src="https://static.igem.org/mediawiki/2021/1/1a/T--Vilnius-Lithuania--3_down_v2_area.png"/> |
<div> | <div> | ||
<b> Fig. 1. | <b> Fig. 1. | ||
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</div> | </div> | ||
<p> | <p> | ||
| − | <a href="https://2021.igem.org/Team:Vilnius-Lithuania/Hardware"> More about hardware | + | <a href="https://2021.igem.org/Team:Vilnius-Lithuania/Hardware"> <b>More about hardware</b> |
</a> | </a> | ||
</p> | </p> | ||
Latest revision as of 13:36, 18 December 2021
CONTRIBUTION
Kill-switch - VapXD system
We characterised and implemented two toxin-antitoxin system parts - toxin VapD (BBa_K3904000) and VapX (BBa_K3904001). The whole network works best for human digestive system and for the bacteria which leaves it. None of the previous teams have used VapXD system and we hope to path the way for comparison of widely used mazEF and novel VapXD.
As similar toxin-antitoxin systems, toxin VapD works as endoribonuclease for cellular RNAs. It’s activity is inhibited by antitoxin VapX which is produced under bile inducible promoter (BBa_K3904002). We hope to introduce chassis for future iGEM teams to work with genetically engineered probiotic bacteria and design further for more applications.
More about toxin-antitoxin system VapXD
Aptamer generation in silico
Since aptamers assume a variety of shapes, they are extremely versatile and can bind targets with high selectivity and specificity. Therefore they can be applied to various areas. One of the methods to generate aptamers is SELEX - an expensive and mostly time-consuming aptamer-generating process. Keeping this in mind, approaches are being created to simulate aptamer evolution in silico - making the aptamer-generation process cheaper and faster.
Actually, computer modelling approaches that include docking techniques to evaluate aptamer affinity in silico take time. Knowing this, we were motivated to run M.A.W.S. - the software of Heidelberg 2015 iGEM team - which implements an idea that provides a fast way to evaluate the affinity of the aptamer sequence to the desired target based on the nucleotide energy entropy. We intended to use this program and check the affinity of the generated aptamer in vitro. Although ours and TU Delft team’s attempts to run the Python script provided in the team’s GitHub repository were not successful. Additionally, we found out that this software tool was improved by Heidelberg iGEM team in 2017, and Athens 2019 iGEM team made a review about this program. We made an assumption that this software should be functional after the correction of disturbing errors, which motivated us to dig deeper into the code.
We can present Transformer Enhanced Aptamer (TEA) generation method that is based on transformer neural network model and genetic algorithm. It has the potential to generate affine aptamer sequences to the target protein.
More information about the performance of the software tool can be found on Software and Results pages.
3D test kit model
For the diagnostic test we created hardware design in which blood can flow from upper layers to bottom layer due to gravitation. Binded in the lower layer the aptamers catch specific biomarker. The whole framework is universal for diseases that can be detected through blood. By discovering aptamer through SELEX method and conjugating it to PDA future research groups can create diagnostic tests for diseases that could not be detected before.
