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<li>Adaptive laboratory evolution for optimization of strains synthesizing membrane proteins</li><br> | <li>Adaptive laboratory evolution for optimization of strains synthesizing membrane proteins</li><br> | ||
<li>Repressor-based light-inducible one-component systems </li><br> | <li>Repressor-based light-inducible one-component systems </li><br> | ||
| − | <li>De novo enzymatic DNA synthesis</li><br> | + | <li><i>De novo</i> enzymatic DNA synthesis</li><br> |
| − | <li>In silico metagenomic mining</li><br> | + | <li><i>In silico</i> metagenomic mining</li><br> |
</ol> | </ol> | ||
<p>To select the best idea and gather the opinions of specialists from different biology-related fields, we sought some of the best minds the faculty we are based in, the Life Sciences Centre can offer. We organized meetings with Prof. Saulius Serva, Dr. Paulius Lukas Tamošiūnas, Prof. Virginijus Šikšnys. Conversations with them helped us develop a better general understanding of their respective subjects and recognize the possible practical risks which they had faced and might be potential threats to our project.</p> | <p>To select the best idea and gather the opinions of specialists from different biology-related fields, we sought some of the best minds the faculty we are based in, the Life Sciences Centre can offer. We organized meetings with Prof. Saulius Serva, Dr. Paulius Lukas Tamošiūnas, Prof. Virginijus Šikšnys. Conversations with them helped us develop a better general understanding of their respective subjects and recognize the possible practical risks which they had faced and might be potential threats to our project.</p> | ||
| − | <p>We came to the conclusion that the best idea is the creation of light-inducible bacterial systems using in silico metagenomic mining.</p> | + | <p>We came to the conclusion that the best idea is the creation of light-inducible bacterial systems using <i>in silico</i> metagenomic mining.</p> |
<p>After coming up with the primary concept, we wanted to discuss it with the people whom this project may impact:</p> | <p>After coming up with the primary concept, we wanted to discuss it with the people whom this project may impact:</p> | ||
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<ul> | <ul> | ||
<li>Together we came up with the Idea of application - light inducible bacterial systems in cancer therapy.</li> | <li>Together we came up with the Idea of application - light inducible bacterial systems in cancer therapy.</li> | ||
| − | <li>Decided not only rely on in silico metagenomic mining but try rational design for creating novel optogenetic tools.</li> | + | <li>Decided not only rely on <i>in silico</i> metagenomic mining but try rational design for creating novel optogenetic tools.</li> |
<li>We welcomed our new PI.</li> | <li>We welcomed our new PI.</li> | ||
</ul><br> | </ul><br> | ||
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<p class="page-heading">Dr. Darius Kazlauskas</p> | <p class="page-heading">Dr. Darius Kazlauskas</p> | ||
| − | <p>We had a meeting with Dr. Darius Kazlauskas, who is a scientist working with in silico metagenomics. In one of his projects, he tried to assemble viral genomes from various metagenomic datasets. He worked with Ebi-metagenomics, IMG/M, iMicrobe.us. However, he had problems with the biggest metagenomic database MG-RAST, which stores more than ten times more information than all the other databases together.</p> | + | <p>We had a meeting with Dr. Darius Kazlauskas, who is a scientist working with <i>in silico</i> metagenomics. In one of his projects, he tried to assemble viral genomes from various metagenomic datasets. He worked with Ebi-metagenomics, IMG/M, iMicrobe.us. However, he had problems with the biggest metagenomic database MG-RAST, which stores more than ten times more information than all the other databases together.</p> |
<p>According to him, there are massive problems with some databases, especially MG-RAST, as the data is not structured. Moreover, data storage and analysis requires enormous amounts of system resources and advanced expertise in the field. | <p>According to him, there are massive problems with some databases, especially MG-RAST, as the data is not structured. Moreover, data storage and analysis requires enormous amounts of system resources and advanced expertise in the field. | ||
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<p><b style="color: #f6cd61!important">Implementations after a meeting:</b></p> | <p><b style="color: #f6cd61!important">Implementations after a meeting:</b></p> | ||
<ul> | <ul> | ||
| − | <li>We understood that in silico metagenomic mining has considerable potential.</li><br> | + | <li>We understood that <i>in silico</i> metagenomic mining has considerable potential.</li><br> |
<li>Our team contacted Google to receive a cloud for performing computations.</li><br> | <li>Our team contacted Google to receive a cloud for performing computations.</li><br> | ||
<li>We started to discuss the possibility of control not only at the transcriptional but also at the translational level.</li><br> | <li>We started to discuss the possibility of control not only at the transcriptional but also at the translational level.</li><br> | ||
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<p class="page-heading">Kotryna Kvederavičiūtė</p> | <p class="page-heading">Kotryna Kvederavičiūtė</p> | ||
| − | <p>In March, the team had a meeting with Ph.D. candidate Kotryna Kvederavičiūtė who works in the field of bioinformatics to discuss our approach to in silico metagenomic mining.</p> | + | <p>In March, the team had a meeting with Ph.D. candidate Kotryna Kvederavičiūtė who works in the field of bioinformatics to discuss our approach to <i>in silico</i> metagenomic mining.</p> |
<p>Firstly, Kotryna suggested finding access to enough computer memory and computational power to store all the necessary data and run the algorithms needed for our software. Together we found a way how to solve this problem. Our team members asked Prof. Juozas Rimantas Lazutka if we could link together the computers in the Life Science Center computer class to perform our calculations.</p> | <p>Firstly, Kotryna suggested finding access to enough computer memory and computational power to store all the necessary data and run the algorithms needed for our software. Together we found a way how to solve this problem. Our team members asked Prof. Juozas Rimantas Lazutka if we could link together the computers in the Life Science Center computer class to perform our calculations.</p> | ||
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<p>However, team members heard from Dr. Dapkūnas that this sort of protein structure modeling is a possible but extremely challenging task. The first difficulty stems from the fact that sometimes completely unrelated protein bind to the same DNA sequences, and other times homologous protein bind to entirely different DNA sequences. Secondly, it could be possible to predict the DNA sequence by docking a very closely related protein with a known structure to genomic DNA from the organism the protein of interest comes from. However, this method would not be possible, as our protein sequences would be gathered from metagenomic databases.</p> | <p>However, team members heard from Dr. Dapkūnas that this sort of protein structure modeling is a possible but extremely challenging task. The first difficulty stems from the fact that sometimes completely unrelated protein bind to the same DNA sequences, and other times homologous protein bind to entirely different DNA sequences. Secondly, it could be possible to predict the DNA sequence by docking a very closely related protein with a known structure to genomic DNA from the organism the protein of interest comes from. However, this method would not be possible, as our protein sequences would be gathered from metagenomic databases.</p> | ||
| − | <p>After this conversation with Dr. Dapkūnas, we understood that we would not be able to find the needed DNA sequence in silico and would have to find them by performing in vitro experimentation. Therefore, we asked Prof. Edita Sužiedėlienė to give us a piece of advice. She suggested performing double-stranded SELEX (Systematic evolution of ligands by exponential enrichment). This approach started by expressing our protein of interest fused with an affinity tag in E. coli. The next step would be immobilization of the protein on beads and bringing double-stranded chemically synthesized random nucleotide containing oligonucleotides. Professor Sužiedėlienė warned that the assay might be noisy; therefore, we should be prepared to optimize the conditions as much as possible.</p> | + | <p>After this conversation with Dr. Dapkūnas, we understood that we would not be able to find the needed DNA sequence <i>in silico</i> and would have to find them by performing <i>in vitro</i> experimentation. Therefore, we asked Prof. Edita Sužiedėlienė to give us a piece of advice. She suggested performing double-stranded SELEX (Systematic evolution of ligands by exponential enrichment). This approach started by expressing our protein of interest fused with an affinity tag in E. coli. The next step would be immobilization of the protein on beads and bringing double-stranded chemically synthesized random nucleotide containing oligonucleotides. Professor Sužiedėlienė warned that the assay might be noisy; therefore, we should be prepared to optimize the conditions as much as possible.</p> |
<p><b style="color: #f6cd61!important">Implementations:</b></p> | <p><b style="color: #f6cd61!important">Implementations:</b></p> | ||
<ul> | <ul> | ||
| − | <li>We understood that finding DNA binding sites in silico would be too challenging. Therefore, we decided to use in vitro method.</li> | + | <li>We understood that finding DNA binding sites <i>in silico</i> would be too challenging. Therefore, we decided to use <i>in vitro<i> method.</li> |
<li>We decided to use the double-stranded SELEX method to find the protein binding sites of the undescribed protein of our research.</li> | <li>We decided to use the double-stranded SELEX method to find the protein binding sites of the undescribed protein of our research.</li> | ||
Revision as of 10:45, 20 October 2019
Human practices
