Difference between revisions of "Team:Jilin China/Project/Overview"
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| − | <td><a href="#pragraph_1" class="clickwave"> | + | <td><a href="#pragraph_1" class="clickwave">SynRT Toolkit 1.0</a></td> |
| + | <td><a href="#pragraph_2" class="clickwave">SynRT Toolkit 2.0</a></td> | ||
| + | <td><a href="#pragraph_3" class="clickwave">SynRT Toolkit 3.0</a></td> | ||
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| − | <li><a href="#pragraph_1"> | + | <li><a href="#pragraph_1">Toolkit 1.0</a></li> |
| + | <li><a href="#pragraph_2">Toolkit 2.0</a></li> | ||
| + | <li><a href="#pragraph_3">Toolkit 3.0</a></li> | ||
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<h2>SynRT Toolkit 1.0</h2> | <h2>SynRT Toolkit 1.0</h2> | ||
| − | <p>This year, we aimed to develop a RNA-based thermosensor toolkit. Based on adding | + | <p>This year, we aimed to develop a RNA-based thermosensor toolkit. Based on adding a stem-loop structure to 5'UTR, the thermosensors achieve the temperature sensing. And we construct the measurement device to characterize these thermosensors.</p> |
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| − | <p> | + | <p>As the temperature rises, the expression intensity of the reporter protein, sfGFP_optimism, increased sharply between 33℃ to 42℃, so we named this kind type of thermosensor as Heat-inducible RNA-based thermosensor. After getting the measurement results of the first batch of heat-inducible RNA-based thermosensors. Then we did some investigation in industry, asking experts for advice about essential temperature in different fields while showing the hopeful results. Through these human practices, we got many significant suggestions.</p> |
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| − | <p>For example, by means of investigation in fermentation industry, the | + | <p>For example, by means of investigation in fermentation industry, the engineer told us that for the potential users like them, the melting temperature of thermosensor should not be the only optional property, intensity and sensitivity should also be thought. </p> |
| − | <p>That really inspired us, we continued to design | + | <p>That really inspired us, we continued to design more thermosensors and measure them. Meanwhile, we also began to think, how to make users select a thermosensor conveniently? How to get the melting temperature, intensity and sensitivity of the thermosensor? To solve these problems, we decided to fit a curve to reflect the relationship between the change of temperature and the expression intensity of thermosensors. </p> |
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| − | <p>When we saw these different | + | <p>When we saw these different curves, there's a new question came up. Some of the thermosensors showed unsatisfactory results -- they don't have melting temperature or the melting temperatures is too high or too low. So how to decrease the undesirable thermosensors? We decide to use random forest algorithm. We want to use this machine to tell us whether the thermosensor is desirable if we only provide the sequence of the thermosensor. And we made it! This algorithm raised the success rate from 47% to 65% successfully.</p> |
| − | <p>After that, we continued to do experiments and got a lot of thermosensors. We chose 51 heat-inducible RNA-based thermosenors and | + | <p>After that, we continued to do experiments and got a lot of thermosensors. We chose 51 heat-inducible RNA-based thermosenors and uploaded them to the parts registry. We also developed a search engine, we called SynRT Explorer. Until here, we have built the RNA-based thermosensor toolkit successfully! </p> |
<div class="overview_nav"> | <div class="overview_nav"> | ||
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<h2>SynRT Toolkit 2.0</h2> | <h2>SynRT Toolkit 2.0</h2> | ||
| − | <p> | + | <p>After further dialogue with potential users, like scientific researchers and medical institutes or companies, who provided meaningful suggestions for our SynRT toolkit. We got the significant imformation that users need not noly the heat-inducible RNA-based thermosensors, but also thermosensors whose expression intensity decreases at elevated temperature. Based on this requirement, we updated our SynRT to version 2.0. We designed heat-repressible RNA-based thermosensors, which based on the RNase E. We designed hundreds of heat-repressible RNA-based thermosensors.We measured them and selected 23 thermosensors for the toolkit. </p> |
<div class="overview_nav"> | <div class="overview_nav"> | ||
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<h2>SynRT Toolkit 3.0</h2> | <h2>SynRT Toolkit 3.0</h2> | ||
| − | <p>We definitely won't stop going, after getting these | + | <p>We definitely won't stop going, after getting these exciting results, we still want to explore further. Can we expand our sensing temperature range? Can we design more different type of RNA-based thermosensors? Finally, we update the SynRT toolkit again. Now we call it SynRT 3.0, which contains four different types of RNA-based thermosensors. The cold-inducible RNA-based thermosensors and cold-repressible RNA-based thermosensors were added to the SynRT toolkit. Cold-inducible RNA-based thermosensors based on the cspA 5'UTR mRNA, and cold-repressible RNA-based thermosensors based on the RNase III. </p> |
<div class="overview_nav"> | <div class="overview_nav"> | ||
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<h2>Postscript</h2> | <h2>Postscript</h2> | ||
| − | <p>After getting these exciting results, we | + | <p>After getting these exciting results, we don't want to stop going. We know there are more things waiting for us to explore. The SynRT toolkit are still updating, we aim to provide users multiple thermosensor Biobricks to select. </p> |
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Revision as of 22:16, 14 October 2018
Overview
-
SynRT Toolkit 1.0
This year, we aimed to develop a RNA-based thermosensor toolkit. Based on adding a stem-loop structure to 5'UTR, the thermosensors achieve the temperature sensing. And we construct the measurement device to characterize these thermosensors.
As the temperature rises, the expression intensity of the reporter protein, sfGFP_optimism, increased sharply between 33℃ to 42℃, so we named this kind type of thermosensor as Heat-inducible RNA-based thermosensor. After getting the measurement results of the first batch of heat-inducible RNA-based thermosensors. Then we did some investigation in industry, asking experts for advice about essential temperature in different fields while showing the hopeful results. Through these human practices, we got many significant suggestions.
For example, by means of investigation in fermentation industry, the engineer told us that for the potential users like them, the melting temperature of thermosensor should not be the only optional property, intensity and sensitivity should also be thought.
That really inspired us, we continued to design more thermosensors and measure them. Meanwhile, we also began to think, how to make users select a thermosensor conveniently? How to get the melting temperature, intensity and sensitivity of the thermosensor? To solve these problems, we decided to fit a curve to reflect the relationship between the change of temperature and the expression intensity of thermosensors.
When we saw these different curves, there's a new question came up. Some of the thermosensors showed unsatisfactory results -- they don't have melting temperature or the melting temperatures is too high or too low. So how to decrease the undesirable thermosensors? We decide to use random forest algorithm. We want to use this machine to tell us whether the thermosensor is desirable if we only provide the sequence of the thermosensor. And we made it! This algorithm raised the success rate from 47% to 65% successfully.
After that, we continued to do experiments and got a lot of thermosensors. We chose 51 heat-inducible RNA-based thermosenors and uploaded them to the parts registry. We also developed a search engine, we called SynRT Explorer. Until here, we have built the RNA-based thermosensor toolkit successfully!
-
SynRT Toolkit 2.0
After further dialogue with potential users, like scientific researchers and medical institutes or companies, who provided meaningful suggestions for our SynRT toolkit. We got the significant imformation that users need not noly the heat-inducible RNA-based thermosensors, but also thermosensors whose expression intensity decreases at elevated temperature. Based on this requirement, we updated our SynRT to version 2.0. We designed heat-repressible RNA-based thermosensors, which based on the RNase E. We designed hundreds of heat-repressible RNA-based thermosensors.We measured them and selected 23 thermosensors for the toolkit.
-
SynRT Toolkit 3.0
We definitely won't stop going, after getting these exciting results, we still want to explore further. Can we expand our sensing temperature range? Can we design more different type of RNA-based thermosensors? Finally, we update the SynRT toolkit again. Now we call it SynRT 3.0, which contains four different types of RNA-based thermosensors. The cold-inducible RNA-based thermosensors and cold-repressible RNA-based thermosensors were added to the SynRT toolkit. Cold-inducible RNA-based thermosensors based on the cspA 5'UTR mRNA, and cold-repressible RNA-based thermosensors based on the RNase III.
-
Postscript
After getting these exciting results, we don't want to stop going. We know there are more things waiting for us to explore. The SynRT toolkit are still updating, we aim to provide users multiple thermosensor Biobricks to select.