Difference between revisions of "Team:Jilin China/Project/Overview"

SynRT Toolkit 1.0

This year, we aimed to develop a RNA-based thermosensors toolkit. Based on adding a stem-loop to 5'UTR, the thermosensors could sense the temperature change. And we constructed the measurement device to characterize these thermosensors.

As the temperature went up, the expression intensity of the reporter protein, sfGFP_optimism, increased sharply between 33℃ to 42℃. Thus, this type of thermosensors was named as Heat-inducible RNA-based thermosensors. After getting the measurement results of the first batch of heat-inducible RNA-based thermosensors, we did some investigations in industry, asking experts for advice about essential temperature sensing in different aspects while showing the hopeful results. Through these human practices, we got some significant suggestions.

For example, by means of the investigation results in fermentation industry, the engineer told us that the melting temperature should not be the only features the potential users would consider when they choosing thermosensors, the intensity and sensitivity were also key factors.

This suggestion really inspired us, we continued to design more thermosensors and measured them. Meanwhile, we also began to think about how to make users select a thermosensor conveniently by getting the melting temperature, intensity and sensitivity of the thermosensors? To solve this problem, we fitted a curve to reflect the relationship between the change of temperature and the expression intensity of thermosensors. In this way, we could get the property of our thermosensors intuitively.

After fitting these different curves, a new question came up. Some of the thermosensors showed unsatisfactory results -- their melting temperatures were too high or too low, which could induce too much burden and costing in large-scale construction. So how to decrease undesirable thermosensors? We decided to use random forest algorithm. We want to use this machine to tell us whether the thermosensor is desirable by only providing the sequence of the thermosensor. Fortunately we made it! This algorithm raised the success rate from 47% to 65%.

In all ,we chose 48 heat-inducible RNA-based thermosenors and uploaded them to the parts registry. We also developed a search engine, which was called SynRT Explorer. Until here, we had built the RNA-based thermosensors 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 information that users need not only the heat-inducible RNA-based thermosensors, but also thermosensors whose expression intensity will decrease with increasement of temperature. Therefore, we updated our SynRT to version 2.0 by adding the heat-repressible RNA-based thermosensors based on the RNase E. We designed hundreds of heat-repressible RNA-based thermosensors. We measured them and selected 22 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 types of RNA-based thermosensors? Finally, we updated 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.