Difference between revisions of "Team:Jilin China/Model"

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       <h2>Background</h2>
 
       <h2>Background</h2>
 
       <p>This year, we focused on building a RNA-based thermosensor toolkit which can sense different temperatures. The principle of RNA-based thermosensors response is that stem-loop structure is melting as temperature increasing. We design and use computational methods(mfold) to identify optimum thermosensors at first. However, it can't meet our needs.Our model is aiming at helping design toolkit with focused features for potential users.</p>
 
       <p>This year, we focused on building a RNA-based thermosensor toolkit which can sense different temperatures. The principle of RNA-based thermosensors response is that stem-loop structure is melting as temperature increasing. We design and use computational methods(mfold) to identify optimum thermosensors at first. However, it can't meet our needs.Our model is aiming at helping design toolkit with focused features for potential users.</p>
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<div align="center"><img src="https://static.igem.org/mediawiki/2018/a/aa/T--Jilin_China--model--yht1.svg" width="100%"/></div>
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<div align="center"><img src="https://static.igem.org/mediawiki/2018/a/aa/T--Jilin_China--model--yht1.svg"></div>
 
<p class="figure">Figure 1. principle of heat-inducible RNA-based thermosensors</p>
 
<p class="figure">Figure 1. principle of heat-inducible RNA-based thermosensors</p>
 
     </div>
 
     </div>

Revision as of 01:25, 18 October 2018

MODEL


Model

  • Background

    This year, we focused on building a RNA-based thermosensor toolkit which can sense different temperatures. The principle of RNA-based thermosensors response is that stem-loop structure is melting as temperature increasing. We design and use computational methods(mfold) to identify optimum thermosensors at first. However, it can't meet our needs.Our model is aiming at helping design toolkit with focused features for potential users.

    Figure 1. principle of heat-inducible RNA-based thermosensors

  • Key Achievement ✓

    • ● Developed a Curve Fitting Model to extract focused feature of Themosensors.
    • ● Developed a quantitative criterion to judge an applicable thermosensor.
    • ● Developed an Intelligent Screening System to avoid undesirable thermosensors, improving our experimental efficiency.
    • ● Found free energy and GC content as crucial factors on RNA-based thermosensors.
    • ● Developed a Thermo Loss System to evaluate the cost effect of temperature.
    • ● Made our script publically available for use by other teams.

  • Modeling

    1.The Curve Fitting Model bridges the gap between experimental data and focused thermal features for potential users. This model provided us a continuous fitting curve to describe the switch behavior of our RNA-based thermosensors in detail. Statistical Postulate on two-state distribution, Thermodynamics, Logistic Regression and least square method were used to fit the curve. We then extracted features from the curve to characterize each thermosensors. Besides, the model provided us quantitative criterion R-squared to judge an applicable thermosensor, which was employed to generate training sets in our Screening System. (see more)

    2.The Intelligent Screening System is based on machine learning to screen the desirable RNA-based thermosensor. Using the random forest algorithm to minimize undesirable samples, we predicted the validity of thermosensors by the sequence and get feedback to improve our Toolkit construction efficiency. (see more)

    3.The Thermo Loss System built a system to estimate the heat dissipation of Yogurt fermenters. Using heat conduction equation, Newtons heat-transfer law and classical difference method, we simulate the heat dissipation and analysis the cost saving when 1℃ is decreased on fermentation temperature. (see more)

  • Results

    Our models are not only closely integrated with our project but also give feedback to our project and human practices in several important ways. The Curve Fitting Model describing the switch behavior played a crucial part in building the Toolkit by providing a set of algorism to deal with our experimental data. The Screening System increased the probability of getting a desirable RNA-based thermosensor from 47% to about 65%, which largely decrease our experiment workload. The model also determined free energy and GC content as the most important features on sequence of RNA-based thermosensors. The Thermo Loss System told us that the 0.9% energy will be saved when 1℃ is decreased on fermentation temperature.This result provided us an important commercial reference to learn the effect of our Toolkit on cost saving and energy conservation in practice .