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

Background

We generated mathematical models of heat-inducible RNA-based thermosensors toolkit. The aim of our models was to build a SynRT toolkit for potential users and develop an intelligent system to screen valid thermosensors, which help the designers avoid the undesirable thermosensors.

We also created a model, which integrated with our HP group by calculating the Thermal Losses of fermentation cylinder. This model is aiming at estimating the cost saving and energy conservation of potential industry applications.

Methodology

We employ numerous technics and equations to have the most accuracy access to the problems raised in our project. Examples include random forest algorithm in machine learning, statistical postrulate, least square estimation, Taylor serious expansion, Van 't Hoff statistical , Hill equation, heat conduction equation and finite difference calculus.

To see detailed information, please follow the links below models and have a try of our database!

Integration

Our models are closely interlinked with our project. Results of our model also feedback to our design and human practices work in several important ways. Some examples include:

1.The Curve Fitting Model bridge the gap between experimental data and valid thermal features for potential users. It provided us a detailed and continuous description of behavior of our RNA thermosensor by fitting a temperature-dependent expression curve. Moreover, it provided us a quantitative criteria to judge a valid thermosensor.

2.The Screening System is an intelligent machine learning system to screen the valid RNA sequence. Using the random forest algorithm, it feedback to the design by minimizing invalid samples.

3.The Cost Analyses built a heat conduction equation to estimate the heat loss power of Yogurt fermenters, providing us an important commercial reference to learn the effect of our production on cost saving and energy conservation during the work of human practice.