Difference between revisions of "Team:HK GTC"

 
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                    <h1>PRACTICAL</h1>
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                    <p>PETase & Related Analogous Chimeras Transfused in Computer and AI Learning</p>
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                <h1>Project Abstract</h1>
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<p>Plastic pollution has been a global issue since the last century. In this study, we provide solutions to alleviate the plastic pollution problem from multiple perspectives. We develop a dual enzyme system as chimeras between PETase and MHETase to degrade polyethylene terephthalate (PET) into its constituent monomers. The performance of PETase and MHETase cocktail mixtures is also compared for the extent of hydrolysis of amorphous PET film, and the mixture exhibits improved depolymerization activity compared with the single enzyme. A survey of 60 items, aimed to investigate knowledge, values and actions of secondary students towards plastic pollution, was designed and conducted in 4 secondary schools. The findings suggest the need for environmental education to engage students to take part in preserving the natural environment. Drone and AI technology was applied to train and develop a deep learning PET bottle detection model, which maps plastic pollution on beaches.</p>
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                        <h1>Hover over the Bottle!</h1>
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                        <h1>Team</h1>
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                        <p>We are team HK_GTC - A secondary school team from Hong Kong. Click to explore more about the
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                        <h1>Project</h1>
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                        <p>Our research team develops different dual enzyme systems of PETase and MHETase to increase the rate of PET depolymerization. We constructed chimeras between PETase and MHETase, with the C-terminus of PETase linked to the N-terminus of MHETase using a 12 amino acid link. The degradation rates of PET film using PETase-MHETase chimera, as well as PETase and MHETase cocktail mixtures, are compared. We have shown that the two enzymes act synergistically, increasing the efficiency of the digestion of PET into its final monomers, TPA and EG, and that the mixture exhibits improved depolymerization activity compared with the single enzyme.
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                        <h1>Safety</h1>
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                        <h1>Parts</h1>
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                        <h1>Human Practice</h1>
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                        <p>Human practice team is responsible for raising the awareness of the plastic pollution problem, and our project. We have interviewed local professionals from relevant fields and integrated their advice into our project to strive for improvement. Aside from that, we have also done some educational work. Click to explore.</p>
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                        <h1>Deep Learning</h1>
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                        <p>Responding to the plastic pollution problem, the team created a plastic bottle detecting deep learning model to map PET bottles on beaches. The data allows government, councils, NGO to have an overview of the current situation and to estimate how effective their proposal is to reduce the impact of plastic wastes. Our ultimate goal is to help to reduce the amount of plastic pollution in the ocean. </p>
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                <h1>GT COLLEGE iGEM2021</h1>
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                <img width="30%" src="https://static.igem.org/mediawiki/2021/7/78/T--HK_GTC--logo.png" alt="">
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                <h1>Follow Us!</h1>
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                <h1>Contact:</h1>
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                <p>igemteam.gt@gmail.com</p>
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Latest revision as of 23:11, 28 November 2021

HK_GTC 2021 Homepage

PRACTICAL

PETase & Related Analogous Chimeras Transfused in Computer and AI Learning

Project Abstract

Plastic pollution has been a global issue since the last century. In this study, we provide solutions to alleviate the plastic pollution problem from multiple perspectives. We develop a dual enzyme system as chimeras between PETase and MHETase to degrade polyethylene terephthalate (PET) into its constituent monomers. The performance of PETase and MHETase cocktail mixtures is also compared for the extent of hydrolysis of amorphous PET film, and the mixture exhibits improved depolymerization activity compared with the single enzyme. A survey of 60 items, aimed to investigate knowledge, values and actions of secondary students towards plastic pollution, was designed and conducted in 4 secondary schools. The findings suggest the need for environmental education to engage students to take part in preserving the natural environment. Drone and AI technology was applied to train and develop a deep learning PET bottle detection model, which maps plastic pollution on beaches.

GT COLLEGE iGEM2021

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Contact:

igemteam.gt@gmail.com