Science & Research
ZPL x iGEM
The GT iGEM team had been working on solutions to address plastic pollution using science and technology since 2018. Besides, several outreach activities and educational events were conducted to create impacts and momentum in the school community.
The team participated in the International Genetically Machine Engineered (iGEM) Competition 2019, 2021 and 2023. In the summer of 2022, ZPL students and 8 G9 to G10 students joined as a team to work for a year on a research project and participated in the iGEM 2023. The team was awarded a GOLD medal in the iGEM 2023 Grand Jamboree which was held on 2-5 Nov 2023 in Paris. It was an honour that GT iGEM team was awarded three consecutive GOLD Medals for the competitions.
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In Jan 2022, ZPL students engaged in a research project to extend the idea of iGEM 2021 to use drone and AI technology to develop a deep learning plastic waste detection model for mapping plastic pollution on beaches. The team initiates Data Science Experience Day to invite student volunteers helping to tag plastics in the images on CVAT. The process not only arouse awareness of plastics pollution in the environment but also contribute data for algorithmic analysis. The team participated in the S.T. Yau High School Science Award (Asia) 2023 and entered the second stage in July 2023.
PLASTERIASE
Mutating a Bacterial PET-degrading Enzyme
2019 iGEM Team
TSANG Hoi Yeung, LEUNG Chun Fung, LEE Chak Hei, TSE Cheuk Lam, CHU Tye, CHAU Hok Hin, FOK Chun Yin, CHAN Cheuk Hei, YEUNG Tsz Lok, SIU Tokyo, WONG Henry
​In our project, we focus on PET degradation capacity of PETase. For now, its degradation rate is too slow for any feasible industrial use. Therefore, we hope to enhance its function by creating PETase mutants. We optimized protocols of protein induction and enzyme essay of WT and mutants of PETase. We successfully purified and quantified recombinant proteins for enzyme assays. Of the 4 mutants that we designed, 2 of them S245I and S245I/W159H exhibited higher enzyme activity than WT PETase. W159H may have a synergistic effect on S245I mutant so that S245I/W159H exhibited higher activity than S245I single mutants. Thus, protein engineering approach of rational modification of certain residues on PETase opens the possibility for efficient degradation of PET plastic waste in an industrial process.
Explore more: https://2019.igem.org/Team:HK_GTC
PRACTICAL
PETase & Related Analogous Chimera Transfused in Computer & AI Learning
2021 iGEM Team
LEUNG Gabriel, CHAU Hok Hin, LIU Ka Kiu, Belovffy, FOK Chun Yin, LOK Callista Estella, WONG Chun Hei, TAM Ching Yeung, LEUNG Wai Tai, HO Chung Hei, WONG Chun Lam, TSE Ching Lam
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.
Explore more: https://2021.igem.org/Team:HK_GTC
QSMID
Quorum Sensing system for Microplastics Detection (QSMiD)
2023 iGEM Team
AU YEUNG Pak Hei, NG Wing Hang, LEUNG Theodore, WONG Ching Tung, NG Cheuk Hin, CHING Lap Fung, CHU Yik Ling, WONG Ching Yuen, TANG Yu Chit, YEUNG Sum Yu, CHAU Ka Yau, LEE Yuen Ting, LEUNG Yu Fung, LUO Yi, NG Ka Chun, NG Tsz Ching
Microplastics are one of the most damaging and lasting legacies emerged from plastic pollution. Identification of microplastics in complex environmental matrices remains a challenge. In this study, we engineered a whole-cell biosensor based on the LasI-LasR quorum sensing system to detect and quantitatively measure the presence of quorum sensing molecules, N-acyl-homoserine lactones (AHL) molecules secreted from P. aeruginosa for the detection of microplastic pollution levels in water samples. The engineered whole-cell biosensor had a strong GFP expression within 3 hours in the presence of AHL molecules, 3OC12-HSL and C10HSL as low as 1x10-11M. In addition, the engineered biosensor had a high sensitivity in response to 3OC12-HSL with an EC50 value of 2.823 x 10-11M. The engineered biosensor has potential applications to provide a rapid, sensitive and quantitative detection of the microplastic pollution levels in environmental samples.
Explore more: https://2023.igem.wiki/hkgtc/index.html
S.T. Yau High School Science Award (Asia) 2023
Performance analysis and comparison of Mask R-CNN and Complex-Valued Neural Network for improved robotic plastic detection.
TAM Ching Yeung, HO Chung Hei, WONG Ching Yuen, CHING Lap Fung, CHU Yik Long
The study aims to evaluate the accuracy and performance of Mask R-CNN in performing the task of detecting plastics. Besides, the potential of combining the two network architectures, R-CNN and CVNN will also be evaluated.
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The ultimate goal is to develop an effective deep-learning plastic waste detection model for mapping plastic pollution on beaches and eventually removed by AI vehicles. 3,000 images from a large and publicly available dataset of plastics from an open-source tool are annotated by student volunteers from our school for training the Mask R-CNN for plastic detection, with ResNet50 being its backbone.
For Mask R-CNN, since the ResNet50 backbone is used, 48 convolutional layers, one MaxPool layer, and one average pool layer within the backbone will be applied. RoIAlign layers will also be used to predict masks from RoI. For combining CVNN with RCNN, a series of complex-valued convolutional layers, as well as complex-valued fully-connected layers will be used, to replace the CNN structure in RCNN with CVNN. A small-size testing code have been done to prove the feasibility of our approach. We believe that more complex designs of mask branches may have the potential to improve object detection performance.
Education
We engage every effort to bridge the gap between scientific community and the public by effective science communication and outreach. Our goals are to educate the public about how science and technology has emerged as an innovative trend to address plastic pollution and to raise public awareness about global environmental issues and maximize their engagement.
GT iGEM team has published 2 educational booklets and released to school community. We are pleased to have GT iGEM 2021 Educational Booklet published in Plastic Free Seas website.
GT iGEM 2021 Educational Booklet
GT iGEM 2023 Educational Booklet
Science & Research