Human Practices

HP Framework Overview

Understanding: Clearly define project objectives at the initial stage (Initial objectives) + Potential impacts, survey results. Analyze and determine the direction of follow-up work. List potential stakeholders (researchers, relevant enterprises, the public, iGEM community teams), and predict and analyze their possible concerns.

Interaction/Participation: Based on previous understanding, we conduct iHP and collect and analyze feedback from activities.

Reflection: Combine the feedback collected from HP with one's own next steps of work, and correct and adjust the deviations.

Actions/Adjustments: Based on the results of adjustments after feedback and how to share these experiences.

Human Practices Overview

Background

Plastic pollution has become a global environmental and social challenge. More than 400 million tons of waste plastic are generated annually, most of which is difficult to recycle or degrade, remaining in the natural environment for a long time and posing potential threats to the ecosystem and human health. Traditional physical and chemical treatment methods are costly, energy-intensive, and may also cause secondary pollution, making them difficult to meet the requirements of sustainable development. In contrast, microbial enzyme degradation of plastics provides a green and viable alternative path.

However, both the scientific research and industrial sectors still face numerous obstacles when advancing in this direction:

Against this backdrop, we have proposed the Plaszyme Project, aiming to accelerate the discovery and application of plastic-degrading enzymes through computational methods. The project objectives include:

  1. Achieve highly reliable prediction under limited sample conditions and shorten the screening cycle of candidate enzymes;
  2. Automatically capture sequence features using Machine Learning and Deep Learning, breaking through the limitations of traditional homology methods;
  3. Provide priority ranking and function prediction of candidate enzymes for researchers, reducing unnecessary experimental consumption;
  4. Provide data foundation for the industrial community and promote the research, development, and application of new degradation enzymes.

Stakeholder Analysis

We identified the key stakeholders and their concerns. To gain a more intuitive understanding of the advantages and challenges of the project from the perspectives of different stakeholders, we further conducted a SWOT analysis.

These insights and the analysis of stakeholders have helped us clarify the scientific objectives and social significance of the project, enabling the team to be guided by actual needs in subsequent model development, online platform development, wet experiment design, community engagement, educational practice, and sustainable development strategies, ensuring that every action is based on a full understanding of the issues, audiences, and potential impacts.

Wet Experiment iHP

In this project, we are committed to building a highly accurate prediction model for plastic-degrading enzymes. Although computational biology methods (such as AI and molecular dynamics simulations) can efficiently screen potential enzyme sequences, their reliability ultimately depends on real and reproducible experimental data. Sequence functional annotations in databases often contain inconsistencies or errors, and direct use may lead to biases in model evaluation. Therefore, wet experiments become an indispensable component - they provide us with an empirically validated test set, ensuring that the model's prediction results are not only based on algorithms but also grounded in biological facts.

Integration of External Feedback and Method Optimization

During the experimental design process, we encountered a critical operational challenge: how to ensure the consistency of the crude enzyme solution prepared by ultrasonic disruption among different samples.

The initially adopted ratio of "1g of bacterial cells : 10mL of buffer" resulted in weak enzyme activity signals in some strains, and we realized that the difference in fragmentation efficiency might stem from an unreasonable dilution ratio.

To solve the problem, we proactively consulted Professor Han and his doctoral student Zhao, who have extensive experience in protein biochemical experiments.

They pointed out that an excessively high cell concentration can easily lead to incomplete disruption and cause heat-induced denaturation, while an excessively low concentration will dilute proteins and reduce detection sensitivity. Finally, they recommended using a standardized ratio of "1g of cells : 5mL of buffer".

We immediately fully adopted the ratio of "1g : 5mL" in subsequent experiments. Practice has proven that this adjustment not only solved the original problem of weak signals but also made the results more consistent across different samples. As a result, the team has become more aware that the optimization of many experimental details often relies on inspiration from external experience.

Experimental Validation and Data Contribution

The wet experiment part is centered around the goal of "constructing a reliable test set", and the specific steps include:

These results validate the effectiveness of the prediction model from three dimensions: sequence consistency, enzyme activity rate, and protein expression level, ensuring that subsequent iterations are based on real and reliable data.

Responsibility and Impact: From Experiment to Society

We recognize that wet experiments are not only technical issues but also involve the responsibility of scientific research towards society and the environment. Throughout the process, we strictly adhere to biosafety regulations: all experiments were conducted in a BSL-1 laboratory, the Escherichia coli BL21(DE3) used was a non-pathogenic engineered strain, and all waste containing bacteria was autoclaved to avoid risks to the environment and personnel.

This sense of responsibility is not only reflected in compliance operations but also in our hope to ensure that research results can be safely adopted and applied by the scientific community and society in the future. By considering both scientific rigor and social responsibility in design and implementation, we ensure that experimental data serves as a solid foundation for Model Iteration and is also a practical demonstration of the team's commitment to "responsible science".

Online Platform iHP

Through extensive research, we have recognized the importance of an online platform. Its convenient operation can enable the product to reach a broader client base, actually assist researchers, and allow more people to experience the ability of artificial intelligence platforms to accelerate the development of synthetic biology. Therefore, we have designed the online platform in detail, making our database and models available online.

After the design was developed, we communicated with Yukun Jiang, a member from ZHU Lab, and separately with Sekar Raju from the School of Science at Xi'an Jiaotong-Liverpool University and his Ph.D. students. They are all engaged in research on the biodegradation of plastics and have in-depth hands-on experience in this field, which is also the important reason for our interviews with these individuals or research groups. We shared the platform design with them and summarized the specific suggestions they put forward:

  1. Optimize the database display mode;
  2. Add more descriptive information and provide web cards with more guidance;
  3. Optimize the usage logic of the website;
  4. Add more diverse model input methods.

These ideas have also been incorporated into the next phase of our platform development:

  1. Considering that most researchers lack specific plastic-degrading enzyme sequences and many biodegradation researchers often obtain input in the form of metagenomic sequences from the environment, we have developed a metagenomic input module using HMM as a new model input method, enabling more researchers to study specific issues more conveniently.
  2. We have also implemented the optimization of the mentioned database display mode, further enriching the content displayed on the details page and adjusting the layout accordingly.

During the final online follow-up visit with Jiang colleagues: She personally experienced the entire process of model prediction and database query on the public domain names we shared http://plaszyme.org/plaszymedb and http://plaszyme.org/plaszyme. After hands-on operation, she gave positive feedback and commended the platform for achieving the design goal of "intuitive and convenient".

Model Section iHP

Under the global challenge of plastic pollution, we proposed the idea of constructing the Plaszyme Model Platform, hoping to accelerate the discovery and application of degrading enzymes through computational methods. However, we soon realized that it would be difficult to solve all problems solely through internal team thinking:

These uncertainties have motivated us to proactively seek external feedback and consult experts from scientific research, environmental studies, and the iGEM community. Through these exchanges, we have not only received valuable advice but also continuously adjusted and optimized the direction and design of our model.

Stakeholder Consultation and Feedback

ZhuLab Researcher Yukun Jiang

Question/Concern: Under the condition of limited samples, how to ensure the reliability of prediction? How to obtain more available data?

Our Actions: We integrate decentralized databases, supplement data using methods such as HMM and sequence alignment, and ensure robustness even on small samples through model architecture optimization.

Professor Sekar Raju and his doctoral student Xiaotian Zhao

Question/Concern: Is the application scenario of the model too single? Can it serve environmental sample research that is closer to the needs of researchers?

Recommendation: Introduce a metagenomic analysis module to directly mine potential degrading enzymes in environmental samples.

Improvement: We added a metagenomic analysis interface, combined with a hidden Markov model to predict degradation enzyme sequences in metagenomes, and automatically input the results into the main model for validation.

BUCT-China 2025 Runze Sun

Problem/Concern: The coverage of plastic type prediction is insufficient, with early versions only supporting 7 categories. For iGEM research, the limited number of predicted categories restricts its application.

Recommendation: Expand the types of plastics covered by the prediction, especially common but data-scarce categories.

Improvement: Plaszyme Alpha and Plaszyme X can ultimately cover 34 types of plastics, greatly enhancing the applicability of the model.

Through communication with researchers, professors, and other iGEM teams, we continuously incorporate external feedback into Model Iteration to make it more in line with real scientific research needs.

Model Iteration and Core Methods

Plaszyme Alpha

Combines ESM protein language model embeddings with SMOTE oversampling under small sample conditions to address the issues of insufficient data volume and class imbalance. By using the Histogram-based Gradient Boosting classifier for modeling, the hit@1 can reach up to 0.88, significantly improving the prediction reliability under limited data conditions.

Plaszyme X

Adopts a dual-tower architecture: one side embeds and represents the enzyme sequence, while the other side generates descriptor vectors for the plastic molecular structure. Features are fused through graph neural networks, with cosine similarity as the optimization objective. The model performs outstandingly on multi-label tasks, with the F1-score reaching up to 0.9, better reflecting the complexity of the enzyme-plastic relationship.

The two types of models are complementary: the Alpha model focuses on rapid classification and sequence prediction, while the X model delves into in-depth modeling of enzyme-substrate interactions. Combined with the metagenomic expansion module, the platform can cover the full range of application scenarios from laboratory data to environmental samples.

Security and Feasibility Assessment

Security

The platform strictly protects user privacy in its design and does not collect personal sensitive data.

Data Source Reliability

PlaszymeDB integrates professional databases such as PlasticDB, PAZy, and PMBD, supplements with data from GitHub open-source projects and literature, and cross-verifies through authoritative databases such as UniProt, NCBI, and PDB. Each piece of data comes with a source and citation link to ensure traceability.

Hypothesis Testing and Verification

We hypothesized that enzyme sequence features could distinguish their plastic degradation capabilities. Five validation sequences obtained through wet experiments were used as an independent test set to evaluate the model's generalization ability on real data. Statistical results showed that the predictions were highly consistent with experimental annotations, providing solid scientific support for the model.

Model Scope of Application

The Alpha model is suitable for rapid classification tasks where the plastic type is known; the X model is suitable for exploring the degradation relationships of new or complex plastics. The combination of the two provides full-process support for scientific research and industrial applications.

Conclusion

The development of Plaszyme has undergone a process that starts from social needs, continuously collects feedback, and then proceeds to continuous optimization. Each adjustment brings the model closer to the needs of scientific research and application, and also makes our work more robust and reliable.

Educational iHP

When initially designing educational work, what we considered was not just classrooms and knowledge, but how to enable more people to truly understand synthetic biology. As an emerging discipline, although excellent scholars have been working hard to promote it, it is still often regarded as "abstruse and difficult to understand." We hope to break this barrier, making synthetic biology like a door that everyone can easily open and discover surprises and value within. Meanwhile, we also see the real challenges posed by plastic pollution. We are well aware that science should not remain in an ivory tower but should engage in dialogue with reality and become a driving force for social progress. Therefore, we attempt to bring synthetic biology to more people through stories, interactions, and exchanges, allowing everyone to feel its connection to life and see the hope it may bring to environmental issues.

Educational Philosophy and Spiral Framework

In the process of practice, we have gradually realized that educational activities for different groups are not isolated but rather a closely linked and progressive whole. Combining Bruner's spiral curriculum theory and constructivist learning theory, we have proposed the team's "educational spiral": starting with interest, centered on understanding, and ending with action. The primary school stage focuses on enlightenment and experience, the middle school stage emphasizes responsibility and exploration, the high school stage highlights critical thinking and value identification, while university and public education move towards cross-border cooperation and social participation.

This framework is not only a conceptual design, but also continuously undergoes feedback testing in practice, and is adjusted and optimized with reflection.

Educational Practice and Feedback Adjustment

First Attempt: Shortcomings of Online Teaching

In the online classes at Beijing Bozhi School, we guided students to understand "the four characteristics of living organisms" through real-life examples. The children showed genuine curiosity, but after class, the teaching assistants reported that the online format lacked interaction, making it difficult for some children to concentrate; the AI and project components were too abstract and beyond their comprehension. This feedback made us realize that real experiences can stimulate interest more effectively than abstract knowledge.

Feedback-based Improvement: Offline Interaction and Bacterial Painting

After reflection, we proactively adjusted our strategy: abandoning the online format and instead launching offline courses, reducing abstract content, and increasing hands-on experiments. In the classroom of the Cold Spring Harbor DNA Learning Center in Suzhou, children completed "bacterial painting" under the guidance of teachers. The sense of participation and artistic form of the experiment brought positive feedback, with students remaining focused during exploration and even expressing a desire for further learning. This improvement has made us deeply realize that listening to feedback and making timely adjustments are the keys to the success of educational practice.

Further Extension: Reflections on the Urban-Rural Education Gap

In the classrooms of Caomiao Middle School, we not only introduced synthetic biology and AI but also incorporated interactive Q&A sessions and personal stories to help students find motivation to learn despite limited educational resources. The sincere responses from students after class made us realize that the value of education lies not only in imparting knowledge but also in igniting hope. This feedback has prompted us to expand our educational vision to areas with relatively weak resources.

High School Stage: Enhancement of Responsibility and Critical Thinking

At Huamai Middle School, we have successfully inspired students' sense of responsibility by taking the national and personal fates as the starting point. However, in our post-class reflection, we realized that we still lacked in inspiring critical thinking. Therefore, in subsequent classes, we added "AI prompt" exercises to guide students to learn how to interact with AI through practice and develop critical thinking in the process. The students' feedback, "This is the first time I've truly learned to use AI," confirmed the effectiveness of this adjustment.

University and the Public: Cross-Boundary Collaboration and Cultural Resonance

During the university stage, we not only offered ML training courses but also attempted cross-boundary initiatives, combining "bacterial painting" with calligraphy and painting arts to hold art exhibitions. Many colleagues reported that this format made them feel for the first time the closeness of science to life. The success of this cross-boundary collaboration stems from our continuous collection of feedback and optimization of design in practice, ultimately finding the most resonant approach.

Conclusion

From interest enlightenment to a sense of responsibility, from a sense of responsibility to critical thinking, and then to cross-border cooperation and social participation, our educational practices continue to progress through feedback and adjustment. Each attempt is not a one-way output, but a two-way dialogue jointly completed with students, teachers, and the public. It is precisely this cycle of "feedback - reflection - improvement" that makes the educational spiral truly become a continuously growing path, and also gradually brings synthetic biology towards "understandable and participatory by all".

Sustainable Development iHP

Sustainable Development Pathway: From Education to Collaboration

We have always believed that sustainable development should not remain a mere slogan but should be reflected in the path of specific practices. Looking back on the entire project, our work has gradually formed a sustainable development route linked by education - research - consumption - cooperation.

Starting from Education

In educational activities that compare urban and rural areas and are segmented by age group, we have reached 1 primary school, 1 junior high school, 1 senior high school, and all classes of 1 educational institution and its summer school. In these activities, students' interests, teachers' teaching feedback, and schools' considerations regarding curriculum arrangements have driven us to continuously revise and refine our design, ultimately resulting in a set of reusable educational frameworks (primary schools focus on hands-on practice, while senior high schools emphasize extended thinking). This framework has been fully presented in the Education section and can be directly referenced by future teams and educators. This is not only the starting point of our social awareness but also makes the connotation of SDG4 "Quality Education" truly take root.

Towards Scientific Research and Innovation

The awareness of the question "how plastics can be truly degraded" inspired during the educational process has driven us to think about the industrial significance of translating scientific research into practical applications. On this basis, we developed the Plaszyme Prediction Model, which has reduced the cost and time of experimental trial and error by approximately 98% through computational simulation. This tool has received positive feedback during exchanges with enterprises: it is not only a scientific research method but also has the potential to become a pre-evaluation tool for the industrial sector. Thus, we have realized that the synergy between scientific research and industry is a key entry point for SDG9 "Industry, Innovation and Infrastructure".

Extending to Responsible Consumption

Based on scientific research and education, we attempt to translate our concepts into the team's own code of conduct. For example, we prioritize the use of environmentally friendly materials in peripheral design and promote low-waste work methods within the team. Although these initiatives are limited in scale, they enable us to achieve "unity of knowledge and action" and influence more people to pay attention to SDG 12 "Responsible Consumption and Production" through the dissemination of peripheral products. This is another way for us to externalize the value of scientific research into social action.

Finally, We Return to Collaborative Co-construction

We are well aware that the strength of a single team is limited, so we actively engage in cross-border cooperation. Through exchanges with multiple teams such as BUCT, ZJUT-China, JLU-NBBMS, Tsinghua, etc., we jointly produced Chassis Bacteria Comic White Paper, Plastic Circular Economy Initiative, Cross-team Recycling Action, and Joint Course Recording, etc. downloadable and reusable outcomes. Meanwhile, through enterprise interviews and community linkages, our scientific research exploration has gradually integrated with social expectations. It is precisely in these collaborations that we have realized that SDG17 "Partnerships" is not just a bonus, but a necessary path to promote the sustainable development of scientific research and society.

This path from start to finish is not only the trajectory of our growth in the one-year project, but also our most authentic understanding of "sustainable development": Educational enlightenment, scientific research innovation, responsible consumption, and collaborative co-construction are not isolated actions, but an integrated whole that supports and advances each other.

Communication and Cooperation

Throughout the journey of the project, we have always reminded ourselves that scientific research cannot be conducted in isolation within the laboratory; it must establish connections with society, industry, teams, and the public to truly realize its value. That is why we have taken the initiative to reach out, continuously engaging in exchanges and cooperation among communities, industries, teams, and on academic platforms. These experiences have not only enriched the connotations of the project but also helped us understand the significance of scientific research from new perspectives.

Community Communication

Plastic Recycling · Starting Point Initiative: Sharing Environmental Responsibility with the Community

Team members organized a "Plastic Recycling Campaign" by Jinji Lake in Suzhou City, Jiangsu Province. We cleaned up scattered plastic waste along the lakeside trail. After several hours of effort, we collected dozens of plastic items in total.

Initially, we only had the idea of "collecting background creatives for the project," but witnessing this trash made us for the first time deeply and intuitively feel the problem of plastic pollution.

Subsequently, we sorted out these plastic samples, conducted preliminary classification of biodegradable and non-biodegradable waste, and transformed them into "creatives" that promote the connection between scientific research and society. This process also made us truly realize that researching plastic degradation is not just a "point of interest" in scientific research; it corresponds to an urgent social pain point. It is precisely this impact that has strengthened our conviction in the significance of the project and made us recognize that scientific research needs to draw problems from reality.

After CCIC, the team, with the help of the synthetic biology community, got to know iGEM teams such as BUCT and BUCT-China. Through discussions and exchanges among the teams, they jointly organized the subsequent "Joint Plastic Product Recycling Activity".

CCiC Exchange Conference: Cross-team Sharing and Collaboration

At the CCiC National Exchange Conference, we first presented our project on a larger stage, sharing the spotlight with iGEM teams from across the country. We shared our research ideas and also raised questions about experimental design and educational activities.

We also engaged in in-depth exchanges with multiple teams on experimental techniques, HP ideas, and science popularization and education methods:

Taking this opportunity provided by CCiC, the XJTLU-AI-China Team engaged in in-depth exchanges with outstanding teams such as BIT, ZJUT, SJU, and BUCT in the biodegradation and artificial intelligence track, and established mutually trusted cooperative relationships with some of them.

Through sharing and feedback, our project has been further optimized from an external perspective, and we have also gained many potential future cooperation opportunities. These suggestions not only help us optimize our research, but also make us realize that scientific exploration is not a one-way output, but continuous evolution through mutual inspiration. CCiC has taught us to step outside our team and view our work from others' perspectives.

Volunteers at Cold Spring Harbor Asia DNA Learning Center Summer Camp: Lighting Up Children's Eyes with Stories

At the summer camp of Cold Spring Harbor Asia DNA Learning Center in Suzhou, Jiangsu Province, China, our team members participated as volunteers, leading campers to visit laboratories, teaching buildings, etc., and introduced through interactive explanations the development history of DNA research. In the communication with primary and secondary school students, our team's volunteers explained complex scientific concepts and issues in easy-to-understand language, acting like little tour guides, leading them to explore the ocean of science and inspiring their interest in life sciences. We firmly believe that scientific communication is not about "dumbing down" complex knowledge, but about finding a bridge to communicate with different audiences. This experience not only improved our communication skills but also reminded us that the value of scientific research results also lies in whether they can inspire the curiosity and sense of responsibility of the next generation.

Enterprise Communication: Two-way Dialogue between Scientific Research and Industry

During the project's advancement, we have always emphasized maintaining close ties with the industry. Only by continuously listening to real-world needs can our work truly solve problems, rather than working in isolation. Based on this understanding, we actively sought to communicate with enterprises in the plastic degradation industry. However, actually taking this step was not easy. After four rejections, we finally established a communication opportunity with Guangdong Zhongsu, a leading enterprise in the plastic degradation industry in Dongguan City, Guangdong Province, China.

During the first online communication, after we introduced the project concept, we unexpectedly received positive and candid feedback from the other party: "If you can develop such a tool, it will surely be a good thing for the industry." This affirmation made us feel the alignment between scientific research results and real-world needs, and also strengthened our confidence to continue exploring. More importantly, their representative put forward a highly valuable suggestion: if our prediction model can take into account the complex conditions of the natural environment, the results will be closer to the application scenarios. This reminder made us reflect that the gap between the perfect settings in the laboratory and the real world may determine whether the project can ultimately truly benefit stakeholders. The "ideal environment" in the laboratory is not reality, and for scientific research to be effective, this gap must be bridged.

Face-to-Face Thinking: Repositioning Amid Industry Pain Points

On this basis, we further went offline and had in-depth face-to-face exchanges with enterprise representatives. During the meeting, they not only shared the current situation of the industry and market pain points: Although policy promotion has gradually reduced the cost of degradable materials, they still remain at a disadvantage compared to traditional plastics; the market demand is strong, but it is restricted by the imperfect industrial chain and inconsistent standards. These real challenges have made us realize that our project cannot merely exist as a scientific research achievement. Enterprises have also helped us think about the positioning of our project within the industrial chain. They further pointed out that if we position our project platform as a scientific research service tool, it can better integrate into the front end of the industrial chain, help research institutions and enterprises collaborate, and thus promote the implementation of research results. This direct feedback from the community and the industry has made us realize that communication is not just about passively acquiring information, but a driving force that propels us to continuously correct our understanding and expand the boundaries of our project. Such communication has not only given us an overall understanding of the industry but also made us more clear about the role we can play in future development.

Through both online and offline communication, we have gradually come to understand the industry's expectations and support for the project, and have also deeply felt that scientific research must respond to real-world issues. It is precisely these conversations that have made us realize that the relationship between scientific research and industry is not a "one-way output," but rather a cycle of mutual empowerment.

Team Communication

If community and enterprise exchanges have allowed us to see the expectations of society and the industry for scientific research, then team exchanges have enabled us to experience the mutual assistance and growth within the scientific research community. In our interactions with other iGEM teams, we have not only shared experiences but also continuously reflected and improved through the collision of ideas with each other.

Cross-team Online Sharing Session: From Doubts to Consensus

We first organized a national online sharing session with multiple teams engaged in research related to plastic degradation (such as BUCT, BUCT-China, ZJUT-China, JLU-NBBMS...).

Everyone introduced their respective projects, sharing the results of their exploration and candidly presenting the challenges they encountered. It was precisely in this "unreserved" communication atmosphere that we gained many insights: (such as the progress of wet experiments, the affirmation of the scientific significance of such a plastic degradation prediction model within the field, and the subsequent HP work based on the synthetic biology community). The discussion gradually evolved from communication to cooperation, ultimately resulting in four practical outputs:

Chassis Bacteria Comic White Paper

Each team is responsible for writing about 1–2 chassis bacteria, introducing them from four dimensions: introduction and core features, application examples and engineering plasticity, industrialization and application prospects, as well as safety and risks. We presented these contents in the form of comics and exhibited them on our respective social media platforms. Finally, we compiled them into a book and made it available for everyone to browse in physical form at the Paris venue. This is not only an organization of knowledge but also an attempt at science popularization for the public.

Enzymatic Cycling: A Synthetic Biology Roadmap and Action Initiative towards a Closed-Loop Plastic Economy

During a previous online sharing session, teams from different tracks and research backgrounds came together to jointly discuss environmental issues and their respective projects, generating a wealth of meaningful intellectual exchanges. Through the collaboration of multiple teams, we organized our ideas and documented them in writing, resulting in this white paper. Our team emphasizes the writing on the integration of AI in the white paper, sharing the overall background and innovative projects. More importantly, as a team in the Software&AI track, we responsibly conduct a detailed analysis of the risks and safety of AI products, especially those related to plastic biodegradation research, to address the lack of awareness among most teams regarding the detailed content of AI risks in this field.

Jointly Recorded Sustainable Development Courses

Among the 7 Sustainable Development Goals (SDG3, 6, 9, 11, 12, 14, 17), each team selected one to record a course. We chose SDG17 (Partnerships for the Goals), focusing on the core topic of "plastic pollution as a global issue that requires international cooperation to address". From how global research teams promote synthetic biology research through collaboration to how governments, enterprises, and social organizations jointly promote technology application and dissemination, we shared real-world cases. The course was finally uploaded to social media platforms by the BUCT team, enabling more people to benefit.

Eco Plastic Recycling · Joint Action

Building on our previous community recycling activities, each team decided to jointly deepen the significance and scope of the activities. We created a "Plastic Type Query Table" as a reference tool and unified all the data collected by the teams into an Excel spreadsheet for everyone to share. These data not only help the teams supplement the background of their respective projects but also form a shared outcome for the community. Through this sharing session, we have deeply felt that cooperation is not merely about making an event "even more beautiful" through teamwork and collecting a larger amount of background data on plastic products with a wider geographical scope, but rather a way to promote scientific research results to reach a broader society.

Peer-to-Peer Communication: In-depth Experiments and Models

In more specific cooperation, we had in-depth discussions with two teams from BUCT regarding the wet lab experiment section. During the communication process, we were able to review and reflect on the work of the wet lab experiment section and envision subsequent summary plans. This helped us realize the importance of an interdisciplinary perspective: teams with different backgrounds can illuminate our blind spots from new angles.

Our Achievements and Reflections

This series of team exchanges has made us deeply realize that:

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