Contribution

Overview

Our project delivers a comprehensive wet lab contribution by establishing Yarrowia lipolytica as a robust, programmable chassis for intelligent therapy, developing a novel set of genetic tools, and integrating them into a functional delivery system. We provide future iGEM teams with a complete and validated toolkit to engineer living therapeutics for dynamic environments.

1.Wet Lab

1.1 Standardized & Validated Part Library: Ready-to-Use for Future Therapeutic Projects

We constructed and rigorously verified a set of functional parts tailored for yeast-based living therapeutics, covering chassis adaptation, gene expression control, protein localization, and functional validation. All parts are annotated with clear activity data and application scenarios, allowing future iGEM teams to directly integrate or modify them without redoing foundational validation.

1.2 Contribution to Chassis Organism Selection: Validating Yarrowia lipolytica as a Robust Platform for Hostile Microenvironments

While S. cerevisiae and E. coli almost dominate iGEM projects, we championed and rigorously validated the non-conventional yeast Yarrowia lipolytica Po1h as an ideal chassis for applications in challenging physiological environments, such as chronic wounds.

(1) A Solution to harsh environment : We demonstrated that its native tolerance to high oxidative stress and glucose is not just a documented trait but a functional advantage, enabling our engineered strains to survive and operate in the harsh diabetic wound milieu where conventional chassis might fail.

(2) Protease Deficiency as a Critical Feature: We actively selected for and utilized the Po1h strain's knockout of major extracellular proteases (AEP, AXP). This decision was crucial for ensuring the stability and bioactivity of our secreted therapeutic proteins (IL-4, VEGF), directly addressing a common hurdle in heterologous protein production—proteolytic degradation.

(3) A New Option for the Community: By providing a complete engineering workflow—from transformation to functional validation—for Y. lipolytica, we lower the barrier for future teams. We offer a proven, rugged alternative for projects targeting high-stress environments, including bioremediation, industrial fermentation, and other medical applications.

1.3 Contribution to the Therapeutic Track: An Integrated and Dynamically Responsive "Living Therapeutic" Paradigm

Our most significant contribution is to the therapeutic application of synthetic biology. We have moved beyond single-function, constitutively active systems to demonstrate a proof-of-concept for a programmable, multi-stage, integrated therapy.

(1) From Static to Dynamic Treatment: We engineered a system that senses and responds to the dynamic state of a wound. Instead of a constant, uncontrolled release of therapeutics, our yeast:

- Provides immediate, physical antimicrobial defense (surface-displayed Pexiganan).

- Automatically initiates anti-inflammatory action when glucose levels are high.

- Delivers pro-regenerative signals on demand via an externally applied, benign thermal trigger.

(2) A Blueprint for Sequential Logic in Therapy: This "sense-and-respond" logic mimics the natural phases of wound healing (hemostasis → inflammation → proliferation). We provide a design framework for creating living therapeutics that can intelligently intervene at the correct pathophysiological stage, a crucial advancement for treating complex, multi-factorial diseases.

(3) Synergy with a Safe Delivery Platform: We did not treat the delivery system as an afterthought. By integrating our engineered yeast with our custom L-DOPA-modified thermosensitive hydrogel (L-HBC), we demonstrate a complete therapeutic package that addresses safety (encapsulation), usability (injectable gel), and enhanced function (photothermal response).

By sharing this work, we aim to lower the technical barrier for therapeutic synthetic biology in iGEM, inspire more teams to explore yeast as a chassis for real-world applications, and contribute to a more collaborative, efficient iGEM community.

2.Dry Lab

For society, our modular wound analysis and processing system is specifically designed to address the critical challenges of diabetic foot care, particularly in underserved and remote areas. This hardware integrates automated debridement, gel spraying, and monitoring functions into a low-cost, highly integrated, and user-friendly platform, significantly reducing the technical and economic barriers to chronic wound management. This innovation not only alleviates the burden on healthcare systems but also empowers more communities to effectively manage chronic wounds, contributing significantly to patient well-being and public health.

It is worth mentioning that our improved wound recognition algorithm, leveraging multi-spectral sensing and temperature matrix compensation technology, effectively reduces identification biases caused by skin tone variations. This technical approach avoids the sensitivity limitations of traditional visual algorithms on darker skin, ensuring consistent diagnostic accuracy for patients of all skin tones—thereby promoting healthcare equity at the technological level.

Globally, the rising prevalence of diabetes has made related complications (such as chronic wounds) an increasingly critical international health issue. With its cost-effectiveness, modularity, and ease of use, our system holds broad application potential and can be deployed and adapted in various resource-limited settings, helping to bridge global healthcare disparities. By advancing standardized and precise care, the device contributes to the sustainable development of global health, aligning particularly with the United Nations Sustainable Development Goal 3 (Good Health and Well-being).

For the iGEM community and the broader scientific and engineering fields, this project serves as an exemplary case of integrating synthetic biology with medical applications through hardware solutions. We demonstrate the entire engineering process from design to implementation: including the initial adoption of commercially available low-cost robotic arms, precision improvements through inverse kinematics algorithms, path planning optimization based on polar coordinates, modular task payload integration (electromagnetically attached disinfectant nozzles/cameras/sensors), and the development of a temperature-controlled bioreactor (gel-yeast mixing and insulation system). By open-sourcing our design concepts, standardized interface protocols, and practical experiences (including failure cases and solutions), we provide valuable references for future iGEM teams and researchers. This project exemplifies how hardware development can effectively support biological application innovation, enabling a closed-loop technological solution from automated treatment to real-time monitoring.

We firmly believe that through continuous hardware research and development efforts, we can advance wound care technology toward greater accessibility, efficiency, and precision. We also hope to inspire more iGEM teams to tackle complex biomedical challenges using a "hardware-software-biology" integrated approach, truly embodying the interdisciplinary spirit of iGEM.

Our project provides a comprehensive, multi-scale computational framework, from AI-driven research assistants to molecular and systems-level models, offering significant contributions to future iGEM teams, the broader scientific community, and society.

For future iGEM teams, our primary contribution is a suite of powerful, adaptable software tools—the AMPilot platform—designed to accelerate the entire Design-Build-Test-Learn cycle.

The AMP Research Agent, built on an Agentic RAG paradigm, offers a robust solution for navigating vast scientific literature, enabling teams to move beyond simple keyword searches to intelligent, context-aware discovery. This framework can be adapted by any team to build their own expert assistant for their specific research topic.

The AMP Rerank Agent introduces an innovative "white-box" methodology for candidate screening. By generating interpretable, natural-language design rules, it bridges the cognitive gap between computational predictions and wet-lab intuition, providing a valuable tool for teams working on protein engineering or directed evolution.

The Data Analysis Agent democratizes data science for biologists. By translating natural language commands into executed Python code, it empowers teams to perform complex data cleaning, analysis, and visualization without requiring extensive programming expertise, dramatically improving the efficiency and rigor of the "Test" and "Learn" phases.

At the level of fundamental science, our multi-scale modeling pipeline provides a complete "digital twin" of our therapeutic system. The molecular molecular dynamics (MD) simulations offer an unprecedented atomic-level view of our L-HBC hydrogel, revealing the microscopic driving forces behind its macroscopic thermo-sensitivity and its high-affinity interactions with bioactive molecules. This work not only provides a solid theoretical foundation for our project but also serves as a methodological blueprint for other teams developing novel biomaterials. Building on this, our reaction-diffusion (PDE) model predicts the system's real-world pharmacodynamics, allowing for in silico optimization of therapeutic delivery. This predictive power helps formulate precise, testable hypotheses and provides clear guidance for designing more efficient and targeted wet-lab and animal experiments.

Most importantly, we have pioneered a novel approach by leveraging the inherent reasoning capabilities of an LLM, combined with the functionality of an Agent's memory module, to learn from antimicrobial peptide sequences. This introduces a new paradigm for the application of AI in synthetic biology: shifting away from implicit, encoding-based learning and instead enabling the LLM to reason about the problem in a human-like manner. This represents a new paradigm for AI in synthetic biology.

For society, our work accelerates the development pipeline for advanced "living therapies." The AMPilot platform can significantly shorten the discovery and design phase for new therapeutics, while our modeling approach aligns with the ethical principles of the 3Rs (Replacement, Reduction, and Refinement) by reducing the reliance on animal testing. By creating a high-fidelity predictive model for a complex wound-healing therapy, we pave the way for more effective and personalized treatments for challenging conditions like diabetic foot ulcers.

Ultimately, for the iGEM community, we contribute not just a single project but an integrated, computationally-driven research philosophy. We provide a validated workflow that seamlessly connects AI-powered literature analysis, deep molecular insight, and systems-level predictive modeling. The methodologies, source code for our agents, and validated models are all valuable resources that can be built upon by future teams. Our project demonstrates how the fusion of artificial intelligence and biophysical simulation can solve complex biological engineering challenges, and we hope to inspire other teams to adopt these tools to push the boundaries of synthetic biology.

The following is a overview diagram of the contribution of the model part：

3.Human Practice

In our 2025 project, we gradually realized that a linear approach to Human Practices—where research, theory, and practice are separated into static steps—was not sufficient to address the complex and evolving challenges of real-world problems. To overcome this gap, we developed and applied a new methodological model: the RTPNR framework (Research–Theory–Practice–New-theory–Re-practice/Re-search).

RTPNR is not merely a theoretical construct; it is a dynamic and spiraling process that integrates feedback loops into every stage of a project. Beginning with Research (collecting genuine needs from diverse stakeholders), moving to Theory (formulating guiding principles), then Practice (conducting experiments and testing solutions), the cycle leads to the generation of New Theory (reflective insights and innovative solutions). This new theory subsequently informs Re-practice or Re-search, enabling projects to ascend to a higher level of development. By employing RTPNR, we ensured that our work on diabetic wound hydrogel dressings was not confined to isolated experiments but became a continuously iterative and socially responsive process.

At the Research stage, we conducted extensive surveys and interviews with patients, family caregivers, doctors, and community health workers. Over 90% of respondents emphasized that safety was prioritized over efficacy and price in wound care products, highlighting a critical market gap. We also visited chitosan dressing companies and medical device developers, learning firsthand about the regulatory distinctions between Class II and Class III devices and their commercialization pathways. Guided by literature and expert consultation, we identified hydrogels as the most suitable material among many candidates, owing to their moisturizing capacity, breathability, biocompatibility, and ability to regulate the microenvironment of diabetic wounds.

In practice, we collaborated with professors, researchers, and companies to experiment with antimicrobial peptides, engineered Yarrowia lipolytica, and chitosan-based hydrogels. From these efforts emerged new insights: by integrating social feedback with experimental results, we proposed a novel vision—combining smart hydrogel dressings with automated mechanical arm applications, aiming to relieve the burden on caregivers and improve patient outcomes. Returning to patient and doctor communities for validation, we continued the cycle of feedback and iteration, demonstrating how RTPNR transforms social needs from static “reports” into drivers of innovation.

On the global scale, RTPNR provides a framework for addressing the broader challenges of synthetic biology: unifying scientific and social value, balancing innovation with ethics, and ensuring sustainability. Our project was closely aligned with multiple United Nations Sustainable Development Goals. It contributed to Goal 3 (Good Health and Well-being) by developing innovative wound care strategies; Goal 8 (Decent Work and Economic Growth) by fostering employment in biomaterials and medical technology; Goal 9 (Industry, Innovation, and Infrastructure) through cross-disciplinary innovation; Goal 10 (Reduced Inequalities) by addressing the needs of vulnerable groups such as the elderly and disabled; and Goal 14 (Life Below Water) by promoting sustainable use of marine-derived chitosan. By framing our work within RTPNR, we offered not only a practical solution but also a universally applicable paradigm to help future researchers and policymakers align technological advancement with ethical and environmental responsibilities.

For the iGEM community, RTPNR is our most important contribution. Unlike isolated surveys or one-time activities, it is a transferable and reproducible methodology that future teams can adopt to structure their Human Practices work. To facilitate adoption, we created a visual spiral ribbon diagram to illustrate the iterative logic of the framework, practical templates and guides for application, and detailed case studies demonstrating how the framework shaped our hydrogel dressing project.

Beyond methodology, we developed an educational game inspired by Flappy Bird to raise public awareness of diabetes. Its open-source code and resources are freely available for future teams to adapt for outreach and education. We also engaged actively with the iGEM community through events such as CCiC, the Four Provinces Exchange, the Northeast China Online Meeting, Synbiopunk 2025 in Beijing, and a joint session with OUC-China. These interactions allowed us to share insights in experimental methods, modeling strategies, Human Practices methodology, and ethical considerations, thereby enriching community knowledge and supporting collective progress. Through the Blue Dream · Haichuang Action initiative, we further bridged university, industry, and society, offering a replicable model of structured stakeholder engagement for other teams.

Through the proposal, application, and sharing of RTPNR, we made contributions on three levels: to society by bridging real-world needs with scientific innovation, to the global community by aligning research with ethics and sustainability, and to the iGEM community by providing reusable methodologies and educational resources. We believe that RTPNR is not only a tool for our project but a universal contribution to synthetic biology. It equips future iGEM teams with a structured, reflective, and iterative pathway to integrate science with society, helping us collectively move forward toward the shared goal of making the world a better place through synthetic biology.

In the 2025 competition, our team regarded education not as a one-time effort, but as a legacy to be passed on from generation to generation. To systematize our educational activities and promote synthetic biology, we originally proposed and developed the "Life Cycle of Fish" education model, which uses a vivid metaphor to illustrate the sustainability and progressive nature of education.

In this model, the seeding of fry symbolizes the starting point of public engagement with synthetic biology. We release these “fry” into diverse waters, enabling more people to encounter synthetic biology for the very first time. Gradually, they swim from the shallows into deeper waters, acquiring different layers of knowledge and skills throughout their learning journey, until they eventually grow into mature fish capable of swimming independently. Finally, these “fish” return to their original waters to spawn new eggs—just like educated individuals, who in turn become communicators and educators themselves, passing on the knowledge and passion for synthetic biology across generations. Through this model, we reached people of different ages and regions both domestically and internationally, ensuring the sustainability of our educational mission.

To put this model into practice, we developed context-specific lesson plans, tailored for middle school classrooms, community streets, and local enterprises. By designing content that fits different audiences and contexts, we ensured that synthetic biology education could serve as scientific inspiration, integrate into everyday life, and even connect with industrial development.

Middle School Classrooms By linking synthetic biology to daily life examples—such as sugar-free drinks, lab-grown meat, and glowing bacteria—we introduced students to the question “What is synthetic biology?”, sparking their curiosity and helping them realize that science is not confined to laboratories but is embedded in every corner of life.

Community Streets: By connecting with topics like food, environmental protection, and healthcare, and presenting them in simple, accessible language, we helped middle-aged and elderly groups understand synthetic biology as a practical force that improves daily life, rather than a distant, abstract science.

Local Enterprises: By addressing concerns related to health, sustainability, and industrial innovation, we guided young professionals to recognize how synthetic biology contributes to both individual well-being and green development, while also helping them envision new opportunities in the future of this field.

In designing these lessons, we incorporated Bybee’s 5E instructional model (Engagement, Exploration, Explanation, Elaboration, and Evaluation), as well as Erikson’s stages of psychosocial development. Together, these frameworks ensured that our activities not only stimulated curiosity and encouraged hands-on exploration, but also matched the psychological and cognitive characteristics of different age groups—achieving progressive, targeted, and human-centered education.

Building on this foundation, we also created an original science picture book, The Secret of Yeast, which introduces children and adolescents to the basics of microbiology and synthetic biology through vivid illustrations and engaging narratives. This not only diversifies our educational approaches but also makes complex scientific knowledge more accessible and easier to understand.

At the same time, we leveraged multiple social media platforms (WeChat, Xiaohongshu, Bilibili, and Weibo) to expand the reach of our educational efforts. Through these platforms, we engaged with more diverse audiences and, importantly, accumulated a large repository of teaching materials, videos, and visual resources. These materials remain available for future iGEM teams or educators to reuse or adapt, significantly lowering the threshold for running similar educational activities and strengthening the reproducibility of our contribution.

Building on this entire body of practice and theory, we compiled the Synthetic Biology Communication Handbook. This handbook provides a comprehensive framework, covering pedagogical philosophy, curriculum design, safety guidelines, and evaluation tools. It not only structured our own educational efforts, but also offers future iGEM teams and educators a resource that can be directly applied or further developed. In this way, education is no longer fragmented or temporary, but transformed into a systematic legacy that can be sustained, borrowed, and expanded.

Through these efforts, we not only achieved precise outreach to diverse groups, but also contributed to the iGEM community a reproducible, expandable, and sustainable set of educational models and resources. Future teams can build upon our work, adapt it to new contexts, and continue to innovate so that education becomes one of the most enduring legacies of the iGEM competition.