Wet Lab We established a breast cancer surveillance system with engineered adipocyte. To make cancer surveillance more accurate, real-time, long-term, and user-friendly, thereby maximizing patient safety and enhancing life quality, we set to develop a diagnostic system that integrates the following key advantages:
  • Multi-Marker Sensor: Accurate sensing to ensure reliability and safety.
  • In Vivo Detection: Long-term, real-time monitoring with rapid response.
  • Ex Vivo Output: Simple operation with clear, visible results.
Highlights of ABCS
We chose adipocytes as the chassis cell for cancer detection in vivo, since autologous transplantation of adipocyte tissues is a well-established practice in breast and plastic surgery. We identified RADAR system as an ideal technique platform to achieve our objective of multi-marker sensor with AND gate logic function, as RADAR system allows convenient design of sensor sequences and flexible choice of output reporters. We utilized Gaussia Luciferase (Gluc) as an output reporter—which produces a luminescent signal upon reaction with its substrate, to enable simple and visual detection of output signals (see Design for details). During the progress of our project development, we have established a complete workflow—from design and construction to validation—to build an in vivo multi-gene sensing system with ex vivo readout, which is adaptable for other iGEM teams that would like to use similar strategy. Adipocytes as Chassis to Detect Breast Cancer As a variety of literatures reported that cancer-associated adipocytes (CAA) are induced in tumor microenvironment of breast cancer, we have generated a novel idea of breast cancer detection: using engineered adipocytes to sense the changes of microenvironment that are caused by breast cancer cells. In addition, two main features of adipocytes make them ideal as chassis cell for in vivo detection of cancer. First, autologous transplantation of adipocyte tissues has been proven to be safe in breast as well as plastic surgeries, and the transplanted adipocytes have been shown to survive for a long time. Second, since mature adipocytes are postmitotic cells, accidental detrimental effects caused during genetic engineering are unlikely to cause catastrophic consequences. Therefore, we engineered mature adipocytes with AAV-mediated gene delivery, and the results showed that our RADAR-based genetic elements could be implanted successfully in these engineered adipocytes. Multi-Marker Detection by RADAR system We have constructed a RADAR sensor (BBa_25C7Z3LD) capable of simultaneously detecting PLOD2 and LIF—two genes specifically upregulated in CAAs. We completed the design, construction, and testing of this part, covering the entire process from biomarker screening and RNA design to functional validation. Finally, we successfully established a sensor that is activated only when both PLOD2 and LIF are present, therefore ensuring the accuracy of detecting tumor microenvironment of breast cancer.
  • Target Identification & Sensor Design: The optimal combination of biomarker genes of CAA (PLOD2 & LIF) was identified using an Attention-MLP model. RADAR sensors with specific responsiveness were constructed based on predicted RNA secondary structures. See Model for more details.
  • Delivery & Functional Validation: The sensor part was delivered into adipocytes alongside ADAR enzyme (BBa_2574CGF3). Following induced expression of PLOD2 and LIF, the conditioned medium from these adipocytes was collected and luminescence generated from output reporter was measured for functional validation. See Results for more details.
  • Part Contribution: Throughout this process, we designed and registered 20 new parts, expanding the iGEM BioBrick registry. See Parts for more details.
New parts in our project
Output strategy for convenient Ex Vivo detection We chose Gluc, a small secretory protein, as output reporter, which catalyze coelenterazine to emit luminescence. Since Gluc has been reported to be able to enter urine in vivo, it provides a none-invasive way of detection for application of our ABCS project in patients of breast cancer in future. Therefore, based on the results of our experiments, we provided a solution to the common challenge of "how to non-invasively retrieve information from engineered cells transplanted inside the body".
New ideas of our project
In summary, our strategy of "detecting upregulation of endogenous genes within engineered cells to probe changes in their surrounding physiological environment" breaks through the limitation of detection of exogenous biomarkers in classical diagnosis approaches. In addition, we provide the iGEM community with an integrated workflow covering cell engineering, sensor design and validation, and detection of output reporter, which empowers other iGEM teams to build upon our work and develop next-generation diagnostic tools for monitoring various pathogenic conditions, such as, chronic inflammation, fibrosis, etc. Dry Lab This year, our dry lab group applied computational methods into the evaluation of ethical responsibility innovatively. We created BEAM–BEAMer, a pioneering quantitative ethics assessment tool that combines rigorous mathematical model reasoning with an easy-to-use web application, transforming abstract ethical evaluation into quantitative analysis, which could also help other iGEM teams evaluate ethical risks of their own projects in a convenient and intuitive way. At the same time, we built DISCERN, a dry lab model that simulates the entire process of RADAR-based detection of target gene expression—from sensing the target genes, to expression and processing of the output signal protain. It gives us a complete framework and strong guidance of wet-lab experiments, while also serving as a safe and efficient alternative to animal experiments to some extent. BEAM–BEAMer: A Pioneering Tool for Quantitative Ethical Evaluation in Synthetic Biology BEAM (Bio-Ethical Assessment Model) BEAM is a methodological breakthrough, which, to our knowledge, is the first quantitative workflow designed to assess the ethical risks of iGEM projects. In today’s ethical assessment practice, we face challenges such as unclear evaluation criteria and difficulty in quantifying complex concerns. BEAM addresses these challenges by combining Structural Equation Modeling (SEM) to calculate the weights of ethical dimensions with Bayesian networks to convert these weights into predicted risk probabilities. In this way, BEAM shifts bioethics evaluation from qualitative assessment to quantitative analysis, establishes an adaptable framework, and provides a new practical approach for advancing the ethical aspects of synthetic biology.
BEAM–BEAMer Workflow: Model Construction and Application
BEAMer (Bio-ethical Assessment Software) To make the BEAM method easy to apply, we designed software called BEAMer, a web-based tool for calculating ethical risk assessments. BEAMer integrates structured data collection, automated Bayesian inference, and interpretable visual outputs into a seamless workflow. It offers iGEM teams a convenient software tool for performing ethical evaluations. Other iGEM teams can use BEAMer to conduct systematic risk assessments of their own projects, ensuring that ethical foresight becomes a part of their design cycle. (For source code of our software, please refer to the GitLab repository.)
Usage process of BEAMer software
New Dynamic Integration of Simulation for Cancer Evaluation and Recognition Model (DISCERN) To support the smooth implementation of wet-lab experiments and validate the efficacy of engineered adipocytes, we innovated an integrative approach utilizing multiple cutting-edge bio-informatics methodologies, successfully simulating the entire process from target gene recognition to reporter production and subsequent excretion into urine through kidney. See DISCERN for more details.
Framework
Addressing the biological limitations of traditional gene screening algorithms, we innovatively introduced deep learning algorithms to substantially improve conventional models. This enhancement not only precisely identified optimal target gene combinations but also endowed the model with robust biological interpretability, effectively resolving the "black box" problem inherent in many models. Subsequently, we further employed the iPKnot++ algorithm, capable of efficiently processing complex long-chain RNA structural information, to design targeted sensor RNA sequences and accurately predict their secondary structures, thereby assessing the accessibility of editing sites and providing a novel methodology for RNA-targeted design. To comprehensively elucidate the reporter secretion mechanism triggered by the RADAR system, we conducted intracellular dynamic simulation analyses, obtaining temporal concentration curves of reporter proteins that provided solid theoretical foundations for wet-lab experimental design. Importantly, whereas traditional wet-lab experiments are often confined to in vitro cellular studies or simplistic metabolic models and lack precise simulation of complex in vivo metabolic processes, we constructed, for the first time, a GLUC protein glomerular filtration barrier model to systematically simulate the complete process from glomerular filtration to urinary excretion of the reporter protein, and calculated the time required for GLUC accumulation in urine to reach detectable visualization levels based on the simulated sieving coefficients.
Structure of glomerular filtration membrane
Simulated process
This model provided robust theoretical underpinnings for wet-lab experimental design. Particularly in the context of time-constrained iGEM projects subject to ethical approval limitations, our model established an efficient and safe alternative to animal experimentation through novel computer-based simulation approaches, offering valuable reference frameworks for other iGEM teams requiring rapid exploration protocols. Education Our Education group didn’t just run outreach—we engineered education. By adopting digital technologies, we advanced educational equity and made learning more accessible. To lower barriers, we created the Virtual Laboratory, offering click-through experiments with step-by-step animations. The Bilingual Education Platform delivers Synthetic Biology in Seven Days in both English and Chinese, with tailored versions for kids, teens, and adults, bridging cultures and age groups. Notably, we developed an AI-assisted Feedback System that not only collected 1,617 responses across five dimensions but also transformed them into systematic evaluations of education quality. Building on this foundation of accessibility, we also created open resources that turn knowledge into lasting impact. The four Handbooks empower teachers, engage families, tackle public misconceptions, and guide frontier research, while the four Games transform abstract biology into hands-on play. Together, these resources form a ready-to-use toolkit that makes education sustainable and engaging for the iGEM community and beyond. Virtual Laboratory To give everyone the chance to practice synthetic biology online, we built the virtual laboratory—an interactive platform with click-through experiments. Users only need to select an experiment to access a full step-by-step animation, learning principles, instruments, and techniques in an intuitive way. We envision that, in the future, more modules will support users in conducting complete experimental workflows entirely online. Thus, we will open-source the platform code on the GitLab repository, enabling the iGEM community to expand it into a shared repository of virtual experiments.
Virtual Laboratory
Education Platform (Bilingual) Our Bilingual Education Platform is designed to lower barriers and bridge cultures, showing how synthetic biology can be taught across languages and age groups. It features our teaching material Synthetic Biology in Seven Days with video lessons in three age-tailored versions (Kids, Teens, Adults) that allows learners to enter the field step by step. By providing resources in both English and Chinese, the platform not only expands outreach from elementary schools to universities, but also gives the iGEM community a practical model for cross-cultural and inclusive science education.
Education Platform
Education Feedback System What we built? We developed a comprehensive framework to standardize and evaluate our educational activities. The system is built upon a feedback model designed to capture a comprehensive view of participants’ experience with multi dimensions: Comprehension, Practicality, Fun & Curiosity, Interactivity, Satisfaction. Across our events, we successfully collected and processed 1,617 valid responses, demonstrating the system’s capability to handle large-scale dataset.
Education Feedback System
How we analyzed? After collecting feedback data, we delivered it to Deep-Seek for analysis. By combining AI insights, we generated a chart across five dimensions, providing a systematic evaluation of our activities. For the iGEM community, AI tools make it much easier for busy teams to quickly turn large amounts of feedback into clear and meaningful insights about their education activities.
Radar Chart for Educational Activity Evaluation
What others can reuse (for iGEM teams)? We packaged our work into a reusable toolkit to empower future iGEM teams in their educational efforts. We provide:
  1. A universal Education activity feedback form, ready for immediate use:
    Education Feedback Form
  2. A step-by-step guide on using our AI-assisted feedback system for any team to efficiently process large-scale feedback and transform data into actionable insights: Instructions for AI-assisted feedback system
Four Handbooks To make synthetic biology accessible across classrooms, homes, and the wider community, we created four open-source handbooks that meet learners’ specific learning demands.
  • The Teachers’ Reference Book has already reached 13 schools and nearly 900 students, demonstrating that empowering educators multiplies education impact.
  • Parent-Child Learning Handbook provides interactive activities and simple at-home experiments, enabling families to learn together beyond events.
  • The Myth-Busting Synthetic Biology Handbook united 35 iGEM teams to confront one of the field’s biggest barriers: misconceptions.
  • The White Paper on Functional Nucleic Acids was co-created with six teams, lowering complexity and safety barriers to open exciting new frontiers.
Together, these four handbooks strengthen the iGEM community with resources that educate, connect, and inspire innovation. All resources are open—discover them on our Education Map - Sustainability page.
The Teachers’ Reference Book
Parent-Child Learning Handbook
Myth-Busting Synthetic Biology
White Paper on Functional Nucleic Acids
Four Games We turned science into games—and games into lasting knowledge. To make synthetic biology not just learned but lived, we designed four open-source educational games: Gene Voyage, Cellular Chess, AdipoAlert: Tumor Signal Strike, and Synthetic Biology Snakes & Ladders. Each game transforms abstract concepts into thrilling, hands-on play, sparking curiosity while cementing key ideas. Together, they offer the iGEM community and educators a ready-made toolkit to teach science with excitement, strategy, and fun. All resources are open—discover them now on our Education Game page.
Gene Voyage Contribution
Cellular Chess Contribution
AdipoAlert Contribution
Synthetic Biology Snakes & Ladders Contribution
Integrated Human Practice Throughout the year, our Integrated Human Practices engaged deeply with a broad range of stakeholders to ensure our project remained both scientifically sound and socially relevant. In parallel, we carried out extensive public engagement through multi-community interviews, online and offline communications with breast cancer patients, and field study at the Yanbian CDC, ultimately reaching thousands of people and making sure that the voices of those most affected were directly reflected in our project.
Map of Stakeholders
As a result of this comprehensive effort, we developed a set of transferable frameworks and practical toolkits. These resources were designed not only to strengthen our own project, but also built a paradigm for the iGEM community, uniting stakeholder voices with responsibility to create projects that make a positive effect to the world. Consideration of Stakeholders Our Human Practices were integrated into every stage of the project to ensure responsibility and real-world relevance. We interviewed 25 stakeholders across 16 fields, including clinicians in breast surgery, plastic surgery, and oncology, as well as experts in IVD, investment, entrepreneurship, mathematics, chemistry, sociology, philosophy, ethics, law, and medical aesthetics. We also engaged 58 breast cancer patients through online and offline interviews, assessing the feasibility of our project directly from their perspective. By involving stakeholders in each step—from design to feedback—we established a complete feedback loop that guided our decisions and strengthened our outcomes. This comprehensive approach demonstrates our commitment to iGEM’s values of responsibility, and integrity. Systematic Theoretical Frameworks To ensure that stakeholders’ perspectives remained embedded at every step, we developed three complementary engagement models:
  • SCQAI Framework: Structuring every dialogue into Situation, Conflict, Question, Answer, and Implementation, ensuring purposeful interviews and actionable outcomes. This framework led us from identifying public needs to targeting recurrence monitoring in clinical care, and ultimately to engineering adipose cells as our core solution.
  • Dual-Track Framework: Keeping scientific experimentation and social needs evolving in parallel. This enabled us to capture community perspectives while refining experiments with expert input, steadily strengthening both human responsiveness and technical rigor.
  • Concentric Stakeholder Engagement Framework: Mapping stakeholders by values and interests to tailor engagement strategies. Using this approach, we expanded beyond breast cancer patients to also include women with breast implants, broadening the project’s reach and relevance.
World Responsibility – Practical Toolkits Our commitment to responsibility at a global level produced four open-sourced frameworks and toolkits, addressing ethics, commercialization, and public health communication: Ethics through Digital Tools: The AI-Powered Ethical Screening Procedure provides a reproducible method to simulate and refine interview questions, removing distressing content and standardizing respectful dialogues with vulnerable groups. The BEAM model translates participant concerns into structured, analyzable data, shifting ethical evaluation from abstract debate to measurable, evidence-based practice. Commercialization for Real-World Impact: By systematically applying entrepreneurial tools such as the Business Canvas, Brand Strategy House, Entrepreneurship Sandbox, and a comprehensive Business Plan, we built a practical commercialization pathway. This toolkit empowers iGEM teams to assess feasibility, plan sustainable ventures, and amplify societal benefits. Please click here to learn more. Public Health Communication: Based on community interviews, we produced a Breast Health Awareness Brochure covering early detection, self-examination, breast protection, and male breast cancer. It not only met immediate public needs but also fostered long-term social impact by raising awareness, encouraging prevention, and stimulating broader dialogue.