Gold Medal
Best Model
Our modeling work goes beyond technical design, integrating biological interpretability, mathematical simulations, and multi-scale system optimization to provide a comprehensive foundation for our project. From biomarker identification to RNA sensor design and physiological-level simulations, our modeling pipeline not only supported feasibility but also created novel perspectives for non-invasive cancer monitoring.
Criterion
Description
Link
Excellence in Synthetic Biology
Impress the judges with your work towards three Special Prizes of your choice. You must demonstrate excellence in both General Biological Engineering and in at least one Specialization. Note: Software and AI Village teams cannot select the Software Special Prize as a Gold medal criterion. By competition rules, teams in the Software and AI Village are not eligible to win the Software Special Prize.
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Through systematic literature research, we identified four candidate genes (FAM3C, PLOD2, LIF, IL6) associated with breast cancer microenvironment regulation. Using an Attention-MLP model, we evaluated all possible combinations of these genes. The model balanced predictive performance and interpretability, ultimately identifying PLOD2+LIF as the optimal gene combination (AUC=0.89). This provided a reliable basis for downstream sensor design.
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Based on the RADAR system, we designed synthetic RNA sensors targeting PLOD2 and LIF mRNAs. Using advanced algorithms such as iPKnot++, we systematically assessed 12 candidate RNA sequences. From these, we identified Seq C as the optimal construct with both high structural stability and good accessibility of the ADAR editing site, laying the foundation for functional validation.
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To predict sensor behavior in living cells, we established ordinary differential equation (ODE)-based simulations of RADAR reactions. These simulations revealed time and concentration-dependent expression curves of reporter genes, providing insights into reaction kinetics, system responsiveness, and detection thresholds. By simulating both single-input and dual-input systems, we optimized sensor responsiveness under in vivo conditions.
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A key innovation of our modeling lies in extending the system to the glomerular filtration barrier (GFB), bridging intracellular RNA editing events to non-invasive clinical detection. Specifically, we modeled how the reporter Gaussia luciferase (Gluc, 19.9 kDa) would be filtered into urine. The simulation quantified its sieving coefficient (SC) and predicted the detection latency. This multi-scale approach ensures that our sensing system is not only functional at the cellular level but also translatable to a practical, physiological readout.
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By combining deep-learning assisted biomarker discovery, RNA structural modeling, reaction dynamics, and GFB-based physiological simulations, we constructed a full pipeline from gene to clinical readout. This comprehensive framework demonstrates not only technical rigor but also biological relevance, offering a generalizable strategy for live-cell cancer monitoring and inspiring new applications of synthetic biology in non-invasive disease detection.
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Broaden: We established an Education Map covering 9 provinces, 23 schools, 2791 participants and other 4 countries, including rural and special education schools. To extend reach beyond classrooms, we maintained active communication on 5 social media platforms and organized public-facing activities, embedding synthetic biology into both cultural and digital spaces.
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Deepen: To move beyond one-way teaching, we developed two interactive learning tools—a Virtual Laboratory for simulating molecular and cell biology experiments, and an Education Platform supporting both self-learning and classroom teaching. We also designed multi-themed activities linking synthetic biology with sports, music and ethics, alongside 4 games (Gene Voyage, Cellular Chess, AdipoAlert: Tumor Signal Strike, Snakes & Ladders) that encouraged playful yet critical engagement, enabling participants to learn in creative contexts.
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Iterate: We adopted a system of real-time feedback and AI-assisted analysis across 5 dimensions—including interactivity and satisfaction. With 1617 responses analyzed, we identified both strengths and areas for improvement. This allowed us to refine activity design continuously, ensuring our programs remained effective, engaging, and accessible to diverse groups of learners.
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Sustain: To preserve educational value beyond individual events, we created 4 practical handbooks, as well as Synthetic Biology in Seven Days for students and Teachers’ Reference book: All-ages Synthetic Biology for teachers. These openly available materials empower students and educators to carry forward synthetic biology independently, leaving a durable impact long after initial participation.
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We developed three proprietary theoretical frameworks (SCQAI, Dual-Track Framework, and the Concentric Stakeholder Engagement Framework) to ensure that our IHP work was strategic, systematic, and aligned with both scientific goals and societal needs, thereby laying a rigorous and replicable foundation for IHP execution.
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Through extensive community interviews and stakeholder engagement, we engaged with 98 members of the public (53 breast cancer patients) to gain insight into their concerns and practical needs. We started from real pain points and centered the project on addressing the unmet needs of the public, rather than pure technical exploration.
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We established a “Concentric Stakeholder Framework” to integrate insights from 25 stakeholders across 16 distinct professional domains, including academic experts, clinicians, lawyer, and project managers from pharmaceutical enterprises. With continuous feedback, ABCS’s features have been iteratively improved.
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Against the backdrop of insufficient ethical tools in synthetic biology, we created the BEAM Ethical Risk Assessment Model through expert interviews and public surveys to ensure responsible innovation.
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We created an “AI-Powered Ethical Screening Procedure” to assist our team in crafting interviews that are both sensitive and impactful when working with vulnerable groups, like breast cancer patients. By using Doubao AI to simulate patient interviews, we identified sensitive questions and refined phrasing to ensure ethical dialogue.
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After interviewing experts from various fields related to commercialization, we developed the “Lab-to-Market Commercialization Toolkit”—which includes the Business Canvas, Brand Strategy House, iGEM Entrepreneurship Practice Simulation, and a Comprehensive Business Plan—to address the challenge of translating lab innovations into real-world applications.
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What we built is not just a technical solution, but a dynamic “demand – feedback – improvement loop” that connects science with people. Each interview, and field research feeds into an ongoing cycle of listening, refining, and applying—growing with every voice we hear and every challenge we face.
Silver Medal
For Engineering Success
iGEM requires all teams to complete the Engineering cycle of Design-Build-Test-Learn on their own projects. We have completed 4 DBLT Engineering cycle to identify biomarkers, design sensor sequence, explore reaction kinetics and ethical assessment. We identify biomarkers with Attention-MLP modeling and verified expression of PLOD2 and LIF via experiments. Based on it, we design our sensor sequence and build whole RADAR system in adipocyte to detect PLOD2 and LIF. To verify our building, we test it by fluorescence microscopy imaging and luminescence measurement under both cytokine and MDA-MB-231 conditioned medium tratement. We built dynamics model of Gluc and test it in wet-lab to provide theoretical and data support for Gluc's detection. Finally we built and tested BEAM to deal with ethical chellenges of our project. In each engineering cycle, we observed and analyze result so that it can promote the next engineering cycle, thereby achieve our project.
For Human Practices
We ensured our project is responsible by actively engaging with diverse stakeholders, including breast surgeons, plastic surgeons, ethics experts, biotech industry professionals, legal advisors, and breast cancer patients. Through this broad consultation process, we refined our project both scientifically and ethically, ensuring that it addresses the real needs of medical professionals and patients alike. By integrating public feedback, scientific advances, ethical principles, and a detailed business plan, we deliver an innovative and responsible approach to breast cancer surveillance that ultimately enhances patient care.
Bronze Medal
Criterion
Description
Link
Engineering Success
Demonstrate engineering success in a technical aspect of your project by going through at least one iteration of the engineering design cycle.
Find our engineering page here.
Human Practices
Explain how you have determined your work is responsible and good for the world.
Find our human practices page here.
Bronze Medal
Criterion
Description
Link
Competition Deliverables
Complete the following Competition Deliverables: Wiki, Presentation Video, Judging Form, and Judging Session.
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We have designed and submitted our wiki.
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Our presentation video will be handed in after the wiki freeze on the iGEM uploads platform.
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Find our judging form.
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We are looking forward to the judging session.
Project Attributions
Describe what work your team members did, as well as what other people did for your project, using the standardized Project Attributions Form.
Find our attributions form here.
