Since its establishment in 1973, synthetic biology has developed rapidly. In 1973, the expression of exogenous plasmids was achieved; in 2002, the first bacteria genome was synthesized; and in 2017, the simplest artificially synthesized life form and the fifth chromosome of yeast were created. Over 50 years, the creation of tens of thousands of microscopic components has brought infinite creativity to synthetic biology and is gradually changing our world. Against the backdrop of significant achievements in synthetic biology, the impact of iGEM cannot be ignored: up to the 21st century, thousands of teams have contributed more than 75,000 components and 4,300 biological projects.

In 2023, when NJU-China participated in the Jamboree, a judge asked them a question, "Over the years, iGEMers have contributed tens of thousands of synthetic biology components. Why didn't your team use them?" This inspired the team to consider what 20 years of iGEM research accumulation can actually bring us.

Before the project was initiated, the Human Practices (HP) team began exploring these issues. We found that the majority of biologists interviewed believed that the discoveries and outcomes of synthetic biology were difficult to fully utilize, the data were mixed and hard to integrate, and the large number of components made it even harder to combine them. A significant portion of scholars with a biological background did not consider synthetic biology to be crucial to life sciences.

Based on this, we distilled three core issues:

1. The component data in synthetic biology is disorganized

2. The complexity of the components makes it difficult for individuals to combine and innovate

3. These high barriers limit the dissemination of the synthetic biology concept and its potential value

Reviewing the history of life sciences, each breakthrough has been accompanied by the birth of new tools. Microscopes propelled the development of microbiology, and sequencing technologies advanced the field of omics. We realized that with the development of information technology and artificial intelligence, we can integrate decades of accumulated history of synthetic biology into a new tool, making it easier for everyone to use.

Based on this, we have formulated the core objective of our project:

Our project aims to revolutionize the field of synthetic biology by developing an advanced AI model that is accessible to everyone. The goal is to empower individuals, regardless of their background or expertise, to harness the potential of synthetic biology and become beneficiaries of this cutting-edge science. By leveraging the capabilities of artificial intelligence, we seek to simplify and democratize synthetic biology, making it a universally accessible tool for innovation and problem-solving.

In summary, our project aims to achieve the following four objectives:

Accessibility: Create an intuitive and user-friendly platform that enables users to engage with synthetic biology effortlessly. This platform will provide resources, tutorials, and support to guide users through the complexities of synthetic biology.

Education and Outreach: Develop comprehensive educational materials and interactive modules to educate a wide audience about the principles and applications of synthetic biology. Our aim is to inspire and train the next generation of synthetic biologists.

Collaboration and Community: Foster a collaborative environment where users can share their projects, ideas, and insights. By building a vibrant community, we aim to accelerate innovation and collective problem-solving.

Ethical and Sustainable Practices: Promote ethical considerations and sustainable practices in synthetic biology. Our project will emphasize responsible use and encourage users to develop solutions that address global challenges such as healthcare, agriculture, and environmental sustainability.

The 2024 NJU-CHINA team used a knowledge graph as the foundational database architecture, leveraging a large language model to facilitate the entire process—from raw data cleaning and database information retrieval to natural language dialogue with users. We created Prometheus, a novice-friendly, professional-grade synthetic biology language model that promotes synthetic biology by assisting users in retrieving components, constructing plasmids, and guiding biological experimental design.

Our project has four features:

AI-Driven Design: Utilize advanced AI algorithms to assist users in designing and optimizing synthetic biological systems. The AI model will provide recommendations, simulate outcomes, and suggest improvements based on user input.

User-Friendly Interface: Develop a seamless and intuitive interface that caters to both novices and experts. Users will have access to a range of tools and resources tailored to their level of expertise.

Extensive Database: Build and maintain a comprehensive database of genetic parts, protocols, and case studies. This repository will serve as a valuable resource for users to explore and reference.

Interactive Tutorials and Support: Offer interactive tutorials, step-by-step guides, and real-time support to help users navigate the platform and achieve their goals in synthetic biology.

By democratizing synthetic biology, our project aims to unlock the creative potential of individuals and communities worldwide. We envision a future where anyone, from students to professionals, can contribute to advancements in medicine, agriculture, and environmental conservation. Through this initiative, we aspire to make synthetic biology an inclusive and transformative force for good.

Join us on this exciting journey to make synthetic biology accessible to all and empower a new wave of innovation and discovery. Together, we can create a better, more sustainable future through the power of synthetic biology.

Looking ahead, NJU-China will continue to promote research and popularization of synthetic biology, bridging the gap between researchers and young people to foster technological innovation and social progress. We anticipate that the Prometheus model will be used by more experts, life science students, youth, and even the general public in the future. By incorporating feedback from the experiments it guides, as well as insights from professionals and the community, we aim to continuously refine the model and make a greater contribution to the advancement of synthetic biology.

Cohen, S. N., Chang, A. C., Boyer, H. W., & Helling, R. B. (1973). Construction of biologically functional bacterial plasmids in vitro. Proceedings of the National Academy of Sciences of the United States of America, 70(11), 3240-3244.

Venter, J. C., Glass, J. I., Hutchison, C. A. 3rd, & Vashee, S. (2022). Synthetic chromosomes, genomes, viruses and cells. Cell, 185(15), 2708-2724.

El Karoui, M., Hoyos-Flight, M., & Fletcher, L. (2019). Future trends in synthetic biology - A report. Frontiers in Bioengineering and Biotechnology, 7, 175.

Endy, D. (2005). Foundations for engineering biology. Nature, 438(7067), 449-453.

Shapira, P., Kwon, S., & Youtie, J. (2017). Tracking the emergence of synthetic biology. Scientometrics, 112(3), 1439-1469.

Google. (2012). A knowledge-based approach to search. In Proceedings of the 21st International Conference on World Wide Web.

Brown, T., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.

Touvron, H., et al. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint.

Bender, E. M., et al. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.