Project Overview

Our project aims to address the time-consuming nature of information retrieval and the inconsistent quality of data often encountered in synthetic biology experiments. The traditional plasmid synthesis process faces challenges such as lengthy database searches, scattered information, and a lack of integrated tools. To overcome these issues, we have developed an intelligent platform based on big data and artificial intelligence, designed to optimize experimental workflows and enhance research efficiency and the quality of results.

ChatParts

All the details are available in our software page

Forum for Collaboration

We have created an open iGEM forum for researchers, students, and enthusiasts in the field of synthetic biology. This platform allows users to share research findings, discuss technical challenges, and exchange competition experiences. Through this forum, we aim to attract more individuals to participate in discussions and exploration within synthetic biology, fostering a vibrant research community and driving the continued development of the field.

AI Chat

Data Collection and Processing

In traditional plasmid synthesis, researchers often face time-consuming database searches, dispersed data, and inconsistent quality. We have not only integrated the existing iGEM parts library but also collected a vast amount of data related to synthetic biology, covering key areas such as gene synthesis, metabolic engineering, and gene regulatory networks. This data has been rigorously filtered and processed to ensure high quality, accuracy, and usability, providing researchers with reliable references that reduce the time spent on data retrieval and organization, thereby improving experimental efficiency.

Open-source Model Code

Our AI model is not only developed to solve specific problems but also serves as an open-source platform that researchers and developers can freely use, modify, and optimize. This helps them to accelerate the validation and optimization of plasmid designs while providing opportunities for innovation based on our AI models. Additionally, we offer detailed documentation and technical support to ensure that anyone can easily get started, promoting the AI-driven research in synthetic biology.

Establishment and Promotion of Evaluation Standards

In the process of plasmid design and synthesis, predicting and resolving potential issues is crucial. We have developed rigorous evaluation standards to help researchers assess model performance in areas such as data processing, prediction accuracy, and experimental feasibility. These standards not only enhance the practical utility of models but also contribute to the collective progress of the field. We have made all evaluation standards open to the community, encouraging the sharing and iteration of these metrics, enabling researchers to continuously optimize their models and accelerate the application of research outcomes.

Social Impact

AI and Biosafety

We recognize the potential safety risks that the application of AI in biology may pose. Therefore, we have collaborated with other teams to organize a roundtable forum on the theme of “AI and Biosafety” inviting experts, researchers, and policymakers to discuss the biosafety concerns related to AI technologies. Additionally, we have authored the “AI and Biosafety” white paper, which helps establish safety guidelines in this field and raises public awareness about the safe use of AI technologies.

Science Education

To enhance the awareness of biosafety and environmental protection among young people, we have organized several educational outreach activities. These events focus on the dangers of invasive species, prevention strategies, and biodiversity conservation, using engaging and interactive formats to spark interest in young participants. These initiatives aim to increase their understanding of synthetic biology and its impact on the environment while fostering ecological protection awareness.

Community Feedback and Contribution

Throughout the development of our project, we have maintained close collaboration with external parties, actively incorporating feedback from the industry and giving back to the community with improved outcomes(details).

Industry Collaboration and Feedback Integration

Through our discussions with YuanGen, we reassessed the core strengths of our project, particularly in terms of improving model simulation and specialization. This has helped us refine our project direction, focusing more on integrating existing tools and innovating to meet the customized needs of users. Meanwhile, our partnership with ShengranWeian prompted us to strengthen data management and compliance, ensuring that our data processing strictly adheres to intellectual property regulations and avoids potential legal risks.

Giving Back to the Community

After incorporating valuable feedback, we not only enhanced the applicability of our models but also shared the improved results with the community through our open-source platform. High-quality datasets, model codes, and evaluation standards have been made available to researchers and developers, helping them to improve research efficiency and accuracy. Additionally, we provide innovative features and customized solutions to promote the broader application and adoption of synthetic biology technologies.

Through this two-way interaction, we ensure that our project continues to progress in terms of technology, legality, and application, while also actively contributing to the community, driving innovation and development in the field of synthetic biology.