Award
Bronze - All criteria have been met.
B1 Competition Deliverables
We have already submitted the Wiki and Judging Form, and we will submit the Presentation Video on time in early October. Additionally, we will attend the Jamboree in person in late October.
- Wiki
- Presentation Video（Submitted）
- Judging Form（Submitted）
- Judging Session（Attending in person）
B2 Project Attributions
We have clearly outlined the division of work and contributions of each team member, and have also acknowledged our advisors, as well as all the experts, entrepreneurs, and the companies and organizations that have provided us with support.
B3 Project Description
We developed a set of biosensors for detecting heavy metal ion pollution and validated their functionality. This is the description of our project.
B4 Contribution
We provided additional data for previous parts (BBa_K1758320) based on our high-throughput screening, offering a large collection of reference sequences and promoter performance data for future iGEM participants in promoter screening. Additionally, we explored the effects of different metal ions (copper, zinc, etc.) on E. coli growth and uploaded the related data.
Silver - All criteria have been met.
S1 Engineering Success
S2 Human Practices
Gold - The selected criteria have been met.
G1 Excellence in Synthetic Biology
New Composite Part
We utilize this part as a promoter responsive to copper ions to initiate the expression of downstream reporter genes. Furthermore, we have demonstrated with this example that endogenous promoters from Escherichia coli can function independently within E. coli without the need for additional expression of their corresponding transcriptional regulatory proteins; they can directly use the proteins already present in the genome.
Model
In this study, we employed machine learning (ML) techniques to analyze the relationship between sequences and responses using our experimental dataset of 88 samples. Our initial objective was to establish a regression model to predict fluorescence intensity based on sequence features. We implemented a feature extraction strategy focusing on k-mers, constructing datasets from various k-mer lengths. However, we found that using 3-mers and 4-mers as features did not yield sufficient predictive power.
In response, we shifted our approach by changing the prediction target to classify promoters into three categories based on fluorescence intensity: high, medium, and low. When utilizing the k-mers feature extraction method for this classification task, we still encountered limited success. Conversely, when we employed one-hot encoding for the promoter sequences, we achieved notable results, with our logit boost model reaching an impressive classification accuracy of 0.96 and an AUC greater than 0.9. This indicates that our refined approach significantly improved the model's ability to predict promoter activity based on sequence information, paving the way for more effective designs in synthetic biology applications.
G2 Specializations
- Integrated Human Practices
Human Practices played a crucial role in our project. We implemented Human Practices through the logic of the DBTL (Design-Build-Test-Learn) cycle, with HP and project design alternating and advancing in conjunction. The project continuously evolved towards practicality and responsible innovation under the guidance of stakeholders.