AI × SynBioDry-Wet Challenge
A global university challenge where AI design drives wet-lab validation and experiments close the model loop.
AI-SynBio connects AI with synthetic biology, taking ideas from computational design to experimental validation for real-world needs.
Two Tracks · Integrated Loop
The core difference from traditional SynBio competitions: projects are encouraged to open both AI and synthetic-biology tracks, with the DBTL loop as the highest aspiration.
AI / Computational
Machine learning and deep learning-driven biological research, from data to insight.
- ML/DL model development & application
- Biological data collection, analysis & visualization
- Algorithm & software tool development
- Fine-tuning LLMs for biology
Synthetic Biology
Standardized engineering biology, from design to validation.
- Standardized DNA parts design & assembly
- Gene circuits / metabolic pathway construction
- Engineered microbe building & testing
- Cell-free in-vitro systems
2026 Season Progress
Registration & Topic
Team registration, PI agreements, consent forms; brainstorming, literature and feasibility validation.
Proposal Defense
Present scientific merit & feasibility; submit ≥3 of: lay summary, schematic, logo, ≤2-min video, poster.
AI Technical Proposal
Submit AI Technical Proposal + preliminary Safety Form, describing AI methods, data sources and safety assessment.
Team Roster Freeze
No additions or removals thereafter; based on enrollment at the deadline.
Final Submission Bundle
Wiki / code / AI model / judging form / safety form — all items auto-lock.
Presentation Video Due
Submit the 15-minute presentation video for expert review and defense preparation.
Judging & Awards
Online/onsite defense (15-min talk + 10-min Q&A), finale roadshow, awards ceremony.
Research Directions
Medicine & Health
Drug design, antibody engineering, gene-therapy vectors.
Agriculture & Environment
Crop improvement, bioremediation, environmental monitoring.
Industrial Biomanufacturing
Metabolic engineering, enzyme engineering, fermentation optimization.
Data-driven Basic Science
Genomics, proteomics, non-coding RNA and related directions.
Virtual Cell
Cell modeling, network simulation, computable cell models.
What the Work Builds
Focused on project training, public presentation and reproducible delivery.
Project Training
Move through topic framing, design, validation and defense as one research project.
- Topic framing
- Method design
- Validation plan
- Teamwork
Public Presentation
Present process and results through Wiki, video, Expo and defense materials.
- Wiki
- Video
- Expo
- Defense deck
Reproducible Delivery
Leave code, data notes, experiment records and method documentation.
- Code repo
- Data notes
- Lab records
- Methods