PlantGPT-Syn: An LLM-Integrated Agent System for Plant Synthetic Biology
Breaking the bottlenecks of the DBTL cycle. Bridging the gap from in silico computational design to wet-lab validation through digitized expert cognitive profiling.
Expert QA Consistency
85% (↑25%)
Core Modules
8
Rice Biomass Maintenance
>95%
Framework Capabilities
Core Bioinformatics Pipeline
An integrated suite of deep-learning agents for end-to-end metabolic engineering.
KEGG Pathway Analysis
Systematic analysis of metabolic networks for mapping synthesis routes.
DLKcat Prediction
Deep-learning based enzyme turnover number (kcat) estimation across diverse species.
Molecular Docking
AutoDock Vina integration for high-throughput protein-ligand binding affinity prediction.
CRISPR Design
Precision guide RNA selection for target gene knockout or activation in plant genomes.
Localization
Predicting subcellular enzyme localization for pathway compartmentalization.
Vector Assembly
Automated modular assembly design for multicistronic plant expression vectors.
Codon Optimization
Host-specific codon adaptation to maximize translational efficiency in plants.
Flux Balance (FBA)
Predictive metabolic flux analysis using genome-scale metabolic models.
Experimental Validation
Case Studies & Wet-Lab Results
Secondary Metabolic Pathway Validation
Reliability of multiple validated secondary metabolic pathways through dry-wet closed loop validation.
- check_circle13-step reaction screening
- check_circle8 candidate enzymes validated
- check_circleSuccessful plastid localization
Rice FBA Validation
Genome-scale metabolic modeling using iOS2164 for metabolic flux accuracy.
- check_circleiOS2164 GSM model stability
- check_circleBiomass maintenance >95%
- check_circleCore metabolic flux prediction
The PlantGPT-Syn Web Application is Launching Soon.
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