Building a comprehensive Biological Knowledge Graph to predict adaptational traits and design optimized enzyme circuits.
Our Dry Lab processes raw biodiversity sequences into functional, design-ready components. By linking genomic databases with environmental factors and physical traits, our neural networks construct a highly detailed **Biological Knowledge Graph**.
Our modular model architecture comprises Large Language Models for hypothesis formulation, Protein Language Models for sequence mutations, DNA generative modules for regulatory circuit layout, and active learning engines to refine next-stage protocols.
Mapping relations: Species has Gene → Gene encodes Protein → Protein catalyzes reaction → Pathway causes Phenotype.
Detecting homolog functions in unstudied species to uncover novel enzymes, heat-resistant catalysts, and structural dyes.
Recombining genomic transcripts from disparate organisms to construct novel light-emitting pathways and metabolic functions.
Feeding experimental success and failure data back into our databases, maximizing information gain for subsequent design loops.