AI-Driven Discovery Platform
Extreme Environment Bio-Resource Database
Since 2003, our database has been built upon long-term microbial sampling from extreme environments worldwide, including deep-sea ecosystems, hydrothermal vents, and polar glaciers. Through continuous expansion and systematic curation, it has grown into a large-scale proprietary resource containing over 1TB of high-quality enzyme gene and sequence data.
Built on decades of extreme-environment exploration since 2003, our proprietary database integrates long-term microbial sampling from deep-sea ecosystems, hydrothermal vents, and polar glaciers. Through continuous expansion and rigorous curation, it has evolved into a large-scale, high-quality data foundation that powers AI-driven enzyme discovery and predictive biomanufacturing design.
SiyoScreen™ Intelligent Enzyme Discovery System
SiyoScreen™ is a proprietary AI-powered enzyme screening platform developed in-house to enable efficient and predictable selection from large-scale enzyme gene datasets.
Built on advanced sequence-based learning architectures, the platform captures latent relationships between enzyme structure and function while integrating multi-dimensional performance metrics—including activity, stability, and substrate compatibility—into a unified decision framework.
By leveraging SiyoScreen™, millions of candidate enzyme sequences can be computationally narrowed down to a small set of top-performing leads, dramatically reducing experimental screening burden and accelerating the transition from data-driven prediction to experimental validation.
SiyoCell™ Virtual Cell & Metabolic Modeling Platform
SiyoCell™ is a proprietary virtual cell modeling platform developed in-house, integrating computational simulation, metabolic network modeling, and multi-omics analysis within a systems biology framework.
The platform systematically captures interactions between heterologous biosynthetic pathways and host cellular metabolism, providing deep insight into the regulation of key elemental cycles, including carbon, nitrogen, and sulfur.
Powered by AI-driven analysis and prediction, SiyoCell™ navigates complex intracellular networks to identify metabolic bottlenecks and repressed flux nodes that extend beyond established pathway knowledge and conventional design paradigms, enabling rational redesign and rebalancing of intracellular fluxes to support high-efficiency pathway engineering.
