Synthetic Biology Platform Services

The DBTL cycle is the foundational framework of synthetic biology. Design involves creating genetic constructs in silico; Build refers to physically constructing those constructs; Test involves measuring the performance of built constructs; and Learn uses the experimental data to inform the next round of design. Our platform supports all four phases with integrated tools and automation.

Our platform leverages ML in multiple ways. During the Design phase, ML models predict construct performance to guide optimization. During the Learn phase, algorithms analyze experimental data to identify patterns and recommend design improvements. We use Gaussian Process Regression for small datasets and neural networks for sequence-function prediction.

We support a wide range of host organisms including E. coli, yeast (Saccharomyces cerevisiae, Pichia pastoris), Bacillus subtilis, and various mammalian cell lines. Our platform also supports non-model organisms with custom optimization workflows.

Our platform offers multiple screening modalities. FACS-based screening can analyze millions of cells per experiment. Droplet microfluidics enables ultra-high-throughput screening in picoliter-scale droplets. Plate-based screening provides flexible format options for various assay types.

Yes, our platform fully supports SBOL (Synthetic Biology Open Language) and SBML (Systems Biology Markup Language) standards. This ensures compatibility with other synthetic biology tools and facilitates data sharing and collaboration.

Our platform is designed for flexibility. We offer both full-service project execution and modular integration with existing workflows. API access and standardized data formats enable seamless integration with laboratory information management systems and external tools.

We support projects ranging from single construct validation to large-scale library construction and screening. Our flexible service tiers are designed to accommodate academic research projects, early-stage biotech, and established pharmaceutical companies.

Quality assurance is built into every step of our workflow. We implement rigorous QC at each phase, standardized protocols, automated documentation, and comprehensive data packages. Our ML models are trained on high-quality datasets and validated against independent test sets.