From initial project scoping to validated biological results, CD Biosynsis executes every synthetic biology engagement through a rigorous, AI-accelerated Design-Build-Test-Learn pipeline. Discover what happens at each stage of your project journey.
Every engagement follows a structured methodology combining computational intelligence with empirical validation. Our framework is built on the internationally recognized Design-Build-Test-Learn cycle, augmented with proprietary AI tools that compress timelines and improve success rates.
AI models generate optimized sequences and pathway designs based on your performance goals and constraints.
Synthesize and clone designed constructs using automated high-throughput molecular biology platforms.
Execute high-throughput functional assays to measure performance against defined success criteria.
Feed experimental data back into AI models to refine predictions for the next design iteration.
Our project execution is organized into four sequential phases, each with defined deliverables, milestones, and quality checkpoints.
Week 1-2 — Defining goals, assessing feasibility, and establishing project parameters
Every project begins with a structured scoping phase. Our scientific team reviews your target molecule, desired performance specifications, and application environment. We assess technical feasibility, identify potential challenges, and define measurable success criteria before any experimental work begins. This phase includes comprehensive literature review, bioinformatics analysis of target sequences, and pathway assessment.
60-minute technical call to understand your goals, constraints, timeline, budget, and intellectual property requirements. NDA execution and data sharing agreements established.
Sequence analysis, structural prediction, literature mining, and pathway assessment. Identification of potential optimization targets and risk factors.
Detailed project proposal including experimental design, milestone schedule, deliverable list, pricing, IP terms, and quality assurance plan. Confirmed within 5 business days.
Receipt of starting materials, reference sequences, proprietary data, and any client-provided screening assays. Wet lab readiness initiated upon material confirmation.
Week 2-4 — Generating optimized designs through proprietary AI models and simulation
This is where computational biology meets artificial intelligence. Our platform leverages protein language models, pathway simulation tools, and generative AI to propose designs that would take traditional methods months to conceive. The output is a prioritized list of constructs ready for synthesis and testing.
Codon optimization for expression host, AI-guided mutational analysis, structural stability prediction, and functional annotation. Multiple sequence variants generated.
Metabolic pathway reconstruction, flux balance analysis, genetic circuit design and simulation. In silico performance prediction for all proposed constructs.
AI-ranked variant library of 50-500 candidates per design target. Machine learning models score each variant for predicted activity, stability, and expressibility.
Client review of computational results, construct selection, and approval before synthesis. Design rationale documentation prepared for regulatory submissions.
Week 3-12 — Synthesis, cloning, expression, and functional characterization
Our automated molecular biology platforms synthesize and test hundreds of designs in parallel. Every variant undergoes rigorous functional screening using assay systems validated against your specific performance criteria. This phase operates on the DBTL cycle internally, iterating within the project timeline as data accumulates.
Automated DNA synthesis, Gibson assembly, and transformation into expression hosts. Full sequence verification of all constructs via NGS or Sanger sequencing.
Expression in E. coli, yeast, or mammalian systems as appropriate. Small-scale purification and QC using SDS-PAGE, HPLC, and mass spectrometry.
Automated microtiter assay execution for activity, selectivity, stability, and kinetic parameters. Robotic liquid handling for consistent, reproducible screening data.
Detailed characterization of top candidates: kinetic analysis, stress tolerance profiling, and scale-up testing. Preparation of production-ready cell banks or protein lots.
Week 12-20 — Comprehensive data package, validated deliverables, and project handoff
The final phase delivers everything you need to reproduce and build upon our work. Complete documentation, raw and analyzed data, and full intellectual property transfer ensure you have everything required for downstream development, regulatory filing, or commercial deployment.
Full experimental records including raw data files, analyzed results, statistical reports, and AI model outputs. Format-ready for regulatory submissions and publication.
Final sequences, expression clones, protein preparations, and cell banks as specified in project agreement. All materials accompanied by Certificate of Analysis.
Detailed protocols, SOPs, and scale-up documentation. Training sessions available for client teams adopting our designs into their production workflows.
Post-project technical support period for implementation questions. Optional ongoing AI model refinement using client production data for continuous improvement.
Enginoma is built on deeply re-engineered industry benchmarks, performance-calibrated with proprietary datasets and augmented by closed-loop experimental feedback.
Protein language models predict the functional impact of mutations, identify hot-spot residues, and generate site-saturation variant libraries ranked by predicted activity improvement.
Structure-aware generative models design novel protein scaffolds and engineer existing proteins for enhanced stability, solubility, and novel functional properties.
Genome-scale metabolic models and pathway simulation tools identify optimal gene knockouts, knock-ins, and regulatory modifications for maximum product yield.
The defining feature of our platform is the closed-loop training pipeline. Every experimental result feeds back into model retraining, creating a continuously improving system that gets smarter with every project.
Enginoma baseline models are trained on curated protein knowledge bases and performance-calibrated on proprietary experimental datasets collected across hundreds of enzyme engineering projects—combining breadth of coverage with depth of prediction accuracy.
Bayesian optimization selects the next round of designs based on all prior experimental data. This means every experiment is maximally informative, reducing the total number of cycles required to reach target performance by up to 60% compared to random screening.
Our integrated platform combines proprietary AI models, validated assay systems, and automated laboratory infrastructure into a cohesive execution engine.
Transformer-based models trained on millions of protein sequences predict functional consequences of mutations and guide variant prioritization.
Probabilistic models coupled with active learning select the most informative experiments, minimizing cycles needed to reach performance targets.
Robotic liquid handling, microplate readers, and custom assay development enable screening of hundreds to thousands of variants per project in a single run.
All experimental data flows into a unified repository where it feeds AI model retraining, enabling continuous improvement across the entire project portfolio.
Every project concludes with a comprehensive package designed to be immediately useful for publication, regulatory submission, or commercial deployment.
Annotated final sequences in FASTA, GenBank, and electronic lab notebook formats, with complete cloning strategy documentation.
Complete screening datasets with statistical analysis, hit rankings, dose-response curves, and kinetic parameters for all lead candidates.
Computational predictions, variant rankings, design rationale reports, and confidence scores for all AI-generated candidates.
Expression clones, protein preparations, and cell banks as specified, all with Certificate of Analysis and full chain-of-custody documentation.
Detailed step-by-step protocols for all experimental procedures, validated for reproducibility in your laboratory environment.
Full assignment of intellectual property rights for all designed sequences and resulting data. Ready for patent filing or commercial licensing.
Traditional synthetic biology relies on trial-and-error iteration. Our AI-accelerated DBTL pipeline systematically converges on optimal solutions faster and more reliably.
| Capability | Traditional Labs | CD Biosynsis Pipeline |
|---|---|---|
| Library size screened per round | 100-1,000 variants | 500-10,000 variants |
| Sequence space explored | Limited by manual design | Millions computationally |
| Prediction accuracy (activity) | Empirical only | ML-guided, validated |
| Cross-project learning | Isolated project data | Portfolio-wide data reuse |
| Regulatory documentation | Manual compilation | Integrated QA system |
Schedule an initial consultation to discuss your goals. Our scientific team will assess feasibility and provide a detailed project proposal within 5 business days.
Tell us about your project and we'll get back within 24 hours.
