AI-Integrated DBTL Workflows

inquiry

Accelerate enzyme engineering through integrated Design-Build-Test-Learn optimization cycles.

Why DBTL?

The Design-Build-Test-Learn cycle is the fundamental engine of enzyme engineering. Each iteration generates knowledge that should inform the next, but traditional implementations fail to realize this potential:

1. Slow Iteration: Manual data transfer between experimental and computational teams creates bottlenecks. Weeks pass between design submission and validated results, with no parallel processing of next-generation designs.

2. Disconnected Stages: Design decisions are made without full access to prior round data. Computational models and experimental teams operate in silos, repeating mistakes and rediscovering insights.

3. Data Fragmentation: Screening results, kinetic data, and sequence information accumulate in disconnected formats. Historical projects provide no reusable training signal for future optimization.

AI-enhanced DBTL transforms this fragmented process into a continuous learning system:

Continuous Learning: Every experimental result updates predictive models in real time, progressively improving design accuracy across cycles
Smarter Iteration: Models identify which mutations succeeded, which failed, and why, transferring mechanistic insight across projects and enzyme families
Reduced Development Cycles: Parallel processing of design, build, and test stages compresses iteration time. Typical projects achieve target performance in 3–4 cycles rather than 8–12 under traditional workflows

Conventional Design-Build-Test-Learn (DBTL) Cycle vs. AI-Integrated DBTL Cycle

Conventional DBTL Cycle

AI-Integrated DBTL Cycle

AI-Integrated DBTL Platform

The platform operates as a closed feedback loop where each stage feeds the next without manual handoff delays:

AI-Integrated DBTL Platform

Design: Computational models propose mutations based on sequence, structure, and evolutionary analysis. Predictions incorporate all prior experimental data from the current project and related historical datasets.

Build: Variant libraries are synthesized with automated construct generation and sequence verification. Library composition is optimized to predicted hit density, avoiding oversynthesis of low-probability variants.

Test: Standardized assays measure target properties under process-relevant conditions. Screening prioritizes high-confidence designs while maintaining capacity for exploratory variants that challenge model boundaries.

Learn: Experimental results update ML model weights, refine hotspot definitions, and identify systematic prediction errors. The refined models immediately inform the next Design cycle, with no manual data transfer or analysis delay.

The feedback loop is the critical distinction. Traditional DBTL treats each cycle as an independent project; AI-integrated DBTL treats the entire campaign as a single continuous optimization with accumulating intelligence.

Workflow Integration

Computational Modeling

All design decisions are grounded in predictive models with explicit uncertainty estimates. Models are selected and combined based on enzyme family, target property, and available data. No black-box predictions are accepted without mechanistic justification.

Wet Lab Validation

Experimental stages operate under standardized protocols with full traceability. Results are captured in structured formats that feed directly into model updates, eliminating transcription errors and interpretation delays.

Iterative Optimization

Cycles proceed automatically until project targets are achieved or diminishing returns are detected. Progress metrics are reported continuously, with transparent communication of model confidence and prediction accuracy trends.

Application Areas

Enzyme Optimization

Multi-property enhancement of industrial biocatalysts through successive refinement of activity, stability, and expression

Synthetic Biology

Pathway enzyme engineering with coordinated optimization of kinetic parameters to balance flux and avoid metabolic bottlenecks

Biocatalyst Development

Conversion of wild-type or commercial enzymes into process-ready catalysts with manufacturability built into optimization targets

Integrated Wet Lab Support

Our AI-integrated DBTL workflows can be combined with experimental Design-Build-Test-Learn cycles, including library generation, recombinant protein expression, enzymatic characterization, high-throughput screening, and iterative optimization workflows for continuous enzyme engineering improvement.

FAQs

Q: How does AI improve DBTL efficiency?

A: AI compresses cycle time and improves per-cycle progress. Predictive models reduce the number of variants requiring experimental evaluation by 90% or more. Continuous learning means each cycle builds on the last, avoiding repeated mistakes and progressively converging on optimal solutions. Typical projects achieve target performance in 3–4 cycles versus 8–12 under traditional disconnected workflows.
Q: Can DBTL workflows support directed evolution?

A: Yes. Directed evolution is a specific application of DBTL where the Design stage emphasizes random or focused mutagenesis. Our platform supports both traditional directed evolution (with AI-enhanced screening and learning) and fully rational design (with ML-guided mutation selection). The workflow adapts to the project's starting point and optimization philosophy.
Q: Do you provide wet lab support?

A: Yes. The platform includes full experimental execution: expression construct generation, heterologous expression, purification, kinetic characterization, stability profiling, and analytical validation. Computational and experimental teams operate as an integrated unit with shared project management and continuous data flow.
Q: What happens if a project stalls after several cycles?

A: Stall detection is built into the platform. If progress metrics indicate diminishing returns, we analyze model predictions versus experimental outcomes to identify fundamental constraints: active-site geometry limits, stability ceilings, or expression bottlenecks. Transparent reporting enables informed decisions about target adjustment, alternative scaffold selection, or project conclusion.
Q: Can historical data from previous projects improve new campaigns?

A: Yes. The Learn stage accumulates knowledge across all platform projects. Enzyme family-specific models, mutation effect databases, and failure mode libraries transfer insight to new campaigns. Clients with proprietary historical data can contribute to customized models with appropriate confidentiality protections.
Q: What is the typical timeline for a complete DBTL campaign?

A: 4–6 months for standard projects with 3–4 cycles. Complex multi-property optimization or novel enzyme families may extend to 8–10 months. Expedited timelines with compressed cycle times are available for prioritized targets.

References:

Chen J, Singh N, Lu J, Lane ST, Zhao H. Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering. Current Opinion in Biotechnology. 2025;96:103380. doi:10.1016/j.copbio.2025.103380

First Name:

Last Name:

Email *

Phone Number:

Company/Institution:

Country or Region:

Quantity:

Services & Products of Interested *

Project Description:

For research and industrial use only. Not intended for personal medicinal use. Certain food-grade products are suitable for formulation development in food and related applications.

Services

Online Inquiry