Program Structure

Module 1: Why High-Throughput Automation Matters in Biotechnology

• High-throughput biotech domains: SynBio, enzyme engineering, strain screening, diagnostics, and bioprocess R&D.
• Manual vs automated workflows: throughput, variability, reproducibility, and cost tradeoffs.
• DBTL cycle: where automation reduces cycle time and improves learning.
• Defining success: throughput, error rate, CV reduction, and decision turnaround time.

Module 2: Core Hardware in Automated Biotech Labs

• Liquid handling basics: pipetting accuracy/precision concepts and common deck components.
• Plate formats: 96/384/1536 well logic and assay miniaturization tradeoffs.
• Key instruments overview: plate readers, incubators, shakers, thermocyclers, centrifuges (conceptual).
• Robotics: colony pickers, tube decappers/cappers, barcode scanners, and robotic arms for lab logistics.

Module 3: Workflow Engineering and Assay Automation

• Turning assays into automation-ready steps: standardization, timing, and mixing control.
• Reagent handling: dead volume planning, evaporation risk, and contamination control logic.
• Sample tracking: barcodes, plate maps, chain-of-custody, and audit trails.
• Automation constraints: carryover, edge effects, and temperature sensitivity (overview).

Module 4: Scheduling, Orchestration, and Throughput Optimization

• Orchestration concepts: queueing, parallelization, and instrument utilization planning.
• Bottleneck identification: which steps limit throughput and how to redesign flows.
• Error handling: retries, fallbacks, and exception workflows.
• Run design: batch size, controls placement, and plate randomization to reduce bias.

Module 5: Quality Control, Calibration, and Reliability

• Calibration concepts: liquid handler verification, instrument baselines, and drift detection.
• QC metrics: CVs, Z’-factor (high-level), controls performance, and acceptance criteria.
• Preventive maintenance planning: consumables, tips, seals, sensors, and routine checks.
• Data integrity: timestamping, versioning, and consistent metadata capture.

Module 6: Data Systems—LIMS/ELN and Automated Data Pipelines

• LIMS/ELN roles: sample registration, traceability, and compliance-friendly documentation.
• Data types: raw reads, processed metrics, QC flags, and run summaries.
• Pipeline design: automatic ingestion, validation, and storage of high-throughput results.
• Dashboards: throughput, failure modes, and performance monitoring for operations.

Module 7: High-Throughput Applications (Use-Case Tracks)

• Enzyme screening: activity assays and variant libraries (conceptual workflow).
• Strain screening: growth/product assays, micro-fermentation, and multi-parameter selection (overview).
• Diagnostics workflows: sample-to-answer automation, plate-based testing, and QC integration.
• Cell culture automation: feeding schedules, imaging concepts, and contamination risk control.

Module 8: AI/ML and Closed-Loop Automation (DBTL at Scale)

• Active learning concepts: choosing the next experiment based on prior results.
• Bayesian optimization intuition: optimizing conditions with fewer experiments.
• Closed-loop DBTL: design suggestions → robot execution → data capture → model update.
• Validation mindset: baselines, uncertainty, and avoiding automation-driven bias.

Module 9: Safety, Compliance, and Responsible Lab Automation

• Biosafety awareness: containment mindset, access control, and safe waste handling concepts.
• Contamination control: clean workflows, segregation, and decontamination planning.
• Compliance-ready documentation: SOP logic, training records, and audit trails (overview).
• Ethics and governance: responsible use, transparency, and reliability expectations.

Final Project

• Create a High-Throughput Biotechnologies Automation Blueprint for a selected application.
• Include: use-case goal, instrument stack, workflow steps, scheduling plan, QC strategy, data architecture (LIMS/ELN), and KPIs.
• Example projects: automated enzyme variant screening pipeline, micro-fermentation strain screening workflow, automated diagnostic plate processing system, or a closed-loop DBTL automation concept with AI-driven experiment selection.