Week 3 HW: Lab Automation

1. Published Paper Using Opentrons for Novel Biological Applications

Paper Reference: “Semiautomated Production of Cell-Free Biosensors” (ACS Synthetic Biology, 2024)

Summary: This study demonstrates the use of the Opentrons OT-2 liquid handling platform to manufacture cell-free biosensor reactions with improved consistency and throughput compared to manual assembly. The researchers compared manual vs. semiautomated approaches for assembling fluoride-sensing biosensors, constructing an entire 384-well plate of reactions. The automation significantly reduced quality control issues and performance variability that typically plague manual biosensor assembly. The study validated that automated liquid handling could achieve detection outcomes close to expected performance while dramatically improving reproducibility and scaling potential.

Key Innovation: The paper demonstrates that automation can overcome major bottlenecks in biosensor development - specifically the variability and time constraints of manual liquid handling that limit both throughput and consistency in biosensor characterization.

2. Final Project Description: Automated PFAS Biosensor Development and Optimization

Project Goal

Develop and optimize engineered bacterial biosensors for detecting PFAS (per- and polyfluoroalkyl substances) in environmental water samples, with specific focus on Michigan’s contaminated waterways. The project will use automation to systematically optimize biosensor performance and establish reliable detection protocols.

Automation Workflow Overview

Phase 1: Biosensor Library Construction

  • Automated transformation of PFAS-responsive genetic circuits into E. coli chassis
  • Systematic construction of promoter-reporter combinations
  • Automated culture propagation and glycerol stock preparation

Phase 2: Screening and Optimization

# Pseudocode for PFAS concentration-response screening
def pfas_screening_protocol():
    # Prepare biosensor cultures
    for strain in biosensor_library:
        protocol.pick_colony(strain, growth_plate)
        protocol.inoculate(LB_media, overnight=True)
    
    # Set up concentration series
    pfas_concentrations = [0, 0.1, 1, 10, 100, 1000]  # ng/L
    
    # Automated screening
    for strain in biosensor_library:
        for pfas_conc in pfas_concentrations:
            well = protocol.get_next_well()
            protocol.transfer(strain_culture, well, 100_ul)
            protocol.transfer(pfas_solution[pfas_conc], well, 10_ul)
            protocol.mix(well, cycles=3)
    
    # Automated monitoring
    for timepoint in [1, 2, 4, 8, 12, 24]:  # hours
        protocol.read_plate(absorbance_595nm)
        protocol.read_plate(fluorescence_gfp)
        protocol.log_data(timepoint, well_id, strain, pfas_conc, signal)

    return optimization_data

Phase 3: Environmental Sample Testing

  • Automated serial dilution of water samples
  • Spiked recovery experiments for validation
  • Cross-reactivity testing with other environmental contaminants

Hardware Configuration

  • Primary Platform: Opentrons Flex with enhanced deck space for multiple plate types
  • Modules:
    • Heater-shaker for bacterial culture incubation
    • Plate reader for automated absorbance/fluorescence measurement
    • Magnetic module for cell washing steps
    • Temperature module for reagent stability
  • Custom Labware: 3D-printed holders for environmental sample vials

Expected Automation Benefits

  1. Reproducibility: Eliminate manual pipetting variability in multi-step protocols
  2. Throughput: Screen 10-20 biosensor variants against 6-8 PFAS concentrations simultaneously
  3. Time Efficiency: Reduce hands-on time from ~8 hours to ~1 hour per screening round
  4. Data Quality: Automated timing ensures consistent incubation periods and measurement intervals
  5. Scalability: Protocol easily adaptable for testing additional PFAS compounds or environmental matrices

Integration with Cloud Laboratory (Future Extension)

  • Use Ginkgo Nebula for automated design-build-test cycles
  • Automated DNA synthesis and cloning of optimized biosensor variants
  • High-throughput screening of next-generation biosensor designs
  • Machine learning integration for predictive biosensor optimization

Validation Metrics

  • Detection limit (target: <10 ng/L PFOA/PFOS)
  • Response time (target: <4 hours for field deployment)
  • Specificity (minimal cross-reactivity with structural analogs)
  • Stability (consistent response over 48-72 hours)

Real-World Application

The optimized biosensors would ultimately be incorporated into portable detection systems for field deployment across Michigan’s water monitoring network, enabling rapid screening that complements traditional analytical chemistry methods.

This automation-centered approach addresses the key challenge in biosensor development: the need for systematic, reproducible optimization across multiple variables (strain, conditions, analyte concentrations) while maintaining the throughput necessary for meaningful statistical analysis.