Week 3 HW: Lab Automation

Opentron Art

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Post-Lab Questions

Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

Summary

This study introduces Pyhamilton, an open-source Python framework that enables flexible programming of liquid-handling robots for high-throughput biological experimentation. Unlike traditional robotic automation, which merely replicates hand-pipetting protocols, Pyhamilton allows for dynamic decision-making, asynchronous execution, and real-time feedback integration.

The authors demonstrate several novel applications:

  • Complex liquid transfer patterns to simulate population dynamics.

  • Real-time feedback-controlled turbidostats maintaining hundreds of bacterial cultures in log-phase growth.

  • Automated metabolic fitness landscape mapping across 100 nutrient conditions in triplicate.

  • Integration with plate readers to dynamically adjust media replacement based on optical density measurements.

Notably, the system enables maintenance of up to 480 parallel cultures with real-time monitoring and feedback control, transforming static protocols into adaptive experimental systems. The paper illustrates how automation becomes transformative when paired with programmable control logic, data-driven feedback, and asynchronous task execution, enabling experiments impossible to perform manually.

Citation

Chory EJ, Gretton DW, DeBenedictis EA, Esvelt KM. Enabling high-throughput biology with flexible open-source automation. Mol Syst Biol (2021).

Write a description about what you intend to do with automation tools for your final project.

Project Title: Automated Combinatorial Optimization of Programmable Host Cell Circuits for Viral Vector Manufacturing

What I Intend to Automate

The goal is to automate the tuning and validation of a programmable host-cell control circuit designed to dynamically regulate viral vector production. The automation workflow will focus on:

  • Combinatorial helper plasmid ratio optimization

  • Promoter and regulatory element tuning

  • Viral yield vs cell viability quantification

  • Iterative design–build–test cycles

Automated Workflow Overview

  1. Construct Assembly & Preparation
  • Use Opentrons to assemble combinatorial promoter/RBS variants.
  • Prepare helper plasmid ratio matrices.
  • Generate condition libraries across 96-well format.
  1. Transfection Optimization Matrix
  • Variable plasmid concentration gradients
  • Helper gene ratio permutations
  • Timing-dependent transfection panels
  1. Automated Assay Execution
  • Dispense transfection mixes
  • Transfer media
  • Sample supernatant for viral quantification
  • Perform viability assays
  1. Measurement Integration
  • Reporter-based viral production proxy
  • Cell viability (fluorescence / luminescence)
  • Growth curves
for condition in design_matrix:
    assemble_transfection_mix(condition)
    dispense_to_plate(condition.well)
    incubate()

    viral_signal = measure_fluorescence(condition.well)
    viability = measure_viability(condition.well)

    record_results(condition, viral_signal, viability)

optimize_parameters(results)
generate_next_iteration()

This automation framework transforms viral vector manufacturing optimization from static parameter tuning into a programmable, feedback-driven engineering process aligned with scalable synthetic biology platforms.