Week 3: Opentrons

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

The paper I’ve chosen is AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots by John A. Bryant Jr., Mason Kellinger, Cameron Longmire, Ryan Miller, and R. Clay Wright.

Published in Synthetic Biology (Volume 8, Issue 1, 2023), the paper presents a new open-source script for the Opentrons OT-2 robot called “AssemblyTron.” The paper overviews automation in the context of synthetic biology’s repeating workflow (the DBTL cycle) and argues that experimental progress is often constrained by the labor and tacit expertise required to carry out repetitive, error-prone bench work. These standards are crucial for reliable experimentation, but the paper suggests automation can help by:

Reducing opportunities for human pipetting error and improving consistency

Lowering the hands-on training burden for repetitive liquid-handling steps

Minimizing time, cost, and waste

The authors describe using AssemblyTron to streamline DNA assembly/cloning workflows on the OT-2, including automating PCR fragment preparation and the setup of multipart DNA assembly reactions from designed DNA parts/fragments. The discussion concludes by emphasizing the potential for lowering barriers and increasing accessibility in synthetic biology experimentation, along with future directions for improving and extending the script.

Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.

One idea that I could explore with automation regarding the Living Scaffold Final Project I suggested in Week 1 would be automated “parameter sweeps” for mapping purposes. Automation would let me treat the living scaffold as a controllable, testable system: running repeatable parameter sweeps, collecting data, and potentially implementing closed-loop control of light and media conditions to map oxygen distribution. Basically what I’m suggesting here is to run the same experiment across many conditions without human variability, to see how oxygen generation/distribution changes with different inputs:

Different light cycles (intensity, pulsing, duty cycle)

Different media compositions (nutrients, buffering, additives that affect scaffold properties)

Different co-culture ratios (photosynthetic layer density vs scaffold thickness)

Different geometry variants (channels/porosity patterns)