Week 3 HW: Lab Automation

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

I picked this paper by the Baker Lab because its definition of automation drew my attention. The paper developed what the authors refer to as a “partial automation instead of complex end-to-end robotics”, claiming that, although they did integrate robotic liquid handlers for some of the characterizations, the workflow should be widely adoptable to streamline workflows, even without the robotic liquid handlers. The argument that a many wet lab workflows are time, energy, and resource intensive resonated with my work.

Problem

Standard biochemistry workflows are the major bottleneck in de novo protein design.

Protein production and characterization are:

• Time-intensive
• Labor-intensive
• Expensive
• Poorly standardized

Iterative design–build–test cycles are slow, limiting throughput and innovation.

Solution: Semi-Automated Protein Production (SAPP)

• A rapid, modular, scalable, cost-effective, assay-agnostic workflow
• Enables end-to-end protein production and characterization in 48 hours
• ~6 hours active bench time
• Designed to be broadly adoptable across academic labs
• Standardized pipeline reduces variability and cost

Demonstration

• Applied to design and test de novo inhibitors of respiratory syncytial virus (RSV)
• Validated high-throughput screening of large design libraries
• Demonstrated robustness across multiple constructs and tagging formats

Major Workflow Components

1. Computational Design & DNA Preparation

• Software for automated plasmid design
• Sequence optimization using:
  • Codon Adaptation Index (CAI)
  • Suppression of alternative start sites
• Modular architecture enables vector swapping for downstream applications

2. Scalable Demultiplexing Protocol (DMX)

• Uses oligo pools as input DNA
• Enables arrayed, clonal recovery of individual constructs
• 1000 designs processed in parallel
• ~5-fold cost reduction
• ~$5 per construct (expression-ready format)

3. Cloning & Expression

• One-pot Golden Gate Assembly (GGA)
• Direct transformation into E. coli expression strain
• Modular tagging compatibility:
  • His-tag
  • Strep-tag
  • Avi-tag
  • NanoBiT
  • Halo-tag
  • MBP

Single design → multiple downstream functional assays.

Automation

Software-Level Automation

• Python scripting to automate plasmid design and sequence processing
• Automated construct annotation and tracking
• Programmatic primer and part design
• Standardized digital handoff between design and wet lab

Workflow Automation

• One-pot cloning reduces manual steps
• Standardized liquid handling–compatible format
• Modular plate-based layout enables robotics integration
• Minimal hands-on time (~6 hours)
• Scalable from tens to >1000 constructs

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.

Below is a protocol for a protein-agnostic directed evolution protocol:

Python: Generate library of starting proteins DMX: Generate genetic parts Python: Reverse translate Python: Codon optimize and remove stop codons Python: Design PCR primers DMX: Generate PCR primers Echo: Perform error-prone PCR Multiflo: dispense the PCR products to all wells containing competent cells to start protein expression. PlateLoc: seal plates. Inheco: incubate. Xpeel: remove seal. Identify variants with desired features (e.g. PHERAstar: measure and compare fluorescence ) Echo525: Amplify best-performing variants via PCR and use these as templates Repeat