Week 3 HW: Lab Automation

Week 3: Lab Automation

Student: Constantin Convalexius
Course: HTGAA Spring 2026
Location: Vienna, Austria

Part 1: Python Script for Opentrons Artwork

I created and tested an Opentrons Python script that generates a dotted skull design for gel art.

1.1 What I completed

  • Designed a skull artwork concept and implemented it in Python for Opentrons (apiLevel 2.20).
  • Used multi-color patterning with helper functions for safer droplet detachment (dispense_and_detach).
  • Simulated the protocol in Colab and fixed simulator compatibility issues (e.g., replacing direct protocol.comment calls with mock-safe logging logic).
  • Generated a higher-resolution version of the skull by increasing point density.

Submission status

  • Artwork script: completed.
  • Opentrons skull design image: completed.
  • I will submit the Python script for robot execution as required by the course submission form.

1.2 Proof of Opentrons skull artwork

Opentrons dotted skull proof Opentrons dotted skull proof

Part 2: Post-Lab Questions

2.1 Published paper using Opentrons/automation for novel biology

Paper selected:
Herzog AE, Zheng S, Warner KA, Vanini JV, Somayaji R, Johnson MR, et al.
“Bmi-1 inhibition sensitizes head and neck cancer stem cells to cytotoxic chemotherapy.”
Translational Oncology. 2026;63:102603. doi:10.1016/j.tranon.2025.102603.

Why this paper is a strong example of lab automation

This study uses automation directly in a cancer-biology workflow, including an Opentrons OT-2 liquid handling robot to standardize and scale an automated orosphere assay in 96-well plates. The authors investigate whether inhibiting Bmi-1 (genetically and pharmacologically with PTC596/unesbulin) can reduce chemotherapy-driven cancer stemness in head and neck squamous cell carcinoma (HNSCC).

Main findings (concise)

  • Platinum chemotherapies (cisplatin/carboplatin) increase stem-like tumor cell populations.
  • Bmi-1 inhibition blocks this chemotherapy-induced stemness increase.
  • Bmi-1 inhibition reduces self-renewal readouts (orosphere formation).
  • Bmi-1 inhibition suppresses protective DNA-damage response signaling and IL-6R/STAT3 pathway activation.
  • In vivo xenograft data supports combining Bmi-1 inhibition with conventional chemotherapy.

Why this is biologically novel and relevant

The key innovation is not only biological (targeting cancer stemness to overcome resistance) but also methodological: integrating an affordable, programmable OT-2 into a translational cancer workflow enables reproducible treatment delivery and phenotyping at scale. This demonstrates how benchtop automation can move from “pipetting convenience” to hypothesis-driven oncology research.

2.2 What I intend to do with automation tools for my final project

My project direction is to use automation for a small combinatorial therapeutic screen focused on therapy resistance biology.

Proposed project concept

Automate a matrix experiment testing combinations of:

  • Cytotoxic drug condition (e.g., cisplatin dose levels),
  • Pathway-modulating small molecule condition (e.g., Bmi-1/STAT3-related perturbation),
  • Optional timing condition (simultaneous vs. staggered treatment).

The readout would be a plate-based viability/survival proxy and, if feasible, a stemness-related assay endpoint.

Why automation is essential

  • Precise liquid handling across many conditions and replicates.
  • Lower human pipetting variability.
  • Easier reproducibility for repeated screens.
  • Structured experimental logs that support downstream analysis.

Planned automation workflow (high-level pseudocode)

load_labware()
load_reagents()
seed_plate_cells()

for condition in treatment_matrix:
    distribute_drugs(condition)

incubate_for_defined_time()
add_assay_reagents()
collect_measurements()
analyze_condition_vs_control()

Potential hardware/software components

  • Opentrons OT-2 for treatment dispensing.
  • Python protocol files for condition mapping and transfer plans.
  • Optional custom 3D-printed holder to stabilize specialized plate formats if needed.
  • Optional cloud-lab extension for higher-throughput follow-up experiments.

AI Disclosure

I used AI tools to assist with coding, debugging, and documentation:

  • Cursor assistant for protocol debugging, formatting, and writeup polishing.
  • AI coding assistance in Colab/Cursor to refine Opentrons script structure and simulation compatibility.

All final scientific framing, selection of paper, and project direction were reviewed and approved by me.