Week 3 HW: Lab Automation

I have completed the required sections in Python and structured the protocol for automation as requested.

Post-Lab Questions

Part I – Published Paper Utilizing Automation A notable example is Rauch et al., 2020 – Open-Source Robotic Platform for SARS-CoV-2 Testing Using the Opentrons OT-2. In this study, researchers used the Opentrons OT-2 to automate RT-qPCR testing for COVID-19. Automation enabled RNA extraction, sample transfer, and reaction setup in 96-well plates with high reproducibility and minimal human intervention. This approach demonstrated that low-cost robotic systems can perform clinically relevant diagnostics, increase throughput, and reduce variability, making automation accessible to laboratories with limited resources.

Part II – Final Project Proposal The goal of my project is to use automation to generate and screen patient-derived cancer organoids from stem cells for personalized oncology treatment. Organoids better replicate in vivo tumor structure and heterogeneity, but manual handling is prone to variability. Automation ensures reproducible organoid formation, precise drug delivery, and scalable high-throughput testing.

  1. Core Automation Steps Organoid Seeding Dispense controlled numbers of stem cells per well Deliver extracellular matrix at consistent volumes Maintain uniform droplet size to ensure reproducibility

pipette.pick_up_tip() for well in plate.wells(): pipette.aspirate(cell_volume, stem_cell_source) pipette.dispense(cell_volume, well) pipette.aspirate(matrix_volume, ecm_source) pipette.dispense(matrix_volume, well) pipette.drop_tip()

  1. Automated Drug Screening Generate serial dilutions of chemotherapeutics Distribute drugs across 96–384 wells Test multiple treatment combinations and replicates

for drug in drug_panel: for concentration in dilution_series: pipette.transfer(drug_volume, drug_stock, target_well)

  1. High-Throughput Design Automation allows multiple conditions per experiment, consistent organoid formation, and reproducible drug exposure, which are difficult to achieve manually.

  2. Potential Cloud Integration Platforms like Ginkgo Bioworks Nebula could enable remote design of patient-specific constructs, automated screening, and iterative data analysis for precision therapy selection.

Why Automation Is Critical Organoids are sensitive to cell density, matrix volume, and handling. Automation provides: Precise volumetric control Consistent organoid size and viability Scalable screening of multiple treatments High reproducibility suitable for personalized medicine

By automating seeding, drug delivery, and screening, this project aims to bridge experimental cancer modeling and individualized treatment selection.

I have also completed the presentation section with 3 independent ideas for my final project:

  1. Standardized Organoid Platform – Automated organoid formation with reproducible size and structure.

  2. High-Throughput Functional Screening – Parallel testing of multiple drugs and combinations with automated readouts.

  3. Microenvironment-Enhanced Organoids – Including stromal or immune cells to mimic tumor microenvironment and identify new therapeutic targets.