Week 3: Lab Automation

Context
This page recasts my Week 3 assignment (Lab Automation) for Hugo. Instructions reflect the original course guidance for this week. :contentReference[oaicite:0]{index=0}

Recitation

• Recitation video: will be posted here. :contentReference[oaicite:1]{index=1}
• Slides: HTGAA Recitation — Opentrons Art, Lab Automation (view-only). :contentReference[oaicite:2]{index=2}

Homework

What to do

1. Pre-lab — Review the week’s materials and workflow. :contentReference[oaicite:4]{index=4}
2. Create your protocol — Write and test a Python routine in a Google Colab notebook. :contentReference[oaicite:5]{index=5}
3. Submit your completed protocol to your TA, and make sure you’ve booked a robot slot. :contentReference[oaicite:6]{index=6}
4. Book & file your submission using the links below.

Links you’ll need

• Submit your code (before lab time):
  https://docs.google.com/forms/d/1AyZBo6G3XbRdq5Yr_q50Ndw6_6_FQdb8Ee-5NXJk1Ko/viewform?edit_requested=true :contentReference[oaicite:7]{index=7}

• Sign up for a robot time slot:
  https://docs.google.com/spreadsheets/d/1F4Ncb6YWxrXFFmS8uzofGqIkHXJh0HDVLzGdp7FfTNI/edit?gid=0 :contentReference[oaicite:8]{index=8}

• Submit your Colab block (form):
  https://docs.google.com/forms/d/e/1FAIpQLScy0WLb-l3SnSzSFs8idvcPG7SgPpIRngpaPvbpsNZrVolWDQ/viewform :contentReference[oaicite:9]{index=9}

About the automation task

You’ll use an Opentrons OT-2–style Python protocol to automate mixing/dispensing steps and generate outputs you’ll report (see Deliverables). If you’re new to the platform, these references help:

• Opentrons docs hub (Protocol Designer + Python API). :contentReference[oaicite:10]{index=10}
• Protocol examples (API v2) for Flex/OT-2. :contentReference[oaicite:11]{index=11}
• Liquid handling commands (aspirate/dispense, mix, distribute). :contentReference[oaicite:12]{index=12}
• Google Colab (run and share notebooks). :contentReference[oaicite:13]{index=13}

Deliverables

Please include (or be prepared to report) the following with your submission:

• A shareable link to your Colab notebook cell that holds the metadata + code (ensure “Anyone with the link” is a Viewer). :contentReference[oaicite:14]{index=14}
• Simulation-derived totals:
  • Number of tips used by your protocol.
  • Volumes (µL) used per color channel: Red, Yellow, Green, Cyan, Blue. :contentReference[oaicite:15]{index=15}
• A short notes/reflection on any issues and fixes.

Post-lab questions (mandatory)

Use a few sentences/bullets for each:

1. What did automation let you do more precisely or reproducibly than manual pipetting?
2. Where did your protocol struggle (e.g., timing, tip use, residuals), and how would you improve it?
3. If you repeated this on the robot with new constraints (less time, fewer tips), what would you optimize first?
4. How might this automation approach accelerate your final project work? :contentReference[oaicite:16]{index=16}

Tips

• Keep commands simple and log outputs you’ll need for reporting (tip counts, per-color volumes).
• Prefer distribute/transfer helpers when appropriate; fall back to explicit aspirate/dispense for tricky steps. :contentReference[oaicite:17]{index=17}
• Test logic in Colab, then export the cell link for submission. :contentReference[oaicite:18]{index=18}

Pages in this week

• Week 3: Protocol Skeleton (Colab/OT-2)

  Prelude to this lab What this is A minimal, well-commented Opentrons Python API v2 protocol that mixes five dye “channels” (Red/Yellow/Green/Cyan/Blue) and logs the total consumables.