Week 3 HW: Lab automation

Week 3 Lab automation: Opentrons artwork;

  Abstract: Laboratory experiments and follow of protocols can lead to errors, thus in wrong assumptions. Automating lab work not only saves precious time, but also ensures quality of information delivered.

I:

  1. I used rcdonovan’s site to iterate a design. Instead of manually creating the image I chose to import it from google and then make as many changes as I desired. Shown image is Aphex Twin’s, a famous British musician (“pioneering figure”), logo. Image copied from wikipedia.
face1 face1

used colors: mkate2; electra2; mturquoise2.

face2 face2

  1. After a really long time, I managed to make the google colab copy code run and output Aphex Twin’s logo as theoretical Opentrons hardware operation on petri dish. opentron.google-colab

  2. A.I. usage was involved in my multiple attempts to understand essential code found in Opentrons Colab, and to later verify and fix code impediments that stopped it from running.

I submitted my code to Google Gemini, including shown error code and asked for explanations to why it wasn’t working properly and modified version of code. (the main problem I had to overcome was using the color library:

To fix the error, we need to synchronize the naming. The visualization tool (Matplotlib) doesn’t recognize protein names like “Electra2,” so we must use standard color names (like “lime”, “cyan”, etc.) in both the well_colors mapping and the all_designs data keys. Here is the fully rewritten, working code. I have mapped each protein to its closest visual color. code Key Improvements Made: • Synchronized Dictionaries: The keys in all_designs now perfectly match the values in well_colors. This prevents the ValueError: No well found… error. • Matplotlib Compatibility: By using colors like “lime” and “crimson”, the protocol.visualize() function can now render the pattern without crashing. • Logic Preserved: The protocol still aspirates in $20 \mu L$ chunks and drops the tip between colors to prevent cross-contamination. Would you like me to help you split the “lime” protein coordinates across two wells to ensure the robot doesn’t run out of liquid?

II:

from opentrons import types

metadata = {
    'author': 'Casian Veselin',
    'protocolName': 'Aphex Twin logo ',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

# --- Robot Deck Setup ---
TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

# Mapping each protein set to a specific well on the 96-aluminum block
# Updated mapping using standard Matplotlib colors
well_colors = {
    'A1': '#0077ff',  # Electra2 (Blue)
    'A2': '#4b0082',  # mKate2 (Deep Purple)
    'C1': '#00ffff'   # mTurquoise2 (Cyan)
}

def run(protocol):
    # --- Load Labware ---
    tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')
    pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])

    temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
    color_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 'Cold Plate')

    agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')
    center_location = agar_plate['A1'].top()
    pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

    # --- Data ---
    # Grouping all point sets into a dictionary for clean iteration
    all_designs = {
     '#4b0082': [(-9.9, 38.5),(-7.7, 38.5),(-5.5, 38.5),(-3.3, 38.5),(-1.1, 38.5),(1.1, 38.5),(3.3, 38.5),(5.5, 38.5),(7.7, 38.5),(9.9, 38.5),(-14.3, 36.3),(-12.1, 36.3),(-9.9, 36.3),(-7.7, 36.3),(-5.5, 36.3),(-3.3, 36.3),(-1.1, 36.3),(1.1, 36.3),(3.3, 36.3),(5.5, 36.3),(7.7, 36.3),(9.9, 36.3),(12.1, 36.3),(14.3, 36.3),(16.5, 36.3),(-16.5, 34.1),(-14.3, 34.1),(-12.1, 34.1),(-9.9, 34.1),(-7.7, 34.1),(-5.5, 34.1),(-3.3, 34.1),(-1.1, 34.1),(1.1, 34.1),(3.3, 34.1),(5.5, 34.1),(7.7, 34.1),(9.9, 34.1),(12.1, 34.1),(14.3, 34.1),(16.5, 34.1),(18.7, 34.1),(20.9, 34.1),(-20.9, 31.9),(-18.7, 31.9),(-16.5, 31.9),(16.5, 31.9),(18.7, 31.9),(20.9, 31.9),(23.1, 31.9),(-25.3, 29.7),(-23.1, 29.7),(-20.9, 29.7),(20.9, 29.7),(23.1, 29.7),(25.3, 29.7),(-27.5, 27.5),(-25.3, 27.5),(-23.1, 27.5),(23.1, 27.5),(25.3, 27.5),(27.5, 27.5),(-29.7, 25.3),(-27.5, 25.3),(-25.3, 25.3),(25.3, 25.3),(27.5, 25.3),(29.7, 25.3),(-31.9, 23.1),(-29.7, 23.1),(-27.5, 23.1),(27.5, 23.1),(29.7, 23.1),(31.9, 23.1),(-34.1, 20.9),(-31.9, 20.9),(-29.7, 20.9),(29.7, 20.9),(31.9, 20.9),(34.1, 20.9),(-34.1, 18.7),(-31.9, 18.7),(-25.3, 18.7),(-23.1, 18.7),(-20.9, 18.7),(-18.7, 18.7),(-16.5, 18.7),(-14.3, 18.7),(-12.1, 18.7),(-9.9, 18.7),(-7.7, 18.7),(-5.5, 18.7),(-3.3, 18.7),(-1.1, 18.7),(1.1, 18.7),(3.3, 18.7),(5.5, 18.7),(7.7, 18.7),(9.9, 18.7),(31.9, 18.7),(34.1, 18.7),(-34.1, 16.5),(-31.9, 16.5),(-27.5, 16.5),(-25.3, 16.5),(-23.1, 16.5),(-20.9, 16.5),(-18.7, 16.5),(-16.5, 16.5),(-14.3, 16.5),(-12.1, 16.5),(-9.9, 16.5),(-7.7, 16.5),(-5.5, 16.5),(-3.3, 16.5),(-1.1, 16.5),(1.1, 16.5),(3.3, 16.5),(5.5, 16.5),(7.7, 16.5),(9.9, 16.5),(31.9, 16.5),(34.1, 16.5),(36.3, 16.5),(-34.1, 14.3),(-20.9, 14.3),(-18.7, 14.3),(-16.5, 14.3),(-14.3, 14.3),(-12.1, 14.3),(-9.9, 14.3),(-3.3, 14.3),(-1.1, 14.3),(1.1, 14.3),(3.3, 14.3),(5.5, 14.3),(7.7, 14.3),(9.9, 14.3),(12.1, 14.3),(34.1, 14.3),(36.3, 14.3),(-34.1, 12.1),(-25.3, 12.1),(-16.5, 12.1),(-14.3, 12.1),(-12.1, 12.1),(-3.3, 12.1),(-1.1, 12.1),(1.1, 12.1),(3.3, 12.1),(5.5, 12.1),(7.7, 12.1),(9.9, 12.1),(12.1, 12.1),(34.1, 12.1),(36.3, 12.1),(-34.1, 9.9),(-5.5, 9.9),(-3.3, 9.9),(-1.1, 9.9),(1.1, 9.9),(3.3, 9.9),(5.5, 9.9),(7.7, 9.9),(9.9, 9.9),(12.1, 9.9),(14.3, 9.9),(34.1, 9.9),(36.3, 9.9),(38.5, 9.9),(-36.3, 7.7),(-34.1, 7.7),(-7.7, 7.7),(-5.5, 7.7),(-3.3, 7.7),(-1.1, 7.7),(1.1, 7.7),(3.3, 7.7),(5.5, 7.7),(7.7, 7.7),(9.9, 7.7),(12.1, 7.7),(14.3, 7.7),(34.1, 7.7),(36.3, 7.7),(38.5, 7.7),(-36.3, 5.5),(-34.1, 5.5),(-7.7, 5.5),(-5.5, 5.5),(-3.3, 5.5),(-1.1, 5.5),(5.5, 5.5),(7.7, 5.5),(9.9, 5.5),(12.1, 5.5),(14.3, 5.5),(16.5, 5.5),(34.1, 5.5),(36.3, 5.5),(38.5, 5.5),(-36.3, 3.3),(-34.1, 3.3),(-9.9, 3.3),(-7.7, 3.3),(-5.5, 3.3),(-3.3, 3.3),(-1.1, 3.3),(7.7, 3.3),(9.9, 3.3),(12.1, 3.3),(14.3, 3.3),(16.5, 3.3),(34.1, 3.3),(36.3, 3.3),(38.5, 3.3),(-36.3, 1.1),(-34.1, 1.1),(-9.9, 1.1),(-7.7, 1.1),(-5.5, 1.1),(-3.3, 1.1),(-1.1, 1.1),(9.9, 1.1),(12.1, 1.1),(14.3, 1.1),(16.5, 1.1),(18.7, 1.1),(34.1, 1.1),(36.3, 1.1),(38.5, 1.1),(-38.5, -1.1),(-36.3, -1.1),(-34.1, -1.1),(-9.9, -1.1),(-7.7, -1.1),(-5.5, -1.1),(-3.3, -1.1),(-1.1, -1.1),(9.9, -1.1),(12.1, -1.1),(14.3, -1.1),(16.5, -1.1),(18.7, -1.1),(34.1, -1.1),(36.3, -1.1),(38.5, -1.1),(-36.3, -3.3),(-34.1, -3.3),(-9.9, -3.3),(-7.7, -3.3),(-5.5, -3.3),(-3.3, -3.3),(-1.1, -3.3),(12.1, -3.3),(14.3, -3.3),(16.5, -3.3),(18.7, -3.3),(34.1, -3.3),(36.3, -3.3),(38.5, -3.3),(-36.3, -5.5),(-34.1, -5.5),(-12.1, -5.5),(-9.9, -5.5),(-7.7, -5.5),(12.1, -5.5),(14.3, -5.5),(16.5, -5.5),(18.7, -5.5),(20.9, -5.5),(34.1, -5.5),(36.3, -5.5),(38.5, -5.5),(-36.3, -7.7),(-34.1, -7.7),(-14.3, -7.7),(-12.1, -7.7),(-9.9, -7.7),(12.1, -7.7),(14.3, -7.7),(16.5, -7.7),(18.7, -7.7),(20.9, -7.7),(34.1, -7.7),(36.3, -7.7),(38.5, -7.7),(-36.3, -9.9),(-34.1, -9.9),(-14.3, -9.9),(-12.1, -9.9),(14.3, -9.9),(16.5, -9.9),(18.7, -9.9),(20.9, -9.9),(34.1, -9.9),(36.3, -9.9),(38.5, -9.9),(-36.3, -12.1),(-34.1, -12.1),(-16.5, -12.1),(-14.3, -12.1),(-12.1, -12.1),(16.5, -12.1),(18.7, -12.1),(20.9, -12.1),(23.1, -12.1),(34.1, -12.1),(36.3, -12.1),(-36.3, -14.3),(-34.1, -14.3),(18.7, -14.3),(20.9, -14.3),(23.1, -14.3),(25.3, -14.3),(34.1, -14.3),(36.3, -14.3),(-34.1, -16.5),(-31.9, -16.5),(18.7, -16.5),(20.9, -16.5),(23.1, -16.5),(25.3, -16.5),(31.9, -16.5),(34.1, -16.5),(36.3, -16.5),(-34.1, -18.7),(-31.9, -18.7),(20.9, -18.7),(23.1, -18.7),(25.3, -18.7),(31.9, -18.7),(34.1, -18.7),(-34.1, -20.9),(-31.9, -20.9),(-29.7, -20.9),(29.7, -20.9),(31.9, -20.9),(-31.9, -23.1),(-29.7, -23.1),(-27.5, -23.1),(27.5, -23.1),(29.7, -23.1),(-29.7, -25.3),(-27.5, -25.3),(-25.3, -25.3),(25.3, -25.3),(27.5, -25.3),(29.7, -25.3),(-27.5, -27.5),(-25.3, -27.5),(-23.1, -27.5),(23.1, 27.5),(25.3, -27.5),(-25.3, -29.7),(-23.1, -29.7),(-20.9, -29.7),(20.9, -29.7),(23.1, -29.7),(-23.1, -31.9),(-20.9, -31.9),(-18.7, -31.9),(-16.5, -31.9),(16.5, -31.9),(18.7, -31.9),(20.9, -31.9),(-20.9, -34.1),(-18.7, -34.1),(-16.5, -34.1),(-14.3, -34.1),(-12.1, -34.1),(-9.9, -34.1),(-7.7, -34.1),(-5.5, -34.1),(-3.3, -34.1),(-1.1, -34.1),(1.1, -34.1),(3.3, -34.1),(5.5, -34.1),(7.7, -34.1),(9.9, -34.1),(12.1, -34.1),(14.3, -34.1),(16.5, -34.1),(18.7, -34.1),(20.9, -34.1),(-9.9, -36.3),(-7.7, -36.3),(-5.5, -36.3),(-3.3, -36.3),(-1.1, -36.3),(1.1, -36.3),(3.3, -36.3),(5.5, -36.3),(7.7, -36.3),(9.9, -36.3),(12.1, -36.3),(14.3, -36.3),(16.5, -36.3),(-9.9, -38.5),(-7.7, -38.5),(-5.5, -38.5),(7.7, -38.5)],
     '#0077ff': [(-16.5, 36.3),(-20.9, 34.1),(-23.1, 31.9),(-36.3, 16.5),(-36.3, 14.3),(-27.5, 14.3),(-25.3, 14.3),(-36.3, 12.1),(-23.1, 12.1),(-20.9, 12.1),(-18.7, 12.1),(-38.5, 9.9),(-38.5, 7.7),(-38.5, 5.5),(-38.5, 3.3),(-38.5, -3.3),(-38.5, -5.5),(-38.5, -7.7),(-38.5, -9.9),(-36.3, -16.5),(34.1, -20.9),(31.9, -23.1),(27.5, -27.5),(25.3, -29.7),(23.1, -31.9),(-16.5, -36.3),(-14.3, -36.3),(-12.1, -36.3),(-3.3, -38.5),(-1.1, -38.5),(1.1, -38.5),(3.3, -38.5),(5.5, -38.5),(9.9, -38.5)],
     '#00ffff': [(-18.7, 34.1),(-23.1, 14.3),(-36.3, 9.9),(-38.5, 1.1)]
    }

    # --- Helper Functions ---
    def location_of_color(color_name):
        for well, protein in well_colors.items():
            if protein.lower() == color_name.lower():
                return color_plate[well]
        raise ValueError(f"No well found for {color_name}")

    def dispense_and_detach(pipette, volume, location):
        assert(isinstance(volume, (int, float)))
        above_location = location.move(types.Point(z=location.point.z + 5))
        pipette.move_to(above_location)
        pipette.dispense(volume, location)
        pipette.move_to(above_location)

    # --- Main Execution ---
    for protein_name, points in all_designs.items():
        if not points:
            continue

        pipette_20ul.pick_up_tip()

        for i, (x, y) in enumerate(points):
            # Aspirate every 20 drops (full pipette capacity)
            if i % 20 == 0:
                volume_needed = min(20, len(points) - i)
                pipette_20ul.aspirate(volume_needed, location_of_color(protein_name))

            target_location = center_location.move(types.Point(x=x, y=y))
            dispense_and_detach(pipette_20ul, 1, target_location)

        # Drop tip after completing one protein color to prevent mixing
        pipette_20ul.drop_tip()
face3 face3

code generated image

II:

1.Published paper review

scientific paper

This paper explores enhancing lipid production in plant cells through automated, high-throughput genome engineering and phenotyping, focusing on maize (Zea mays) and Nicotiana benthamiana. Rather than industrial manufacturing, the research develops a conceptual framework for accelerating biological Design-Build-Test-Learn (DBTL) cycles to improve the yield of lipid macromolecules. The system measures editing efficiency by targeting the photosynthetic gene HCF136; its knockout produces distinct changes in chlorophyll fluorescence intensity, serving as a high-throughput proxy for successful genomic modification and subsequent analysis of lipid metabolic pathways.

The study utilizes the iBioFAB biofoundry, an automated platform integrated with Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry (MALDI-MS) to expedite genome characterization. These biofoundries operate as specialized informatics-driven workstations that combine robotics with high-throughput instrumentation to scale repetitive protocols. By automating the transition from genome editing to cellular effect characterization, the system reduces human error and significantly optimizes the time required to identify high-yielding lipid variants, transforming traditionally manual plant engineering into a scalable, iterative process.

2. Final individual project idea: