Week 03 — Opentrons: Automation Art + Post-Lab Questions

Part 1 — Automation Art (OT-2 “printing” a design)

This week I designed a microscope icon as “automation art” and converted it into a grid of XY dot coordinates that can be dispensed by the Opentrons OT-2 onto an agar plate.

1) Design → coordinate map

I started from the course Automation Art Interface, which makes it easy to draw a dot pattern on a circular “canvas.”

2) Convert the pattern into points + sanity-check in Python

To avoid trial-and-error on the robot, I used a Colab notebook to:

convert pixels/dots → (x, y) coordinate lists
preview the design as a scatter plot
separate two colors (main shape vs highlights)

Colab notebook:
https://colab.research.google.com/drive/1tLENS2Rs0mxdN-pJp5QNfm1K6dfg9xsS?usp=sharing

The preview below shows the final point-map I used:

Green = main “microscope” body
Red = highlight/accent points (mScarlet)

3) Implement in an OT-2 protocol

In my OT-2 protocol, the key idea is:

store the design as coordinate lists (e.g., electra2_points, mscarlet_i_points)
aspirate enough volume for a “chunk” of dots (so we don’t aspirate for every single point)
dispense each dot using a small helper that moves down to dispense and back up to detach the droplet cleanly

Snippet (from my protocol):

# --- parameters ---
DOT_UL = 0.8      # volume per dot
GRID_MM = 1.0     # coordinate units → mm

designs = [
    ("Green", electra2_points),
    ("Red",   mscarlet_i_points),
]

for color_label, pts in designs:
    source = location_of_color(color_label)
    pipette.pick_up_tip()

    dots_per_chunk = int(pipette.max_volume // DOT_UL)

    i = 0
    while i < len(pts):
        chunk = pts[i:i + dots_per_chunk]
        vol = DOT_UL * len(chunk)

        pipette.aspirate(vol, source)

        for (x, y) in chunk:
            dest = center_location.move(types.Point(x=x * GRID_MM, y=y * GRID_MM, z=0))
            dispense_and_detach(pipette, DOT_UL, dest)

        i += len(chunk)

    pipette.drop_tip()

Part 2 — Post-Lab Questions (Opentrons paper + how it connects to my final project)

2.1 A published paper using Opentrons for a novel bio application

I chose Brown et al. (2025), “Semiautomated Production of Cell-Free Biosensors” (ACS Synthetic Biology) because it shows the OT-2 being used not just for “routine liquid handling,” but as a manufacturing platform for synthetic biology diagnostics.

In the paper, the authors use an Opentrons OT-2 to assemble large batches of cell-free biosensor reactions, then process them through a deployment-style pipeline: assemble → (optionally) lyophilize → rehydrate → measure output. They compare manual vs automated preparation and demonstrate reliable, scaled production (including a full 384-well plate format), which is exactly the kind of reproducibility you want when moving from “cool demo” to “repeatable product”.

2.2 How Opentrons could be “perfect” for producing a BC skincare sheet mask (pouch mask)

For my final project direction, I’m thinking of a skincare sheet mask, using bacterial cellulose (BC) as the carrier material. The OT-2 is a great fit because it turns a “handmade one-off” into a repeatable, batchable fabrication workflow.

Where OT-2 helps most

Standardized loading of serum / actives: dispense precise volumes of humectants (e.g., glycerol), buffers, preservatives (if used), fragrance-free additives, etc. into pouches or soaking trays so every mask gets the same dose.
Patterned deposition (“pixel printing”) onto BC: print micro-spots or zones of different formulations (e.g., soothing zone vs brightening zone) or a visible “QC pattern” to confirm even loading.
Built-in controls + QC: include calibration spots or a reference color patch on each sheet (so each mask is self-verifiable in documentation/photos).

How this connects to the Brown et al. OT-2 paper Brown et al. use the OT-2 as a manufacturing platform for cell-free biosensor reactions (assemble → process → rehydrate → readout). My mask workflow is conceptually similar, just with a different substrate:

assemble formulations (or cell-free mixes for R&D prototypes)
deposit onto/into BC in a controlled way
package / dry / store
rehydrate on use (when the sheet mask is applied)

What I would document as “automation value”

Repeatability across a batch (mass gain of BC after dosing, or volume dispensed per pouch)
Uniformity (image-based check of a printed pattern across masks)
Optional: a simple visual indicator that activates upon rehydration (e.g., a time/usage indicator patch for R&D proof-of-concept)

This makes the OT-2 useful not only for lab experiments, but for building a small-scale manufacturing pipeline for BC skincare sheet masks.

Reference

Brown, D. M. et al. (2025). Semiautomated Production of Cell-Free Biosensors. ACS Synthetic Biology. DOI: 10.1021/acssynbio.4c00703

Links (for citation / screenshots):

PubMed: https://pubmed.ncbi.nlm.nih.gov/40073441/
ACS (journal page): https://pubs.acs.org/doi/10.1021/acssynbio.4c00703
PDF: https://jewettlab.org/wp-content/uploads/2025/06/brown-et-al-2025-semiautomated-production-of-cell-free-biosensors.pdf

Final Project Ideas

Idea 1 — OT-2 “manufactured” BC skincare sheet masks (pouch masks)

Concept: Use the Opentrons OT-2 as a small-scale manufacturing tool to reproducibly load / pattern skincare formulations onto bacterial cellulose (BC) sheet masks that come in a sealed pouch and sit on skin for ~1–2 hours.

Problem: BC have excelant water holding capacity however handmade BC sheet masks are hard to standardize (dose, uniformity, repeatability across a batch).
Hypothesis: Automation + coordinate-based dispensing can turn BC sheet masks into a consistent, documented “biofabrication pipeline.” bacteria can be engineered to “read” your skin health and express it in simple color cues.
embed a cell-free color indicator patch as a “time / health/ hydration indicator.
Approach (R&D workflow):
- Grow/harvest BC sheets → press to target thickness → load into a deck jig/holder.
- OT-2 dispenses exact volumes of serum/actives into:
  - (A) the pouch (soak method), and/or
  - (B) directly onto the BC in patterns/zones (“forehead zone”, “cheek zone”, etc.).
MVP demo: 6–12 masks with identical dosing; photo + mass-gain and uniformity checks.
What to measure: repeatability (dispensed volume, BC mass gain), uniformity (image analysis), user-facing consistency (feel, tack, wetness over time).

Idea 2 — Water-resistant BC “leather” via in-growth synbio

Concept: Reduce BC water uptake during growth by programming the system to deposit a cellulose-bound amphiphilic layer (e.g., a hydrophobin–cellulose binding domain fusion) that self-assembles on/within the BC network.

Problem: When using BC as leather substitude (material production) one of the main problems is that it absorbs a lot of water + swells; tradtionally the solution have been different post-coatings different oils or waxes however they tend to not be very long lasting.
Hypothesis: A cellulose-binding, self-assembling protein layer produced during growth period can reduce wetting and wicking without heavy post-treatment.
Approach:
- Engineer a production strain or a modular functionalization step to present hydrophobin–CBD/CBM at the BC interface.
- Compare conditions:
  1. control BC
  2. BC + in-process hydrophobin–CBD functionalization
  3. BC + conventional post-coat (baseline comparison)
MVP demo: small “bag panel” swatch set + simple rain/soak tests.
What to measure: water uptake %, wicking height, thickness change after wetting, flex/crack after dry–wet cycles.
Stretch goal: combine with in-growth pigment or optogenetic patterning for functional + aesthetic “self-finished” BC.

Idea 3 — Light-input → color-output BC bio-print for moiré effects (BC + engineered E. coli)

This project is based on week01 homework

Concept: A co-culture “living printer”: Komagataeibacter grows the BC sheet while engineered E. coli produces pigments under light control, enabling projected patterns. Two patterned layers with slightly different line frequencies create moiré interference when stacked.

Problem: Dyeing BC is slow/uneven; patterning usually requires post-processing.
Hypothesis: Optogenetics enables spatial control: light patterns → localized gene expression → localized color on/within a growing material.

Approach (research plan):
- Build/borrow a light-gated expression system in E. coli (red/green/blue input).
- Drive a visible output (pigment pathway or chromoprotein).
- Pattern with projector/photomask onto a co-culture or onto E. coli deposited on BC.
- Grow/prepare two sheets with slightly offset gratings → overlay for moiré visuals.
MVP demo: one light-patterned colored sheet + photo documentation of resolution/contrast.
What to measure: pattern sharpness (edge blur), color contrast, stability after drying, moiré strength with layer overlay.
Stretch goal: multi-color “logic-like” prints (different wavelengths → different pigments).

Reff. project 1:

Brown, D. M. et al. Semiautomated Production of Cell-Free Biosensors. ACS Synthetic Biology (2025). https://pubmed.ncbi.nlm.nih.gov/40073441/
Amnuaikit, T. et al. (2011). Effects of a cellulose mask synthesized by a bacterium on facial skin characteristics and user satisfaction. https://pmc.ncbi.nlm.nih.gov/articles/PMC3417877/
Nguyen, P.Q. et al. (2021). Wearable materials with embedded synthetic biology sensors for biomolecule detection. Nature Biotechnology. https://www.nature.com/articles/s41587-021-00950-3
Pardee, K. et al. (2014). Paper-Based Synthetic Gene Networks. Cell. https://pubmed.ncbi.nlm.nih.gov/25417167/
Ba, F. et al. Chromoproteins: visible tools for advancing synthetic biology. https://pubmed.ncbi.nlm.nih.gov/41309430/

Reff. project 2:

Puspitasari, N. Class I hydrophobin fusion with cellulose binding domain… (PDF thesis/report, 2021). https://repositori.ukwms.ac.id/id/eprint/31910/1/1-Class_I_hydrophobin_fusion_with_%28Nathania%29.pdf

Reff. project 3:

Walker, K. T., Li, I. S., Keane, J., Goosens, V. J., Song, W., Lee, K.-Y., & Ellis, T. (2025). Nature Biotechnology, 43, 345–354. https://doi.org/10.1038/s41587-024-02194-3
Zhou, H., Lin, P., Jeong, K. J., & Lee, S. Y. (2025). Trends in Biotechnology. https://doi.org/10.1016/j.tibtech.2025.09.019
Levskaya, A. et al. Synthetic biology: engineering Escherichia coli to see light. Nature (2005). https://pubmed.ncbi.nlm.nih.gov/16306980/