Week 1 HW: Principles and Practices

The halfpipe of Doom- How to grow good?

For the first weeks lecture we had an introduction to the fundamental principles of synthetic biology and the HTGAA program. The focus of the lecture was on the governance and ethics of synthetic biology. David S. Kong discussed the balance between decentralized and centralized synBio development and the importance of thrust (something we are lacking these days). As a global community we have largely agreed to certain rules (e.g. bioweapon treaty 1975) however emerging synBio technologies also allow a much broader audience to participate in the development (e.g. community labs/ biohackers) that might not necessary always align with large governmental policies. He draws the parallel to how the early governance of the internet have allowed for a decentralized scaling that have contributed to an increased “computer literacy”. This might allow us to make better (although not perfect) personal decisions for how to use this new technology. Coming from a background of community focused biolab practice this was an interesting topic and made me think of the importance for a global bio-literacy. It also got me to think about the importance to apply these principals in a simple enough way that it doesn’t stifle participation.

Questions that I tried to include in my homework:

1. Describe a biological engineering application

Programmable colors for bacterial cellulose production

The textile dyeing industry is a major source of chemical pollution and water use. Coloration of bacterial cellulose (BC) can also be technically challenging because pigments often diffuse slowly into the material’s dense nanofibrillar network, making post-growth dyeing difficult and time consuming. This project proposes a bioengineering approach to generate color in situ during BC growth, eliminating conventional dyeing steps.

TerraPods

Prior work demonstrates the feasibility of embedding pigmentation into BC production. Walker et al.(2025) ¹ engineered the cellulose-producing bacterium Komagataeibacter rhaeticus to generate melanin during BC growth, producing pigmented material. Zhou et al. (2025) ² demonstrated a “one-pot” co-culture strategy coupling BC production by Komagataeibacter xylinus with pigments synthesised in engineered E. coli, enabling a broader palette by combining violacein derivatives (green/blue/navy/purple) and carotenoids (red/orange/yellow).

Zhou et al. (2025)

Building on these studies, the core concept here is light-patterned control of pigment production during BC formation. A cellulose-forming culture generates the sheet while a pigment-producing bacteria is engineered to be light-responsive, so that pigmentation occurs in illuminated regions. Patterned illumination via projection enables spatial control of coloration. Furthermore this technique would also enable varying projected patterns across growth phases that could yield multi-layer visual effects, (e.g. moiré-like effects).

Walker et al.(2025)

Drawing from my previous experiences on working in various community biolab the project is framed as a distributed biofabrication platform for community labs, which creates governance questions around biosafety practice in a decentralized settings, concider the relative complex technique I was for this excersice imagining a centralized organization providing the framework and digital infrastructure for the community labs to safetly experiment with the protocol. Although consumer product are less ethically complicated then for example medicine or bioweapon their came up important questions concerning consumer/skin-contact safety, environmental release and waste handling, and norms for responsible dissemination of methods and bacteria strains.

2. governance/policy goals

                CENTRAL PLATFORM / ORG
     (protocol repo + training + registry + reporting)
            |           |               |
     SOP minimums   pigment safety   open hardware stack
     (Option 1)      (Option 2)          (Option 3)
            |           |               |
            +-----------+---------------+
                        |
        -----------------------------------------
        |                  |                   |
   Community Lab A     Community Lab B     Community Lab C...
 (local biosafety)   (local biosafety)   (local biosafety)
   - containment        - containment       - containment
   - waste handling     - waste handling    - waste handling
   - incident reports   - incident reports  - incident reports
   - minimal tests + labeling (skin-contact, leaching, etc.)
                        |
  Local authorities / partners / funders
  (disposal rules, validation support, incentives)

• Actors: Community labs and networks, open-hardware designers, academic partners, funders, and (optionally) insurers.

A. Biosecurity

• A1: Reduce risk of malicious repurposing of organisms, materials, or protocols.
• A2: Improve traceability and incident reporting to support response.

B. Lab safety

• B1: Standardize safe practices (training, containment, waste handling) across labs.
• B2: Establish clear response procedures for spills, exposures, and contamination.

C. Environmental protection

• C1: Prevent release of organisms or harmful pigments/byproducts.
• C2: Enable remediation and corrective action after incidents.

D. User/consumer protection and social trust

• D1: Ensure skin-contact safety (low leaching, low irritation risk, stability).
• D2: Maintain low barrier access; avoid governance that excludes low-resource labs.
• D3: Require transparency and avoid misleading sustainability claims.

E. Feasibility and innovation

• E1: Keep requirements simple for community labs.
• E2: Avoid unnecessary friction to legitimate research and education.

3. Governance actions (three options)

➡️ Option 1 — Network baseline: certification + SOP minimums

Purpose: Reduce variability in biosafety practice across distributed labs.

Design: A lightweight participation standard for labs using the platform including training checklist; Standard operating procedure (SOP) templates for handling, contamination response, waste logs and periodic documentation checks.

Assumptions: Labs will opt in if benefits are tangible and the extra admistrive work is not to burdensome.

Risks: Uneven enforcement; exclusion of under-resourced labs if standards become to complex.

➡️ Option 2 — Pigment/material safety standard: whitelist + minimal testing + labeling

Purpose: Address the most important downstream risk for the product: skin-contact, pigment safety and environmental implications.

Design: Shared “allowable pigment classes” (whitelist) plus minimum evidence requirements for testing (basic leach, washfastness, disposal guidance, documentation of lab status). Standard labeling for intended use and safety-relevant claims.

Assumptions: Low-cost testing tools or institutional partners are available; whitelist stays current and not to restrictive.

Risks: The process to complex and hindering community engagement, or weak tests gives unreliable results, slowed innovation if the whitelist narrows too far.

➡️ Option 3 — Open-source hardware standards for safe, distributed BC biofabrication

Purpose: Reduce reliance on expensive proprietary equipment while lowering barriers to participation without lowering safety. The goal is to make safe practice easier by default through standardized, well-documented hardware and workflows suitable for community labs.

Design: an open-source “reference stack” that includes:

• Validated hardware designs for core needs (e.g., enclosed growth modules with spill containment, filtered airflow concepts, light/projection enclosures to reduce eye/UV exposure, basic sensing/logging for temperature/pH proxies where appropriate).
• A documentation package: build BOMs with substitutions, maintenance/calibration checklists, cleaning/decon compatibility notes, and safety labels.
• Inter-lab benchmarking: common test artifacts and reporting templates so labs can compare performance and identify failure modes early.

Assumptions:

• Standardizing equipment and documentation will reduce accidents and variability more effectively than rules alone.
• Community labs have enough fabrication capacity (or partner access) to build/maintain hardware.
• A shared reference design can remain adaptable across different local constraints.

Risk:

• Hardware reliability varies; incomplete documentation leads to unsafe modifications; lack of maintenance causes drift in performance.
• Lowered barriers increase scale of adoption faster than training capacity; designs are copied without safety context; fragmentation into many forks undermines standardization.

4. Score

5. Prioritization and recommendation

I would prioritize Option 1 + Option 2 as the baseline governance package, with Option 3 as a longer-term technical pathway. Option 1 provides uniform safety culture and response capacity across labs; Option 2 directly governs consumer-contact risks and environmental externalities specific to pigment-enabled textiles. Option 3 is desirable for uniformed implementation of option 1 and 2 in a community lab setting.

Primary audiences: community lab networks and lab leads (implementation), funders/partners, and local safety/environment authorities (alignment on waste and disposal practices).

ChatGBT 5.2 was used for brainstorming bioengineering ideas for BC production in a community based setting

Aswell as searching for academic literature

and correct spelling error and double checking if I understood the research correctly

and to make the code for the governance chart:

It was also used for debugging some of the problems that I had with the website build, I am not including those prompts here…

Homework Questions from Professor Jacobson

Homework Questions from Dr. LeProust:

Homework Question from George Church:

Week 2 HW: dna read write and edit

Part 1: Benchling & In-silico Gel Art

My original idea was to make a circle, but after some trial and error I realized it would be a bit too complicated—so I settled on an arch (bridge).

1a) I imported the sequence for lambda DNA.

1b) In Benchling, I ran all 7 restriction enzymes we had available to see which ones gave:

• a busy lane (many bands) → use as the “background” in most lanes
• a cleaner lane (fewer bands) → use to “carve out” the interior of the arch

Note:

• Lane = the vertical track DNA runs down from a single well
• Bands = the horizontal lines within a lane (different fragment sizes)

Based on the results above, I rearranged the enzymes to create the pattern:

Although it’s not the most beautiful arch, this was a great exercise for understanding the basics of in-silico digests and gel band patterns.

This tool is also great for quickly iterating on gel-art layouts: https://rcdonovan.com/gel-art

3.1. Choose your protein

In recitation, we discussed picking a protein for the homework that you personally find interesting. I chose CBM3.

Why CBM3?
CBM3 is interesting because it works like a modular “cellulose anchor”: you can fuse it to other proteins so they reliably stick to cellulose (including bacterial cellulose). Beyond simple labeling, CBM fusions are used as fluorescent probes to visualize cellulose organization and dynamics, as affinity tags for low-cost purification on cellulose, and as anchoring domains to immobilize enzymes on cellulose scaffolds—turning cellulose into a reusable biocatalyst support or functional capture material.

Simply put: it’s short, often expresses well, and it sticks to cellulose.
Reference: CBM3 (example paper)

In UniProt, I searched for “carbohydrate-binding module CBM cellulose-binding protein” and got many hits. A good way to narrow the options is to pick something that is:

1. Reviewed (Swiss-Prot) (more reliable annotation)
2. Short / manageable (ideally ~80–250 aa)
3. Clearly annotated as a CBM domain (cellulose-binding)

The UniProt entry I used was Q06851. The full protein is long, but UniProt makes it possible to extract only the domain/region relevant to the application:

1. Open the UniProt entry
2. Scroll to Family & Domains
3. Find the feature you are interested in (domain boundaries)

I chose the CBM3 (carbohydrate-binding module family 3) from the cellulosome scaffoldin CipA, because CBM3 specifically binds cellulose and is relevant for bacterial cellulose materials.

3.2. Reverse translate: Protein (amino acid) → DNA (nucleotide)

To extract only the CBM3 region, I downloaded the sequence and used the Gao Lab WebLab tool:
WebLab – range_extract_protein

I entered the range 365–523, which returned:

>CBM3_CipA_Q06851_res365-523
GAYAITKDGVFAKIRATVKSSAPGYITFDEVGGFADNDLVEQKVSFIDGGVNVGNATPTKGATPTNTATPTKSATATPTRPSVPTNTPTNTPANTPVSGNLKVEFYNSNPSDTTNSINPQFKVTNTGSSAIDLSKLTLRYYYTVDGQKDQTFWCDHAAI

Next, I pasted the CBM3 amino-acid sequence into the Sequence Manipulation Suite reverse-translation tool: bioinformatic – Reverse Translate

Finally, I double-checked the result in Benchling by pasting the reverse-translated DNA into a new sequence and using Benchling’s Translate feature to confirm it produced the same amino-acid sequence.

3.3. Codon optimization

I decided to codon-optimize for E. coli because it’s a common protein-expression host with well-established tools. Codon optimization matters because organisms have different codon bias / tRNA abundances, and matching preferred codons often improves translation efficiency, protein yield, and reduces stalling during expression. To do this, I used Twist’s codon-optimization workflow and selected Host: Escherichia coli. The optimization completed successfully (“Optimization was successful”) and the sequence scored Standard, indicating it is considered synthesize-able under Twist’s constraints. I then selected Use the optimized sequence and (as a sanity check) confirmed that the translated amino-acid sequence remained unchanged—only synonymous codons were swapped.

“I optimized for E. coli because it’s a common protein-expression host with well-established tools; the purified CBM can then be applied to bacterial cellulose to bind it.”

3.4. You have a sequence! Now what?

Now that I have a DNA sequence encoding CBM3, the next step is to express the protein. In a typical cell-dependent (in vivo) workflow, the codon-optimized CBM3 coding sequence is cloned into an E. coli expression plasmid under a promoter (e.g., T7/lac).

-An expression plasmid is designed to make lots of protein.

-A promoter is a DNA “on-switch” that tells the cell when to start making RNA from your gene.

-T7/lac is a common strong promoter system used to tightly control expression.

After transforming the plasmid into an expression strain, the cells are grown and expression is induced (often with IPTG).

IPTG releases repression in the lac system so the promoter becomes active, and the cells start producing CBM3.

Inside the cell, the DNA is transcribed by RNA polymerase into mRNA, and the mRNA is then translated by ribosomes into the CBM3 protein as tRNAs deliver amino acids according to the codons. The protein can then be purified (for example via an affinity tag such as His-tag) and used to bind/functionalize bacterial cellulose.

-His-tag lets you purify CBM3 using a matching resin (Ni-NTA), washing away everything else.

Alternatively, CBM3 could be produced using a cell-free expression system (TX-TL), where the DNA template (plasmid or linear) is added directly to a lysate containing RNA polymerase, ribosomes, and all required cofactors.

required cofactors: -RNA polymerase

-ribosomes

-tRNAs, amino acids

-energy + cofactors

In this setup the same steps—transcription to mRNA and translation to protein—happen in a test tube rather than inside living cells, which can be faster and easier for prototyping, though often at smaller scale.

Why do cell-free?

• Often faster for prototyping (no transformations, no growing cells).
• Convenient when testing multiple designs quickly.
• Downsides: usually more expensive per mg and often smaller scale/yield than growing E. coli.

Part 4 — Build an E. coli expression cassette (Benchling → Twist-ready)

For this step I designed a complete E. coli expression DNA insert in Benchling by assembling the required genetic parts in the correct order:

1. Promoter (BBa_J23106)
2. RBS (BBa_B0034 + spacer)
3. Start codon (ATG)
4. Coding sequence: replaced the template CDS with my codon-optimized gene (from Part 3)
5. C-terminal His-tag (7×His)
6. Stop codon (TAA)
7. Terminator (BBa_B0015)

After pasting each piece, I annotated every region (promoter, RBS, start, CDS, His-tag, stop, terminator) directly on the Benchling sequence.

benchling

I also used Benchling’s Analyze/Translate to confirm the ATG (Open Reading Frame) is in frame from the ATG (Start codon) and that the sequence ends with the His-tag followed by a stop codon.

Benchling link: https://benchling.com/s/seq-YgFm33VIxUzvPdZpyOKk?m=slm-lYfXGHAomlD9Go7bgPWh

Part 5 — DNA Read / Write / Edit (pigment-colored SCOBY / bacterial cellulose sheets)

This builds directly on my Week 1 project idea (“Programmable colors for bacterial cellulose production”):
https://pages.htgaa.org/2026a/alve-lagercrantz/homework/week-01-hw-principles-and-practices/index.html

5.1 DNA Read (sequencing)

(ii) What sequencing technology would you use and why?
Because SCOBY is a mix of different types of DNA (bacteria, yeast etc) I would use Oxford Nanopore long-read sequencing with shotgun metagenomic DNA from the SCOBY. One run can tell me both who is present (community composition) and help reconstruct full plasmids/inserts, which matters for checking stability during long fermentations.

• Generation: Third-generation (single-molecule, long-read sequencing).
• Input: Total genomic DNA extracted from the SCOBY (mixed community DNA).
• Essential prep steps: Extract DNA carefully (aim for high molecular weight) → optionally size-select / gently shear if needed → ligate Nanopore adapters (or use rapid prep) → load on flow cell.
• How bases are decoded (base calling): DNA passing through a nanopore changes the ionic current; a basecaller converts the signal into A/C/G/T sequences.
• Output: FASTQ (reads + quality scores) (often plus raw signal files) → downstream: taxonomic profiling + assembly to recover plasmids/contigs and verify constructs.

5.2 DNA Write (synthesis)

The Part 4 cassette I built is an E. coli expression-style design (promoter/RBS/terminator suited for E. coli). To make color, I can keep the same cassette architecture but swap the coding sequence to a pigment gene (or pathway). For SCOBY/BC specifically, there are two realistic “write” directions:

1. In-situ pigmentation inside the cellulose producer
   Engineer a cellulose-producing Komagataeibacter strain to biosynthesize pigment while it grows the pellicle. A strong example is melanin via tyrosinase expression, which yields dark, robust coloration in BC.¹

2. Co-culture / division-of-labor pigmentation
   Keep the cellulose producer focused on making BC, and pair it with a second microbe engineered to produce pigments (broad palette). A published example uses E. coli strains producing violacein derivatives and carotenoids alongside Komagataeibacter xylinus to generate multiple BC colors.²

Important design note: If the target host is Komagataeibacter (not E. coli), the regulatory parts (promoters/RBS/terminators, plasmid backbone) must be chosen for that host; otherwise the pigment genes may not express even if the coding sequence is correct.

Material/safety note (relevant for textiles/skin contact):

• Some pigments (e.g., violacein) are bioactive, so “write” decisions should also consider leaching, irritation risk, and safe handling/disposal pathways. ³

5.3 DNA Edit (genome editing)

For stable, repeatable colored BC (especially over long growth periods), genome editing can be attractive because it can:

• reduce dependence on plasmid maintenance,
• improve stability across generations,
• enable more predictable performance in a mixed or semi-open fermentation context.

Conceptually, “edit” could mean integrating a pigment function into the cellulose-producer genome, or tuning regulatory control (e.g., linking pigment production to growth phase or light-patterning concepts used in engineered living materials).

Bonus — a bacterial-cellulose (BC) face mask that changes color via cell-free pigment expression

BC is already a compelling cosmetic substrate because it holds a lot of water, conforms well to skin, and has been tested as a moisturizing sheet mask material. In one evaluation, a single application of a bacterial-cellulose mask increased facial skin moisture more than a moist towel control.⁴

Instead of putting living engineered cells on the face, a safer “synthetic biology” route is to embed freeze-dried cell-free gene expression (TX-TL) into the BC sheet as small patterned “sensor dots.” These cell-free circuits stay inactive when dry, then turn on when the mask hydrates during wear; outputs can be colorimetric (visible) or optical.⁵

Because freeze-dried cell-free circuits activate upon rehydration, a conventional pre-hydrated sheet mask would trigger prematurely during storage. A practical design might be a dry-stored BC mask (or a separate paper sensor tab) that is activated only at time of use by releasing fluid.

How it could work:

• Input (skin/sweat biomarker): pH (skin barrier/irritation proxy), lactate (sweat/metabolic proxy).
• Sensing layer (cell-free circuit): a biomarker-responsive regulatory element controls whether a reporter is expressed.⁶
• Output (visible color): express a chromoprotein (strong color under normal light) so the mask visibly shifts color in specific zones without any instrument; chromoproteins are attractive for “naked-eye” readouts.⁷

Why this is interesting for BC masks:

• The mask provides hydration + intimate contact, which can reactivate freeze-dried cell-free systems.
• Patterning multiple “dots” enables a simple visual map (e.g., pH zones at cheeks vs T-zone), turning the mask into a wearable readout rather than just a carrier.

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References (footnotes)

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:

1. Brown, D. M. et al. Semiautomated Production of Cell-Free Biosensors. ACS Synthetic Biology (2025). https://pubmed.ncbi.nlm.nih.gov/40073441/
2. 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/
3. 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
4. Pardee, K. et al. (2014). Paper-Based Synthetic Gene Networks. Cell. https://pubmed.ncbi.nlm.nih.gov/25417167/
5. Ba, F. et al. Chromoproteins: visible tools for advancing synthetic biology. https://pubmed.ncbi.nlm.nih.gov/41309430/

Reff. project 2:

1. 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:

1. 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
2. Zhou, H., Lin, P., Jeong, K. J., & Lee, S. Y. (2025). Trends in Biotechnology. https://doi.org/10.1016/j.tibtech.2025.09.019
3. Levskaya, A. et al. Synthetic biology: engineering Escherichia coli to see light. Nature (2005). https://pubmed.ncbi.nlm.nih.gov/16306980/

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project