Week 1 HW: Principles and Practices

Table of Contents

• 1) Biological engineering application / tool
• 2) Governance / policy goals
• 3) Governance actions
• 4) Risk analysis
• 5) Scoring governance actions
• 6) Prioritization recommendation
• 7) Reflection
• 8) Project + governance overview
• Homework Questions from Professor Jacobson
• Homework Questions from Dr. LeProust
• Homework Question from George Church

1) Biological engineering application / tool I want to develop (and why)

Application / tool: Growing interactive surfaces from bacterial cellulose

I already know how to grow 3D artifacts in bacterial cellulose (BC). In this project, I want to develop a biological functionalization workflow that turns grown BC artifacts into interactive surfaces, measured through impedance-based sensing (e.g., tactile volumetric response, sensitivity to pressure), with minimal embedded electronics.

The goal is not to integrate electronic components into the object, but to make the material itself behave as an interface. Electrodes are used only for readout and characterization, not as active elements.

Concretely, this toolchain would include:

• Biological functionalization workflow
  Modulating the extracellular matrix during growth (for example through protein secretion into the BC network) to tune impedance-based electrical behavior and enable material-level interaction.

• Measurement interface (readout only)
  A simple electrode setup and impedance readout used to validate, compare, and characterize material responses across different growth and functionalization conditions.

• Optional integration (if time allows)
  Integrating functionalization into my existing 3D growth workflow (molds, scaffolds, and growth conditions), so objects can be grown already functionalized rather than treated after fabrication.

Why this matters

Most interactive devices are built by assembling electronics into synthetic materials. This project proposes a different paradigm:

Instead of fabricating objects and then adding interactivity, objects are cultivated so that interactivity emerges from biological growth and organization.

This matters for several reasons:

• Sustainable interfaces: biodegradable material base, low-energy fabrication, and the possibility of metabolic degradation or recycling.
• New interface paradigms: artifacts become processual and time-based, where growth history and material state shape interaction.
• Research tool: impedance acts as a lens to study how morphology, hydration, and growth dynamics become measurable signals.

2) Governance / policy goals for an ethical future

Inspired by the iGEM Safety framework, the ethical challenges of this project are framed around biosafety, environmental responsibility, and responsible use of engineered living materials. Because this work lowers the barrier to growing functional living artifacts, governance should guide not only what is built, but how materials are grown, handled, and disposed of.

Goal A — Ensure biosafety and non-malfeasance during cultivation and use

Although bacterial cellulose–producing organisms are generally considered low-risk, improper handling or uncontrolled experimentation could lead to contamination or unintended exposure.

Sub-goals

• A1. Safe handling and containment
  Ensure cultivation, functionalization, and testing are conducted using appropriate hygiene, containment, and separation practices, especially when combining multiple biological agents.
• A2. Transparency about biological modification
  Maintain a clear distinction between engineered biological components and non-biological elements (e.g., electrodes used only for readout).

Goal B — Prevent environmental harm and unintended release

A key motivation of this work is sustainability: growing interactive surfaces from living matter that can eventually be degraded or recycled. This benefit only holds if environmental risks are managed.

Sub-goals

• B1. Controlled disposal and deactivation Ensure BC artifacts, growth media, and byproducts are deactivated or degraded before disposal, preventing accidental release into wastewater or compost systems.
• B2. Avoid persistent or toxic additives Favor biological or biodegradable functionalization methods over persistent chemicals or heavy metals to preserve viable end-of-life pathways.

Goal C — Promote responsible development of living interactive interfaces

Because this project treats living materials as sensing and computational substrates, governance should guide how such systems are framed and used, particularly outside the lab.

Sub-goals

• C1. Prevent misleading claims
  Avoid presenting biologically functionalized materials as autonomous, intelligent, or fully controllable systems.
• C2. Limit sensing to material interaction
  Encourage applications focused on material state (touch, pressure, hydration).

3) Governance actions (comparative overview)

The table below summarizes three governance actions addressing biosafety, environmental impact, and responsible use of grown bacterial cellulose artifacts. Each action is evaluated across its purpose, design, assumptions, and potential risks.

4) Potential risks, use, misuse, and governance considerations

5) Scoring governance actions

Scoring: 1 = best, 2 = moderate, 3 = weak

6) Prioritization and recommendation

Priority order

1. Option 2 — Deactivation & disposal protocols
2. Option 1 — Provenance documentation
3. Option 3 — Readout-limited norms

Option 2 addresses the most immediate and likely harm: environmental release and waste. Option 1 supports accountability and response without limiting research. Option 3 addresses longer-term risks if the technology scales.

7) Reflection

This exercise highlighted that the main risks of growing interactive living artifacts arise from success and scale, not malicious intent. Even biodegradable materials can become harmful when produced or discarded in large quantities. Another key insight is how easily biological sensing can be reframed beyond material interaction.

Governance should therefore focus on practice, documentation, and lifecycle management, rather than strict prohibition.

At this stage, the cultivation of interactive BC artifacts should remain confined to controlled research and educational contexts, and not be deployed in public or commercial environments without additional review.

8) Project + governance overview

mindmap
  root((BC Interactive Artifacts))
    Growth
      3D molds
      Functionalized matrix
    Interaction
      Impedance
      Tactile response
    Risks
      Biosecurity
      Waste
      Misuse
    Governance
      Disposal protocols
      Provenance docs
      Readout limits

Strategies to ensure an ethical biological future (project-integrated)

The table below summarizes the concrete strategies embedded in my final project to ensure it contributes to an ethical biological future. Rather than external rules, these strategies are implemented through design choices, constraints, and documentation practices.

Homework Questions from Professor Jacobson:

Question 1 — Polymerase error rate, genome size, and biological error correction

DNA polymerase, the molecular machinery responsible for copying DNA, has a non-zero error rate. As presented in the lecture slides, the error rate of an error-correcting DNA polymerase is approximately 1 error per 10⁶ base incorporations. While this error rate is very low at the scale of individual nucleotides, it becomes significant when compared to the size of the human genome, which is on the order of 3.2 × 10⁹ base pairs. If replication relied solely on polymerase accuracy, this discrepancy would imply the accumulation of thousands of errors during each complete genome replication, which would be incompatible with stable inheritance and organismal viability.

Biology resolves this apparent mismatch through multiple layers of error correction rather than relying on perfect synthesis. First, many DNA polymerases include proofreading activity (3′→5′ exonuclease) that detects and removes incorrectly incorporated bases during replication. Second, additional post-replication repair systems, such as mismatch repair pathways (e.g., the MutS system shown in the slides), identify and correct errors that escape polymerase proofreading. Together, these layered mechanisms dramatically reduce the effective error rate of DNA replication, allowing large genomes like the human genome to be copied reliably despite the intrinsic imperfection of polymerase activity.

Question 2 — Coding redundancy and why most theoretical DNA codes do not work in practice

Due to the redundancy of the genetic code, an average human protein can, in theory, be encoded by an extremely large number of different DNA sequences. As shown in the slides, an average human protein is approximately 1036 base pairs long, corresponding to roughly 345 amino acids. Because many amino acids can be encoded by multiple synonymous codons, the number of possible nucleotide sequences that map to the same amino acid sequence grows combinatorially. In principle, this means there are astronomically many distinct DNA sequences that could encode the same protein sequence.

In practice, however, most of these theoretically valid codes do not function effectively in living systems. The lecture slides highlight several biological constraints that limit which sequences work. Certain DNA or RNA sequences form secondary structures with unfavorable free energy that interfere with transcription or translation. GC content bias can destabilize sequences or alter expression efficiency. Additionally, synthesis and assembly processes have their own error profiles, making some sequences more fragile than others. Finally, translation depends on interactions with cellular machinery such as ribosomes and tRNA pools, meaning that codon usage and sequence context matter beyond simple amino acid encoding. As a result, although the genetic code is redundant in theory, only a narrow subset of possible DNA sequences reliably produce functional proteins in practice.

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently?

The most commonly used method for oligonucleotide synthesis today is solid-phase phosphoramidite chemical synthesis, originally developed by Caruthers in the early 1980s and still the industry standard. In this approach, DNA is synthesized base by base on a solid support (typically controlled pore glass or functionalized silica). Each synthesis cycle consists of four repeated chemical steps: coupling of a phosphoramidite nucleotide, capping of unreacted chains, oxidation to stabilize the backbone, and deprotection (deblocking) to expose the next reactive site. This cycle is repeated sequentially to build the desired oligo.

This method dominates because it is highly automatable, scalable, and compatible with parallelization, especially in modern platforms such as silicon-based microarrays (e.g., Twist Bioscience). However, it is fundamentally a stepwise chemical process, meaning that each added nucleotide has a non-zero failure rate. Even with very high per-step efficiencies (>99.5%), errors accumulate with length, which directly constrains how long oligos can be synthesized reliably. This intrinsic accumulation of errors explains many of the downstream limitations discussed in the following questions.

2. Why is it difficult to make oligos longer than ~200 nt via direct synthesis?

The primary difficulty in synthesizing oligos longer than ~200 nucleotides arises from the cumulative error rate of stepwise chemical synthesis. Each coupling step has a small probability of failure (incomplete coupling, side reactions, or truncation). While a single error is unlikely at short lengths, the probability of obtaining a full-length, error-free molecule drops exponentially as the number of synthesis cycles increases. Beyond ~200 nt, the fraction of correct full-length molecules becomes very low, even if average synthesis efficiency remains high.

In addition to error accumulation, sequence-dependent effects further complicate long oligo synthesis. Regions with high or low GC content, homopolymers, inverted repeats, or strong secondary structures (e.g., hairpins) reduce coupling efficiency and increase truncation or deletion events. Purification also becomes more challenging: separating full-length products from truncated sequences is increasingly inefficient as length grows. As a result, while advances in chemistry have pushed this limit somewhat (e.g., validated synthesis of ~300–500 nt in controlled contexts), ~200 nt remains the practical and economical upper bound for routine, high-fidelity direct synthesis.

3. Why can’t you make a 2000 bp gene via direct oligo synthesis?

A 2000 bp gene cannot be synthesized directly because chemical oligo synthesis does not scale linearly with length. At that size, the compounded error rate would make the probability of producing even a single correct full-length molecule essentially negligible. Even if synthesis chemistry allowed chain extension to 2000 nt, the resulting product pool would be dominated by truncated, mutated, or rearranged sequences, rendering it unusable without extensive correction.

Instead, long genes are produced through a hierarchical assembly strategy: shorter oligos (typically 40–200 nt) are first synthesized, then enzymatically assembled using methods such as PCR-based gene assembly or ligation. These assembled fragments are subsequently cloned and sequence-verified, allowing error correction through selection rather than chemistry. This separation of concerns—chemical synthesis for short, precise building blocks, and biological processes for long-range assembly and error correction—is fundamental. It reflects a broader principle emphasized in the course: chemistry writes locally, biology validates globally. Direct chemical synthesis alone cannot replace this division of labor for long DNA constructs.

Homework Question from George Church:

Given the examples in slides #2 and #4, where biological systems use structured “codes” to mediate interactions between polymers (NA:NA via base pairing and AA:NA via translation and binding domains), an AA:AA interaction code would most plausibly rely on side-chain chemistry and spatial complementarity rather than a discrete symbolic alphabet.

Unlike nucleic acids, amino acids do not pair through a uniform geometry or hydrogen-bonding scheme. Instead, proteins interact through combinations of hydrophobicity, charge, polarity, aromatic stacking, and steric fit. Therefore, an AA:AA code is inherently analog and multidimensional, not digital. A reasonable “code” would be based on classes of side-chain interactions—for example, positively charged residues pairing with negatively charged ones, hydrophobic patches aligning with hydrophobic patches, or aromatic residues engaging in π–π stacking. This resembles a physicochemical code rather than a base-pair code.

In practice, such a code already exists implicitly in protein–protein interaction domains, coiled-coil motifs, antibody–antigen interfaces, and self-assembling protein systems. From a design perspective, an explicit AA:AA code could be engineered by constraining proteins to limited alphabets or repeat motifs, where interaction specificity emerges from repeating patterns of side-chain chemistry and geometry. This mirrors how TALE proteins use a simple amino-acid pair code to recognize DNA, but adapted to protein–protein recognition. Thus, AA:AA coding is best understood not as a lookup table, but as a designed interaction grammar grounded in chemistry and structure.

PROMPT :

you can found here the slides of Reading & Writing Life from Goerge Church. Normally I’ve to answers to one of this 2 questions (only one of those not the both) : A. [Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”? B. [Given slides #2 & 4 (AA:NA and NA:NA codes)] What code would you suggest for AA:AA interactions?

So, I don’t know which one is easiest to answer at the moment. We will use the PDF to determine which questions are most likely to be contained within it and reduce the uncertainty. To do this, we will

1. Break down the problem into sub-elements.
2. Treat each of them with an explicit confidence level (0.0 - 1.0).
3. Verify by checking the logic, facts, completeness, possible biases, what could be done, and how to achieve the objective (determine which question is easiest to answer without being wrong and give the answer).
4. Synthesize and combine using weighted confidence levels.
5. If confidence is below 0.8, identify weaknesses and how we could proceed to achieve a better level. Formulate clear answers, the level of confidence, and key points for vigilance.

REFERENCES

• https://2022.igem.wiki/paris-bettencourt/index.html

Week 02 HW: DNA Read, Write, & Edit

Table of Contents

• 0. Basics of Gel Electrophoresis

• 1. Benchling and In-silico Gel Art

• 2. Gel Art Restriction Digests and Electrophoresis

• 3.1 Choose Your Protein

  • Protein sequence (from Supplementary Data 1)
  • Structural micro-analysis: copper coordination and catalysis
  • Why this protein matters

• 3.2 Reverse translate

• 3.3 Codon optimization

• 3.4 You have a sequence now what

• 3.5 Optional how does it work in nature

• 4. Prepare a Twist DNA Synthesis Order

  • 4.1 Create accounts
  • 4.2 Build the DNA insert sequence

• 5.1 DNA Read

• 5.2 DNA Write

• 5.3 DNA Edit

• 6. Exploration of others strategies for my project

0. Basics of Gel Electrophoresis

Gel electrophoresis is a fascinating process that allows DNA fragments to be separated according to size. The migration of fragments through agarose reveals invisible molecular differences as visible banding patterns. What interests me most is how information encoded in DNA becomes a spatial structure that can be interpreted visually.

1. Benchling and In-silico Gel Art

Following the protocol Gel Art: Restriction Digests and Gel Electrophoresis, I explored Benchling to simulate restriction enzyme digestion.

I imported Lambda DNA and simulated restriction digests using:

• EcoRI
• HindIII
• BamHI
• KpnI
• EcoRV
• SacI
• SalI

At first, I was not entirely sure what I was doing — I explored the platform experimentally, clicking through the interface to understand how the enzymes cut and how the DNA fragments were segmented.

The origin of the fragment.

See the operons targetted.

How it could/ should looks on gel.

After generating fragment sizes, I moved to Ronan Donovan’s gel art tool:

👉 https://rcdonovan.com/gel-art

There, I entered fragment sizes from Benchling to simulate gel patterns. Interestingly, some of the same enzymes were already available in the tool. I experimented with fragment combinations, spacing, and contrast to create structured visual patterns. I tried composing a geometric “invader”-like form inspired by Paul Vanouse’s aesthetic approach.

This exercise made clear how restriction logic can generate latent images embedded in genomic structure.

2. Gel Art Restriction Digests and Electrophoresis

This tutorial was particularly helpful:

👉 https://www.youtube.com/watch?v=TIZRGt3YAug

Although I do not currently have access to a laboratory to run physical gels, I have previously seen Paul Vanouse’s Latent Figure Protocol artworks in person. A curator friend owns one and showed it to me during a bioart conference. Seeing the material presence of gel-based imagery deeply informed how I approached this in-silico exercise.

3.1 Choose Your Protein

Protein chosen: Tyrosinase (Tyr1) from Bacillus megaterium

I chose the tyrosinase Tyr1 enzyme from Bacillus megaterium because it directly enables the biosynthesis of eumelanin, a redox-active polymer with electronic and material properties.

This protein is particularly relevant to my project, which explores the biological functionalization of bacterial cellulose. By expressing Tyr1 in Komagataeibacter rhaeticus, the cellulose-producing bacterium can generate eumelanin within or around the extracellular matrix, thereby modifying the optical and potentially electroactive properties of the material.

Tyr1 can encode a macroscopic material property in the cell of Komagateibacter Rhaeticus.

This strategy has been experimentally validated in:

Walker et al., 2024 — Self-pigmenting textiles grown from cellulose-producing bacteria with engineered tyrosinase expression The authors engineered K. rhaeticus strains expressing Tyr1 and demonstrated eumelanin production under buffered neutral pH conditions (Supplementary Data 1, 41587_2024_2194_MOESM1_ESM).

The paper is here 👉 https://www.nature.com/articles/s41587-024-02194-3 Details are in Supplementary information / Supplementary information / Supplementary Figs. 1–5, Tables 1–3 and Supplementary Data 1 and 2.

Or here : 📄 Supplementary Walker et al

The principale idea :

DNA (tyr1) ↓ transcription mRNA ↓ translation Tyrosinase enzyme ↓ catalytic activity Melanin polymer ↓ integration into cellulose matrix Modified impedance ↓ Human interaction (touch sensing)

Rather than maximizing conductivity, the goal is to explore how living metabolism can reorganize electrochemical properties within a self-grown material matrix. Maybe a hybrid approach combining enzymatic melanin production with controlled ionic modulation of the growth medium may provide a balance between biological emergence and measurable electrical response.

Impedance vs Conduction (for BC-based “electronic” biofilms)

Quick intuition

• Conduction (DC): current flows through a continuous conductive path (like a wire).
  → You usually need a percolating network (CNT/graphene/metal paths).

• Impedance (AC): the material reacts to an alternating signal.
  → It mixes resistance + capacitance + ionic diffusion, and is extremely sensitive to: hydration, ions, porosity, and microstructure.

My goal is a tactile / capacitive surface then impedance is usually the most realistic target.

Concept map (what you measure defines what you build)

flowchart TB
A[BC biofilm / pellicle]--> B{What do I measure}

B-->|DC or very low frequency| C[Conduction]
B-->|AC multi-frequency| D[Impedance]

C-->C1[Electronic transport]
C-->C2[Needs a percolating path]
C2-->C3[CNT graphene Ag nanowires PEDOT]

D-->D1[Resistance R]
D-->D2[Capacitance C double layer]
D-->D3[Ionic diffusion]
D-->D4[Strongly affected by water ions porosity]
D4-->D5[Good for touch pressure hydration sensing]

Protein Sequence (from Supplementary Data 1)

>sp|Tyr1_BACME|Tyrosinase OS=Bacillus megaterium
MGNKYRVRKNVLHLTDTEKRDFVRTVLILKEKGIYDRYIAWHGAAGKFHTPPGSDRNAAHMSSAFLPWHREYLLRFERDLQSINPEVTLPYWEWETDAQMQDPSQSQIWSADFMGGNGNPIKDFIVDTGPFAAGRWTTIDEQGNPSGGLKRNFGATKEAPTLPTRDDVLNALKITQYDTPPWDMTSQNSFRNQLEGFINGPQLHNRVHRWVGGQMGVVPTAPNDPVFFLHHANVDRIWAVWQIIHRNQNYQPMKNGPFGQNFRDPMYPWNTTPEDVMNHRKLGYVYDIELRKSKRSS*

In the app bencling ==> https://benchling.com/s/seq-cSFfevSwjHFxf6KwwXGx?m=slm-D8VDordW2oyuqdI6bH4r

Structural Micro-Analysis: Copper Coordination and Catalysis

Tyrosinases are type-3 copper enzymes. Their catalytic activity depends on two copper-binding sites (CuA and CuB), each coordinated by conserved histidine residues. These histidine-rich motifs enable:

• Binding of molecular oxygen
• Oxidation of L-tyrosine to L-DOPA
• Conversion to dopaquinone
• Spontaneous polymerization into eumelanin

The presence of conserved histidines in the Tyr1 sequence reflects this copper-dependent catalytic architecture. The structure of the enzyme directly determines its ability to generate a redox-active polymer. Here, protein folding and metal coordination translate genetic code into material transformation.

Why This Protein Matters

1️⃣DNA → enzyme → material property The tyr1 gene encodes an enzyme. The enzyme catalyzes a redox reaction. The reaction produces a polymer. The polymer modifies material properties.

Thus: Genetic sequence → enzyme structure → catalytic activity → polymer formation → macroscopic material pigmentation and electroactivity This directly embodies the theme of “Reading & Writing Life.”

2️⃣ Minimal synthetic biology intervention Unlike multi-gene systems (e.g., the curli operon), Tyr1: Requires only one coding sequence Does not modify the cellulose biosynthesis operon (bcsABCD) Adds a new functional layer to the extracellular matrix This makes it an elegant example of additive biological functionalization, where a single gene expands the material phenotype of a living system.

3️⃣ Connection to biofabrication and living materials

Walker et al. demonstrate patterned melanin production using optogenetic control of tyr1 expression. This shows that: Gene expression can spatially control material coloration Biological programming can encode textile-level patterning Material properties can be written through genetic regulation This aligns directly with my research interest in growing functionalized 3D artifacts and interactive living interfaces.

Beyond color: material programming

In this project, the goal is not simply to encode color in DNA. The visible pigmentation is only a surface effect. What is actually encoded is a redox-active polymer production pathway inside living matter.

Melanin modifies:

• local redox properties,
• hydration-dependent conductivity,
• and potentially the impedance behavior of the cellulose matrix.

So pigmentation here is not aesthetic encoding, but electrochemical encoding. The gene does not just change appearance — it changes how the material behaves electrically. In that sense, DNA is not only encoding a protein, but indirectly programming a material state.

3.2 Reverse Translate

Protein (amino acid) sequence → DNA (nucleotide) sequence

Because of codon degeneracy, there is not a single unique DNA sequence for a given protein sequence. Many different nucleotide sequences can encode the exact same amino acid sequence. To reverse-translate Tyr1, I used a reverse-translation approach (equivalent to common online reverse-translation tools) by selecting one plausible codon per amino acid (a “standard/high-frequency codon” strategy).

The protein sequence used is Tyr1 from Bacillus megaterium as reported in Walker et al. 2024 (Supplementary Data 1). :contentReference[oaicite:1]{index=1}

Reverse-translated nucleotide sequence (one valid coding DNA sequence)

ATGGGCAATAAATACCGCGTGCGTAAGAATGTTCTGCACCTGACAGATACCGAGAAGCGTGACTTCGTGCGCACTGTACTGATTTTGAAAGAGAAGGGCATTTACGATCGTTACATCGCATGGCACGGCGCCGCGGGTAAGTTTCACACCCCGCCCGGTAGTGACCGTAACGCGGCGCACATGTCGAGTGCGTTCTTGCCTTGGCACCGCGAATATCTGCTGCGCTTTGAGCGCGATCTGCAATCGATTAACCCTGAGGTGACTCTGCCGTACTGGGAGTGGGAAACCGATGCTCAAATGCAAGACCCTAGCCAGTCGCAGATCTGGAGCGCCGACTTCATGGGCGGCAATGGCAACCCAATTAAGGACTTCATTGTAGACACGGGCCCGTTCGCTGCCGGCCGTTGGACAACCATTGACGAGCAGGGTAACCCGTCAGGCGGCTTAAAGCGCAACTTCGGTGCGACTAAGGAAGCCCCCACCCTGCCGACGCGCGACGACGTGCTGAACGCACTTAAGATTACCCAATACGACACCCCACCCTGGGACATGACGTCCCAGAATAGTTTCCGCAACCAACTCGAGGGTTTCATCAATGGCCCGCAACTGCATAACCGTGTGCATCGCTGGGTCGGTGGCCAAATGGGTGTCGTCCCTACCGCGCCCAACGACCCGGTGTTCTTCCTGCATCATGCGAACGTTGACCGCATCTGGGCCGTGTGGCAGATCATCCACCGCAACCAGAATTACCAACCAATGAAGAATGGCCCGTTCGGCCAGAATTTCCGTGACCCAATGTATCCATGGAACACCACGCCTGAGGATGTAATGAATCACCGTAAACTGGGCTATGTTTATGACATCGAGTTGCGTAAGTCGAAGCGCAGCTCTTGA

Degeneracy and probabilistic encoding

During this process, I realized that reverse translation is not deterministic. The genetic code is degenerate: multiple DNA sequences can encode the same protein. Choosing a DNA sequence is therefore not neutral. Codon usage can influence expression level, folding efficiency, and metabolic burden in the host organism. I am only beginning to understand how codon bias shapes expression outcomes. What appears to be a simple reverse translation step is in reality a probabilistic optimization problem shaped by cellular context.

3.3 Codon Optimization

Why codon optimization is necessary

Although multiple DNA sequences can encode the same protein due to codon degeneracy, not all synonymous codons are used equally in different organisms.

If a gene uses codons that are rare in the host organism:

• Translation can be slow
• Ribosomes may stall
• Protein yield can decrease
• Misfolding may increase

Codon optimization ensures that the nucleotide sequence:

• Matches the host organism’s codon usage bias
• Improves translation efficiency
• Reduces metabolic burden
• Avoids problematic motifs (e.g., strong secondary structures, cryptic promoters, restriction sites)

Thus, codon optimization improves its expression in a chosen host.

Organism chosen for optimization

I chose to optimize the tyr1 gene for expression in: Komagataeibacter rhaeticus

This organism is the cellulose-producing bacterium used in Walker et al. (2024) and is central to my broader project on biologically functionalized bacterial cellulose.

Because K. rhaeticus has a relatively high GC content and a codon usage profile distinct from Bacillus megaterium, optimization is required to:

• Improve translation efficiency
• Ensure stable protein production
• Support effective melanin biosynthesis

Additional design constraints

Following best practices (e.g., Twist Bioscience optimization tools), the optimized sequence was also designed to:

• Avoid Type IIS restriction enzyme recognition sites:
  • BsaI
  • BsmBI
  • BbsI
• Avoid internal repetitive motifs
• Maintain balanced GC distribution

Codon-Optimized DNA Sequence (for K. rhaeticus)

ATGGGCAACAAGTATCGCGTCCGCAA GAACGTGCTGCACCTGACCGACACCGAGAAGCGCGACTTCCGCAGCACCTGCACCATTCTGAGGAGATCGGCTACGACCGCATCTACATCGGCGCGGCGGGCAAGTTCCACACCCCCACCGGCAGCGACCGCAACGCGGCACACTCGATCGCGCGTTCACCTGGCAACGCTACCTGCGCTTCGAGCGCTACCAGAGCATCAACCCCGAGGTGACCCTGCCGTACTGGGAGTGGGAGACCGACGCGCAGATCGACCCCGACAGCCAGATCTGGAGCGCGGACTTCATCGGCGGCAACGGCAACCCCATCAAGGACTTCATCGTCGACACCGGCCCCGTC TTCGCGGCGGGCCCCACCATCGACGAGCAGGGCAACCCCAGCGGCGGCCTGAAGCGCAACTTCGGCGCGACCAAGGAGGCGCCGACCCCCGCGCGCTCCGACGACGTGCTGAAGCGCCTCAAGATCTACC
CCAACATCGACACCCCCCTGGACATGACCTCCAACAGCTTCCGCAACCAGCTCGAGGGCATCTACAACGGCCCCCAGATCTTCAACAACCGCGTGCACCGCTGGGGCGGCCAGATGGGCGTGGTGCCCACCCCCGCGCCCAACGACCCCGTCTTCTCCTGCACCATGCCAACGTGGACCGCTACTGGGCCGTGTGGCAGATCCACCGCAACCAGAACTACCCCCATGAAGAACGGCCCCTTTGGCCAGAACTTCCGCTACCCCAACGTCTCCTGGAATACCACCGCGGAGGACGTCATGAACCACCGCAAGCTGGGCTACGTGTACGACATCGAGCTGCGCAAGAGCAAGCGCAGCAGCTAA

3.4 You have a sequence / Now what?

Now that we have a codon-optimized DNA sequence encoding Tyr1, the next step is to express the protein.

How can this protein be produced?

There are two main strategies:

A. Cell-dependent expression (in vivo)

In this approach, the DNA sequence is inserted into a plasmid vector containing:

• A promoter (constitutive or inducible)
• A ribosome binding site (RBS)
• The coding sequence (our optimized tyr1)
• A transcription terminator

The plasmid is then introduced into a host organism (e.g., Komagataeibacter rhaeticus).

What happens biologically?

1️⃣ Transcription
RNA polymerase binds to the promoter and transcribes the DNA sequence into messenger RNA (mRNA).

2️⃣ Translation
Ribosomes bind to the mRNA at the ribosome binding site. The mRNA codons (triplets) are read. tRNAs deliver amino acids corresponding to each codon. The ribosome assembles the amino acids into the Tyr1 protein.

3️⃣ Protein folding and function
The translated Tyr1 protein folds into its 3D structure. Copper ions bind to the active site. The enzyme becomes catalytically active.

In the case of Tyr1, the enzyme then catalyzes: L-tyrosine → L-DOPA → dopaquinone → eumelanin Thus, genetic information becomes a catalytic material transformation.

B. Cell-free expression (in vitro)

Alternatively, the DNA sequence can be expressed using a cell-free transcription-translation system.

These systems contain:

• RNA polymerase
• Ribosomes
• tRNAs
• Amino acids
• Energy sources

The optimized DNA is added directly to the mixture.

Transcription and translation occur outside living cells.

Advantages:

• Rapid prototyping
• No need for transformation
• Easier control of conditions

For exploratory material functionalization, cell-free systems can serve as a fast validation step before in vivo implementation.

From DNA to Protein: Summary

DNA (double-stranded)
→ transcription →
mRNA (single-stranded)
→ translation →
Protein (amino acid chain)

This is the operational flow of the Central Dogma.

From DNA to material interface

If we follow the chain of transformations:

DNA (tyr1 gene)
→ mRNA
→ Tyrosinase enzyme
→ Melanin polymer formation
→ Integration into the cellulose matrix
→ Modified impedance behavior
→ Human touch interaction

This illustrates that writing DNA is not only about modifying organisms. It is about programming how matter self-organizes over time.

My interest is not genetic novelty for its own sake, but how cellular metabolism reorganizes matter into functional biofilms.

4. Prepare a Twist DNA Synthesis Order

4.1. Create a Twist account, and Benchling account

To prepare the DNA synthesis order, I created:

• A => Twist Bioscience account https://ecommerce.twistdna.com/app
• A => Benchling account https://benchling.com

Benchling was used to design and annotate the DNA construct.
Twist was used to validate and prepare the sequence for synthesis.

The idea is to make a manufacturable artifact, marking the transition from theory to industrial bioengineering.

4.2. Build Your DNA Insert Sequence

The expression cassette was assembled in Benchling as a linear DNA construct including:

• Promoter
• RBS
• Coding sequence (Tyr1)
• Stop codon
• Terminator

Benchling file:

🔗 Benchling sequence link

ZIP version here : 📄 Tyr1 ZIP

Annotated sequence overview

Benchling allows visualization of the construct with annotated features and automatic translation of the coding sequence.

This confirms:

• Correct open reading frame
• Proper annotation of CDS
• Expected protein translation

Twist synthesis validation

The sequence was uploaded to Twist Bioscience for synthesis validation and plasmid build preparation.

Twist checks for:

• Sequence length constraints
• GC content
• Secondary structures
• Restriction site conflicts
• Synthesis compatibility

This step transforms a conceptual genetic design into a manufacturable DNA construct.

Files

📄 Download: Tyr1 synthesis package (ZIP)

Optional: SBOL visualization

To visually represent the genetic architecture, the construct can also be recreated using: 🔗 https://sbolcanvas.org
SBOL Canvas enables graphical design of: Promoter → RBS → CDS → Terminator
This helps communicate the logic of the construct clearly and aligns with synthetic biology design standards.

5. DNA Read/Write/Edit

5.1 DNA Read

(i) What DNA would I want to sequence and why?

I would sequence synthetic DNA used for digital data storage. Instead of reading DNA from living organisms, I am interested in DNA that has been artificially synthesized to encode digital information (text, images, archives). In this case, DNA is not used as biology — it is used as a storage material.

Why this matters

DNA can store enormous amounts of information in a very small volume and can remain stable for long periods of time. Unlike hard drives or servers, it does not depend on electronic infrastructure. Sequencing such DNA means reading information written into molecules. It turns a biological technology into a digital decoding tool. This connects biology with computation and raises important questions about how information can be stored, preserved, and accessed in the future.

(ii) Which sequencing technology would I use and why?

I would use Illumina sequencing.

Why Illumina?

DNA used for digital storage is usually made of short fragments. Illumina sequencing works very well for short pieces of DNA and provides very high accuracy, which is essential when decoding stored data. Small sequencing errors could corrupt the recovered file, so accuracy is more important than read length.

What generation is it?

Illumina is considered a second-generation sequencing technology because it reads millions of DNA fragments at the same time using amplified clusters and fluorescent signals.

What is the input and preparation?

Input:
Synthetic double-stranded DNA fragments (around 100–200 base pairs).

Basic preparation steps:

1. Add short adapter sequences to the DNA fragments
2. Amplify the fragments
3. Load them onto the sequencing machine

How does Illumina read DNA?

The machine copies the DNA one base at a time.
Each base (A, T, C, G) produces a specific fluorescent signal.

A camera records the signal after each step, and software converts these signals into a DNA sequence.

What is the output?

The output is a digital file containing:

• The DNA sequences
• A quality score for each base

In DNA data storage, these sequences are decoded back into binary information using error-correction algorithms.

Why not Nanopore?

Nanopore sequencing can read longer DNA fragments, but it generally has higher error rates. For digital data storage, accuracy is more important than read length, so Illumina is currently more suitable.

Conceptual note

In this case:

DNA is not a gene.
It is a storage medium.

Sequencing is not diagnosing life.
It is decoding information.

5.2 DNA Write

(i) What DNA would I want to synthesize and why?

I would synthesize a genetic construct enabling melanin production in cellulose-producing bacteria.

More specifically, I would synthesize a codon-optimized version of the tyrosinase gene (tyr1) from Bacillus megaterium, placed inside an expression cassette designed for bacterial hosts.

The goal is to enable engineered Komagataeibacter strains (cellulose-producing bacteria) to produce eumelanin, a dark redox-active polymer, directly within the growing cellulose matrix.

This would allow:

• Pigmented living materials
• Potentially electroactive cellulose composites
• A direct link between gene expression and macroscopic material properties

In this case, DNA is used not only to encode a protein, but to program a material property.

Example genetic construct (simplified expression cassette)

Promoter → RBS → tyr1 CDS → Stop → Terminator This DNA construct would enable constitutive expression of tyrosinase, leading to melanin formation when L-tyrosine is available.

(ii) What DNA synthesis technology would I use and why?

I would use phosphoramidite-based chemical DNA synthesis, the standard method used by companies like Twist Bioscience. This is currently the most reliable and scalable way to synthesize custom genes.

Essential steps of DNA synthesis

1. Chemical synthesis of short oligonucleotides
2. Assembly of oligos into full-length genes
3. Error correction and amplification
4. Cloning into a plasmid vector
5. Sequence verification

Limitations

• Error rates increase with sequence length
• GC-rich or repetitive sequences are harder to synthesize
• Cost increases with size
• Large constructs require assembly from smaller fragments
• Scalability

Scaling biological material production is not a simple matter of increasing volume. In living systems, morphology, oxygen gradients, metabolic stress, and contamination risks can fundamentally alter structural outcomes. A construct that works at lab scale does not automatically behave the same way at industrial scale. Understanding small-scale control is therefore a necessary first step before considering scalability.

Despite these limits, gene-scale synthesis (1–3 kb) is highly robust today.

5.3 DNA Edit

My ambition is not maximal intervention, but controlled understanding. Before redesigning entire biosynthetic networks, I prefer mastering one enzymatic layer and observing its material consequences. Editing cellulose crystallinity, secretion pathways, or c-di-GMP regulation may be possible in the future. But at this stage, focusing on a single added function (tyr1 expression) allows a clearer understanding of cause and effect.

(i) What DNA would I want to edit and why?

I would want to edit the genome of a cellulose-producing bacterium (Komagataeibacter) to integrate a functional gene such as tyr1 directly into its chromosome.

Rather than keeping the gene on a plasmid, chromosomal integration would:

• Increase genetic stability
• Reduce reliance on antibiotic selection
• Make the engineered trait more sustainable

More broadly, DNA editing could be used to:

• Improve cellulose yield
• Modify crystallinity or fiber structure
• Control secretion of functional proteins
• Enable responsive or patterned material growth

The objective is material programming through living systems such as Komagataeibacter and raises technical and ecological questions regarding containment, stability, and unintended environmental interactions.

(ii) What technology would I use and why?

I would use CRISPR-Cas9 genome editing. CRISPR allows precise insertion or modification of DNA at specific genomic locations.

How CRISPR edits DNA

1. Design a guide RNA (gRNA) targeting a specific genomic sequence
2. Deliver Cas9 protein and guide RNA into the cell
3. Cas9 creates a double-strand break at the target site
4. Provide a donor DNA template containing the new gene
5. The cell repairs the break using the donor template (homology-directed repair)

Required inputs

• Guide RNA sequence
• Cas9 enzyme (plasmid or ribonucleoprotein complex)
• Donor DNA template
• Competent cells
• Transformation system

Limitations

• Editing efficiency may be low in non-model organisms
• Off-target edits are possible
• Homology-directed repair is not always efficient
• Delivery systems can be challenging

Despite these challenges, CRISPR remains the most precise and flexible editing tool currently available.

Conceptual note

DNA writing defines what is possible.
DNA editing reshapes what already exists.

Together, they allow us to move from reading biology to programming living materials.

Personnal note

Writing DNA is not merely modifying organisms; it is programming how matter self-organizes over time.

6. Exploration of others strategies for my project

Compare 4 Conductivity Strategies (What They Really Do)

Reading this table

• If the goal is to create a wire-like conductive material, strategies 1–2 are the most effective.
• If the goal is to create a living sensing surface, strategy 3 (and potentially 4 later) is more coherent.

Is Tyr1 Enough for a Tactile Sensor?

What I am actually building with Tyr1 + BC

With Tyr1/melanin integrated into bacterial cellulose, I am not creating a high-performance conductor. Instead, I am most likely creating an impedance-based sensor.

Two realistic sensing behaviors:

1. Touch or pressure → impedance change
   Mechanical compression modifies microstructure and redistributes water and ions.
   → Impedance changes, especially at specific AC frequencies.

2. Hydro-tactile response (touch + humidity)
   BC is highly sensitive to hydration.
   → Strong signal variation, but humidity must be controlled to avoid false readings.

Does the material need to remain alive?

Not necessarily. Two modes are possible:

• Living mode
  The material continues to grow and self-organize.
  Advantage: conceptual strength, biological emergence.
  Limitation: signal drift over time due to metabolism and morphology changes.

• Stabilized mode
  Washed and partially dried but rehydratable.
  Advantage: more stable electrical behavior and easier reproducibility.

For experimental validation, a stabilized but responsive material may be more reliable.

Is electrodes + amplifier + ADC sufficient?

Yes — provided that impedance (AC) is measured correctly.

Basic workflow:

• Inject a small, known AC excitation signal
• Measure amplitude (and ideally phase)
• Test at 2–3 different frequencies

Possible hardware:

• Dedicated impedance IC (e.g., AD5933 / AD5940)
• Or a simple bridge with sinusoidal excitation and amplitude measurement

Electrode configurations:

• 2-electrode setup
  Simpler, but more sensitive to contact variability.

• 4-electrode setup
  Better separation of excitation and measurement, more stable results.

  See what I’ve already done with Bacterial cellulose and electrodes signals :

Minimal validation experiment

To test whether Tyr1 truly contributes:

• BC control (no Tyr1)
• BC + Tyr1 + Cu²⁺/tyrosine development step
• Compare impedance vs pressure and humidity

If Tyr1 produces a measurable difference compared to control, the biological contribution is validated.

Why Copper Matters and Hybrid Strategies

Do I need to add copper?

Yes.

Tyrosinase is copper-dependent.
Without sufficient Cu²⁺, enzyme activity drops significantly.

However:

• Too little copper → weak melanin synthesis
• Too much copper → toxicity and growth inhibition

Copper availability must therefore be optimized, not maximized.

Can other dopants improve impedance?

Yes, but carefully.

Impedance in BC systems is strongly influenced by:

• Hydration level
• Ionic concentration
• Microstructure and porosity

A mild hybrid strategy can be interesting:

• Tyr1 provides a biologically synthesized redox layer
• Controlled ionic tuning (salts, buffering, hydration control) shapes electrical response

This maintains biological emergence while improving signal stability.

Long-Term Direction: Redox → c-di-GMP → Cellulose Organization

c-di-GMP is a central intracellular regulator controlling biofilm formation and cellulose production.

In Komagataeibacter, cellulose synthesis is tightly linked to c-di-GMP levels.

A future direction could be:

• A redox or electrical stimulus alters intracellular c-di-GMP levels
• This modifies cellulose production or organization
• Which then changes macroscopic impedance

This would represent a deeper form of biological programming, where structure and electrical behavior emerge from intracellular regulation.

However, this approach is high complexity (4/5–5/5) and better suited as a long-term research direction.

Week 03 HW lab-automation:

table of matter

Assignment: Python Script for Opentrons Artwork

https://opentrons-art.rcdonovan.com/?id=sux110hip535fnx

Statement of Intent — Why Reaction–Diffusion

I have been playing with reaction–diffusion algorithms for a long time. I keep returning to them out of curiosity — both for their history (their direct link to Alan Turing’s work on emergent patterns) and for what they suggest in terms of morphogenesis.

What interests me is not the naïve idea that “everything reduces” to these equations, but rather the fact that a very simple model can produce rich structures that resemble certain biological patterns. In developmental biology, reaction–diffusion models are often invoked to explain parts of gradient formation, repetition, or textural organization (and, to a limited extent, aspects of differentiation). Of course, real biological systems are far more complex: mechanical constraints, multi-scale signaling, feedback loops, and energetic limitations all play crucial roles.

Precisely for this reason, in the context of this Opentrons experiment, I am interested in translating a dynamic of emergence into a very concrete material gesture — an image composed of discrete deposits, where a continuous phenomenon (reaction and diffusion) becomes a physical field of dots.

Best website for undertsand Reaction-diffusion equation: https://www.karlsims.com/rd.html

from opentrons import types
import subprocess, sys
import numpy as np


metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
    'author': 'Vivien',
    'protocolName': 'Gray-Scott Reaction-Diffusion Pattern',
    'description': 'Generates a reaction-diffusion pattern on an agar plate using the Gray-Scott model, pipetting where the B concentration is high.',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

##############################################################################
###   Gray-Scott Model Global Parameters and Functions
##############################################################################

# Gray-Scott Model Parameters
GS_DA = 1.0
GS_DB = 0.5
GS_F = 0.014 # Feed rate. Experiment with values like 0.035 (spots), 0.014 (worms), 0.062 (moving spots)
GS_K = 0.045 # Kill rate. Experiment with values like 0.065 (spots), 0.045 (worms), 0.061 (moving spots)
GS_DT = 1.0

#1. Mitosis / spots : f=0.0367, k=0.0649
#2. Solitions / worms : f=0.030, k=0.062
#3. Coral / dense : f=0.0545, k=0.062
#4. Vibe : f = 0.029, K = 0.057
#5. labyrythn : f = 0.060, K = 0.063

def laplace(Z):
    """Weighted 3x3 Laplacian (Karl Sims style) using numpy.roll."""
    return (
        -1.0 * Z +
        0.2 * (np.roll(Z,  1, axis=0) + np.roll(Z, -1, axis=0) +
               np.roll(Z,  1, axis=1) + np.roll(Z, -1, axis=1)) +
        0.05 * (np.roll(np.roll(Z,  1, axis=0),  1, axis=1) +
                np.roll(np.roll(Z,  1, axis=0), -1, axis=1) +
                np.roll(np.roll(Z, -1, axis=0),  1, axis=1) +
                np.roll(np.roll(Z, -1, axis=0), -1, axis=1))
    )

def simulate_gray_scott(A, B, num_iterations):
    """
    Runs the Gray-Scott simulation for a given number of iterations.
    Modifies A and B arrays in place.
    """
    for _ in range(num_iterations):
        lapA = laplace(A)
        lapB = laplace(B)

        reaction = A * B * B
        A += (GS_DA * lapA - reaction + GS_F * (1 - A)) * GS_DT
        B += (GS_DB * lapB + reaction - (GS_K + GS_F) * B) * GS_DT
    return A, B # Return A and B for clarity, though they are modified in-place

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

well_colors = {
    'A1' : 'Red',
    'B1' : 'Green',
    'C1' : 'Orange'
}


def run(protocol):
  ##############################################################################
  ###   Load labware, modules and pipettes
  ##############################################################################

  # Tips
  tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')

  # Pipettes
  pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])

  # Modules
  temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)

  # Temperature Module Plate
  temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul',
                                                      'Cold Plate')
  # Choose where to take the colors from
  color_plate = temperature_plate

  # Agar Plate
  agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')  ## TA MUST CALIBRATE EACH PLATE!
  # Get the top-center of the plate, make sure the plate was calibrated before running this
  center_location = agar_plate['A1'].top()

  pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

  ##############################################################################
  ###   Patterning
##############################################################################

  ###
  ### Helper functions for this lab
  ###

  # pass this e.g. 'Red' and get back a Location which can be passed to aspirate()
  def location_of_color(color_string):
    for well,color in well_colors.items():
      if color.lower() == color_string.lower():
        return color_plate[well]
    raise ValueError(f"No well found with color {color_string}")

  # For this lab, instead of calling pipette.dispense(1, loc) use this: dispense_and_detach(pipette, 1, loc)
  def dispense_and_detach(pipette, volume, location):
      """
      Move laterally 5mm above the plate (to avoid smearing a drop); then drop down to the plate,
      dispense, move back up 5mm to detach drop, and stay high to be ready for next lateral move.
      5mm because a 4uL drop is 2mm diameter; and a 2deg tilt in the agar pour is >3mm difference across a plate.
      """
      assert(isinstance(volume, (int, float)))
      above_location = location.move(types.Point(z=location.point.z + 5))  # 5mm above
      pipette.move_to(above_location)       # Go to 5mm above the dispensing location
      pipette.dispense(volume, location)    # Go straight downwards and dispense
      pipette.move_to(above_location)       # Go straight up to detach drop and stay high

  ###
  ### YOUR CODE HERE to create your design (Gray-Scott Reaction-Diffusion Model)
  ###

  # Grid size for simulation
  size = 100 # Reduced size to make simulation faster and denser pattern on plate
  A = np.ones((size, size))
  B = np.zeros((size, size))

  # Create a central square seed for B
  r = 5
  A[size//2-r:size//2+r, size//2-r:size//2+r] = 0.5
  B[size//2-r:size//2+r, size//2-r:size//2+r] = 0.25

  # Add small random noise to the entire grid to introduce variation
  # This helps break symmetry and encourages diverse pattern formation
  A += np.random.rand(size, size) * 0.05 # Add noise up to 0.05
  B += np.random.rand(size, size) * 0.05 # Add noise up to 0.05

  B_min = float(B.min()); B_max = float(B.max());
  B_mean = float(B.mean())
  print("B stats:", B_min, B_mean, B_max)

  # Ensure values remain within bounds [0, 1] after adding noise
  A = np.clip(A, 0.0, 1.0)
  B = np.clip(B, 0.0, 1.0)

  print("Running Gray-Scott simulation...")

  # Define the number of simulation iterations here
  SIMULATION_ITERATIONS = 8000 # Initial run for 400 iterations

  A, B = simulate_gray_scott(A, B, SIMULATION_ITERATIONS)

  print(f"Gray-Scott simulation complete after {SIMULATION_ITERATIONS} iterations. Starting patterning.")
  print(f"To run more iterations, change the 'SIMULATION_ITERATIONS' variable in the code and re-run this cell and the visualization cell below.")


  # Patterning Parameters for Opentrons
  PIPETTE_VOLUME = 1 # 1uL per dot
  ASPIRATE_VOLUME = 16 # Aspirate up to this much at a time
  MAX_DOTS_PER_COLOR = 300 # Maximum number of dots to dispense per color

  # Colors to use for B components
  COLOR_FOR_PRIMARY_PATTERN = 'Green' # For the 'thick black lines' pattern
  COLOR_FOR_SECONDARY_PATTERN = 'Orange' # For other B concentrations
  COLOR_FOR_TERTIARY_PATTERN = 'Red' # For the highest B concentrations

  # Thresholds for B values
  # For 'thick black lines' (Green color)
  PRIMARY_PATTERN_B_LOWER_THRESHOLD = 0.40 * B_max
  PRIMARY_PATTERN_B_UPPER_THRESHOLD = 0.70 * B_max # This value will also define the lower bound for Red

  # For the secondary pattern (Orange color)
  # This will catch 'B' values that are above this, but not in the primary or tertiary pattern bands
  SECONDARY_PATTERN_B_THRESHOLD = 0.20 * B_max # Adjust this percentage as needed

  print("Using Red Pattern (Highest B) threshold: B >=", PRIMARY_PATTERN_B_UPPER_THRESHOLD)
  print("Using Green Pattern (Mid B - 'thick lines') B thresholds: (", PRIMARY_PATTERN_B_LOWER_THRESHOLD, ",", PRIMARY_PATTERN_B_UPPER_THRESHOLD, ")")
  print("Using Orange Pattern (Lower B) threshold: (", SECONDARY_PATTERN_B_THRESHOLD, ",", PRIMARY_PATTERN_B_LOWER_THRESHOLD, "]")


  # Agar plate dimensions (estimated for a standard 90mm round agar plate,
  # patterning in a 70x70mm square area roughly) to fit the pattern
  PATTERN_AREA_WIDTH_MM = 55 # Reduced from 85mm to fit within 40mm radius safe area
  PATTERN_AREA_HEIGHT_MM = 55 # Reduced from 85mm to fit within 40mm radius safe area

  # Calculate scaling factor to map grid coordinates to millimeters on the plate
  scale_x = PATTERN_AREA_WIDTH_MM / size
  scale_y = PATTERN_AREA_HEIGHT_MM / size

  # Add sampling step for clearer dots
  SAMPLING_STEP = 4 # Pipette every Nth pixel to create distinct dots
  DOT_SPACING_MM = SAMPLING_STEP * scale_x # Actual physical spacing between dot centers
  print(f"Desired dot spacing for distinct patterns: {DOT_SPACING_MM:.2f} mm (approx. 2mm drop diameter).")

  pipetted_count_primary = 0 # Green
  pipetted_count_secondary = 0 # Orange
  pipetted_count_tertiary = 0 # Red

  # --- Pipetting for Tertiary Pattern (Red component - highest B concentration) ---
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(ASPIRATE_VOLUME, location_of_color(COLOR_FOR_TERTIARY_PATTERN))
  current_pipette_volume = ASPIRATE_VOLUME

  print(f"Starting patterning for Tertiary Pattern with {COLOR_FOR_TERTIARY_PATTERN}.")

  for y in range(0, size, SAMPLING_STEP):
      for x in range(0, size, SAMPLING_STEP):
          if B[y, x] >= PRIMARY_PATTERN_B_UPPER_THRESHOLD:
              if current_pipette_volume < PIPETTE_VOLUME:
                  pipette_20ul.aspirate(ASPIRATE_VOLUME, location_of_color(COLOR_FOR_TERTIARY_PATTERN))
                  current_pipette_volume += ASPIRATE_VOLUME

              x_offset_mm = (x - size / 2) * scale_x
              y_offset_mm = (y - size / 2) * scale_y

              adjusted_location = center_location.move(types.Point(x=x_offset_mm, y=y_offset_mm))
              dispense_and_detach(pipette_20ul, PIPETTE_VOLUME, adjusted_location)
              current_pipette_volume -= PIPETTE_VOLUME
              pipetted_count_tertiary += 1
          if pipetted_count_tertiary >= MAX_DOTS_PER_COLOR:
              break
      if pipetted_count_tertiary >= MAX_DOTS_PER_COLOR:
          break
  pipette_20ul.drop_tip()
  print(f"Total {pipetted_count_tertiary} drops pipetted for Tertiary Pattern using {COLOR_FOR_TERTIARY_PATTERN}.")


  # --- Pipetting for Primary Pattern (Green component - 'thick lines') ---
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(ASPIRATE_VOLUME, location_of_color(COLOR_FOR_PRIMARY_PATTERN))
  current_pipette_volume = ASPIRATE_VOLUME

  print(f"Starting patterning for Primary Pattern with {COLOR_FOR_PRIMARY_PATTERN}.")

  for y in range(0, size, SAMPLING_STEP):
      for x in range(0, size, SAMPLING_STEP):
          if (B[y, x] > PRIMARY_PATTERN_B_LOWER_THRESHOLD) and (B[y, x] < PRIMARY_PATTERN_B_UPPER_THRESHOLD):
              if current_pipette_volume < PIPETTE_VOLUME:
                  pipette_20ul.aspirate(ASPIRATE_VOLUME, location_of_color(COLOR_FOR_PRIMARY_PATTERN))
                  current_pipette_volume += ASPIRATE_VOLUME

              x_offset_mm = (x - size / 2) * scale_x
              y_offset_mm = (y - size / 2) * scale_y

              adjusted_location = center_location.move(types.Point(x=x_offset_mm, y=y_offset_mm))
              dispense_and_detach(pipette_20ul, PIPETTE_VOLUME, adjusted_location)
              current_pipette_volume -= PIPETTE_VOLUME
              pipetted_count_primary += 1
          if pipetted_count_primary >= MAX_DOTS_PER_COLOR:
              break
      if pipetted_count_primary >= MAX_DOTS_PER_COLOR:
          break
  pipette_20ul.drop_tip()
  print(f"Total {pipetted_count_primary} drops pipetted for Primary Pattern using {COLOR_FOR_PRIMARY_PATTERN}.")

  # --- Pipetting for Secondary Pattern (Orange component) ---
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(ASPIRATE_VOLUME, location_of_color(COLOR_FOR_SECONDARY_PATTERN))
  current_pipette_volume = ASPIRATE_VOLUME

  print(f"Starting patterning for Secondary Pattern with {COLOR_FOR_SECONDARY_PATTERN}.")

  for y in range(0, size, SAMPLING_STEP):
      for x in range(0, size, SAMPLING_STEP):
          # Dispense Orange if B is above its threshold, AND NOT in the Green or Red band
          if (B[y, x] > SECONDARY_PATTERN_B_THRESHOLD) and (B[y, x] <= PRIMARY_PATTERN_B_LOWER_THRESHOLD):
              if current_pipette_volume < PIPETTE_VOLUME:
                  pipette_20ul.aspirate(ASPIRATE_VOLUME, location_of_color(COLOR_FOR_SECONDARY_PATTERN))
                  current_pipette_volume += ASPIRATE_VOLUME

              x_offset_mm = (x - size / 2) * scale_x
              y_offset_mm = (y - size / 2) * scale_y

              adjusted_location = center_location.move(types.Point(x=x_offset_mm, y=y_offset_mm))
              dispense_and_detach(pipette_20ul, PIPETTE_VOLUME, adjusted_location)
              current_pipette_volume -= PIPETTE_VOLUME
              pipetted_count_secondary += 1
          if pipetted_count_secondary >= MAX_DOTS_PER_COLOR:
              break
      if pipetted_count_secondary >= MAX_DOTS_PER_COLOR:
          break
  pipette_20ul.drop_tip()
  print(f"Total {pipetted_count_secondary} drops pipetted for Secondary Pattern using {COLOR_FOR_SECONDARY_PATTERN}.")

# Execute Simulation / Visualization -- don't change this code block
protocol = OpentronsMock(well_colors)
run(protocol)
protocol.visualize()

 B stats: 1.1769677182305039e-07 0.02738330028607403 0.2993330786497384
Running Gray-Scott simulation...
Gray-Scott simulation complete after 8000 iterations. Starting patterning.
To run more iterations, change the 'SIMULATION_ITERATIONS' variable in the code and re-run this cell and the visualization cell below.
Using Red Pattern (Highest B) threshold: B >= 0.20953315505481687
Using Green Pattern (Mid B - 'thick lines') B thresholds: ( 0.11973323145989537 , 0.20953315505481687 )
Using Orange Pattern (Lower B) threshold: ( 0.059866615729947684 , 0.11973323145989537 ]
Desired dot spacing for distinct patterns: 2.20 mm (approx. 2mm drop diameter).
Starting patterning for Tertiary Pattern with Red.
Total 0 drops pipetted for Tertiary Pattern using Red.
Starting patterning for Primary Pattern with Green.
Total 0 drops pipetted for Primary Pattern using Green.
Starting patterning for Secondary Pattern with Orange.
Total 0 drops pipetted for Secondary Pattern using Orange.

=== VOLUME TOTALS BY COLOR ===
	Orange:		 aspirated 16	 dispensed 0		##### WASTING BIO-INK : more aspirated than dispensed!
	Green:		 aspirated 16	 dispensed 0		##### WASTING BIO-INK : more aspirated than dispensed!
	Red:		 aspirated 16	 dispensed 0		##### WASTING BIO-INK : more aspirated than dispensed!
	[all colors]:	[aspirated 48]	[dispensed 0]

=== TIP COUNT ===
	 Used 3 tip(s)  (ideally exactly one per unique color)

I used Gemini (2.5) to help translate a Gray-Scott reaction–diffusion model into a stable Opentrons protocol and to choose a robust rendering strategy (iso-contour band → dot sampling) that produces reliable aesthetic output under time/volume constraints.

Post-Lab Questions

Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

Published Example: Automation in Synthetic Biology

Write a description about what you intend to do with automation tools for your final project.

Automation as a Tool to Explore the Behavioral Landscape of Living Materials

Automation tools such as Opentrons have been widely used for:

• High-throughput DNA assembly
• CRISPR editing workflows
• Combinatorial genetic library screening
• Automated protein expression testing

These systems increase reproducibility, reduce human variability, and enable scalable experimentation. In most cases, automation serves to optimize molecular workflows and accelerate genetic engineering cycles.

However, my interest in automation is different.

Moving Beyond Optimization

In classical bioprocess engineering, automation is used to:

• Optimize growth conditions
• Reduce experimental noise
• Standardize reproducibility
• Improve yield

But living materials — such as bacterial cellulose biofilms — do not behave like linear industrial systems.

They exhibit:

• Non-linear responses
• Narrow stability windows
• Emergent morphologies
• Phase transitions under small parameter shifts

Traditional experimental design (e.g., Taguchi matrices) assumes relatively smooth and predictable response surfaces. In living systems, this assumption often fails. Small changes in pH, oxygen availability, carbon source, or metal ions can lead to abrupt structural transitions.

Automation, in this context, is not merely an efficiency tool. It becomes a way to systematically explore instability and emergence.

My Intended Use of Automation

1. Mapping Morphogenetic Regimes of Bacterial Cellulose

Instead of optimizing for maximum growth, I aim to use automation to:

• Vary glucose concentration
• Modulate copper availability (for Tyr1-dependent melanin production)
• Adjust nitrogen sources
• Introduce controlled gradients

The objective is to map how living cellulose changes:

• Thickness
• Porosity
• Impedance
• Mechanical anisotropy
• Conductive behavior

This transforms automation into a cartographic tool:

Not optimizing yield, but mapping the behavioral topology of a living material.

2. Spatial Programming of Living Matter

A more ambitious direction is to use liquid handling automation to:

• Deposit gradients of dopants
• Create patterned functional zones
• Introduce local conductivity modulation
• Encode anisotropy into growing pellicles

Instead of post-processing materials (e.g., adding graphene or PEDOT in situ), this approach attempts to let functionality emerge during growth. Automation allows spatial control. Living matter performs the structuration.

3. Toward a Cybernetic Living Material System

A future extension would integrate measurement and feedback:

1. Grow bacterial cellulose
2. Measure impedance or electrical response
3. Adjust copper or nutrient concentration automatically
4. Iterate

This creates a cybernetic loop: Living material → Measurement → Algorithmic adjustment → Modified growth Automation becomes a mediator between biological behavior and computational control.

Why This Matters

This project shifts the role of automation from:

Eliminating biological variability
to: Engaging systematically with biological variability.

Rather than forcing the living system into industrial predictability, automation is used to:

• Detect bifurcations
• Explore phase transitions
• Reveal hidden regimes
• Enable programmable morphogenesis

In this sense, automation becomes a bridge between:

• Synthetic biology
• Biofabrication
• Morphogenesis
• Cybernetic design

It allows living matter to be explored not as a static substrate, but as a dynamic, programmable system.

Final Project Ideas

https://docs.google.com/presentation/d/1FAFN4YYisOcso3CI5F3W3Z7hj6_n9D1vAhVUywQXKPU/edit?slide=id.g3c8d4cf45e8_0_55#slide=id.g3c8d4cf45e8_0_55