Week 1 HW: Principles and Practices

1. Biological Engineering Application/Tool I Want to Develop

I would like to explore developing bioadaptive smart textiles that can dynamically regulate heat and cold based on environmental and bodily signals. The core idea is: a textile that behaves less like fabric and more like a living interface, sensing temperature/humidity (and eventually physiology like skin temp or sweat composition), then changing state the way nature does when it crosses thresholds (like phase transitions: water to ice, or protein conformation shifts). I’m fascinated by systems that compute through material behavior, not just microcontrollers. What draws me to this is the idea of information processing in matter. In synthetic biology, we learn how cells act like computers: inputs, logic, outputs, composable hierarchies. I’m interested in translating that logic into materials - textiles that sense, compute, and respond through their structure, not just software. This would be a distributed system that alters function based on the signals it receives.Beyond clothing, I see this evolving as a platform for designing systems that sit at the intersection of climate, comfort, and human experience - while forcing us to confront what it means to design with materials that are adaptive, persistent, and potentially biological.

2. Governance/Policy Goals

Goal A - Prevent blind spots harm through safety-by-design Sub-goals:

• Constrain what the system can do (no uncontrolled growth, replication, or mutation).
• Ensure predictable failure modes (the system fails inertly, not biologically).
• Require testing across real-world contexts (skin contact, heat stress, washing, degradation).

Goal B - Protect the environment across the full lifecycle Sub-goals:

• Clear biodegradation and disposal pathways.
• Prevent micro-shedding or release of bioactive components.
• Avoid shifting environmental risk to under-regulated supply chains.

Goal C - Preserve autonomy, privacy, and equity Sub-goals:

• Prevent bioadaptive textiles from becoming covert biometric surveillance tools.
• Ensure user consent and control over sensing and data.
• Avoid creating a luxury-only “bio-enhancement” layer.

3. Three Governance Actions

Option 1 - Staged safety case + material risk tiers

• Purpose:

Current regulations treat textiles as non-reactive. I propose a staged approval process that evaluates biological behavior and lifecycle risks before scaling.

• Design:
  • Regulators + standards bodies require a “safety case” describing sensing, actuation, containment, and end-of-life.
  • Risk tiers (inactive → bio-derived → living systems) determine scrutiny level.
• Assumptions:
  • Regulators can adapt beyond device/drug frameworks.
  • Testing infrastructure can scale without blocking research.
• Risks:
  • Too burdensome means innovation moves elsewhere.
  • Compliance becomes box-checking instead of real safety.

Option 2 - Built-in technical containment

• Purpose:

  Embed governance directly into the material, not just policy.

• Design:

  • Favor non-replicating or cell-free systems.
  • If living components exist: layered containment, bounded responses, limited lifetimes.
  • No default connectivity or networking.

• Assumptions:

  • Containment remains robust under wear, washing, and misuse.

• Risks:

  • Degradation over time weakens safeguards.
  • “Safety” branding creates false confidence.

Option 3 - User rights + ban on covert physiological surveillance

• Purpose:

Prevent textiles from becoming invisible sensing infrastructure.

• Design:
  • Clear labeling of sensing capabilities.
  • Opt-in consent, local processing, and repairability.
  • Prohibit mandatory biometric clothing in workplaces/schools.
• Assumptions:
  • Users can meaningfully exercise choice.
  • Enforcement exists.
• Risks:
  • Consent fatigue.
  • Privacy theater instead of real protection.

4. Governance Actions Matrix (1=best) (3=problematic)

5. What I Would Prioritize

I would prioritize Option 2 (technical containment) first, supported by Option 1 (staged safety cases), with Option 3 (user rights) as a necessary social layer once sensing enters the picture.

• Option 2 (technical containment) is the most “biofabrication-aligned” governance move: it treats the technology as a living/active system and puts guardrails inside the material itself. This feels like the most honest approach to working with biology - you don’t just regulate it externally; you design constraints into it.

• Option 1 (safety case + staged release) is essential because consumer contexts are chaotic. Even well-designed containment can fail across manufacturing variation, wear, and ecosystems. We need a lifecycle framework: testing, monitoring, iteration.

• Option 3 (user rights) becomes critical the moment sensing/physiology enters the picture - and I think it will, because markets will push textiles toward “personalization,” and personalization tends to become surveillance if you don’t actively prevent it.

  This aligns with what stood out to me in class: when you design with biology, you’re not just shipping a product, you’re stewarding behavior over time. Emergence isn’t a failure; it’s a property. That shifts responsibility from one-time approval to continuous governance.

Trade-offs I’m accepting:

• More friction early on (especially for smaller teams). I’d offset this by advocating for public funding, shared testing infrastructure, and open safety standards** so compliance doesn’t become a paywall.
• Some capabilities might be delayed (especially “living” versions).

Key uncertainties:

• Whether containment strategies remain robust under repeated washing / abrasion.
• How “bioadaptive” a textile can be without drifting into biological persistence.
• Whether governance keeps pace with product cycles and hype.

Audience I’d address: If I’m writing this as a recommendation, it’s to a standards consortium + federal agency leadership (in the spirit of how safety standards shaped aviation, electronics, and medical devices). I’d want a shared playbook so innovation can move fast without externalizing risk.

Weekly Reflection: ethical concerns that surfaced for me

The ethical concern that felt most new to me this week is how easily adaptive systems blur the line between tool and organism. When you engineer biology, you’re not only designing function, you’re designing behavior over time. It also means you can build systems that behave in ways you didn’t fully anticipate once they meet real environments. Emergence unpredictability is a property of this process and requires continuous stewardship.

Governance has to evolve from controlling static artifacts to managing living or semi-living systems - through design constraints, lifecycle thinking, and explicit boundaries around consent and use.

Week 2: Lecture Prep

Homework Questions from Professor Jacobson

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

DNA polymerase has an intrinsic error rate of approximately 1 in 10⁶ base pairs, while the human genome is roughly 3.2 billion base pairs long. On its own, this would result in thousands of errors per replication. Biology resolves this through proofreading polymerases, post-replication mismatch repair systems, and evolutionary selection, which together reduce the effective error rate to near one mutation per genome per replication cycle

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

An average human protein can theoretically be encoded by ~10¹⁶⁵ different DNA sequences due to codon redundancy. In practice, most of these sequences do not function because DNA and RNA are physical molecules subject to constraints from physics such as translation-dependent protein folding. As a result, only a small subset of possible sequences produce functional protein expression.

Homework Questions from Dr. LeProust

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

The dominant method is solid-phase phosphoramidite chemical synthesis, where DNA is built one nucleotide at a time on a solid support through repeated cycles of coupling, capping, oxidation, and deprotection.

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

It’s a probability limit that starts at the fundamental level as errors compound with each step. Because chemical DNA synthesis is stepwise and imperfect, and small inefficiencies at each nucleotide addition accumulate exponentially with length. Beyond ~200 nt, the yield of full-length, error-free molecules becomes extremely low due to base errors.

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

We chemically synthesize oligos, not genes. Genes are built by assembling short oligos because direct chemical synthesis does not scale. A 2000 bp gene would require thousands of sequential chemical coupling steps, causing error rates. As a result, genes are not synthesized directly but are assembled hierarchically from many shorter oligos using enzymatic processes such as PCR.

Homework Question from George Church

1. What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

   The 10 essential amino acids across animals

   Histidine - Isoleucine - Leucine - Lysine - Methionine - Phenylalanine - Threonine - Tryptophan - Valine - Arginine

   The “lysine contingency” proposes lysine as a potential dependency point because animals cannot synthesize it and must obtain it externally. It is already deeply embedded in biological environments (food chains, microbes, decay)

   Source:

   [(https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2017.4858)] [(https://www.purina.com/articles/dog/health/nutrition/dog-nutrition-basics)] [(https://www.ncbi.nlm.nih.gov/books/NBK557845/)]

Week 2 HW: DNA Read Write and Edit

Part 3: DNA Design Challenge

3.1. Choose your protein.

Chosen protein: TlpA (temperature-sensing transcriptional repressor) from Salmonella typhimurium (UniProt: Q56080)

Why I chose it:

• TlpA is a protein “thermometer”: it changes its oligomeric/structural state with temperature, which is exactly the kind of temperature-triggered phase/structure shift that maps conceptually to smart textiles that respond to heat/cold.
• In synthetic biology, thermosensitive repressors like TlpA are used as temperature-controlled switches (gene expression ON/OFF based on temperature).

TlpA_Salmonella_typhimurium protein sequence (FASTA-style; source: UniProt Q56080):

https://www.ncbi.nlm.nih.gov/protein/Q56080

 mrpatyepeq iieaglalqa egrnitgfal rnqvgggnpt rlrqiwdeyq asqstvvtep
       61 vaelpvevae evkavsaals eritqlatel ndkavraaer rvaevtraag eqtaqaerel
      121 adaaqtvddl eekldelqdr ydsltlales erslrqqhdv emaqlkerla aaeentrqre
      181 eryqeqktvl qdalnaeqaq hkntredlqk rleqisaean arteelkser dkvntlltrl
      241 esqenalase rqqhlatret lqqrleqaia dtqarageia lerdrvsslt arlesqekas
      301 seqlvrmgse iaslterctq lenqrddarl etmgeketva dlrgeaealk rqnqslmaal
      361 sgnkqtggqn a

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

Due to codon degeneracy, this is one possible DNA sequence encoding TlpA. Multiple valid nucleotide sequences could encode the same protein depending on codon choice.

Using the benchling back-translation tool I generated the DNA coding sequence corresponding to the TlpA amino acid sequence.

This version uses standard bacterial codons suitable for E. coli expression.

tlpA_reverse_translated_DNA ATGCGTCCGGCGACCTACTACGAACCGGAACAGATTATTGAAGCGGGCCTGGCGCTGCAG GCGGAAGGCCGTAACATTACCGGCTTTGCGCTGCGTAACCAGGTGGGCGGCGGCAACCCG ACCCGTCTGCGTCAGATTTGGGATGAATACCAGGCGTCGCAGTCTCAGACCGTGGTCACC GAACCGGTGGCGGAGCTGCCGGTGGAGGTCGCGGAGGAGGTGAAAGCGGTGTCGGCGGCG CTGTCGGAGCGTATTACCCAGCTGGCGACCGAGCTGAACGACAAGGCGGTGCGTGCGGCG GAGCGCGTGGCAGAGGTGACCCGTGCGGCGGAGCAGACCGCGCAGGCGGAGCGCGAGCTG GCGGACGCGGCGCAGACCGTGGACGACCTGGAGGAGAAGCTGGACGAGCTGCAGGACCGC TACGACAGCCTGACCCTGGCGCTGGAGTCGGAGCGTTCGCTGCGTCAGCAGCACGACGTG GAGATGGCGCAGCTGAAGGAGCGTCTGGCGGCGGAGGAGAACACCCGTCAGCGTGAGGAG CGTTACCAGGAGCAGAAGACCGTGCTGCAGGACGCGCTGAACGCGGAGCAGGCGCAGCAC AAGAACACCCGTGAGGACCTGCAGAAGCGTCTGGAGCAGATTTCTGCGGAGGCCAACGCG CGTACCGAGGAGCTGAAGTCGGAGCGTGACAAGGTGAACACCCTGCTGACCCGTCTGGAG TCGCAGGAGAACGCGCTGGCGTCGGAGCGTCAGCAGCACCTGGCGACCCGTGAGACCCTG CAGCAGCGTCTGGAGCAGGCGATCGCGGACACCCAGGCGCGTGCGGGCGAGATCGCGCTG GAGCGCGACCGCGTGTCGTCGCTGACCGCGCGTCTGGAGTCGCAGGAGAAGGCGTCGTCG GAGCAGCTGGTGCGCATGGGCTCGGAGATCGCGTCGCTGACCGAGCGTTGCACCCAGCTG GAGAACCAGCGCGACGACGCGCGTCTGGAGACCATGGGCGAGAAGGAGACCGTGGCGGAC CTGCGCGGCGAGGCGGAGGCGCTGAAGCGTCAGAACCAGTCGCTGATGGCGGCGCTGTCT GGCAACAAGCAGACCGGCGGCCAGAACGCGTAA

3.3. Codon optimization.

Although multiple codons encode the same amino acid, different organisms do not use synonymous codons equally. This is known as codon bias.

Codon optimization rewrites the DNA sequence without altering the amino acid sequence, selecting synonymous codons that match the host organism’s translational machinery. This increases protein expression efficiency while preserving protein function.

Codon optimization does not change the protein itself, it changes how efficiently the biological system produces it.

I optimized the TlpA coding sequence for Escherichia coli (E. coli, K-12 strain).

Why? E. coli is the most widely used host organism for recombinant protein expression. It allows rapid prototyping and testing of engineered constructs. Its codon usage bias is well-documented, making optimization straightforward. Since TlpA is a bacterial protein, expressing it in E. coli maintains a compatible folding and regulatory environment. Given that TlpA is a temperature-sensitive transcriptional repressor, optimizing for E. coli allows efficient production and functional testing of its temperature-responsive behavior under controlled lab conditions.

3.4. You have a sequence! Now what?

Once the codon-optimized DNA sequence for TlpA is designed, it can be used to produce the protein using either cell-dependent or cell-free expression systems.

Cell-Dependent (In Vivo) Expression

The optimized TlpA gene is inserted into a plasmid containing a promoter and ribosome binding site, then transformed into E. coli.

Inside the cell:

1. Transcription : RNA polymerase reads the DNA and produces mRNA (T is replaced by U).
2. Translation : Ribosomes read the mRNA in 3-nucleotide codons and assemble amino acids into the TlpA protein.
3. Folding : The protein folds into its functional, temperature-sensitive structure.

This allows the engineered cells to produce TlpA using their natural molecular machinery.

Cell-Free (In Vitro) Expression

Alternatively, the DNA can be added to a cell-free transcription–translation system containing purified RNA polymerase, ribosomes, tRNAs, and amino acids.

In this setup, DNA → mRNA → protein occurs in a test tube, allowing rapid and controlled protein production without living cells.

Part 5: DNA Read/Write/Edit

5.1 DNA Read

(i) I want to read the biological code behind adaptive matter. I would sequence DNA from organisms that naturally exhibit signal-responsive material behavior, such as:

• Thermosensitive bacteria
• Phase-separating proteins
• Elastin-like polypeptides (ELPs)
• Stress-responsive regulatory networks

Specifically, I would sequence genes involved in:

• Temperature-sensitive protein folding
• Phase transitions
• Environmental sensing pathways

These systems encode how matter changes state in response to information. Sequencing them allows us to understand the genetic instructions that enable biological materials to:

• Fold differently at different temperatures
• Assemble or disassemble
• Switch functions based on environmental inputs

ii) Based on the sequencing technologies mentioned, I would use Illumina and Nanopore to enable genome-scale decoding of signal-responsive systems.

1. What generation is this?

• Illumina → Second-generation sequencing
• Nanopore/PacBio → Third-generation sequencing

Illumina provides high accuracy and deep coverage.

Long-read sequencing captures full-length genes and structural context.

2. What is the input and preparation?

Input: Extracted genomic DNA or plasmid DNA.

Preparation steps (Illumina):

1. DNA extraction
2. Fragmentation
3. Adapter ligation
4. PCR amplification
5. Cluster generation (bridge amplification on flow cell)

3. How does it decode bases?

Sequencing by synthesis (Illumina):

• Fluorescently labeled reversible terminator nucleotides are added one at a time.
• After each incorporation, a camera detects fluorescence.
• Each color corresponds to A, T, C, or G.
• Software converts fluorescence signals into base calls.

This transforms chemical events into digital sequence information.

4. What is the output?

• Millions of short DNA reads
• FASTQ files with quality scores
• Digital base sequences for downstream analysis

5.2 DNA Write

(i) I would synthesize a temperature-responsive genetic circuit, combining:

• TlpA (temperature-sensitive repressor)
• A regulatory promoter
• A structural protein domain (e.g., elastin-like polypeptide)
• A reporter gene (GFP)

This would encode a system where:

Temperature signal → Gene regulation → Material phase shift

This directly supports programming matter to change state based on environmental signals.

(ii) For the DNA synthesis I would use :

Silicon-based high-throughput oligo synthesis (Twist Bioscience model)

Gene assembly (Gibson Assembly)

1. Essential steps of DNA synthesis

• Chemical synthesis of short oligonucleotides
• Cleavage and deprotection
• Assembly into longer constructs
• Cloning into plasmid
• Sequence verification

2. Limitations

• Error rates increase with length
• Repetitive sequences are difficult
• Cost increases with scale
• Large constructs require hierarchical assembly

5.3 DNA Edit

(i) I would edit microbial genomes to embed temperature-sensitive regulatory modules directly into the chromosome. This would allow cells to produce structural or phase-transition proteins only under defined environmental conditions, enabling adaptive biological materials.

ii) I would use CRISPR-Cas9 genome editing tool potentially for base editing and prime editing

1. How does CRISPR edit DNA?

• A guide RNA targets a specific DNA sequence.
• Cas9 creates a double-strand break.
• The cell repairs the break:
  • NHEJ → insertions/deletions
  • HDR → precise edits using a repair template

2. Preparation and input

Required components:

• Guide RNA
• Cas9 protein or plasmid
• Donor DNA template (for precise edits)
• Target cells

Design steps include PAM identification and off-target analysis.

3. Limitations

• Off-target edits
• Variable efficiency
• Delivery challenges
• HDR is less efficient than NHEJ

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project