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/)]