Week 1 HW: Principles and Practices

title: ‘Week 1 HW: Principles & Practices’ weight: 10

Introduction and Motivation

This week emphasized that biological engineering is not only about what we can build, but how and why we choose to build it. The lectures and recitation highlighted that ethics, safety, and governance should not be treated as external constraints applied after a technology is developed, but rather as integral design dimensions from the earliest stages of a project.

Revisiting a previous biosensing project through the HTGAA framework allowed me to explicitly articulate design decisions that were originally motivated by technical performance, but which also carry strong ethical, safety, and governance implications. This exercise helped me move beyond a purely technical evaluation and reflect more deeply on responsibility, context, and downstream impact.

Biological Engineering Application

The biological engineering application I focus on is a cell-free biosensor based on a Pb²⁺-specific DNAzyme coupled to CRISPR-Cas12a, designed for the ultrasensitive detection of lead in water.

Lead contamination represents a serious public health concern, with no safe threshold for chronic exposure. While analytical techniques such as ICP-MS or atomic absorption spectroscopy provide high sensitivity, they require centralized laboratories, specialized equipment, and trained personnel, limiting their accessibility for frequent or decentralized monitoring.

Previous generations of biological sensors, including whole-cell bacterial biosensors, demonstrated the feasibility of biological detection but suffered from long response times, higher detection limits, and biosafety concerns related to the use of living genetically modified organisms. In contrast, this project deliberately adopts a cell-free, in vitro architecture, translating the presence of Pb²⁺ into a fluorescent signal in under one hour.

The motivation behind this application is to combine high sensitivity, portability, and safety by design, enabling environmental monitoring in settings where conventional laboratory infrastructure is unavailable, while minimizing biological risks.

Governance and Policy Goals

Reframing this project within the HTGAA framework led to the identification of several governance and policy goals that extend beyond technical performance.

Goal A – Prevent harm and misuse (Non-malfeasance)

• Avoid enabling biological manipulation or amplification of hazardous agents.
• Prevent repurposing of the sensing platform for unintended or harmful biological activities.

Goal B – Enhance biosafety and biosecurity

• Minimize risks associated with handling living organisms by using a fully cell-free system.
• Reduce the likelihood of accidental environmental release or uncontrolled replication.

Goal C – Promote constructive and equitable use

• Enable access to sensitive environmental monitoring tools without requiring advanced infrastructure.
• Support public health and environmental decision-making rather than surveillance or coercive applications.

Option 1 – Safe-by-design, cell-free system architecture

Purpose
Many biosensing platforms rely on living cells, which introduce biosafety, containment, and regulatory challenges. This project replaces whole-cell systems with a fully cell-free, non-replicative architecture.

Design
This approach is implemented directly by academic researchers during the design phase and can be reinforced by funding agencies that prioritize safe-by-design technologies.

Assumptions

• Eliminating living components significantly reduces biosafety risks.
• Performance can be maintained or improved in vitro.

Risks of Failure and “Success”

• Failure: reduced robustness in complex environmental matrices.
• Success risk: overconfidence in technical safeguards without complementary governance measures.

Option 2 – Transparent documentation of limitations and failures

Purpose
Scientific reporting often emphasizes successful outcomes while underreporting failures. This project explicitly documents experimental failures, matrix effects, and design trade-offs.

Design
Implemented through detailed lab records and public documentation on the course website, supported by academic training and publication norms.

Assumptions

• Transparency improves reproducibility, safety awareness, and ethical reflection.

Risks of Failure and “Success”

• Failure: documentation becomes superficial or performative.
• Success risk: increased reporting burden for early-stage researchers.

Option 3 – Context-specific deployment guidelines

Purpose
Environmental biosensors may be deployed in diverse contexts with different ethical implications. This option proposes context-aware guidelines distinguishing research, environmental monitoring, and regulatory use.

Design
Developed by public health and environmental agencies in collaboration with researchers and adapted to local regulatory frameworks.

Assumptions

• Misuse risk depends strongly on deployment context.
• Local institutions have the capacity to enforce guidelines.

Risks of Failure and “Success”

• Failure: inconsistent enforcement across regions.
• Success risk: delayed deployment in high-need environments.

Scoring Matrix

Prioritization and Recommendation

Based on this analysis, the highest priority should be given to Option 1 (cell-free, safe-by-design architecture), complemented by Option 2 (transparent documentation). Together, these strategies embed ethical and governance considerations directly into technical design and research practice, rather than relying solely on downstream regulation.

This combined approach is particularly relevant for academic research institutions and funding agencies, where early design choices strongly influence future applications. While these decisions may introduce additional development effort, they significantly enhance safety, trust, and long-term societal benefit.

Weekly Reflection

A key insight from this week is that biosensing technologies are not ethically neutral, even when developed for public health or environmental protection. Portability and accessibility, while beneficial, can also enable misuse if deployment contexts are not carefully considered.

Engaging with the recitation examples reinforced the importance of situating my project at the detection and prevention end of the biological intervention spectrum. This week shifted my perspective from asking only “can this work?” to also asking “should it work this way, and under what conditions?”, a mindset I intend to maintain throughout the course and into the final project.

Documentation Practice

In alignment with the course emphasis on documentation, I am recording all in-silico design steps, experimental iterations, failed conditions, and troubleshooting decisions. This documentation is intended to support reproducibility, collaborative learning, and ethical transparency, and to make visible the full experimental journey rather than only successful outcomes.

George Church – Homework Question

Question chosen: (AA:AA and NA:NA codes) What code would you suggest for AA:AA interactions?

Why we need a code (and what it can/can’t do)

Protein–protein interactions are not “pairwise letters” like Watson–Crick base pairing. They depend on 3D context (distance, solvent exposure, orientation, dynamics, PTMs, local environment). Still, a useful AA:AA “code” can exist as a coarse-grained interaction alphabet: a compact way to describe which residue pairs are likely to attract/repel or stabilize contacts, similar in spirit to how other biological codes map chemistry into discrete symbols.

So the goal is not a perfect predictor of structure, but a portable interaction language that is:

• symmetric (A–B = B–A),
• composable (many contacts → one interface),
• extendable (can include non-standard amino acids / PTMs),
• and human-usable (a small alphabet rather than a 20×20 table).

Proposed AA:AA interaction code (two-layer)

Layer 1 — Assign each amino acid to an “interaction class”

Define a small set of classes that reflect dominant chemistry:

H = hydrophobic aliphatic (A, V, L, I, M)
Ar = aromatic (F, Y, W)
P = polar uncharged (S, T, N, Q)
D+ = cationic / H-bond donor-leaning (K, R, H, plus N-termini)
A− = acidic (D, E, plus C-termini)
S = sulfur/thiol special (C)
G = glycine (conformational special)
Pro = proline (conformational breaker)

Note: H and Ar are separated because π-stacking and cation-π interactions are distinct modes; Cys is treated separately because it can form disulfides and participate in redox/metal interactions.

Layer 2 — Use a compact “interaction operator” between classes

Use a small set of operators that describe the type of contact:

⊕ = favorable hydrophobic packing (H–H, H–Ar, Ar–Ar stacking)
± = electrostatic attraction (D+–A− salt bridge)
≠ = electrostatic repulsion (D+–D+, A−–A−)
⋯ = hydrogen bonding (P–P, P–D+, P–A−; and some aromatic H-bonding cases)
π+ = cation-π (D+–Ar)
S–S = disulfide bond (S–S; context-dependent oxidation/geometry)
⟂ = conformational modulation (Pro/Gly effects; Pro–X, G–X)

This yields a compact grammar:

• Contact = Class(residue1) OP Class(residue2)
• Example: Lys–Glu → D+ ± A−
• Example: Leu–Ile → H ⊕ H
• Example: Arg–Trp → D+ π+ Ar
• Example: Cys–Cys → S–S (only if oxidation state and geometry allow)

Why this code is useful

1. Small alphabet, big coverage: compresses 20×20 possibilities into a readable set of “interaction modes.”
2. Extendable to non-standard amino acids / PTMs: you can add classes/operators for modified residues (e.g., phospho-Ser behaving more A−-like; methyl-Lys tuning D+ strength).
3. Bridges to protein design: interface reasoning often uses these primitives (hydrophobic core + H-bond networks + salt bridges + cation-π + disulfides).

Known limitations (important)

• Context dependence: the same pair can change behavior depending on burial, pH, dielectric, water mediation, and geometry.
• Not a folding code: this is an interaction vocabulary, not a full structural specification.
• Many-body effects: cooperative networks (packing + H-bond chains) are only approximated by pairwise labels.

Optional refinement (if more precision is needed)

Add an environment tag:

• (B) buried, (E) exposed
  Example: D+ ± A−(B) often stronger than D+ ± A−(E).

AI / Prompt citation

I used ChatGPT to draft and structure this answer. Given Church’s lecture framing of codes beyond DNA→AA, propose a concise, extensible AA:AA interaction code that captures major interaction types (hydrophobic, salt bridges, H-bonds, cation-π, disulfide).