Week 1 HW: Principles and Practices

HTGAA Week 1 Banner HTGAA Week 1 Banner

Week 1 HW — Principles & Practices

Biological Engineering Application & Governance Analysis

1. Biological Engineering Application / Tool

Application:
I want to develop a bacterial biosensor for rapid detection of antibiotic-resistant pathogens in clinical samples. The biosensor uses engineered E. coli containing genetic circuits that activate fluorescent protein expression when they detect beta-lactamase activity or other resistance markers from nearby bacteria.

Why this application:
As a Pharm.D student, I’ve witnessed how current resistance testing takes 24-48 hours using culture-based methods. This delay forces physicians to prescribe broad-spectrum antibiotics empirically, which often fails and accelerates resistance development. A rapid biosensor could provide results within 2-4 hours, enabling targeted antibiotic selection on the same day. The technology is HTGAA-feasible because it uses standard E. coli chassis, well-characterized promoters (like those responsive to beta-lactamase degradation products), and simple fluorescent reporters (GFP/RFP). This addresses a critical clinical gap—the time between infection diagnosis and appropriate treatment—using accessible synthetic biology techniques that I can learn and implement during the course.

2. Governance / Policy Goals

Primary Goal:
Ensure the bacterial biosensor contributes to better patient outcomes and antimicrobial stewardship without creating environmental or biosecurity risks.

Sub-Goals

Goal 1: Enhance Biosecurity

  • Sub-goal 1a: Prevent the engineered biosensor strain from surviving outside laboratory/clinical settings
  • Sub-goal 1b: Ensure the technology cannot be easily modified to detect or enable harmful applications

Goal 2: Ensure Equitable Access

  • Sub-goal 2a: Make the biosensor affordable for resource-limited clinics where resistance is often highest
  • Sub-goal 2b: Share genetic circuit designs openly to enable local production and adaptation

Goal 3: Protect Environmental Health

  • Sub-goal 3a: Prevent accidental release of engineered bacteria into wastewater or soil
  • Sub-goal 3b: Ensure biosensor components are properly sterilized after use

3. Governance Actions

Option 1: Standardized Biosafety Containment Protocols

Purpose:
Currently, different labs use varying containment practices for engineered bacteria. I propose standardized protocols specifically for clinical biosensor applications that mandate genetic kill switches, auxotrophy (nutritional dependency), and proper waste sterilization.

Design:

  • All clinical biosensor strains must include auxotrophy for a non-natural amino acid
  • Genetic kill switches activated after 48 hours or upon temperature change
  • Clinical users receive pre-packaged, single-use biosensor kits with built-in sterilization (autoclave bags)
  • Implemented by clinical microbiology labs, hospital infection control committees, and research institutions

Assumptions:

  • Kill switches and auxotrophy reliably prevent environmental persistence
  • Clinical staff can follow standardized disposal protocols
  • Containment measures don’t significantly increase costs

Risks of Failure:

  • Kill switches fail due to genetic mutation
  • Users skip sterilization steps due to time pressure
  • Bacteria escape before kill switch activates

Risks of “Success”:

  • Over-engineering containment makes biosensor too expensive for routine use
  • Complexity of safety features reduces reliability of detection function

Option 2: Open-Source Design Registry with Safety Review

Purpose:
Create a public database (similar to iGEM Registry) where biosensor genetic circuits are shared, peer-reviewed for safety, and rated for performance. This promotes equitable access while maintaining safety oversight.

Design:

  • Researchers submit biosensor designs to registry before publication
  • Community safety review board (academic institutions, biosafety officers) evaluates dual-use risks
  • Approved designs receive “safety rating” and recommended containment level
  • Low-risk designs freely downloadable; high-sensitivity designs require institutional approval
  • Implemented by academic consortia, journals (Nature Biotech), funding agencies (NIH)

Assumptions:

  • Community review effectively identifies safety concerns
  • Researchers comply voluntarily with registry submission
  • “Safety rating” system can be objectively defined

Risks of Failure:

  • Malicious actors access designs and remove safety features
  • Review process becomes bottleneck, slowing innovation
  • Inconsistent safety standards across jurisdictions

Risks of “Success”:

  • Too many low-quality designs clutter registry
  • Safety ratings create false sense of security
  • Commercial entities avoid registry to protect IP, limiting access

Option 3: Tiered Clinical Validation Requirements

Purpose:
Establish validation standards matched to biosensor application setting. Point-of-care devices require more stringent testing than research-grade sensors, ensuring patient safety without hindering basic research.

Design:

  • Tier 1 (Research only): Basic characterization, standard lab biosafety
  • Tier 2 (Clinical research): Sensitivity/specificity testing, IRB approval, medical waste protocols
  • Tier 3 (Clinical diagnostic): FDA/regulatory approval, clinical trial validation, quality control systems
  • Academic labs can operate at Tier 1; clinical deployment requires Tier 3
  • Implemented by hospital IRBs, regulatory agencies (FDA, equivalent bodies), clinical microbiology professional societies

Assumptions:

  • Tiered system balances innovation with patient safety
  • Clear criteria exist for moving between tiers
  • Regulatory bodies develop biosensor-specific guidelines

Risks of Failure:

  • Academic sensors prematurely used clinically without validation
  • Tier 3 requirements too expensive for resource-limited settings
  • Regulatory uncertainty delays deployment

Risks of “Success”:

  • Only large diagnostic companies can afford Tier 3, limiting innovation
  • Overly conservative standards delay life-saving applications
  • Tiering creates quality perception gap harming Tier 1 research funding

4. Scoring Governance Actions

Scale: 1 = best alignment with goal, 3 = weakest, n/a = not applicable

Policy GoalOption 1: ContainmentOption 2: RegistryOption 3: Validation
Enhance Biosecurity
• Prevent incidents122
• Enable response221
Ensure Equitable Access
• Affordable access213
• Local adaptation212
Protect Environment
• Prevent release13n/a
• Containment response13n/a
Other Considerations
• Minimize burden213
• Feasibility122
• Not impede research123
• Promote applications211

Scoring Rationale:

  • Option 1 provides strongest environmental protection through physical/genetic containment but doesn’t address equitable access
  • Option 2 excels at promoting access and knowledge sharing but has weaker environmental safeguards once designs are public
  • Option 3 ensures patient safety through validation but creates cost barriers and may slow beneficial research

5. Prioritized Recommendation

Recommended Strategy:
Implement Option 1 (Containment Protocols) combined with Option 2 (Open Registry) for research phases, followed by Option 3 (Tiered Validation) for clinical translation.

Rationale:

For my biosensor project specifically, I would:

  1. During HTGAA development: Use Option 1 containment (auxotrophy + kill switches) and share my circuit design via Option 2 registry for peer feedback
  2. If pursuing clinical application: Progress through Option 3 tiers, starting with research validation (Tier 1), then clinical research (Tier 2) if results are promising

This layered approach allows me to innovate safely during the course while establishing pathways to clinical impact. The containment features protect against accidental release, open sharing promotes equitable access and scientific improvement, and tiered validation ensures patient safety without stopping early-stage research.

Target Audience:

  • HTGAA instructors and peers: For research-phase safety practices
  • MIT/Hospital IRBs: If transitioning to clinical testing
  • Clinical microbiology professional societies: For eventual diagnostic standards

Trade-offs & Uncertainties:

  • Kill switch reliability: Current technology has ~1-5% failure rate; need backup containment (auxotrophy)
  • Balancing openness vs. security: Sharing designs enables both beneficial adaptation and potential misuse; registry review helps but isn’t foolproof
  • Clinical validation costs: Tier 3 requirements may be prohibitive for academic proof-of-concept; might need industry partnership or grant funding for translation

6. Ethical Reflection

New Ethical Concern:
This week’s discussions highlighted the “edgeless” quality of engineered organisms—once released, bacteria don’t respect geographical or temporal boundaries. Unlike chemical diagnostics that degrade predictably, live biosensors could theoretically persist and spread if containment fails. This made me realize that even diagnostic applications (which seem purely beneficial) carry environmental responsibilities that extend beyond the immediate user.

Proposed Governance Action:
Require environmental impact assessments even for contained clinical applications. Specifically:

  • Before deploying biosensors in any clinical setting, model worst-case release scenarios (e.g., improper waste disposal, accidental spill)
  • Establish monitoring protocols for detecting engineered strains in local wastewater
  • Create rapid-response plans if biosensor bacteria are detected outside intended use areas

This shifts thinking from “it’s contained so it’s safe” to “what if containment fails, and how do we detect and respond?” As a future Pharm.D working at the interface of biology and medicine, I want to build the habit of anticipating unintended consequences, not just assuming good intentions equal good outcomes.

Subsections of Week 1 HW: Principles and Practices

Week 1: Professor Questions

Homework Questions from Professor Jacobson

Question 1: DNA Polymerase Error Rate

Question:
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?

Answer:

The error rate of DNA polymerase with proofreading is approximately 1 error per 10⁶ base pairs. The human genome is about 3.2 billion base pairs long. At this error rate, a single round of genome replication would introduce roughly 3,200 errors, which would be incompatible with stable life.

How Biology Resolves This Discrepancy

Biology relies on layered error correction mechanisms rather than polymerase accuracy alone:

  1. Polymerase Proofreading
    Many DNA polymerases possess 3′→5′ exonuclease activity, which removes incorrectly incorporated nucleotides immediately during synthesis.

  2. Mismatch Repair (MMR)
    Errors that escape proofreading are corrected post-replication by the mismatch repair system, involving proteins such as MutS, MutL, and MutH, which detect mismatches, excise the incorrect segment, and resynthesize it correctly.

Net Result:
These combined systems reduce the final mutation rate to approximately 1 error per 10⁹–10¹⁰ base pairs, resulting in only a few errors per genome replication.

Question 2: Coding Diversity for Human Proteins

Question:
How many different DNA sequences can encode an average human protein, and why do most of these sequences fail in practice?

Answer:

An average human protein is encoded by approximately 1,036 base pairs of DNA (about 345 amino acids). Because the genetic code is redundant, with 61 codons encoding 20 amino acids, there are an astronomical number of possible DNA sequences that can theoretically encode the same protein.

Why Most Synonymous Sequences Fail

Despite this theoretical diversity, most synonymous sequences do not function properly due to several biological constraints:

  1. mRNA Secondary Structure
    Different sequences fold into different mRNA structures. Stable hairpins or loops can block ribosome binding, slow translation, or destabilize the transcript.

  2. GC Content and Stability
    Extreme GC or AT content alters nucleic acid stability. Excessive GC content makes DNA and RNA difficult to unwind, while low GC content reduces structural stability.

  3. RNA Cleavage Rules
    Certain sequences form structures recognized by RNases (e.g., RNase III), leading to premature mRNA degradation.

  4. Codon Usage Bias
    Organisms prefer specific codons. Rare codons slow translation due to limited tRNA availability, reducing protein yield or causing misfolding.

  5. Translation Kinetics and Folding
    Translation speed affects co-translational protein folding. Incorrect synonymous choices can produce misfolded, non-functional proteins.

Together, these constraints explain why only a small fraction of synonymous DNA sequences successfully produce functional proteins.

Homework Questions from Dr. LeProust

What’s the most common method for oligonucleotide synthesis?

The most common method is solid-phase phosphoramidite synthesis, a chemical process in which nucleotides are added stepwise to a growing DNA strand. Modern platforms perform this synthesis on silicon chips, enabling the parallel production of millions of oligonucleotides.

Why is it difficult to synthesize oligonucleotides longer than ~200 nucleotides?

As oligonucleotide length increases, small errors accumulate and coupling efficiency decreases, leading to truncated and incomplete products. This limits reliable direct synthesis to a few hundred nucleotides.

Why can’t a 2000 base-pair gene be made by direct oligonucleotide synthesis?

Chemical synthesis is limited to short DNA fragments. A 2000 base-pair gene must be constructed by synthesizing shorter oligonucleotides and assembling them into the full-length gene using enzymatic assembly and ligation methods.

Homework Questions from Professor Jacobson

Natural vs. Synthetic Biocontainment Strategies

Amino Acid Essentiality & Biocontainment

The ten essential amino acids for animals—those that must be obtained through diet—are phenylalanine (F), valine (V), threonine (T), tryptophan (W), isoleucine (I), methionine (M), histidine (H), arginine (R), leucine (L), and lysine (K).

The Lysine Contingency and Biological Reality

The “lysine contingency,” popularized by Jurassic Park, proposes limiting survival by making organisms dependent on lysine. In reality:

  • Natural dependency: All animals are already dependent on lysine and other essential amino acids obtained from the environment.
  • Poor containment: Lysine is abundant in nature, making this an ineffective biocontainment strategy.
  • Synthetic solutions: Research on genomically recoded organisms (GROs) replaces natural amino acid dependence with reliance on non-standard amino acids (NSAAs) that do not exist outside controlled environments.

This creates a synthetic contingency, ensuring engineered organisms cannot survive beyond the laboratory or production setting.

Citations and AI Prompt Disclosure

Key References:

  • Lajoie et al. (2013), Genomically Recoded Organisms Expand Biological Functions
  • Nyerges et al. (2022), Swapped genetic code blocks viral infections & gene transfer

AI Usage Disclosure:
Standard biological facts were retrieved using internal knowledge. Google NotebookLM was used as a study aid. Lecture slides were uploaded to ChatGPT, and the following prompt was used:

“Teach me this lecture as a coherent essay. Explain all concepts from first principles, and clearly explain any new or technical terms when they appear.”

The connection to Prof. Church’s work and the lysine contingency was synthesized directly from the provided source materials.