Week 1 HW: Principles and Practices

1. Modular Biological Sensing and Response Platform

I want to create a modular biological sensing and response platform that detects environmental or industrial chemicals and produces a programmable biological output. The idea is to create interchangeable modules in a biological system that include:

• A sensing module
• A processing module
• A response module

These modules would be engineered so they can be interchanged based on:

• The target chemical
• The desired response output

This idea excites me because it moves beyond creating one-off biosensors and instead focuses on programmable and engineered biological systems that can be used for real-life applications. My interest lies in combining biological networks with engineering logic to build systems that are predictable, controlled, and reusable.

2. Goals

Goal 1: Ensure Safety and Prevent Harm

Sub-goal 1:

The model should require in-built safety mechanisms in case of issues such as contamination.

Sub-goal 2:

Clear safety benchmarks must be met before any scaling is allowed.

Goal 2: Prevent Misuse

Sub-goal 1:

Resist use cases that could enable environmental manipulation or toxic/harmful chemical amplification.

3. Actions

Action 1: Mandatory Safety-by-Design Criteria

Proposed:
Introduce mandatory safety-by-design requirements where every project must demonstrate built-in contamination control and fail-safe mechanisms.

Design:
Institutional Biosafety Committees (IBCs) should evaluate system behavior, not just the organism.

Assumptions:
Incorporating safety at the early design stage reduces downstream risks.

Risk of Failure and Success:

• Risk: Some designs may not fit strict safety rules.
• Success: Safety measures become a mandatory checklist in biological engineering projects.

Action 2: Knowledge Sharing and Transparency

Proposed:
Share circuits and protocols openly, while identifying sensitive components that could be misused.

Design:
Review and categorize components based on their risk and sensitivity.

Assumptions:
Partial transparency supports research progress while maintaining safety.

Risk of Failure and Success:

• Risk: Excessive restriction could slow research.
• Success: Increased engagement, awareness, and interest in addressing complex challenges.

Action 3: Collaborations

Proposed:
Introduce public recognition for practical applications of this platform.

Design:
Provide grants, innovation challenges, and encourage strong industry–academia collaborations.

Assumptions:
Ethical goals can align with long-term innovation when supported institutionally.

5. Combined Strategy

A combined strategy that enforces mandatory safety-by-design standards along with controlled transparency is recommended. This analysis helped me understand that ethical governance is a core factor in shaping how such ideas can responsibly come to life.

Pre-Lecture Questions

Homework Questions from Dr. LeProust

1.## Error Rate of DNA Polymerase

Replicative DNA polymerases make an intrinsic mistake approximately once every
10⁴ to 10⁵ nucleotides (10,000 to 100,000).

The human genome is roughly 3 billion base pairs (3 × 10⁹).

Without correction, an error rate of 10⁻⁵ would result in hundreds of thousands of mistakes per cell division, which is unsustainable for life.

High-Fidelity DNA Replication System

Biology employs a multi-layered, high-fidelity error-correction system:

1. Proofreading

• DNA polymerases possess 3′ → 5′ exonuclease activity
• Incorrect nucleotides are removed immediately during replication
• Improves accuracy by approximately 100-fold

2. Mismatch Repair (MMR)

• Post-replication repair machinery
• Scans newly synthesized DNA
• Removes errors missed by proofreading

2. Degeneracy of the Genetic Code and Protein Encoding

An average human protein (approximately 400–500 amino acids) can, in theory, be encoded by an astronomically large number of different DNA sequences, potentially exceeding 10¹⁰⁰ possibilities, due to the degeneracy of the genetic code.

However, most of these alternative sequences fail in practice because they may:

• Disrupt proper protein folding
• Introduce premature stop codons
• Contain suboptimal codons that slow translation
• Reduce overall translation efficiency and accuracy

Homework Questions from Dr. LeProust

1. Oligonucleotide Synthesis

The most commonly used method for oligonucleotide synthesis is solid-phase phosphoramidite chemistry.

2. Limitations of Chemical Oligonucleotide Synthesis

Producing oligonucleotides longer than ~200 nucleotides (nt) by direct chemical synthesis
(typically using phosphoramidite technology) is challenging.

The primary reason is the cumulative, step-wise inefficiency of the synthesis process.
With each nucleotide addition having a small failure rate, the overall yield of the full-length product drops dramatically as length increases.

As a result:

• The desired full-length oligo is produced at very low yield
• The reaction mixture becomes dominated by truncated failure sequences
• Purification of the correct product becomes extremely difficult, if not impractical

3. Direct, single-step chemical synthesis of a 2000 base pair (bp) DNA molecule is generally not feasible because the phosphoramidite chemistry used for oligonucleotide synthesis suffers from accumulating efficiency losses and errors as the chain length increases.

Homework Question from George Church

Essential Amino Acids

The 10 essential amino acids that must be consumed in the diet because animals cannot synthesize them in sufficient amounts are:

• Lysine
• Threonine
• Isoleucine
• Leucine
• Methionine
• Tryptophan
• Valine
• Histidine
• Phenylalanine
• Arginine (often considered essential in young or growing animals)

These amino acids are sometimes referred to as essential or indispensable amino acids.

The “Lysine Contingency” and Its Scientific Flaws

The “Lysine Contingency” from Jurassic Park is a fictional control mechanism in which engineered dinosaurs are designed to require dietary lysine to survive. This idea, while compelling narratively, is scientifically flawed.

Why the Lysine Contingency Fails

1. Ubiquity in Diet

Lysine is a common and widely available amino acid found in most protein-rich foods, including:

• Plants
• Animals
• Microorganisms

An organism engineered to require lysine could easily obtain it from natural food sources, making this control mechanism ineffective.

2. Biological Reality vs. Fiction

The concept treats lysine as if it were a rare, artificial, or synthetic compound, whereas in reality it is a fundamental nutrient present throughout the food chain.

3. Flawed Control Method

As implied even within the movie, the dinosaurs could theoretically survive by:

• Eating prey containing lysine-rich proteins
• Consuming plants that naturally supply lysine

Thus, lysine dependence alone cannot function as a reliable biological fail-safe.

Conclusion

While the Lysine Contingency works as a dramatic plot device, it does not align with real biological principles. Effective biological control systems would require far more stringent, multi-layered, and biologically realistic safeguards than dependence on a universally available essential amino acid.

2. NA:NA → Base-Pairing Rules

• Discrete, low-alphabet
• Geometry + hydrogen-bond constrained
  • A = T
  • C ≡ G
• Expanded alphabets possible later

AA:NA → Genetic Code / Recognition Code

• Many-to-one mapping
  • 64 codons → 20 amino acids
• Implemented physically via aminoacyl-tRNA synthetase recognition
  • Shape
  • Charge
  • Chemistry

Shared Properties (NA:NA and AA:NA)

• Symbolic → physical
• Error-tolerant
• Composable
• Readable / writable by biology

Goal for AA:AA

We want something analogous:

A low-dimensional, physically grounded, interaction-predictive code

Suggested AA:AA Interaction Code

Side-Chain Interaction Alphabet (SCIA)

Instead of 20 × 20 explicit pairwise rules, compress AA–AA interactions into interaction classes, defined by side-chain physics, not amino-acid identity.

Step 1: Reduce Amino Acids to Interaction Primitives

Each amino acid is encoded as a vector of interaction features:

Example:
Lysine ≠ just K, but:

Step 2: Define an Interaction Grammar (Not Pairs)

Instead of explicit pairings like:

Define rules:

• ( + ↔ − ) → electrostatic attraction
• ( donor ↔ acceptor ) → hydrogen bond
• ( aromatic ↔ aromatic ) → π–π stacking
• ( hydrophobic ↔ hydrophobic ) → packing
• ( bulky ↔ bulky ) → steric exclusion (negative rule)

This generalizes Watson–Crick pairing logic beyond nucleic acids.

Step 3: Encode AA:AA Interactions Symbolically

Interaction Symbols