I am a Biotechnology undergraduate with a strong interest in synthetic and systems biology. I enjoy understanding biological networks, designing genetic circuits, and exploring how engineered biological systems can solve real-world problems. I’m down to learn new technologies and inovations
Week 1 HW: Principles and Practices
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:
These modules would be engineered so they can be interchanged based on:
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.
The model should require in-built safety mechanisms in case of issues such as contamination.
Clear safety benchmarks must be met before any scaling is allowed.
Resist use cases that could enable environmental manipulation or toxic/harmful chemical amplification.
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:
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:
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.
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.
| Does the option: | Option 1 | Option 2 | Option 3 |
|---|---|---|---|
| Enhance Biosecurity | |||
| • By preventing incidents | 1 | 1 | 3 |
| • By helping respond | 2 | 2 | 1 |
| Foster Lab Safety | |||
| • By preventing incident | 1 | 2 | 3 |
| • By helping respond | 1 | 2 | 2 |
| Protect the environment | |||
| • By preventing incidents | 1 | 2 | 2 |
| • By helping respond | 2 | 2 | 1 |
| Other considerations | |||
| • Minimizing costs and burdens to stakeholders | 2 | 3 | 1 |
| • Feasibility? | 1 | 2 | 2 |
| • Not impede research | 2 | 3 | 1 |
| • Promote constructive applications | 2 | 2 | 1 |
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.
Biology employs a multi-layered, high-fidelity error-correction system:
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:
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 10 essential amino acids that must be consumed in the diet because animals cannot synthesize them in sufficient amounts are:
These amino acids are sometimes referred to as essential or indispensable amino acids.
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.
Lysine is a common and widely available amino acid found in most protein-rich foods, including:
An organism engineered to require lysine could easily obtain it from natural food sources, making this control mechanism ineffective.
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.
As implied even within the movie, the dinosaurs could theoretically survive by:
Thus, lysine dependence alone cannot function as a reliable biological fail-safe.
We want something analogous:
A low-dimensional, physically grounded, interaction-predictive code
Instead of 20 × 20 explicit pairwise rules, compress AA–AA interactions into interaction classes, defined by side-chain physics, not amino-acid identity.
Each amino acid is encoded as a vector of interaction features:
| Feature | Examples |
|---|---|
| Charge | + / − / 0 |
| Polarity | polar / nonpolar |
| H-bond role | donor / acceptor / both / none |
| Aromaticity | yes / no |
| Size class | small / medium / bulky |
| Flexibility | rigid / flexible |
Example:
Lysine ≠ just K, but:
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.
| Code | Interaction |
|---|---|
| E | Electrostatic (+/−) |
| H | Hydrogen bonding |
| Φ | Hydrophobic packing |
| π | Aromatic stacking |
| S | Steric clash |
| Ø | Neutral / weak |