Week 1 HW: Principles and Practices

1. BioBoard

want to develop a modular, plug and play platform for biological engineering inspired by how Arduino made electronics easy to access and build. Today, even though synthetic biology is advancing quickly, the ability to design and test biological systems is still limited to well funded labs with specialized equipment. The tool I imagine, which I conceptually call “BioBoard”, would act as a simple interface between users and biological systems. Instead of mastering complicated workflows, users could connect optimized biological modules such as growth sensors, environmental monitors, light control units, or cell free expression kits into a central platform. This would allow faster experimentation, easier data collection, and more room for creativity.

This idea is also deeply personal to me. Since childhood, I have enjoyed building small projects using sensors and breadboards. My mother is an electronics and communication engineering professor, and growing up around that environment encouraged me to explore, create, and solve problems in frugal ways. I learned that with the right platform, even complex technology can become approachable. However, when I became involved in biotechnology through synthetic biology, I noticed a sharp difference. The ability to prototype ideas was far more difficult than in electronics. Experiments required expensive tools, controlled lab spaces, and long preparation times. While biology is naturally complex and often costly, electronics once faced similar challenges in the 1970s before accessible platforms transformed the field. That shift allowed more people to innovate and ultimately accelerated technological progress.

Making biological engineering more structured and accessible could open the door for students, educators, community labs, and interdisciplinary innovators to participate. This vision is not about making biology careless or uncontrolled. Rather, it is about designing tools that encourage responsible experimentation while lowering unnecessary barriers. By focusing on modularity, usability, and thoughtful design, we can move toward a future where interacting with biological systems becomes more approachable without losing the care and responsibility the field requires.

2. Governance and Policy Goals for an Ethical Future

Goal 1: Prevent misuse while encouraging responsible experimentation

Sub goal 1: Build safety into the platform itself The platform should guide users toward safe practices. For example, modules could be limited to non harmful organisms, cell free systems, or educational kits unless users have verified training. Built in checks can reduce the chance of accidental misuse without stopping curiosity.

Sub goal 2: Encourage basic biosafety training Access to advanced modules could require simple certification, similar to how many labs require safety training before allowing equipment use. This ensures that as more people enter biology, they also understand their responsibility.

Goal 2: Make access fair without removing oversight

Sub goal 1: Support education and community labs Lower cost tools can help students and smaller institutions participate in biological engineering. This helps prevent innovation from being limited only to wealthy organizations.

Sub goal 2: Avoid unrestricted distribution of sensitive tools Some biological capabilities should remain controlled. A tiered system, where beginner modules are widely available but higher risk tools require approval, can balance openness with caution.

Goal 3: Promote a culture of responsibility, not just rule following

Sub goal 1: Normalize ethical thinking early When tools are designed for learners, they should also introduce ethical reflection. Short guidance built into the platform can remind users to consider environmental impact, safety, and long term effects.

Sub goal 2: Encourage transparency and shared learning Open documentation of experiments, successes, and failures can help communities learn from each other and avoid repeating mistakes. Transparency also builds trust as the technology spreads.

3. Governance Actions

Action 1: Tiered Access Based on Training and Use

Purpose Today, biological tools are either tightly restricted to formal labs or freely available as simple kits. As platforms become more powerful, this gap becomes risky. I propose a tiered access system where basic modules are open to everyone, while more advanced capabilities require proof of basic biosafety training.

Design Tool developers, academic institutions, and community labs could jointly define simple training requirements. Completing a short biosafety course would unlock access to higher level modules. This is similar to how drone users can fly basic models freely, but must register or train for more advanced use.

Assumptions This assumes that users will take training seriously and that certification improves responsible behavior. It also assumes that shared standards across institutions are possible.

Risks of Failure and “Success”
If the system becomes too strict, it could discourage learners and reduce creativity. If it is too relaxed, certification may become a formality without real impact. Even if successful, unequal access to training could create new barriers.

Action 2: Built in Technical Safety Limits

Purpose Current biosafety relies heavily on user behavior and lab rules. For a more accessible platform, safety should be built directly into the technology rather than enforced only through policy.

Design Platform developers could limit compatibility to low risk organisms, cell free systems, or educational strains by default. Software interfaces could flag potentially unsafe experiments or require additional confirmation steps. This is similar to how financial systems automatically detect and flag unusual transactions.

Assumptions
This assumes that common risks can be identified and translated into technical safeguards. It also assumes developers are willing to invest in safety features even if they increase cost or reduce flexibility.

Risks of Failure and “Success” Overly strict limits may block legitimate experimentation or creative exploration. Users may also attempt to bypass safeguards. If successful, there is a risk that users rely too heavily on the system and stop thinking critically about safety.

Action 3: Platform Certification Model

Purpose
As biological platforms become more modular, there is a risk that unsafe third party modules could enter the ecosystem. A certification model, similar to how app stores review software or how hardware devices receive safety marks before entering the market, could help prevent harmful tools from spreading while still encouraging innovation.

Design
Platform providers could require third party biological modules to pass a safety review before being listed in an official marketplace. Reviews could check for biosafety risks, environmental concerns, and proper documentation. Certified modules would receive a visible trust label so users can easily identify safer options. This approach mirrors how smartphone app stores approve applications and how electronic devices often require regulatory certification before sale.

Assumptions
This assumes developers are willing to submit their modules for review and that certification bodies can evaluate risks effectively. It also assumes users will prefer trusted modules over uncertified alternatives.

Risks of Failure and “Success"
If certification becomes slow or expensive, it could discourage small innovators and reduce creativity. Developers might also distribute tools outside official marketplaces to avoid oversight. However, if successful, this model could create a safer ecosystem where users naturally gravitate toward trusted components without requiring constant regulatory intervention.

No single action is sufficient on its own. A layered approach that combines user training, technical safeguards, and ecosystem level certification provides stronger protection while still supporting innovation and creativity.

5. Governance Priority and Recommendation

Based on the scoring, I would prioritize Option 2 (Built in Technical Safety Limits) and Option 3 (Platform Certification Model), with Option 1 (Tiered Access)playing a supporting role.

Built in safety are the most effective because they make safer behavior the default and do not rely entirely on user behaviour. Certification further protects the ecosystem by ensuring that third party modules meet basic safety and documentation standards before being widely shared. Together, these options reduce risk while still allowing broad access and creativity.

The main trade off considered is between accessibility and control. Strict access requirements can slow learning and exclude students or small labs, while overly open systems increase the chance of misuse. Embedding safety into the platform helps balance this tension without recreating high barriers.

This recommendation is most relevant for organizations that actively shape biotechnology standards, such as the Engineering Biology Research Consortium (EBRC), for its guidance on responsible innovation in engineering biology. International coordination from groups like the World Health Organization through its biosafety and biosecurity programs is also important to support shared safety expectations as biological platforms become more accessible. The United Nations Cartagena Protocol on Biosafety further helps address ecological risks as these technologies scale globally.

AI Prompts Used

Used ChatGPT and Gemini for brainstorming and reasoning. I used it mainly for discussing the Governance Measures needed for my idea and its effect on the success of the idea, and also policy measures needed for ethical synthetic biology concepts.

Prompts: Analyze the risks and benefits of democratizing biological engineering tools, using analogies from electronics, software platforms, or open-source hardware. Focus on how accessibility can increase innovation while also introducing governance challenges.

Break down the concept of “ethical synthetic biology” into concrete policy goals that apply to accessible, modular biological platforms. Avoid medical use cases and focus on education, experimentation, and creativity.

Propose governance actions for an Arduino-like biological platform by drawing from existing models in other fields such as app stores, drone regulation, or open-source software communities.

HOMEWORK QUESTIONS-PRE LECTURE PREP

Homework Questions from Professor Jacobson

1. 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 makes roughly 1 error per 100,000 bases. The human genome has around 3.2 billion base pairs, which would cause around 30,000 mutations per division. Biology corrects this via proofreading and mismatch repair.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? Why don’t all of these codes work in practice?

An average human protein requires about 1036 base pairs, and it is 375 amino acids long, and each amino acid requires 3 codons, tso here can be 3 DNA sequences for an average protein. But, not all sequences work well due to some sequences forming secondary structures that interfere with proper function and differences in GC content or repeated regions can make synthesis difficult.

Homework Questions from Dr. LeProust

1. What’s the most commonly used method for oligo synthesis currently?

The standard method is phosphoramidite solid-phase synthesis. In this process:

• Nucleotides are added one at a time to a growing chain attached to a solid support.
• Each addition involves deprotection, coupling, capping, and oxidation steps.

This chemistry enables precise base addition but introduces cumulative inefficiency with each cycle.

2. Why is it difficult to make oligos longer than ~200 nt via direct synthesis?

Several factors limit direct synthesis:

• Each synthesis cycle is less than 100% efficient, with typical stepwise yields around 99.5% or lower.
• Small inefficiencies compound exponentially: (0.995)²⁰⁰ ≈ 37% full-length product.
• Errors such as deletions, insertions, and incomplete coupling accumulate.
• Longer sequences can form secondary structures that interfere with synthesis chemistry.

As a result, the yield of correct full-length products becomes very low beyond ~200 nt, making longer direct synthesis impractical.

3. Why can’t you make a 2,000 bp gene via direct oligo synthesis?

Direct synthesis above ~200 nt is already inefficient, so synthesizing a 2,000 bp gene is not feasible because:

• Too many synthesis steps lead to near-zero correct material.
• Error accumulation becomes extremely high.
• Current methods cannot reliably remove truncated or faulty sequences.

Instead, long genes are typically assembled from short overlapping oligos, using methods such as PCR assembly or Gibson Assembly.

Homework Question from George Church

What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

Essential amino acids in humans and most animals:

• Histidine
• Isoleucine
• Leucine
• Lysine
• Methionine
• Phenylalanine
• Threonine
• Tryptophan
• Valine
• (Arginine is sometimes considered essential in children under certain conditions.)

Since these amino acids cannot be synthesized de novo, animals must obtain them through their diet.

The “Lysine Contingency” refers to engineering organisms so they require lysine (or another essential metabolite) for survival as a containment strategy. Because lysine is essential, this dependency leverages a real biological limitation.

However, organisms may evolve bypass mutations or obtain nutrients from their environment, which reduces the reliability of this strategy. Therefore, lysine dependency should be combined with additional safeguards, as essential-amino-acid requirements alone may not prevent survival in complex environments.

This highlights that synthetic containment strategies must be robust and multilayered, especially when based on fundamental metabolic needs.

Cited source for amino acid list: Lehninger Principles of Biochemistry.

What code would you suggest for AA:AA interactions?

Unlike nucleic acids, amino acids do not follow a single fixed pairing rule like A–T or G–C. Protein interactions depend on chemistry, shape, and structure, which makes them harder to control but also more flexible for design.

I would suggest an AA:AA interaction code built in layers.

At the base level, interactions should follow chemical properties:

• Positively charged amino acids interact with negatively charged ones.
• Hydrophobic amino acids tend to cluster together away from water.
• Hydrogen bonding supports stable contacts between compatible residues.

These rules provide a predictable foundation, similar to how base pairing provides stability in DNA.

On top of this, I would use structure-based motifs as higher-level interaction units. Repeating structural patterns such as coiled-coils, helix-turn-helix motifs, or engineered binding domains could act as modular “connectors” between proteins. These motifs are already used in nature and protein engineering to achieve specific and reliable binding.

Developing such an AA:AA code would be valuable for synthetic biology because it moves protein design toward modular, predictable systems, similar to how defined pairing rules enabled advances in DNA and RNA engineering.