Week 1 HW: Principles and Practices

1. First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

I am curious aboutI the develop of engineered biological decoys systems designed to reduce infections by preventing bacteria or viruses from attaching to human cells. Instead of destroying pathogens these systems would work by mimicking the cellular receptors that microorganisms need to initiate infection. It would consist of synthetic type of cells that display molecules similar to those found on real cell surfaces. Pathogens would bind to these decoys rather than to human tissues, thereby reducing the likelihood of infection and lowering the initial pathogen load. This is inspired by natural protective mechanisms such as mucosal barriers and could help reduce antibiotic use. By not directly killing microorganisms, it would reduce evolutionary pressure and potentially slow the development of antimicrobial resistance.

2. Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

Ensure biosafety and biosecurity by preventing misuse or unintended biological effects of decoy technologies. Promote constructive medical use as an alternative or complement to antibiotics, rather than as an unregulated biomedical product. Support equity and responsible access and ensuring that these tools are deployed based on public health needs and not only commercial interests.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.). Purpose: What is done now and what changes are you proposing? Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc) Assumptions: What could you have wrong (incorrect assumptions, uncertainties)? Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

Action 1: Controlled clinical and environmental regulation of bioengineered decoys

Purpose: Currently, many decoy-based technologies fall between medical devices and biological products. This action proposes clear regulatory pathways before clinical use.

Design: Health regulators including lab testing, clinical trials, and post-deployment monitoring.

Assumptions: This assumes existing regulatory institutions can adapt to novel bioengineered tools.

Risks of Failure and “Success”: If regulation is too strict, innovation may be slow. If successful then early deployment may still miss rare long-term effects.

Action 2: Mandatory impact assessments focused on resistance and evolution

Purpose: Unlike antibiotics, decoys aim to reduce selective pressure, but this is not guaranteed. This action proposes assessing evolutionary and ecological risks.

Design: Researchers would be required to model pathogen adaptation and submit monitoring plans as part of approval.

Assumptions: It assumes resistance can be predicted and monitored with current scientific models.

Risks of Failure and “Success”: Pathogens may evolve in unexpected ways as that its their purpose, success could lead to overconfidence and reduced surveillance.

Action 3: Limits on non-medical and non-approved uses

Purpose: This action aims to prevent decoy technologies from being used outside regulated medical or public health contexts.

Design: Governments and funding agencies restrict access to production protocols and require licensing for manufacturing and distribution.

Assumptions: It assumes enforcement is possible and effective.

Risks of Failure and “Success”: Over-restriction could push development underground and success could concentrate control among few institutions.

5. Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties. For this, you can choose one or more relevant audiences for your recommendation, which could range from the very local (e.g. to MIT leadership or Cambridge Mayoral Office) to the national (e.g. to President Biden or the head of a Federal Agency) to the international (e.g. to the United Nations Office of the Secretary-General, or the leadership of a multinational firm or industry consortia). These could also be one of the “actor” groups in your matrix.

Regulated deployment of non-living biological decoys is prioritized because it allows these tools to reduce infection risk without directly modifying human immune systems or releasing living organisms. The main trade-off is that strict regulation may slow innovation and limit rapid deployment during outbreaks, but this is balanced by increased biosafety and public trust, impact assessments are prioritized alongside regulation to address uncertainties about long-term ecological and evolutionary effects, such as whether pathogens could adapt to the decoys over time. The trade-off here is added cost and administrative burden, but this is justified by the need to anticipate unintended consequences before large-scale use.

Key assumptions include the belief that pathogens will preferentially bind to decoys instead of host cells, and that resistance will evolve more slowly than with traditional antibiotics. There is also uncertainty about how these tools might interact with existing immune responses or medical treatments prioritizing regulated use with strong assessment frameworks balances innovation with precaution, helping ensure that bioengineered decoys contribute to public health without creating new biological risks.

Trade-offs:

Innovation vs. safety: Strong regulation increases safety but may limit early-stage research capacity.

Cost vs. oversight: Surveillance systems improve accountability but require sustained funding and coordination.

Equity vs. speed: Incentive-based approaches promote broader participation but may not prevent all risks.

Reflection

While watching the class on Principles and Practices and learning about interventions involving Indigenous communities, I was reminded of a concern from my own community. In my community, there is a plant that is culturally important because it is used to decorate a symbol with strong meaning in our Indigenous traditions. For many years, part of the tradition involved walking long distances to collect this plant. However it is now becoming scarce due to its uncareful extraction from outsiders . To protect the plant, some people have started growing it at home, and there are discussions about conserving it in new ways. While this may help preserve the species, it could also change or weaken the tradition connected to how the plant was originally collected. This raised an ethical question for me: how can we protect living organisms without losing the cultural practices linked to them? One possible governance response is to combine conservation efforts with cultural participation, instead of fully replacing traditional practices. Regulations could focus not only on protecting the plant but also on supporting community led conservation that allows traditions to adapt while still being respected.

Assignment

(Week 2 Lecture Prep)

Professor Jacobson - Gene Synthesis

1. 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?

DNA polymerase has an error rate of 1 in 10⁶ bases. The human genome is about 3.2 × 10⁹ base pairs so errors would be tend to be frecuent if no proofreading and DNA repair mechanisms existed, so these dramatically reduce the final error rate and keep replication a part of biology.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

An average human protein with around 345 amino acids can be encoded in 4 eleveted to the amino acids to have different DNA sequences, because most amino acids are coded by more than one codon this makes the number of possible DNA codes very large. However, not all of these codes work in practice. Cells prefer certain codons, and different DNA sequences can affect how stable the mRNA is, how efficiently the protein is made, and how the gene is regulated. Also, while many DNA changes do not affect the amino acid or only change it to a similar one, some changes can still disrupt protein production. As a result, only a subset of DNA sequences can properly code for a functional protein.

Dr.LeProust - DNA Synthesis Development and Application

1. What’s the most commonly used method for oligo synthesis currently? The most commonly used method for oligo synthesis is phosphoramidite chemical synthesis. However Twist approach: silicon-based synthesis platforms are starting to outperform traditional methods by offering better scalability and control.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis? Because errors and inefficiencies add up at each step, making long, error-free sequences very unlikely.

3. Why can’t you make a 2000bp gene via direct oligo synthesis? The error rate and low yield make long sequences impractical. Instead genes are built by assembling many shorter, verified oligos.

George Church - Reading & Writing Life

1. [Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

All animals require the same set of essential amino acids, including lysine, because they cannot synthesize them on their own. This means animals already depend on getting lysine from their diet. As discussed in the Reddit thread, this makes the “Lysine Contingency” from Jurassic Park biologically weak: removing lysine wouldn’t act as a special genetic control, it would simply cause general protein failure, not a precise safety switch. So while the idea works for science fiction, it doesn’t make much sense as a real biological containment strategy.

References

Use of gemini AI on double checking english grammar and help on website style view

Acevedo-Rocha, C. G., & Budisa, N. (2016). Xenomicrobiology: a roadmap for genetic code engineering. Microbial biotechnology, 9(5), 666–676. https://doi.org/10.1111/1751-7915.12398

A Simple Guide to Phosphoramidite Chemistry and How it Fits in Twist Bioscience’s Commercial Engine. (n.d.). https://www.twistbioscience.com/blog/science/simple-guide-phosphoramidite-chemistry-and-how-it-fits-twist-biosciences-commercial