Week 1 HW: Principles and Practices

HOMEWORK 1 02.10.2026

1. Describe the biological engineering application or tool you would like to develop and why. The biological engineering application proposed in this project is a programmable CRISPR-based antimicrobial platform designed to combat multidrug-resistant (MDR) bacteria. Rather than broadly killing bacterial populations, this system would selectively target and cleave antibiotic resistance genes (e.g., beta-lactamase or carbapenemase genes), thereby restoring antibiotic susceptibility. The motivation for this approach stems from the global antimicrobial resistance (AMR) crisis. AMR is responsible for over one million direct deaths annually and poses systemic risks to modern medicine, including surgery, chemotherapy, and organ transplantation. Traditional antibiotics exert broad ecological pressure, disrupting microbiota and accelerating resistance evolution. A gene-targeted CRISPR strategy represents a precision intervention that reduces ecological collateral damage. However, this technology is not merely therapeutic. Because it enables precise genetic manipulation, it also raises concerns related to dual-use potential, ecological uncertainty, and global inequities. Therefore, it must be analyzed not only as a biomedical innovation but as a socio-technical intervention requiring robust governance.
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. To ensure this CRISPR antimicrobial platform contributes to an ethical future, three overarching governance goals are proposed, grounded in precautionary governance, global justice theory, and responsible innovation. Goal 1: Enhance Biosecurity and Biosafety (Precautionary Principle) This goal is informed by Ulrich Beck’s Risk Society theory, which argues that modern societies increasingly confront “manufactured risks” generated by technological advancement. Sub-goals: · Minimize dual-use potential and misuse risks. · Prevent unintended ecological spread and horizontal gene transfer. Given the uncertainty surrounding long-term ecological consequences, governance should operate under the precautionary principle: regulatory measures should be implemented even in the presence of scientific uncertainty. Goal 2: Promote Global Equity and Distributive Justice Drawing on Rawlsian distributive justice and global health ethics, this goal aims to prevent the concentration of benefits in high-income countries. Sub-goals: · Ensure access in low- and middle-income countries. · Prevent monopolistic patent control. Without deliberate equity-oriented governance, advanced gene-editing technologies risk exacerbating global health disparities. Goal 3: Ensure Transparency and Democratic Accountability (Responsible Innovation) · Inspired by anticipatory governance and responsible innovation frameworks, this goal emphasizes early-stage oversight and public engagement. · Establish transparent reporting systems. · Implement independent ethical review mechanisms. Because CRISPR technologies reshape biological systems, their legitimacy depends on inclusive and accountable governance structures.
3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Action 1: Embedded Biosecurity through Mandatory Genetic Kill-Switches Purpose: To integrate safety mechanisms directly into the technological design, ensuring containment at the molecular level. Design: · Regulatory agencies require validated genetic kill-switch systems prior to clinical approval. · Systems must include environmentally dependent activation and mutation-resistant safeguards. · Academic and industry actors collaborate on standardized testing protocols. Assumptions: · Technical containment can significantly reduce ecological risk. · Laboratory validation adequately predicts real-world behavior. Failure Risks: · Evolutionary escape mutations may disable safety features. · False confidence in technical containment may reduce oversight vigilance. “Success” Risks: · Perceived safety may legitimize rapid expansion without sufficient public deliberation. · Smaller research groups may be disproportionately burdened. This approach reflects precautionary governance and the concept of “embedded governance,” where regulation is integrated into design rather than imposed externally. Action 2: Transnational CRISPR Monitoring and Licensing Regime Purpose: To establish international oversight for therapeutic CRISPR applications. Design: · A WHO-coordinated global registry for clinical CRISPR sequences. · Mandatory sequence submission prior to therapeutic deployment. · International certification requirements. Assumptions: · States will cooperate within a transnational governance framework. · Centralized oversight enhances crisis response. Failure Risks: · Regulatory evasion and black-market development. · Sovereignty conflicts between states. “Success” Risks: · Regulatory capture by dominant corporations. · Excessive bureaucracy stifling innovation. This model draws on global risk governance and parallels international monitoring regimes in nuclear and financial systems.

Action 3: Equitable Licensing and Global Access Fund Purpose: To ensure the technology does not exacerbate global inequality. Design: · Publicly funded compulsory licensing mechanisms. · Tiered pricing structures. · Capacity-building and technology transfer programs. Assumptions: · Incentive structures can balance innovation and equity. · Public funding remains politically sustainable. Failure Risks: · Political instability undermining funding mechanisms. · Insufficient technology transfer leading to dependency. “Success” Risks: · Market distortion reducing private investment. · Over-reliance on centralized funding bodies. This action aligns with global justice frameworks and distributive ethics.

4.Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own: 1 = Strongly addresses the policy goal (best performance) 2 = Moderately or partially addresses the goal 3 = Weakly addresses the goal (weakest performance) Option 1: Embedded Biosecurity Option 2: Transnational Monitoring Option 3: Equitable Licensing

Analytical Evaluation Option 1: Embedded Biosecurity This option represents the strongest mechanism in terms of preventive security. By integrating kill-switch systems and safety features directly into the technological design, it aims to reduce risk before incidents occur. For this reason, it received the best score (1) in the “preventing incidents” category across biosecurity, laboratory safety, and environmental protection.

However, once an incident has occurred, its response capacity is more limited; therefore, it received a moderate score (2) in the “aiding response” category.

Additionally, the technical requirements associated with embedded safety mechanisms may create financial and procedural burdens for smaller research groups, which explains why it received a score of 2 in categories related to feasibility and avoiding impediments to research.

This governance model aligns closely with precautionary governance, as it prioritizes early intervention and risk containment under conditions of uncertainty.

Option 2: Transnational Monitoring and Licensing Regime This option does not primarily function as a preventive mechanism; rather, it strengthens monitoring and crisis response capacity. Post-incident tracking, recall procedures, and oversight mechanisms are comparatively robust, which is why it received the best score (1) in the “aiding response” category. However, because it does not operate at the molecular design level, its ability to prevent incidents is more limited (score of 2). Furthermore, the bureaucratic burden and licensing procedures may slow research processes, resulting in a weaker score (3) in the “not impeding research” category. This model is consistent with the risk society framework and transnational governance literature, which emphasize oversight and coordination in managing globally distributed technological risks. Option 3: Equitable Licensing and Global Access Mechanism This option focuses primarily on justice and access rather than direct security interventions. As a result, it received weaker scores (3) in most prevention-based biosecurity categories. However, it performs best (score of 1) in promoting constructive applications, minimizing stakeholder burdens, and supporting equitable access. It also received strong scores (1–2) in feasibility and avoiding impediments to research. By prioritizing distributive fairness and access, this model is grounded in global justice and distributive ethics frameworks. 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.

Final Recommendation and Governance Prioritization Drawing upon the scoring analysis, I would prioritize a hybrid governance model centered on Option 1 (Embedded Biosecurity) as the foundational layer, complemented by Option 2 (Transnational Monitoring and Licensing), and selectively supported by Option 3 (Equitable Licensing and Access Mechanisms). This recommendation is directed primarily to national regulatory agencies (e.g., the U.S. Food and Drug Administration or equivalent public health authorities) and secondarily to international bodies such as the World Health Organization, given the transnational nature of antimicrobial resistance and gene-editing technologies. Primary Priority: Embedded Biosecurity as the Core Layer Option 1 should serve as the baseline governance requirement. Because it received the strongest score (1) in preventing incidents across biosecurity, laboratory safety, and environmental protection, it directly addresses the highest-risk dimension of CRISPR-based antimicrobial systems: unintended release or misuse. By embedding kill-switch mechanisms and design-level safeguards into the technology itself, risk mitigation occurs upstream rather than reactively. This aligns with precautionary governance and anticipatory regulation principles, which argue that safety should be incorporated during the design phase rather than imposed only after deployment. However, I recognize the trade-off: stricter design requirements may increase development costs and create barriers for smaller research groups. To mitigate this, regulatory agencies could provide technical guidance, shared safety platforms, or grant incentives to support compliance. Secondary Layer: Transnational Monitoring and Crisis Response While embedded biosecurity minimizes the probability of incidents, Option 2 enhances accountability and response capacity if an incident occurs. Given the global circulation of genetic materials and the cross-border nature of antimicrobial resistance, no single nation can manage the risks independently. A WHO-coordinated registry and certification system would strengthen transparency, enable traceability, and support coordinated crisis response. This option scored best (1) in aiding response capacity. The trade-off here is bureaucratic burden and the risk of regulatory capture. Excessive oversight could slow innovation or concentrate power in dominant corporate actors. Therefore, monitoring systems should be adaptive, interoperable, and proportionate to risk levels.

Supporting Layer: Equitable Licensing and Global Access Option 3 is essential for ethical legitimacy. Although it does not directly enhance biosecurity, it scored strongest (1) in promoting constructive applications and minimizing burdens. Without equity-oriented governance, advanced CRISPR therapies risk exacerbating global health disparities. However, prioritizing access without adequate safety oversight could undermine public trust. Therefore, equity mechanisms should be implemented after foundational safety standards are established. This approach acknowledges a central ethical tension: promoting rapid access to life-saving technologies while ensuring adequate safeguards against misuse and ecological harm. Trade-Offs Considered Several trade-offs informed this prioritization: Prevention vs. Innovation Speed Strong embedded safeguards may slow development but significantly reduce systemic risk.

Global Coordination vs. Sovereignty Transnational monitoring improves accountability but may generate political resistance.

Equity vs. Market Incentives Licensing reforms enhance access but may reduce private investment incentives.

Balancing these tensions requires layered governance rather than a single dominant intervention. Assumptions and Uncertainties This recommendation relies on several assumptions: That technical containment mechanisms (e.g., kill-switches) remain evolutionarily stable.

That international institutions can effectively coordinate oversight.

That financial incentives can balance equity and innovation.

Uncertainties remain regarding long-term ecological effects, evolutionary escape mechanisms, and geopolitical cooperation. Because of these uncertainties, governance structures must remain adaptive rather than fixed.

Conclusion Given the scoring results and broader theoretical considerations (risk society, precautionary governance, and anticipatory regulation), I recommend a layered governance approach: Embedded biosecurity as a mandatory baseline.

Transnational monitoring for accountability and crisis response.

Equity-oriented licensing to ensure global justice. This hybrid model most effectively balances prevention, response capacity, and distributive fairness, thereby increasing the likelihood that CRISPR-based antimicrobial technologies contribute to an ethical and sustainable future.

Homework Questions from Professor Jacobson 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? Biological DNA synthesis using an error-correcting polymerase has an error rate of approximately 1 in 10⁶ (1:10⁶) per base added. This means that, on average, one incorrect nucleotide is incorporated for every one million nucleotides synthesized. When this fidelity is compared to the size of the human genome, which is shown on the “Next Generation DNA Synthesis” slide as approximately 3.2 gigabase pairs (3.2 × 10⁹ bp) , a discrepancy becomes clear. If replication occurred with an error rate of 1:10⁶ and no further correction, a single round of genome replication would introduce roughly 3,200 errors (3.2 × 10⁹ ÷ 10⁶). Such a mutation burden would be biologically catastrophic. Biology resolves this discrepancy through multiple layers of error correction. First, DNA polymerase itself contains a 3′→5′ proofreading exonuclease activity, which removes incorrectly inserted nucleotides immediately during replication. Second, post-replication mismatch repair systems, such as the MutS repair pathway shown on the “Error Correction” slide,detect and correct base-pair mismatches that escape proofreading. These layered correction mechanisms dramatically reduce the effective mutation rate to approximately 10⁻⁹–10⁻¹⁰ per base per generation, making faithful replication of a multi-gigabase genome possible. In other words, while polymerase fidelity alone is insufficient for accurate replication of the human genome, the combined proofreading and repair systems ensure genomic stability.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice, why don’t all of these different codes work? The slide titled “How Many Base Pairs Do We Need to Synthesize?” indicates that the average human protein is approximately 1036 base pairs long. Since three nucleotides encode one amino acid, this corresponds to roughly 345 amino acids. Because the genetic code is degenerate (64 total codons, 61 encoding amino acids), most amino acids can be encoded by multiple synonymous codons. If we assume an average redundancy of approximately three codons per amino acid, then the number of possible DNA sequences that could encode a 345-amino-acid protein is astronomically large — on the order of 3³⁴⁵ possible nucleotide combinations. Even with conservative assumptions, the combinatorial space is effectively exponential and extremely vast. However, in practice, most of these theoretically valid coding sequences do not function well. One reason is codon usage bias: different organisms preferentially use certain codons, and rare codons can reduce translation efficiency. A second constraint is mRNA secondary structure, which is influenced by GC content and base-pairing energetics. Highly stable secondary structures can interfere with ribosome binding and translation initiation. Additionally, extreme GC content (e.g., 90% GC) can create strong base pairing (G/C ≈ −2.0 kcal/mol) making sequences difficult to synthesize or express. Other practical constraints include regulatory motifs, splice sites, repetitive sequences, cryptic promoters, and sequence elements that trigger degradation or silencing. Thus, although the genetic code allows an enormous number of possible nucleotide sequences for any given protein, only a small subset of those sequences will be biologically functional, efficiently expressed, structurally stable, and synthetically accessible.

Homework Questions from Dr. LeProust

1.What’s the most commonly used method for oligo synthesis currently? The most commonly used method for oligonucleotide synthesis today is chemical phosphoramidite synthesis on solid support. In this approach, nucleotides are added one at a time through repeated chemical cycles that include coupling, capping, oxidation, and deprotection steps. Each cycle extends the growing DNA strand by one base. This method is highly automated and widely used because it is reliable, scalable, and compatible with high-throughput platforms such as DNA microarrays and chip-based synthesis. Despite newer enzymatic approaches being developed, phosphoramidite chemistry remains the dominant industrial standard for short oligo production.

2.Why is it difficult to make oligos longer than 200 nt via direct synthesis? It is difficult to synthesize oligos longer than approximately 200 nucleotides because chemical synthesis is not 100% efficient at each base addition step. Even if each cycle has a high coupling efficiency (for example, 99%), the small error rate compounds exponentially with length. Over hundreds of cycles, incomplete coupling, side reactions, and chemical degradation accumulate, dramatically reducing the proportion of full-length product. In addition, longer sequences are more likely to form secondary structures such as hairpins or contain problematic motifs like high GC regions or homopolymers, which further reduce synthesis efficiency and purity. As a result, beyond ~200 nucleotides, the yield of perfectly synthesized full-length oligos drops significantly.

3.Why can’t you make a 2000 bp gene via direct oligo synthesis? A 2000 base pair gene cannot be made directly through single-step oligo synthesis because the cumulative error rate and yield loss would be catastrophic. With chemical synthesis, even a small per-base error rate multiplied over 2000 sequential coupling cycles would result in extremely low full-length accuracy. The probability of synthesizing a completely correct 2000 bp molecule in one continuous process would be near zero. Instead, long genes are constructed by synthesizing many shorter oligos (typically 100–200 nt), then assembling them using enzymatic methods such as PCR-based assembly or Gibson assembly. This modular approach allows error correction, sequence verification, and scalable production of long DNA fragments. Direct synthesis at that length is not chemically or practically feasible due to compounding errors and low yield.

Homework Question from George Church [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 Contingenc 10 essential amino acids are arginine; methionine; histidine; phenylalanine; isoleucine; threonine; leucine; tryptophan; lysine; andvaline;. Lysine contingency is knocking out lysine biosynthesis genes making targets dependent on lysine supplements. However, lysine dependent organisms can survive by eating other organism abındent in lysine.

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project