Week 1 HW: Principles and Practices

1. First, describe a biological engineering application or tool you want to develop and why

   Pattern-Based Rapid Diagnostic Platform for Dengue Virus: A rapid diagnostic platform for dengue virus (DENV) that integrates innate immune recognition, molecular recognition, and biosensor engineering to address key limitations of existing diagnostic methods. The proposed system combines mannose-binding lectin for the recognition of viral glycoproteins, dengue-specific aptamers targeting conserved regions of viral proteins, and signal transduction through a portable biosensor to enable rapid readout. This approach is motivated by the fact that current dengue diagnostics are often expensive and exhibit reduced sensitivity and reliability in dengue-endemic regions, particularly in countries like mine (Colombia), where prior flavivirus exposure compromises serological test performance and access to reliable diagnostics is limited by public healthcare infrastructure (Terenteva et al., 2025).

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.

GOAL 1: Ensure that diagnostic accuracy supports appropriate clinical decision-making, minimizing the risk of misdiagnosis, delayed care, and public health mismanagement.

Subgoals: Diagnostic Reliability: Standards validated in endemic populations and ongoing monitoring of false positives and false negatives.

Prevention of clinical misinterpretation: Support test results with clear and accessible interpretive guidance.

GOAL 2: Ensure to promote equitable access and global health justice in the development and use of the diagnostic technology.

Subgoals: Affordability and Accessibility: Promote public–private collaboration for the deployment of dengue diagnostics in high-burden countries, without dependence on specialized infrastructure.

Prevent Technological Exclusion: Ensure that the diagnostic tool is usable in decentralized healthcare settings, such as rural clinics and community health centers

3.Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”)

Establish context-specific validation requirements for diagnostic deployment:

The objective of this action is to prevent clinical harm caused by diagnostic failures that persist under current practices in endemic settings, particularly false negatives in secondary dengue infections and false positives due to flavivirus cross-reactivity. To achieve this, regulatory agencies and public health institutions should require that rapid dengue diagnostics be validated directly in endemic communities—especially among people with prior flavivirus exposure—before they’re approved or rolled out. This means shifting approval pathways so they rely on real‑world performance data, not just controlled lab studies or trials in non‑endemic settings. Assuming that regulators can review performance data in the specific contexts where diagnostics will be used, that accuracy can vary across different populations, and that manufacturers will adapt their designs to meet these requirements. Even so, a “false success” could occur if compliance is limited to minimal testing in some endemic populations. True success would mean diagnostics that work reliably across diverse populations, helping to reduce misdiagnosis and support appropriate clinical decisions.

Ensuring Transparency and Open Validation in Diagnostic Development:

The purpose of this action is to promote responsible innovation and build trust by ensuring that the limitations of diagnostics are openly documented and shared before large-scale deployment. By making failure modes, cross-reactivity profiles, and other constraints visible early, developers, regulators, and clinicians can make better-informed decisions and reduce risks to patients and public health. Funding agencies and scientific journals should require transparent reporting of assay limitations as part of the evaluation and publication process. Shared validation datasets could be established. Incentives—such as eligibility for specific funding programs, prioritized review, or formal recognition—can encourage participation from researchers and companies while improving the overall reliability and comparability of diagnostic technologies. This approach assumes that increased transparency improves diagnostic quality and that researchers and companies will share performance data to reduce failure across the sector. Potential failure points include limited industry participation due to intellectual property concerns or competitive pressures. Success is defined by establishing routine, independent testing of diagnostic performance claims, creating a standard expectation of reliability prior to clinical adoption.

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: –>

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

   Based on the above, the following actions would be prioritized: Establish context-specific validation requirements for dengue diagnostics. This action has the highest priority because it directly ensures diagnostic reliability in endemic populations. Requiring validation in communities with prior flavivirus exposure reduces false negatives, false positives, and clinical mismanagement. It provides the regulatory foundation necessary for safe, equitable deployment. Without this step, large-scale implementation could compromise patient care and public health decision-making.

Ensure transparency and open reporting of diagnostic limitations. This action strengthens accountability and trust by requiring disclosure of performance data, cross-reactivity profiles, and failure modes. However, its effectiveness depends on clear validation standards. Properly designed, it improves long-term reliability and supports informed clinical use, while balancing industry participation and innovation incentives.

6. Ethical concerns that arose, especially any that were new to you.

   This week, I learned that developing a diagnostic platform is more than just a technical or experimental challenge; it is also a matter of governance and biosecurity—areas that were largely new to me. Previously, I focused mainly on protocol design, molecular mechanisms, and performance metrics. In class and doing the homework, I began to understand that every diagnostic tool exists within a broader regulatory, ethical, and public health framework that determines how it is validated, deployed, and monitored in real-world settings. One key ethical concern that emerged for me is that diagnostic errors are not just laboratory inaccuracies—they can produce systemic harm. Poor validation, lack of transparency, or weak oversight can lead to misdiagnosis, inequitable access, and loss of public trust. To address these issues, some governance actions that I think should be required are context‑specific validation before any large‑scale deployment, establishing clear transparency standards to ensure diagnostic limitations are openly reported, and implementing post‑market monitoring to quickly identify and respond to performance gaps.

7. References

   Terenteva, S., Golani-Zaidie, L., Avivi, S., Lustig, Y., Indenbaum, V., Koren, R., Hoa, T. M., Tuyen, T. T. K., Huyen, M. T., Hoan, N. M., Hoi, L. T., Trung, N. V., Schwartz, E., & Danielli, A. (2025). Sensitivity and Cross-Reactivity analysis of Serotype-Specific Anti-NS1 serological assays for dengue virus using optical modulation biosensing. Biosensors, 15(7), 453. https://doi.org/10.3390/bios15070453

WEEK 2 LECTURE PREP

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?

The error rate of replicative DNA polymerases is roughly 10⁻⁵ errors per nucleotide incorporated. With proofreading (3’→5’ exonuclease activity), this improves to about 10⁻⁷, and after post-replicative mismatch repair, the final error rate drops to approximately 10⁻⁹ to 10⁻¹⁰ per base per replication cycle.

Comparing that to the human genome, which is about 3 × 10⁹ base pairs per haploid. If replication occurred at 10⁻⁵ error frequency with no correction, that would mean tens of thousands of mutations per cell division, incompatible with genomic stability. Even at 10⁻⁷, you would expect hundreds of mutations per division.

Biology resolves this discrepancy throug: 1. Polymerase selectivity 2. Proofreading activity 3. Mismatch repair (MMR) 4. Cell cycle checkpoints and apoptosis

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 400 amino acids long. Because of the degeneration of the genetic code, most amino acids are specified by multiple synonymous codons.

In practice, most of these sequences do not function equivalently because: 1. Codon usage bias 2. mRNA secondary structure 3. GC content constraints 4. Regulatory elements within coding regions

Questions from Dr. LeProust

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

The most common method for oligo synthesis is solid-phase phosphoramidite chemistry. Nucleotides are added stepwise in the 3’→5’ direction on a solid support, with cyclic coupling, capping, oxidation, and deprotection steps.

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

Because each coupling step is not 100% efficient. The yield drops with length. At 99% efficiency per step, a 200-mer has only a few full-length products. Beyond that, truncated products dominate, and purification becomes inefficient.

Why can’t you make a 2000bp gene via direct oligo synthesis?

Because the cumulative stepwise loss would make the full-length product essentially nonexistent. Long genes are built by assembling shorter oligos not by single continuous chemical synthesis.

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”?

The 10 essential amino acids in animals are: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine. These cannot be synthesized de novo and must be obtained from the diet. Respect “Lysine Contingency”: Lysine is not the only essential amino acid; it is one of several indispensable amino acids. This means that engineering a specific dependence on lysine is conceptually no different from creating dependence on any other essential amino acid. Its practical value does not lie in biochemical uniqueness, but in its controllability: environmental availability can be tightly regulated, making it a useful strategy. Source: ChatGPT (Open AI)