Week 1 HW: Principles and Practices

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.

This project focuses on developing a wearable biosensor patch designed to detect early signs of muscle stress and overload in athletes. The patch would be worn on the skin over active muscle groups and would monitor biochemical markers present in sweat or interstitial fluid, such as lactate, pH changes, or inflammation-related molecules.

Wearable biosensors for monitoring physiological biomarkers during exercise are an active and rapidly growing area of research. In particular, lactate and pH levels in sweat have been shown to correlate with exercise intensity and muscle fatigue, making them useful indicators of muscle stress (Yang et al., 2024; Shen et al., 2022). Recent studies have demonstrated flexible, skin-mounted patches capable of continuously measuring these biomarkers during physical activity (Xuan et al., 2021).

The sensing mechanism of the proposed patch would rely on using genetic circuits embedded in a cell-free system rather than living engineered organisms. Cell-free biosensors are increasingly recognized as a safe and flexible platform for biological sensing, as they eliminate risks associated with replication or environmental release while maintaining high sensitivity (Zhang et al., 2020; Wang & Lu, 2022). Similar approaches have already been demonstrated in wearable materials embedded with synthetic biology sensors, supporting the feasibility of this design strategy (Nguyen et al., 2021). These biological components would be integrated into a soft, biocompatible, and potentially biodegradable material suitable for prolonged skin contact.

Many muscle injuries occur because physiological stress accumulates before pain or visible symptoms appear. The motivation for this application is rooted in injury prevention. Current diagnostic methods are often invasive, expensive, or retrospective. A non-invasive, real-time monitoring tool could help athletes and clinicians make better training and recovery decisions, supporting long-term health and performance.

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.

The main governance goal is to ensure that this technology contributes to an ethical and beneficial future, prioritizing athlete health, safety, and autonomy while minimizing biological, environmental, and social risks.

Biosecurity: By relying on cell-free synthetic biology systems, the patch avoids the use of living genetically modified organisms, significantly reducing biosecurity concerns. Clear system behavior and visible outputs allow potential malfunctions to be identified quickly.

Environmental friendly: The use of sealed biological components and biodegradable materials limits environmental exposure. Safe disposal procedures reduce the risk of environmental contamination.

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.).

1. Governance Action Actors: Academic researchers and biotechnology developers Purpose: This action focuses on reducing risk directly through technical design rather than relying solely on external regulation. Design: The biosensor patch would use non-replicating, cell-free biological components, physically sealed within the biomaterial. Assumptions: This approach assumes that technical safeguards would reduce risk and that developers are willing to prioritize safety even when it increases development costs. Risk of failure and success: Higher costs could limit accessibility, and strong safety-by-design approaches could lead to overconfidence and reduced oversight. 2. Governance Action Actors: Sports federations (Sports minister) Purpose: This option aims to prevent misuse of the technology, such as coercive monitoring or excessive performance pressure. Design: Guidelines would restrict use to health and injury prevention, require informed consent. Assumptions: It assumes that institutions will enforce these guidelines and that athletes are able to provide meaningful consent. Risk of failure and success: Enforcement may vary across organizations and countries, and guidelines may lag behind technological developments or the person in charge of the regulatory agency. 3. Governance Action Actors: Universities, public funding agencies Purpose: Encourage constructive and equitable applications of the technology. Design: Public funding and institutional support could be tied to safety standards, open technical approaches, and affordability, helping extend benefits beyond elite sports contexts. Assumptions: Incentives influence research priorities and promote socially beneficial outcomes. Risk of failure and success: Increased administrative requirements may reduce private-sector participation.

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:

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 table above, the most effective governance approach combines option 1 with option 3. But it would be useful to use the ethical guidelines from option 2 to support the idea.

Integrating security into the technology reduces biological and environmental risks before deployment, while incentive-based strategies promote accessibility and socially beneficial uses. Ethical guidelines protect its misuse.

References

Nguyen, P. Q., Soenksen, L. R., Donghia, N. M., Angenent-Mari, N. M., de Puig, H., Huang, A., Lee, R., Slomovic, S., Galbersanini, T., Lansberry, G., Sallum, H. M., Zhao, E. M., Niemi, J. B., & Collins, J. J. (2021). Wearable materials with embedded synthetic biology sensors for biomolecule detection. Nature biotechnology, 39(11), 1366–1374. https://doi.org/10.1038/s41587-021-00950-3 Shen, Y., Liu, C., He, H., Zhang, M., Wang, H., Ji, K., Wei, L., Mao, X., Sun, R., & Zhou, F. (2022). Recent Advances in Wearable Biosensors for Non-Invasive Detection of Human Lactate. Biosensors, 12(12), 1164. https://doi.org/10.3390/bios12121164 Wang, T., & Lu, Y. (2022). Advances, Challenges and Future Trends of Cell-Free Transcription-Translation Biosensors. Biosensors, 12(5), 318. https://doi.org/10.3390/bios12050318 Xuan, X., Pérez-Ràfols, C., Chen, C., Cuartero, M. and Crespo, G. (2021). Lactate biosensing for reliable on-body sweat analysis. ACS Sensors. 6 (7), 2763-277. DOI: 10.1021/acssensors.1c01009 Yang, G., Hong, J., & Park, S. B. (2024). Wearable device for continuous sweat lactate monitoring in sports: a narrative review. Frontiers in physiology, 15, 1376801. https://doi.org/10.3389/fphys.2024.1376801 Zhang, L., Guo, W., & Lu, Y. (2020). Advances in Cell-Free Biosensors: Principle, Mechanism, and Applications. Biotechnology journal, 15(9), e2000187. https://doi.org/10.1002/biot.202000187

Week 2 Lecture Prep

Homework Questions from Professor Jacobson:
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 with proofreading has an error rate of about 10⁻⁶ mistake per base pairs copied. Comparing to the size of the human genome, which is roughly 3.2 billion base pairs long, at that error rate, thousands of mistakes would be introduced every time a human genome is replicated. This discrepancy is solved by its proofreading 3’ – 5’ activity that removes misincorporated bases as replication occurs, and any remaining errors are further corrected by post-replication DNA repair systems such as mismatch repair.

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 is encoded by about 1,036 base pairs, and because most amino acids are specified by multiple synonymous codons, the same protein sequence can be written in an huge number of different nucleotide combinations. In practice, however, only a small fraction of these sequences work well in living cells, and each organism have preferred codons in order to produce a successful protein. Many synonymous DNA sequences form problematic secondary structures, especially those with extreme GC content, which can interfere with transcription and translation. Others use rare codons that slow translation, or accidentally introduce regulatory signals that disrupt expression. As a result, although the genetic code is highly redundant in theory, biological and physical constraints drastically limit which DNA sequences can successfully produce a functional protein.

Homework Questions from Dr. LeProust:
1. What’s the most commonly used method for oligo synthesis currently?

The most commonly used method is solid-phase phosphoramidite synthesis. DNA is built one nucleotide at a time on a solid support through repeated chemical cycles.

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

Each step in chemical DNA synthesis has a small chance of error. As the oligo gets longer, these small errors add up, leading to lots of truncated or incorrect sequences. Beyond ~200 nucleotides, the yield and accuracy of full-length oligos drop sharply, and purification becomes much harder

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

A 2000 bp gene would require thousands of chemical synthesis steps, which would result in extremely low yields and very high error rates. Because of this, long genes are not made directly. Instead, they are assembled from shorter, synthesized oligos or fragments using enzymatic methods, followed by sequence verification

Homework Question from George Church:
1. 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 His, Ile, Leu, Lys, Met, Phe, Thr, Trp, Val, and Arg (often conditionally essential). Lysine’s strict essentiality and central role in protein–DNA interactions support the idea of a real “lysine contingency,” where growth and regulation are limited by lysine availability.