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 want to develop synthetic chemical compounds that enhance membrane visibility in electron microscopy imaging of neural tissue. Current EM images of brain tissue suffer from poor contrast between adjacent neurons, making boundary identification difficult for both human annotators and automated segmentation algorithms. This creates a significant bottleneck for large-scale connectomics.
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.
Primary Goal: Establish standards and oversight that ensure contrast agents are scientifically valid, minimize animal use, and create responsible pathways for technology adoption
- The governance challenge is that improved imaging methods can be adopted quickly without adequate validation. Without coordinated policies, each lab develops proprietary methods that can’t be compared across studies, potentially leading to wasted resources and unnecessary animal use.
Goal 2: Implement oversight requiring validation before widespread use
- Current situation is that novel chemical methods can be published and adopted without independent verification. Labs might use agents that seem to work but actually create subtle artifacts. The policy need is to require demonstration that contrast agents actually improve scientific outcomes before they become standard practice. This means funding agencies like NIH and NSF should require validation data for grants using novel contrast agents, IACUCs need to add review criteria specific to imaging methods, and we need multi-lab validation before methods get recommended as field standards. There should also be public databases where negative results and failures get reported.
Goal 3: Establish protocols that minimize animal use through data quality
- Poor imaging quality leads to larger sample sizes and repeated experiments, but there’s no systematic framework for quantifying how imaging improvements reduce animal numbers. The policy need is to create requirements that researchers demonstrate and report animal use reductions enabled by better methods. Grant applications should include power analyses showing how improved imaging affects required sample sizes, and publications should report imaging quality metrics and how they influenced statistical needs. We also need tissue sharing mandates so one specimen’s data serves multiple research groups.
Goal 3: Create open-access standards for reproducibility
- Proprietary imaging methods fragment the field and results from different labs can’t be directly compared, leading to redundant animal use. Policy need is to require that imaging methods be openly documented and standardized. Journals should require detailed protocols as supplementary materials, funding agencies should prioritize open-source methods, and professional societies should maintain registries of validated protocols.
Goal 4: Develop staged approval pathway for human tissue applications
- There’s currently no clear regulatory path for when animal-validated methods can be applied to human brain tissue from brain banks, autopsy, or preservation cases. We need clear criteria and oversight for transitioning from animal to human applications. This means FDA or equivalent oversight if methods will be used on human tissue, IRB review frameworks specific to connectomics methods, required safety data from animal studies before human tissue use, and brain banks establishing review boards before adopting novel methods.
3. 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:
| Does the option: | Action 1: Validation | Action 2: Open Protocols | Action 3: Human Pathway |
|---|---|---|---|
| Ensure scientific validity | |||
| • By preventing artifacts/bad methods | 1 | 3 | 2 |
| • By enabling error detection | 1 | 2 | n/a |
| Minimize animal use | |||
| • By preventing wasteful experiments | 1 | 2 | n/a |
| • By enabling tissue/data sharing | 2 | 1 | n/a |
| Enable reproducibility across labs | |||
| • By standardizing methods | 2 | 1 | n/a |
| • By making protocols accessible | 3 | 1 | 2 |
| Responsible human tissue use | |||
| • By establishing oversight | n/a | n/a | 1 |
| • By requiring safety validation | 2 | n/a | 1 |
| Other considerations | |||
| • Minimizing costs and burdens to researchers | 3 | 2 | 3 |
| • Feasibility of implementation? | 2 | 1 | 3 |
| • Not impede research progress | 2 | 1 | 3 |
| • Promote beneficial applications | 1 | 1 | 2 |
| • Promote constructive applications |
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.
Target audience: NIH program officers for neuroscience grants
I recommend: Start with Action 2 (open protocols), add Action 1 (validation) later, skip Action 3 for now
Action 2 scores best overall and NIH could implement it pretty quickly - just add protocol transparency to grant requirements and work with journals to strengthen methods reporting. This gives you reproducibility benefits without much friction.
Action 1 (validation) scores better for scientific rigor and preventing animal waste, but it creates real burden. Better to wait 2-3 years until the field matures and there are established protocols to validate against. Otherwise you’re asking labs to validate methods when nobody knows what “good” looks like yet.
Action 3 (human tissue pathway) isn’t needed for years since I’m working with animals. It also scores worst on feasibility and would slow everything down. Hold off until human applications actually become relevant.
Trade-offs:
The main tension is between getting rigorous validation (Action 1) versus letting the field move quickly (Action 2). I’m prioritizing speed early on because bad methods tend to get filtered out naturally - if something doesn’t replicate, labs stop using it. Once methods mature and get used in big studies, that’s when you need formal validation to prevent waste.
Implementing everything at once would be most protective but probably kills adoption. Phased approach seems more realistic.
What I’m assuming:
NIH will actually enforce open protocol requirements (their track record with data sharing isn’t great). The field won’t push back too hard against validation standards. Human applications are far enough out that we have time. Current animal ethics frameworks work for near-term stuff.
Uncertainties:
Whether better imaging actually reduces animal numbers or labs just ask more questions with the same n. Whether open protocols improve reproducibility or just create documentation burden. When human applications become practical - could be sooner if preservation methods advance fast.
6. Ethical Concerns from This Week
New stuff I hadn’t considered:
The 3Rs principle seems simple but applying it to imaging is messy. If contrast agents improve image quality 50%, how do you actually calculate animal number reduction? Risk is assuming better methods automatically mean fewer animals when labs might just use the extra power to expand their studies.
The validation burden issue is weird - requiring extensive validation might use more animals in validation studies than you’d save by preventing failures. Being more rigorous could paradoxically increase animal use short-term.
I hadn’t thought through the open science tension. Making protocols available helps reduce redundant animal use but also makes methods accessible to people outside research ethics frameworks.
What to do about it:
For animal numbers: make grant applications include actual statistical analysis showing projected sample size reductions, not just claims about “reducing animal use.”
For validation burden: tier requirements by study scale. Small pilots can use unvalidated methods, large multi-site studies need validation. Front-loads cost where savings are biggest.
For open science risks: keep protocols open but tissue access controlled. You can replicate the chemistry but still need institutional approval for tissue. 7. Reflecting on what you learned and did in class this week, outline any ethical concerns that arose, especially any that were new to you. Then propose any governance actions you think might be appropriate to address those issues.