Week 1 HW: Principles and Practices

I am excited to try to develop a 3D bioprinted cardiac organoid model inspired by the frog’s three-chambered heart structure (two atria and one ventricle), integrated with data from the Human Cell Atlas (HCA) to accurately replicate human cardiac cell types. This tool would use scaffold-free 3D cell culture techniques, such as spheroid aggregation, to create a simplified heart-like structure where human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (Mills et al., 2019), endothelial cells, and fibroblasts self-assemble into chambers mimicking the frog’s efficient mixing and pumping system. To incorporate cancer research, the organoid could be co-cultured with patient-derived cancer cells (e.g., breast or lung cancer lines) to simulate tumor microenvironments and test cardiotoxic effects of chemotherapy drugs such as doxorubicin.

Why this tool? During my undergraduate studies, I was fascinated by the frog’s three-chambered heart as a model for vertebrate cardiac evolution and congenital diseases like hypoplastic left heart syndrome (HLHS), where a single ventricle dominates (Nie, 2023). This simplicity allows for easier bioengineering compared to complex four-chambered mammalian models, reducing fabrication challenges while maintaining key functions like partial blood mixing for studying hypoxia in tumors. Leveraging HCA’s single-cell atlas of human heart regions (e.g., detailing 75+ cell states including conduction system cells) ensures the model is human-relevant, bridging gaps in current 2D cultures that fail to capture 3D tissue interactions. In health applications, this could advance cardio-oncology by modeling how cancer treatments damage heart tissue, addressing a growing issue in Indonesia where cancer incidence is rising (e.g., over 400,000 cases annually) and heart disease is a top killer. Ultimately, it promotes personalized medicine through drug screening on patient-specific organoids, reducing animal testing and accelerating therapeutic development.

To ensure this 3D cardiac organoid tool contributes to an “ethical” future, I focus on the governance goal of non-malfeasance (preventing harm), broken down into two sub-goals: safety and equity. • Sub-goal 1: Safety Prevent unintended harm by ensuring the tool’s design minimizes biosecurity risks, such as accidental release of engineered cancer cells that could mutate or contaminate environments. This aligns with promoting constructive uses, like safe drug testing, while avoiding malfeasance (e.g., dual-use as bioweapons). • Sub-goal 2: Equity Promote fair access to the technology, ensuring it doesn’t exacerbate global health disparities. For instance, open-source protocols could allow low-resource labs in developing countries to replicate the model, fostering autonomy and reducing dependency on expensive proprietary systems.

These goals draw from synthetic genomics frameworks, adapting them to bioengineered tissues by emphasizing rigorous testing and inclusive distribution. I outline three potential governance actions for this tool, mixing strategies from different actors and drawing analogies from 3D printing (regulating printed biomaterials) and financial systems (incentivizing ethical R&D).

Action 1: Academic Researchers – Open-Source Protocol Development (Technical Strategy) • Purpose: Currently, many 3D culture protocols are proprietary; I propose mandatory open-sourcing of organoid designs inspired by public datasets like HCA to accelerate global research while preventing harm from opaque methods. • Design: Researchers must upload protocols to platforms like GitHub, with ethics boards (e.g., university IRBs) approving before publication. Actors involved: Opt-in by academics, funded by grants like NIH or Indonesian Kemenkes. • Assumptions: Assumes researchers will comply if incentivized by citations and collaborations. • Risks of Failure & “Success”: Failure could lead to fragmented standards; “success” might slow innovation if over-bureaucratic, but it prevents misuse.

Action 2: Companies – Incentive Programs for Ethical Commercialization (Incentive Strategy) • Purpose: Biotech firms often prioritize profit; propose tax incentives for companies developing affordable versions of the organoid for low-income regions, changing from high-cost models to equitable access. • Design: Governments fund via subsidies; companies implement by partnering with NGOs, with approval from bodies like WHO. Actors: Companies opt-in, regulators approve. • Assumptions: Assumes market incentives drive ethical behavior. • Risks of Failure & “Success”: Failure risks monopolies; “success” could over-commercialize, raising privacy issues with patient-derived cells.

Action 3: Federal Regulators – New Biosafety Rules (Regulatory Strategy) • Purpose: Existing biosafety guidelines (e.g., BSL levels) don’t cover hybrid frog-inspired models; propose updated rules requiring pre-release testing for cancer cell containment. • Design: Enforced by agencies like the FDA or BPOM Indonesia; labs must fund and approve via audits. Actors: Mandatory for all, with law enforcement monitoring. • Assumptions: Assumes regulators have resources to enforce. • Risks of Failure & “Success”: Failure could allow biohazards; “success” might stifle small labs, but ensures security.

Source:

  • Mills, Richard J. et al. 2019. Drug Screening in Human PSC-Cardiac Organoids Identifies Pro-proliferative Compounds Acting via the Mevalonate Pathway.*Cell Stem Cell*, Volume 24, Issue 6, 895 - 907.
  • Nie S. 2023. Use of Frogs as a Model to Study the Etiology of HLHS. *J Cardiovasc Dev Dis*. 29;10(2):51
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Pre-lecture HW week #2

HW 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? Answer: DNA polymerase error rate: ~1 in 104-105 bases. Human genome: ~3 billion bp. Discrepancy: Would cause ~105-106 errors per replication. Biology deals via proofreading (exonuclease), mismatch repair, and DNA repair pathways.
  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? Answer: Average human protein: ~400 aa. Ways to code: Huge (e.g., if average 3 synonyms/aa, ~3^400 possibilities). Reasons not all work: Codon bias for efficient translation, mRNA stability/secondary structure, regulatory motifs, and avoidance of rare tRNAs.
HW Questions from Dr. LeProust:
  1. What’s the most commonly used method for oligo synthesis currently? Answer: Most common method: Phosphoramidite solid-phase synthesis.
  2. Why is it difficult to make oligos longer than 200nt via direct synthesis? Answer: Difficult >200nt: Coupling efficiency ~99%, yield drops exponentially (e.g., 0.99^200 ~13%), plus depurination/errors accumulate.
  3. Why can’t you make a 2000bp gene via direct oligo synthesis? Answer: Can’t make 2000bp gene directly: Yield near zero (0.99^2000 negligible), excessive errors, depurination; requires assembly from shorter oligos.
HW Questions from George Church:
  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”? Answer: 10 essential amino acids in animals: Histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, arginine (arginine conditional in some). Affects view of “Lysine Contingency” (Jurassic Park): Already essential in all animals (can’t synthesize), so dependency on diet is natural; contingency flawed as animals can source lysine from food like plants/meat.