Subsections of Homework

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
flow chart Ethics Principles flow chart Ethics Principles

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.

Week 2/Part-1 HW: DNA Write, Read, and Edit

Part 0: Basics of Gel Electrophoresis

  • Watch all lecture and recitation videos.
    • Optionally watch bootcamp

Part 1: Benchling & In-silico Gel Art

See the Gel Art: Restriction Digests and Gel Electrophoresis protocol for details.

Overview:

  • Make a free account at benchling.com
  • Import the Lambda DNA.
  • Simulate Restriction Enzyme Digestion with the following Enzymes:
    • EcoRI
    • HindIII
    • BamHI
    • KpnI
    • EcoRV
    • SacI
    • SalI
  • Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks. You might find Ronan’s website a helpful tool for quickly iterating on designs!

Import Lambda DNA to Benchling site Import Lambda DNA to Benchling site Fig. 1 Import Lambda DNA Sequence

On the left-hand side of the website display when you access benchling.com and create an account, choose create tabs (symbolized as: +) → choose DNA/RNA sequence → Import DNA/RNA sequence, then, you can upload any DNA files that you have downloaded from database (NCBI or Genbank in FASTA format etc) → choose nucleotide type, drag and drop files to upload and then done!

Digest enzyme feature Digest enzyme feature Find and run enzyme digest Find and run enzyme digest Figure 2. Selecting Enzymes

Now, on the right-hand side of the website display, choose the digest tool (symbolized with scissors) → find and choose the enzyme you want to try → click run digest

if you find a Hi-Fidelity options on the Enzyme details information, this means that this restriction enzymes have a very low probability of cutting at off-target sites.

Run virtual enzyme digest Run virtual enzyme digest Fig 3. Perform Digest

After you click run digest, the digest tab and virtual digest tab will appear. Switch the ladder to NEB 2-Log. Now you can download the visual depiction of your restriction digest of your own by clicking on the Download PNG (next to the ladder options)

Visual Depiction of your Restriction Digest Visual Depiction of your Restriction Digest Figure 4. Virtual Depiction of your Restriction Digest

This is my random trial, first timers of using benchling-things and the restrictions enzymes.

Week-2/Part-2 HW: DNA Read, Write, and Edit

5.1 DNA Read

    (i) What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank).

answer: I’m interested in sequencing the PIK3CA gene of cancer cells (e.g., breast/colorectal cancer cells) from the co-culture version in 3D cardiac organoid. Because the PIK3CA mutations (common in 10-20% cancers) drive tumor growth and cardiotoxicity, sequencing helps study interactions in cardio-oncology models, advancing human health research (Keraite et al., 2020). It’s common to sequence it using NGS for the prognosis data, while another interesting fact that the DNA of the frog’s heart (xenopus) is also available for heart development studies (Hellsten et al., 2010).

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?

answer: I’d like to use the Nanopore sequencing technology since it’s more practical compared to the Sanger method. Nanopore offers long reads (up to Mb), real-time results, portable, and also cheaper for batch with >30 samples, and also sensitive for the variants/mixed samples (Layyaroz et al., 2025). I’ll use the nanopore technology to sequence the PIK3CA/Xenopus genome, because of the long reads help to proceed full gene assembly and detect mutations.

Also answer the following questions:

  1. Is your method first-, second- or third-generation or other? How so? What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps. answer: Nanopore is third-generation sequencing: real-time, long-read (up to Mb), single-molecule without amplification bias. Input: Native DNA/RNA (e.g., PIK3CA amplicons or Xenopus extracts) (Alberto et al., 2018). Preparation: Shear/fragmentation (optional for long reads), end-repair, adapter ligation (motor protein/leader), optional PCR for low-input. Essential steps:
    • Extract DNA;
    • Fragment (e.g., shear)
    • End-repair/dA-tailing
    • Ligate adapters
    • Load onto flow cell
  1. What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)? answer: Essential steps: 1) DNA/RNA translocates through nanopore, disrupting ionic current; 2) Raw signal captured; 3) Segmentation into events; 4) Neural network basecalling (e.g., transformer models preprocess signal via convolutions, encode with transformers, decode to bases). Base calling: Translates raw electrical signals to nucleotides using ML models (e.g., RNN/Transformers) predicting bases from current changes (Zhang et al., 2020).
  1. What is the output of your chosen sequencing technology? answer: the output are FASTQ/BAM files with sequences, quality scores, and optional alignments; real-time or post-run via basecallers like Dorado.

5.2 DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why? These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :)

answer: DNA to synthesize: CRISPR gRNA targeting PIK3CA H1047R mutation (sequence: CGAACAGGTATCTACCATGG), plus telomere repeat inhibitor (TTAGGG repeats fused to TERT-targeting gRNA). Why? bacuse it can be utilized for cancer therapeutics—edit PIK3CA in 3D cardiac organoids to model cardio-oncology, inhibit telomerase (TERT) to shorten telomeres in cancer cells, reducing proliferation while studying heart effects (Wang et al., 2025).

(ii) What technology or technologies would you use to perform this DNA synthesis and why?

answer: Phosphoramidite-based solid-phase DNA synthesis (e.g., via Twist Bioscience). It has high-fidelity for short oligos like gRNA (~20-100 bp), scalable for custom sequences targeting PIK3CA/TERT, cost-effective ($0.03/base), and enables rapid iteration in cancer therapeutics without enzymatic errors.

Also answer the following questions:

  1. What are the essential steps of your chosen sequencing methods? answer: these are the essential steps of my chosen sequencing methods including extract and purify DNA (from cancer cells / organoid culture), ligate sequencing adapters + motor protein, load library onto the flow cell, apply voltage → DNA translocates through nanopore, and real-time electrical signal recording → basecalling (Dorado / Guppy).

  2. What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability? answer: I chose Oxford Nanopore (third-generation) because it gives long reads and real-time results, ideal for full-length PIK3CA and telomere analysis in organoids. The main limitation is slightly lower single-read accuracy (~90%), which is easily solved with higher coverage.

5.3 DNA Edit

(i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

answer: I would like to sythesize CRISPR gRNA targeting PIK3CA H1047R mutation (sequence: CGAACAGGTATCTACCATGG), plus telomere repeat inhibitor (TTAGGG repeats fused to TERT-targeting gRNA). I’d like to use it for cancer therapeutics—edit PIK3CA in 3D cardiac organoids to model cardio-oncology, inhibit telomerase (TERT) to shorten telomeres in cancer cells, reducing proliferation while studying heart effects.

(ii) What technology or technologies would you use to perform these DNA edits and why?

answer: I’d like to use Phosphoramidite-based solid-phase DNA synthesis (e.g., via Twist Bioscience) technology, because high-fidelity for short oligos like gRNA (~20-100 bp), scalable for custom sequences targeting PIK3CA/TERT, cost-effective ($0.03/base), and enables rapid iteration in cancer therapeutics without enzymatic errors.

Also answer the following questions:

  1. How does your technology of choice edit DNA? What are the essential steps? answer: CRISPR-Cas9 edits DNA by creating targeted double-strand breaks (DSBs), triggering cellular repair. Essential steps: 1) gRNA binds target sequence via base-pairing; 2) Cas9 nuclease cleaves DNA 3 bp upstream of PAM (NGG); 3) DSB repaired by NHEJ (indels) or HDR (precise edits).

  2. What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing? answer: Preparation: Design gRNA (e.g., for PIK3CA H1047R: CGAACAGGTATCTACCATGG; telomere: TTAGGG repeats + TERT gRNA); synthesize via phosphoramidite; form RNP complex. Input: gRNA, Cas9 protein (RNP), DNA template (for HDR), primers (verification), cells (cancer/organoid culture).

  3. What are the limitations of your editing methods (if any) in terms of efficiency or precision? answer: Limitations: Efficiency—low HDR (~1-20%), high NHEJ preference, mosaicism in founders. Precision—off-target effects (≥50%), unintended mutations, PAM restrictions.

Source: Alberto Magi, Roberto Semeraro, Alessandra Mingrino, et al., 2018. Nanopore sequencing data analysis: state of the art, applications and challenges, Briefings in Bioinformatics, 19(6), pp. 1256–1272. Keraite, I., Alvarez-Garcia, V., Garcia-Murillas, I. et al. 2020. PIK3CA mutation enrichment and quantitation from blood and tissue. Sci Rep, 10, 17082. Hellsten U, Harland RM, Gilchrist MJ, et al. 2010. The genome of the Western clawed frog Xenopus tropicalis. Science, 328(5978): pp. 633-6. Larráyoz MJ, Luri-Martin P, Mañu A, et al. 2025. From Sanger to Oxford Nanopore MinION Technology: The Impact of Third-Generation Sequencing on Genetic Hematological Diagnosis. Cancers (Basel), 17(11): pp. 1811. Zhang YZ, Akdemir A, Tremmel G, et al. 2020. Nanopore basecalling from a perspective of instance segmentation.BMC Bioinformatics, 21:136.