Week 1 HW: Principles and Practices

Assignment (Class - Ethics)

1. Describe a biological engineering application or tool you want to develop and why.

• I want to develop a bioengineered architectural substrate that accelerates coral colonization and marine habitat formation on submerged structures. I call this system Reef-Transition Building Skin (RTBS) - a modular “living interface” that can be attached to coastal buildings, seawalls, piles, and pier foundations, biologically tuned to support coral settlement and marine habitat formation once those structures are periodically or permanently submerged. Sea-level rise guarantees that many coastal structures will eventually enter the water, yet most existing hard infrastructure is ecologically sterile or actively harmful. RTBS responds to this reality by operationalizing the idea that architecture can also serve non-human systems. It is based on the idea that architecture should be designed for more than its period of human occupation: structures that serve people today should be capable of transforming into productive marine habitat in the future, so that when they are submerged they contribute to ecological life rather than becoming inert debris or pollution.

2. 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 A — Prevent harm to ecosystems and people

  • Avoid invasive impacts: Check local ecological compatibility before deployment to prevent disrupting existing species and habitats.
  • Avoid toxic pollution: Set strict limits on harmful chemicals and materials that could leach into the water.
  • Avoid false ecological claims: Require measurable evidence before projects can claim environmental benefits.

• Goal B — Ensure real ecological benefit

  • Prove habitat improvement: Measure ecological conditions before and after installation to confirm actual gains.
  • Design for local conditions: Adapt structures to each site’s water quality, climate, and wave patterns.
  • Plan for long-term care: Require maintenance and repair plans if systems fail or cause harm.

• Goal C — Support fairness and local control

  • Benefit local communities: Ensure projects create jobs or ecological benefits for nearby residents.
  • Include local decision-making: Involve community stakeholders in design and approval.
  • Share information openly: Publish monitoring results, including failures, not just successes.

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

• Action 1 - Habitat-based permitting rule

  • This action would change coastal permits so projects must help marine ecosystems, not just avoid damage. Regulators would require new seawalls, piers, and coastal structures to show measurable ecological benefit, such as increased coral growth or biodiversity, supported by surveys before construction and monitoring for several years after installation. Independent reviewers would verify results, and projects that fail would need to be repaired or redesigned. This assumes ecological performance can be measured fairly and that communities can support monitoring. The risk is weak enforcement, where monitoring becomes symbolic. Even if it works, developers might focus on easy metrics instead of long-term ecosystem health.

• Action 2 - Eco-material certification standard

  • This action creates a certification system to ensure coastal building materials are safe for marine life. Materials would be tested for toxicity, chemical leaching, and durability, and proven in real-world pilot projects before approval. Certified materials would be labeled and traceable, and public infrastructure projects would prioritize their use. This assumes companies are willing to share information and certification bodies stay independent. The risk is greenwashing if standards are weak. Even success could create problems if certification becomes expensive and excludes smaller producers.

• Action 3 - Financial incentives for transition-ready architecture

  • This action makes ecological coastal design financially attractive. Governments and insurers would provide grants, insurance discounts, and performance-based funding to projects that demonstrate real ecological benefits. Funding would depend on monitoring results and require open data and local job training. This assumes insurers accept ecological performance as reducing risk and that ecosystem benefits can be valued financially. The risk is funding projects that don’t actually help ecosystems. Even if successful, incentives could unintentionally encourage more development in vulnerable coastal areas.

4. 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. 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 scoring, I would prioritize the habitat-based permitting rule as the primary governance action, supported by certification and financial incentives. Permitting is the strongest safeguard because it directly prevents ecological harm and sets enforceable minimum standards. Without a regulatory baseline, certification and incentives risk becoming optional or symbolic. A permitting framework ensures that every coastal project must meet habitat-performance thresholds, making environmental protection non-negotiable rather than market-dependent.

• Certification and incentives still play important supporting roles. Certification reduces uncertainty about material safety, making compliance easier and more consistent, while incentives help offset costs and encourage adoption. However, relying too heavily on financial incentives could unintentionally encourage risky development in vulnerable coastal areas. Permitting acts as the necessary boundary that keeps ecological goals from being undermined by economic pressures.

• The main trade-off is feasibility: strong permitting requires institutional capacity, reliable monitoring, and long-term enforcement. It assumes ecological performance can be measured fairly across diverse sites and that regulators have resources to enforce standards. Uncertainty remains around scaling this system globally and maintaining political support over time. Still, prioritizing permitting creates a stable ethical foundation, with certification and incentives functioning as tools that operate within those ecological limits rather than replacing them.

Assignment (Lab Preparation)

• Complete Lab Specific Training in Person.
• Complete Safety Training in Atlas

Assignment (Your HTGAA Website)

• Personalizing your HTGAA website

Assignment (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 makes about one error per 10⁴–10⁵ bases, while the human genome is about 3 × 10⁹ bases long. Cells correct this mismatch through proofreading and mismatch repair, reducing the final mutation rate to roughly one error per genome replication.
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 can be encoded by an astronomically large number of DNA sequences because of codon redundancy, roughly 3ᴸ for a protein of length L. Most of these sequences do not work well in practice due to codon bias, unstable mRNA structure, extreme GC content, hidden regulatory motifs, or disrupted translation kinetics.

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently?
   • The most common oligo synthesis method is solid-phase phosphoramidite chemistry.
2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
   • Oligos longer than about 200 nucleotides are difficult because small stepwise synthesis errors accumulate exponentially with length.
3. Why can’t you make a 2000bp gene via direct oligo synthesis?
   • A 2000 bp gene cannot be made by direct synthesis because yield collapses and errors dominate, so long genes must be assembled from shorter oligos.

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 arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. Lysine dependence highlights a potential containment strategy because organisms that cannot synthesize lysine cannot grow without an external supply.

Week 2 HW: Read, Write, and Edit DNA

Part 1: Benchling & In-silico Gel Art

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

See Week 2 Lab.

Part 3: DNA Design Challenge

3.1. Choose your protein.

• Skeletal aspartic acid-rich protein 1 Acropora millepora (Staghorn coral)

-sp|B3EWY6|SAAR1_ACRMI Skeletal aspartic acid-rich protein 1 OS=Acropora millepora OX=45264 PE=1 SV=1 MAFVSCFHLRLLFLCLALFMAAECRPDELNKKVDSDETISDDDVSARVQPNGGKIMIVRD NDYDASDDNDNDNDDDDNNDNDNDNDDDNDVDRDNDNDDDDFDDSNDDMLSFELDSIEEK DSDGNDVGSTEGHSVESFEDRPFSLSSVDRNSNALGVAAINVNLSTKLEDSNADVDIMLY LFREDGTISFGNETFDVQAGTVKFNIKISNWDFCDGSAQDCSEAKAGEYLDVNIKFKSKD TPIEVTDEERKSQNKPAVCKDKDTPDTDSDPDDSSDNANDGDDDDDDDCPHIYNMGGDSE MLLNRGVMNGDTYTAMPFGFPKVEIEDGEKKIKFRVPKFDDNVNIDPSVTPGRVPKNASP SPALCLKIHILFIALLQAVTLFINSW

• I chose skeletal aspartic acid rich protein 1 (SAARP1) because it is directly involved in coral calcification and biomineralization, linking a specific protein to the physical formation of the coral skeleton. Skeletal aspartic acid rich protein 1 (SAARP1) is a major component of the skeletal organic matrix (SOM) in the staghorn coral Acropora millepora. It is a highly acidic protein with roughly 20 percent aspartic acid residues and is involved in coral calcification and biomineralization. SAARP1 belongs to a conserved family of acidic proteins that likely helps regulate the formation, structure, and deposition of aragonite crystals in the coral skeleton.

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

• NCBI Reference Sequence: XM_029346345.2
• acaccggaaa ccagagattt gcagcagaaa tgtgacataa ggctgctttc cgagctagca cttatcagcg tccattcgcc atttggcagc attgtttatg gccgctgaat gtcgcccaga cgagctgaac aaaaaagtgg acagtgatga aacaattagc gatgatgacg tctccgcaag agttcagccc aatggcggga aaatcatgat cgtccgcgac aatgactacg atgcctccga cgataatgat aatgatgatg atgacaataa cgataacgat aacgataacg atgatgataa tgatgtcgat cgtgataatg ataatgatga cgacgatttt gacgacagta acgacgatat gctctcgttt gagttggata gcatagaaga gaaggactcg gatggaaatg acgtagggag cactgaagga cattctgtag aatcatttga agatcgacca ttttctctgt cttccgttga tcgcaacagc aatgctttag gtgtcgcggc catcaatgtc aacctgtcaa cgaaacttga ggattctaac gcagacgtag acatcatgtt gtacctgttt cgcgaagatg gaaccatttc ctttggtaac gaaacatttg atgtacaagc tggaactgtt aagtttaata ttaagattag caactgggat ttctgtgatg gttcagcgca agactgcagc gaggcaaaag caggcgaata ccttgatgta aacatcaaat tcaagagtaa agacacacca atcgaagtaa cagacgagga acgaaaaagt cagaataaac cggcagtgtg caaggacaaa gacacaccag atactgacag cgatccagat gatagcagtg ataatgccaa tgatggggat gacgatgacg atgacgactg tcctcacatc tataacatgg gtggagattc agagatgtta ctcaacagag gggtaatgaa tggtgacacc tacactgcta tgccgtttgg attccctaaa gtcgaaattg aggatggaga gaagaaaata aagtttcgcg ttcccaagtt tgacgataac gtaattatag accctagcgt gaccccagga agagtgccaa agaatgcttc gccttcacct gccctttgcc tgaaaattca catcctcttc attgcactac ttcaagctgt taccctattt atcaacagtt ggtaacactc aaggggtttt aaacattact atgtggaatt tgaggcgaaa ataagcgaag gcaatacttg cttcgattcc tctttgcact gaattgccat ctgtaatttt gacagagaat agcatcgtta acaccacgtg ttaaagtata caccatagtc tcgagcaatt gtcgtaacag gagataacaa agttatcaac gaaacgttta cactcaggaa tgacatcagt attagtttca gttttgagaa tgaacagacg cgctctccat ggaaaccatg gtgttgttag ggtttattta aattttaaat aaagtaatgc taaccaccaa cctacctacc tacctacatt cgttttacaa ttcagaatga gttttctttt taccatggct caacctccat gcgccaccat tgctaggctt gctacacaca tcgcgcgttt attcagtatc tttcttatta ccgtatatgg tgtttcacca aaatagttga agatcgaagg ccactatcga agattaagtt gatattatgc aaaacaggtc attcactaat taagagtctt gtttgatgtg aatccctacc gcaagctaaa actaatcttg tcatgaactt ttaggcaatg tattgctgaa gattggagaa agcagcgtag ggattatgct ccaaatgtca cctttgcaat tttcttacgg ttgtaattta accctgttat gtttaatatc aaattttgtt atccaattaa attaattgaa acactgcc

3.3. Codon optimization.

• Even though multiple codons can encode the same amino acid, different organisms prefer different codons. That preference tracks with tRNA abundance and other expression constraints. If you use a DNA sequence with many codons that are “rare” in your expression host, translation can stall, causing lower yield, more misfolding/aggregation, and sometimes premature termination. The codon is optimized for E. Coli because we will be using it in labs as the host.

3.4. You have a sequence! Now what?

• Once I have a codon-optimized SAARP1 DNA sequence, I can produce the protein by placing the gene into an E. coli expression plasmid that includes a promoter, ribosome binding site (RBS), transcription terminator, origin of replication, and an antibiotic resistance marker, often plus a purification tag such as 6×His. The plasmid can be introduced into E. coli either via heat shock or electroporation. After the plasmid is inside E. coli, the gene is transcribed into mRNA when the promoter is active (often induced in expression systems). The mRNA is then translated by E. coli ribosomes, which read the mRNA codons and use tRNAs to assemble the SAARP1 amino-acid chain. Codon optimization improves this step by matching E. coli’s preferred codons, reducing ribosome stalling and increasing the likelihood of higher protein yield.

Part 4: Prepare a Twist DNA Synthesis Order

Part 5: DNA Read/Write/Edit

5.1 DNA Read (sequence)

• (i) What DNA would you want to sequence and why?
• • I would sequence some of coral’s DNA like the SAARP1 (skeletal aspartic acid-rich protein 1) gene from Acropora millepora, because I want to explore coral’s potential as architectural material or learn about their calcification performance and stress response under warming and acidification to ensure green future shoreline development.
• (ii) What sequencing technology would you use and why?
• • I would use Sanger sequencing to read the SAARP1 gene because it is accurate, cost-effective, and ideal for sequencing individual gene-length fragments amplified by PCR. PCR and synthetic oligos allow me to isolate the specific gene region, and Sanger sequencing provides precise base-level confirmation of the amplified or edited DNA. Sanger sequencing is considered first-generation sequencing, because it reads one DNA fragment at a time with high accuracy. The input is genomic DNA extracted from coral tissue. The essential preparation steps are: design oligo primers → PCR amplify the SAARP1 locus → purify the PCR product → add sequencing primers → load into the Sanger reaction. Sanger sequencing uses chain-terminating nucleotides labeled with fluorescent dyes. When DNA synthesis stops at each base position, the fragments are separated by size and read by a detector that converts fluorescence into a DNA sequence. The output is a chromatogram and a high-accuracy DNA sequence of the amplified SAARP1 fragment, typically in FASTA format.

5.2 DNA Write (synthesize)

• (i) What DNA would you want to synthesize and why?
• • I would synthesize a codon-optimized SAARP1 coding sequence so I can express it in a chosen host (or produce peptides/biomineralization assays) and test how SAARP1 chemistry affects mineral deposition and coral-like calcification cues.
• (ii) What synthesis technology would you use and why?
• • I would use phosphoramidite oligo synthesis plus assembly (the standard commercial approach) because it is fast, scalable, and ideal for making gene-length constructs via assembly from shorter oligos.
• • Design sequence in silico → synthesize short oligos → assemble into the full gene (PCR/Gibson-like assembly) → clone into plasmid → sequence-verify.
• • Long sequences accumulate errors and often need verification and rebuilding. Repetitive/low-complexity regions and extreme GC can reduce synthesis success. Scaling to many variants is feasible, but costs and validation time increase quickly.

5.3 DNA Edit (edit)

• (i) What DNA would you want to edit and why?
• • I would edit the SAARP1 regulatory region (or specific coding residues) in coral to test whether higher or better-timed SAARP1 expression improves calcification under heat/acidification stress.
• (ii) What editing technology would you use and why?
• • I would use CRISPR-based editing, ideally prime editing (or a high-fidelity Cas9 + HDR if prime editing isn’t feasible), because it can make targeted changes without needing large insertions.
• • 1) How does it edit DNA, and what are the essential steps?
• • CRISPR targets a specific locus with a guide RNA and edits it using an editor enzyme and a repair template or edit-encoding RNA. Key steps are guide design → delivery into cells/embryos → editing reaction → screening/validation.
• • 2) What prep and inputs are needed?
• • Inputs include a target sequence, guide RNA, editor protein (Cas9/prime editor), and (if needed) a repair template plus the recipient cells/embryos and delivery method (microinjection/electroporation/viral vectors depending on system).
• • 3) Limitations (efficiency/precision)?
• • Delivery and survival in coral embryos can be difficult and can cause mosaic edits. Off-target edits and unintended repair outcomes can occur. Even precise edits may have ecological and ethical constraints if used outside controlled research settings.

Week 3 HW: Lab Automation

Post-Lab Questions

1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

• Dettinger et al. (2022), “Open-source personal pipetting robots with live-cell incubation and microscopy compatibility,” published in Nature Communications.

• The authors introduce PHIL (Pipetting Helper Imaging Lid), an open-source, low-cost pipetting robot designed for liquid handling during live-cell experiments and microscopy workflows. PHIL is important because it addresses a real problem in academic labs: many experiments are small-scale, frequently changing, and not well suited to large industrial automation systems, which are often expensive and hard to adapt.

• The paper shows that PHIL can automate tasks such as media exchange, stimulation, and immunostaining while remaining compatible with time-lapse microscopy. This makes it possible to run dynamic live-cell experiments with less manual intervention and better reproducibility. Another key strength is accessibility: the system is built from 3D-printable parts and low-cost components, making advanced lab automation more realistic for smaller or resource-limited research labs.

2. Write a description about what you intend to do with automation tools for your final project.

• Automate a screening workflow for coral-related biomineralization conditions

• • I want to use an automation tool (such as Opentrons) to set up and run a small screening experiment using coral proteins. The robot would prepare multiple reaction conditions (different buffers, salts, and controls) in a consistent and repeatable way.

• Compare material or condition combinations in a plate-based format

• • I want to test which conditions may better support coral-relevant mineralization behavior (for example, comparing protein vs control conditions across different solution chemistries). Automation helps because it can precisely mix and dispense many combinations with less manual error.

• Use a custom holder / insert setup for non-standard samples

• • If needed, I may design a simple 3D-printed holder to keep small material samples or test coupons in a fixed position during pipetting. This would make the workflow more reproducible and easier to scale across replicates.

Final Project Ideas

See slide deck

Week 4 HW: Protein Design Part 1

Part A. Conceptual Questions

1. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)

2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?

3. Why are there only 20 natural amino acids?

4. Can you make other non-natural amino acids? Design some new amino acids.

5. Where did amino acids come from before enzymes that make them, and before life started?

6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

7. Can you discover additional helices in proteins?

8. Why are most molecular helices right-handed?

9. Why do β-sheets tend to aggregate?

• What is the driving force for β-sheet aggregation?

10. Why do many amyloid diseases form β-sheets?

• Can you use amyloid β-sheets as materials?

11. Design a β-sheet motif that forms a well-ordered structure.

Part B: Protein Analysis and Visualization

1. Briefly describe the protein you selected and why you selected it. 2. Identify the amino acid sequence of your protein.

• How long is it? What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.
• How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.
• Does your protein belong to any protein family? 3. Identify the structure page of your protein in RCSB
• When was the structure solved? Is it a good quality structure? Good quality structure is the one with good resolution. Smaller the better (Resolution: 2.70 Å)
• Are there any other molecules in the solved structure apart from protein?
• Does your protein belong to any structure classification family? 4. Open the structure of your protein in any 3D molecule visualization software:
• PyMol Tutorial Here (hint: ChatGPT is good at PyMol commands)
• Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.
• Color the protein by secondary structure. Does it have more helices or sheets?
• Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?
• Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?

Part C. Using ML-Based Protein Design Tools

• C1. Protein Language Modeling

1. Deep Mutational Scans

• a. Use ESM2 to generate an unsupervised deep mutational scan of your protein based on language model likelihoods.
• b. Can you explain any particular pattern? (choose a residue and a mutation that stands out)

2. Latent Space Analysis

• a. Use the provided sequence dataset to embed proteins in reduced dimensionality.

• b. Analyze the different formed neighborhoods: do they approximate similar proteins?

• c. Place your protein in the resulting map and explain its position and similarity to its neighbors.

• C2. Protein Folding

1. Folding a protein

• a. Fold your protein with ESMFold. Do the predicted coordinates match your original structure?

• b. Try changing the sequence, first try some mutations, then large segments. Is your protein structure resilient to mutations?

• C3. Protein Generation

Inverse-Folding a protein: Let’s now use the backbone of your chosen PDB to propose sequence candidates via ProteinMPNN

• a. Analyze the predicted sequence probabilities and compare the predicted sequence vs the original one.
• b. Input this sequence into ESMFold and compare the predicted structure to your original.

Part D. Group Brainstorm on Bacteriophage Engineering

Brainstorm Session

• Choose one or two main goals from the list that you think you can address computationally (e.g., “We’ll try to stabilize the lysis protein,” or “We’ll attempt to disrupt its interaction with E. coli DnaJ.”).
• Write a 1-page proposal (bullet points or short paragraphs) describing:
• Which tools/approaches from recitation you propose using (e.g., “Use Protein Language Models to do in silico mutagenesis, then AlphaFold-Multimer to check complexes.”).
• Why do you think those tools might help solve your chosen sub-problem?
• Name one or two potential pitfalls (e.g., “We lack enough training data on phage–bacteria interactions.”).
• Include a schematic of your pipeline.

3.1. Choose your protein.

• Skeletal aspartic acid-rich protein 1 Acropora millepora (Staghorn coral)

Week 1 Lab: Pipetting

Week 2 Lab: Gel Art

LU (Love U)/upsidedown

Pipette

Incubation

Electrophoresis

Final Product

Week 3 Lab: Opentrons Art

Week 4 Lab: Protein Design Part-1

See Week 4 Homework.

Individual Final Project

Group Final Project