Week 1 HW.1: Class assignment

1. Describe an application

Identify a biological engineering tool or application you wish to develop and explain your motivation.

I would like to develop a way to make plants grow 100x faster. I find this a very interesting and ambitious question. Perhaps you reverse-engineer the genome, morphological development and constraints, proteins/enzymes/catalysts for growth. Perhaps you design a separate organism (two bacterium?) which produces biomass - a combination of a carbon sequester and a cellulose printer. Perhaps you attempt to design a minimal artificial cell, like a Xenobot / JCVI minimal cells - using new AI design software, you create a minimal genome/DNA, design your own morphological topology through simulation, which is compiled down to gene regulatory networks (GRN’s), transcription factors/thresholds, and DNA.

Why? Because trees and plants are great. They are calming, they look beautiful, they are functionally useful. Originally I wanted to build my own house, and was wondering - why is wood so expensive? If we could grow wood more quickly and effectively, that would be useful. It would also be fun to rapidly green certain areas of the world to produce arable land - the Australian desert, for example.

2. Establish governance goals

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.

• Enhance biosecurity (prevent misuse and uncontrolled spread)

  • Prevent incidents

    • Restrict access to engineered strains, protocols, and enabling tools
    • Use genetic containment (kill-switches, auxotrophy, sterility)
    • Avoid traits that increase invasiveness or persistence outside intended settings

  • Help respond

    • Establish monitoring and reporting systems for unexpected dissemination
    • Maintain traceability (registries, audit logs, chain-of-custody)

• Foster lab safety (reduce accidents during development)

  • Prevent incidents

    • Standard biosafety training and conservative organism/chassis selection
    • Physical containment and phased testing (lab → greenhouse → controlled trials)
    • Explicit evaluation of failure modes in growth and developmental pathways

  • Help respond

    • Clear spill/escape response protocols and emergency shutdown procedures
    • Regular safety reviews and independent oversight

• Protect the environment (minimize ecological externalities)

  • Prevent incidents

    • Ecological risk assessment: gene flow, non-target effects, ecosystem disruption
    • Prohibit open release until long-term impacts are understood
    • Prefer reversible or self-limiting designs over permanent alterations

  • Help respond

    • Post-deployment surveillance and remediation plans
    • Defined liability and responsibility for environmental harms

• Equity, autonomy, and constructive use (ensure benefits are fairly distributed)

  • Minimizing burdens to stakeholders

    • Community consultation for land-use and deployment decisions
    • Avoid shifting risks onto local ecosystems or vulnerable populations

  • Feasibility without blocking research

    • Clear regulatory pathways that enable safe experimentation
    • Transparency and documentation to support responsible scaling

  • Promote beneficial applications

    • Prioritize reforestation, sustainable materials, and climate-positive outcomes
    • Discourage purely extractive or destabilizing commercial deployment

3. Design governance actions

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

1. Purpose: What is done now and what changes are you proposing?
2. Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)
3. Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?
4. Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

1. Containment-by-design + staged release

• Actors: Institutional Biosafety Committees (IBC), national GMO regulators (e.g., OGTR/USDA), lab leads, funders
• Design: engineered sterility/kill-switches, greenhouse-only trials, stepwise permits before field testing
• Assumptions: containment works reliably; lab phenotypes predict outdoor behavior
• Risks: safeguard failure, gene flow, invasive advantage, unexpected ecosystem effects

2. Access control + biosecurity screening

• Actors: DNA synthesis firms, biosecurity agencies, research institutions, grant/journal oversight
• Design: sequence screening, restricted strain distribution, dual-use review processes
• Assumptions: misuse is limited by controlling access to key materials/information
• Risks: leakage, uneven enforcement globally, slowing benign research

3. Environmental monitoring + liability framework

• Actors: environmental agencies, local governments/landholders, independent ecologists, insurers/courts
• Design: required impact studies, long-term surveillance, clear remediation liability
• Assumptions: harms are detectable early and manageable with monitoring
• Risks: underfunded surveillance, delayed ecological damage, liability discouraging deployment

4. Score against rubric

Evaluate each action against objectives including:

• Biosecurity enhancement
• Lab safety
• Environmental protection
• Cost/burden minimization
• Feasibility and research impact

5. Prioritize options

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.

I would prioritise Containment-by-design + staged release. Given that there is immense uncertainty in how this project could be achieved, it is a waste of resources to consider other governance actions for now. Rapid iteration to reduce uncertainty is the path towards achievement. As part of this - a scalable safety protocol throughout this process facilitates rapid experimentation without risk of ruin, until the project can achieve milestones necessary for unlocking funding and revenue.

Week 1 HW.2: Lecture prep for W2

Answer prep questions from three faculty members:

Homework Questions from Professor Jacobson:

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?

Error rate refers to errors per nucleotide added per replication. An error could be a misincorporation (wrong base expressed for a pair), for example.

Error rate of polymerase synthesis is 1/1e7 (1:10^7).

Human genome has 3.1-3.2 Gbp or 3e9 base pairs.

The rate of errors in polymerase copying the human genome’s DNA is 1/1e7 * 3e9, which is nonzero.

Biology deals with the likely error through multiple levels of mitigation:

• Proofreading during synthesis corrects errors
• Mismatch repair after synthesis repairs errors
• Redundancy and selection at multiple levels - DNA is double-stranded, cells exist in huge populations, misfolded proteins get degraded, defective RNAs are destroyed, faulty cells undergo apoptosis
• Damage repair system

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?

Our assumptions:

• Average Human Protein: 1036 bp.
• ~30,000 proteins observed in mammalian genome.
• A protein of length L = 3L nucleotides (bases) + a stop codon in the genome

Coding is the process by which DNA is transcribed into mRNA (triplets / codons), and mRNA (codons) is translated into a linear chain of amino acids (polypeptides), which folds into 3D protein structures.

How many different ways are there to code for an average human protein, meaning how many different DNA encodings would compile (transcribe and translate) down to the same protein (chain of amino acids) of length 1036 bp?

Codons are 3 nucleotides, each which have a base (A,C,G,T). There are 64 possible triplet combinations (codons) using the four bases (A, U, G, C). Each codon encodes one amino acid. An amino acid can be encoded by multiple codons. For instance, codons GAA and GAG both specify glutamic acid and exhibit redundancy. This is referred to as degeneracy.

The degeneracy of an amino acid refers to the number of codons which encode it. ie. d(Leu)=6, meaning Leucine has 6 codons which encode it.

Average codon degeneracy across amino acids is roughly 3.

So to calculate the number of possible encodings for a protein of length L=5 amino acids, we compute the degeneracy of each amino acid, and compute their product to find the maximum number of permutations. ie. for a protein of L=5, average degeneracy d(*)=3, num_permutations=d(*) * d(*) * d(*) * d(*) * d(*) = d(*)^L = 3^L

So for an average human protein of L=1036 bp, the number of possible encodings could be 3^L = 3^1036.

There is an intractable number of possible encodings. However, functional “good” encodings are a tiny subset constrained by expression, folding, RNA processing, regulation, and host biology.

Homework Questions from Dr. LeProust:

What’s the most commonly used method for oligo synthesis currently?

solid-phase chemical synthesis with phosphoramidite chemistry

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

Because direct phosphoramidite synthesis has a per-step yield <1.0, errors compound exponentially with length. P(success)=(1-e)^200 is improbable (e ~= 0.01)

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

(1-e)^2000 is near impossible, due to errors accumulating from each synthetic cycle/step.

• expected number of cleavage events scales ~linearly with cycle count and purine content
• Misincorporations accumulate (wrong base addition)

Homework Question from George Church:

Choose ONE of the following three questions to answer; and please cite AI prompts or paper citations used, if any.

[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”?

[Given slides #2 & 4 (AA:NA and NA:NA codes)] What code would you suggest for AA:AA interactions?

[(Advanced students)] Given the one paragraph abstracts for these real 2026 grant programs sketch a response to one of them or devise one of your own:

https://arpa-h.gov/explore-funding/programs/boss

https://www.darpa.mil/research/programs/smart-rbc

https://www.darpa.mil/research/programs/go

What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

Histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine (+ arginine conditional).

Out of the 20 amino acids needed, the body synthesizes 11-12, while the remaining 8-9, known as essential amino acids, must be obtained through diet.

This is not accurate to all animals, it seems? Counterexample: cats. Cats require taurine.

The Lysine Contingency was a genetic alteration Henry Wu performed in the dinosaur genome. The modification knocked out the ability of the dinosaurs to produce the amino acid Lysine.

This forced the dinosaurs to depend on lysine supplements provided by the park’s veterinary staff. In this way, dinosaurs could never escape from the park because they would never survive long without the food supplements.

Haha, I have to rewatch this film.

The way I would hack around this would be to introduce a substance containing the microbes that cows digest and feed it to the dinosaurs. These microbes synthesise the essential amino acids from nitrogen, thus mitigating the need for the dinosaurs to produce Lysine themselves, instead forming a symbiotic relationship with the microbes in their gut.

I don’t know what this question means, but it reminds me also of Liebig’s law - would the restriction of one amino acid necessarily debilitate the dinosaurs so they can’t escape, or is nature more nonlinear and complex than that?

LLM prompts used:

• 10 essential amino acids in all animals?
• across all animals?
• cows can synthesise most of their needed amino acids? how many which ones
• how long can you survive without just one of the amnio acids ?

Week 1 HW.3: Setup your website

CHECK IT OUT

https://pages.htgaa.org/2026a/liam-edwards-playne/

Week 2 HW.1: Benchling & In-silico Gel Art

• 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.

Benchling screenshots.

Experimental design for Gel art.

Week 2 HW.2: Gel Art - Restriction Digests and Gel Electrophoresis

In the wet-lab perform the lab experiment you designed in Part 1 and outlined in this week’s lab protocol “Gel Art: Restriction Digests and Gel Electrophoresis”.

N/A - no access to BioClub Tokyo Lab.

Week 2 HW.3: DNA Design Challenge

3.1. Choose your protein.

In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose.

Miraculin - https://rest.uniprot.org/uniprotkb/P13087.fasta https://rest.uniprot.org/uniprotkb/P13087.txt

>sp|P13087|MIRA_SYNDU Miraculin OS=Synsepalum dulcificum OX=3743 PE=1 SV=3
MKELTMLSLSFFFVSALLAAAANPLLSAADSAPNPVLDIDGEKLRTGTNYYIVPVLRDHG
GGLTVSATTPNGTFVCPPRVVQTRKEVDHDRPLAFFPENPKEDVVRVSTDLNINFSAFMP
CRWTSSTVWRLDKYDESTGQYFVTIGGVKGNPGPETISSWFKIEEFCGSGFYKLVFCPTV
CGSCKVKCGDVGIYIDQKGRRRLALSDKPFAFEFNKTVYF

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

Using https://www.bioinformatics.org/sms2/rev_trans.html:

atgaaagaactgaccatgctgagcctgagctttttttttgtgagcgcgctgctggcggcg
gcggcgaacccgctgctgagcgcggcggatagcgcgccgaacccggtgctggatattgat
ggcgaaaaactgcgcaccggcaccaactattatattgtgccggtgctgcgcgatcatggc
ggcggcctgaccgtgagcgcgaccaccccgaacggcacctttgtgtgcccgccgcgcgtg
gtgcagacccgcaaagaagtggatcatgatcgcccgctggcgttttttccggaaaacccg
aaagaagatgtggtgcgcgtgagcaccgatctgaacattaactttagcgcgtttatgccg
tgccgctggaccagcagcaccgtgtggcgcctggataaatatgatgaaagcaccggccag
tattttgtgaccattggcggcgtgaaaggcaacccgggcccggaaaccattagcagctgg
tttaaaattgaagaattttgcggcagcggcttttataaactggtgttttgcccgaccgtg
tgcggcagctgcaaagtgaaatgcggcgatgtgggcatttatattgatcagaaaggccgc
cgccgcctggcgctgagcgataaaccgtttgcgtttgaatttaacaaaaccgtgtatttt

3.3. Codon optimization.

Describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?

Proteins are translated from mRNA by tRNA’s. The tRNA’s “pair” with codons from the mRNA. A codon is a 3-base sequence which is then mapped onto a single amino acid. As we covered last week, there are 64 different codons (permutations of a string of 3 nuceleotide bases) which map down to only 20 amino acids. The degeneracy means we can swap out parts of the DNA/mRNA to express the same amino acids aka proteins. Why would we do this? Because mRNA codons are translated into amino acids by the available tRNA in the organism. Each tRNA matches a codon (or several synonymous codons, see wobble pairing at 3rd base). There is not a uniform concentration of tRNA for all codons. So some mRNA codons will translate more efficiently than others, because there is more tRNA.

To restate:

1. DNA encodes triplet codons.
2. mRNA is transcribed from DNA.
3. Ribosomes read mRNA in triplets.
4. tRNAs carrying amino acids base-pair with codons (binding with the tRNA’s complementary anticodon)
5. Translation rate is approximately proportional to local charged tRNA abundance and ribosomal processivity.

Multiple codons encode the same amino acid, yet different organisms use these synonymous codons at different frequencies (codon usage bias). If a gene from organism A is expressed in organism B without modification, the codon distribution may not match the tRNA pool of B.

You need to optimize codon usage in order to achieve (good) yields from your biomanufacturing process.

I choose Escherichia coli (E. coli) as the target host for optimization:

• Takes less time
  • Cell division is faster
• Well established protocols to isolate plasmid
  • Each cell has single chromosome
  • Single circular plasmid
  • Each replicated cell has exact copy of DNA
• Easy method

3.4. You have a sequence! Now what?

What technologies could be used to produce this protein from your DNA? Describe in your words how the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both.

Recombinant expression in a host organism like E. Coli.

1. Clone the coding sequence into an expression vector (a plasmid).
   • Promoter - T7 under lac control: binds the RNA polymerase
   • Ribosome binding site - Shine–Dalgarno AGGAGG: recruits ribosome
   • Coding sequence - see Miraculin DNA sequence above.
   • Terminator - hairpin-forming sequence: stops transcription
   • Antibiotic resistance gene - ampR: for selection of culture
2. Transform into E. Coli (transform the plasmid into host cells.)
   1. Bacteria are given a heat shock.
   2. Colonies grow.
   3. Pick colonies.
      1. Plate on ampicillin → only plasmid-containing cells survive.
   4. Inoculate the liquid cultures (by introducing single colonies)
3. Induce expression (e.g., add IPTG if T7/lac system).
   • T7 RNA polymerase binds promoter
   • DNA is transcribed into mRNA
   • Ribosome binds RBS on mRNA.
   • tRNA translates into protein, stop at terminator.
     • tRNAs decode codons
     • Amino acids polymerize into polypeptide
4. Harvest. Cells are lysed. Protein is purified.
   1. Lyse cells (sonication or chemical lysis).
   2. Purify protein (e.g., His-tag + Ni-NTA affinity column).

Apparently E. coli is possible but non-ideal for a cysteine-rich, glycosylated plant secreted protein like miraculin.

3.5. [Optional] How does it work in nature/biological systems?

Describe how a single gene codes for multiple proteins at the transcriptional level. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!! See example below.

Week 2 HW.4: Twist DNA Synthesis Order

Prepare a Twist DNA Synthesis Order

This is a practice exercise, not necessarily your real Twist order!

4.1. Create a Twist account and a Benchling account click through for Twist signup

click through for Twist signup click through for Benchling signup

click through for Benchling signup

4.2. Build Your DNA Insert Sequence

For example, let’s make a sequence that will make E. coli glow fluorescent green under UV light by constitutively (always) expressing sfGFP (a green fluorescent protein):

In Benchling, select New DNA/RNA sequence

Give your insert sequence a name and select DNA with a Linear topology (this is a linear sequence that will be inserted into a circular backbone vector of our choosing).

Go through each piece of the given DNA sequences highlighted below (Promoter, RBS, Start Codon, Coding Sequence, His Tag, Stop Codon, Terminator) and paste the sequences into the Benchling file one after the other (replacing the coding sequence with your codon optimized DNA sequence of interest!). Each time you add a new piece of the sequence, make sure to annotate by right clicking over the sequence and creating an annotation that describes what each piece (e.g., Promoter, RBS, etc.) is (see image below).

Promoter (e.g. BBa_J23106): TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC
RBS (e.g. BBa_B0034 with spacers for optimal expression): CATTAAAGAGGAGAAAGGTACC
Start Codon: ATG
Coding Sequence (your codon optimized DNA for a protein of interest, sfGFP for example): AGCAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCCGTGGAGAGGGTGAAGGTGATGCTACAAACGGAAAACTCACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCGTGGCCAACACTTGTCACTACTCTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCACATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGACCTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAGGGTATTGATTTTAAAGAAGATGGAAACATTCTTGGACACAAACTCGAGTACAACTTTAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACGTTGAAGATGGTTCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGTCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAA
7x His Tag (Let’s add a 7×His tag at the C-terminus of the protein to enable protein purification from E. coli): CATCACCATCACCATCATCAC
Stop Codon: TAA
Terminator (e.g. BBa_B0015): CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

Once you’ve completed this, click on Linear Map to preview the entire sequence. If you intend to have a TA review a sequence in the future, this is a good way to verify that all sections are annotated!

This is not required for this exercise, but to share your design with others, please ensure that link sharing is turned on! (Optional) Share your final sequence link with a TA for review!

This insert sequence you built is commonly referred to as an expression cassette in molecular biology (a sequence you can drop into any vector and it’ll perform its function). Go ahead and download the FASTA file for the sequence you made.

It’s helpful to visualize DNA designs using SBOL Canvas (Synthetic Biology Open Language) to convey your designs. Here’s an example of what you just annotated in Benchling:

4.3. On Twist, Select The “Genes” Option

4.4. Select “Clonal Genes” option

For this demonstration, we’ll choose Clonal Genes. You’ll select clonal genes or gene fragments depending on your final project.

Historically, HTGAA projects using clonal genes (circular DNA) have reached experimental results 1-2 weeks quicker because they can be transformed directly into E. coli without additional assembly.

Gene fragments (linear DNA) offer greater design flexibility but typically require an assembly or cloning step prior to transformation. An advantage is If designed with the appropriate exonuclease protection, gene fragments can be used directly in cell-free expression.

4.5. Import your sequence

You just took an amino acid sequence of interest and converted it into DNA, codon optimized it, and built an expression cassette around it! Choose the Nucleotide Sequence option and Upload Sequence File to upload your FASTA file.

4.6. Choose Your Vector

Since we’re ordering a clonal gene, you will need to refer to Twist’s Vector Catalog to choose your circular backbone. You can think of this as taking your linear expression cassette for your protein of interest, and completing the rest of the circle!

The backbone confers many special properties like antibiotic resistance, an origin of replication, and more. Discuss with your node to decide on appropriate antibiotic options. At MIT/Harvard, you can use Ampicillin, Chloramphenicol, or Kanamycin resistance.

Twist vectors do not contain restriction sites near the insert fragment, so make sure to flank your design with cut sites if you are intending to extract this DNA insert fragment later.

For this demonstration, choose a Twist cloning vectors like pTwist Amp High Copy.

Click into your sequence and select download construct (GenBank) to get the full plasmid sequence:

Go back to your Benchling account. Inside of a folder, click the import DNA/RNA sequence button and upload the GenBank file you just downloaded.

This is the plasmid you just built with your expression cassette included. Congratulations on building your first plasmid!

Week 2 HW.5: DNA Read/Write/Edit

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).

No idea. Possibly my basil plant.

(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?

I would use long-read sequencing (1–100+ kb). Even though it is more expensive, it would provide greater accuracy.

The way that DNA sequencing works currenly is by taking DNA, lysing it, and then reassembling fragments based on probabilistic approaches. The “read length” refers to how large these fragments are in terms of base pairs. A fragment of length = 1 bp would be near useless, since there is no way to “place” it probabilistically within the greater genome. A fragment of length = 150bp map well because apparently the human genome is largely non-repetitive at that scale.

Short-read sequencing is a read of 50–600 bp. Long-read sequencing is 1-100 kb.

Technologies:

• Polymerase-based sequencing
• Enzymatic digest sequencing
• Nanopore sequencing
• DNA microarrays

DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why?

I have no idea.

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

• Recombinant DNA synthesis
• Oligonucleotide synthesis - can make complex motifs, extremely large DNA molecules (1kbp+)

DNA Edit

(i) What DNA would you want to edit and why?

I have no idea. Potentially plant DNA. I don’t know anything about what DNA plants have. I would like to figure out how to increase the growth speed, change the bark texture. Or even doing experiments on yeast. Perhaps I could figure out the enzymes/proteins and what DNA/genes code for it, and then edit that.

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

CRISPR-Cas9

Week 3 HW.1: Python Script for Opentrons Artwork

• Review recitation materials and lab documentation.
• Design artwork using the GUI at opentrons-art.rcdonovan.com.
• Write a Python script using coordinates from the GUI via the “HTGAA26 Opentrons Colab”.
• Sign up for a robot time slot and run the script on the Opentrons robot.
• Submit Python file via provided form.

Artwork Design

Python Script

Week 3 HW.2: Post-Lab Reflection

2.1. Find and describe a published paper utilizing Opentrons or similar liquid handling automation tools.

Paper:

Description:

2.2. Describe your intended automation use for your final project, including pseudocode, scripts, or implementation plans.

Week 3 HW.3: Final Project Ideas

Submit 1–3 slides with three individual project concept ideas.

Idea 1

Idea 2

Idea 3