Week 1 HW: Principles and Practices

Part I

1. First, describe a biological engineering application or tool you want to develop and why.

I am interested in the development of engineered bee gut bacteria or similar that help bees resist viral infections, pesticide stress but especially harmful varroa mites. The presence of varroa mites in bee colonies place an important pressure on bee health since they attack and feed on them in a parasitism relationship. ¹ Instead of genetically modifying bees themselves, I aim to modify their symbiotic bacteria to strengthen colony resilience while minimizing ecological risks. Bees are increasingly threatened by habitat loss, unsustainable agricultural practices, climate change and pollution. Their decline jeopardizes food production, increases costs and exacerbates food insecurity, particularly for rural communities. I am convinced that supporting pollinators will get more and more critical for global food systems and biodiversity and this approach could offer a scalable and ecologically sensitive alternative to chemical treatments currently used in agriculture. Even if it needs human intervention into nature to keep our ecosystem in balance, I think supporting these small often unnoticed pollinators could make a real difference.

Inspiration: Leonard, S. P., Perutka, J., Powell, J. E., Geng, P., Richhart, D. D., Byrom, M., Kar, S., Davies, B. W., Ellington, A. D., Moran, N. A., & Barrick, J. E. (2018). Genetic engineering of bee gut microbiome bacteria with a toolkit for modular assembly of broad-host-range plasmids. ACS Synthetic Biology, 7(5), 1279–1290. https://doi.org/10.1021/acssynbio.7b00399

1: Le Conte, Y., Ellis, M. & Ritter, W. (2010). Varroa mites and honey bee health: can Varroa explain part of the colony losses?. Apidologie, 41, 353–363. https://doi.org/10.1051/apido/2010017

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.

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

Transparency Standards for DNA Synthesis Providers

Purpose

I believe that many DNA synthesis companies voluntarily screen orders against limited threat models to block synthesis of harmful sequences, but standards vary widely. That’s why, we need an universal regulatory requirement for all commercial DNA synthesis providers (domestic and international selling into regulated markets) to implement robust sequence screening, standardized reporting and data sharing with trusted authorities to build transparency.

Design

What is needed to make it work?

• A regulatory body defines minimum screening criteria and risk thresholds.
• All providers must opt-in by compliance through certification, non-compliant firms cannot legally sell into the regulated market.
• Public funding or tax credits to support smaller providers implementing screening software.

Actors involved:

• Federal regulators (standard setting and enforcement)
• DNA synthesis companies (compliance)´
• Independent auditors (certify implementation)

Assumptions

• That most synthesis providers will respond to regulatory pressure and that screening software is reliable.
• That standards can keep pace with rapid advances in gene editing and novel organisms.
• That international firms will comply or that governments will enforce import controls tied to compliance.
• That smaller companies can hold against the competitiveness of certification costs.

Risks of Failure & “Success”

Failure risks

• Providers find loopholes or perform minimal compliance without effective safety.
• Adversaries migrate to unregulated markets or underground vendors, worsening risk.
• High compliance costs drive small innovators out of business, reducing competition.

Risks of “success”

• Genuine research slows due to increased cost and time to order DNA.
• Fragmented global adoption creates asymmetries: robust safety in some countries, weak in others.

Involvment of local stakeholders & community

Purpose

From what I have read, bee-related synbio solutions are mostly developed in labs and tested with limited involvement of local beekeepers or communities who depend on pollinators. The proposed change is to actively involve beekeepers, farmers and local communities (most practical knowledge because of living with/around them) before deploying biotech solutions affecting bee populations.

Design

• Projects deploying engineered microbes or treatments in hives must include local beekeeper collaboration.
• Workshops and pilot projects with beekeeping associations allow practical feedback.
• Farmers, urban beekeepers, and conservation groups participate in decision-making.

Actors involved

• Researchers & biotech companies
• Local governments & environmental authorities
• Farmers & beekeper associations

Assumptions

• Beekeepers are willing and able to participate.
• Public engagement improves trust and project outcomes.
• Communication between scientists and practitioners works effectively.

Risks of Failure & “Success”

Failure risks

• Engagement becomes symbolic rather than meaningful.
• Misinformation or fear blocks beneficial projects.
• Participation dominated by a few voices, not representative groups.

Risks of “success”

• Projects become slowed by lengthy consultation processes.
• Communities may expect veto power over projects beyond reasonable risk concerns.

Secure Testing & Containment Framework for Bee Biotechnology

Purpose

Currently, biotechnology innovations may move from lab testing to real hives without fully coordinated safeguards if unexpected ecological effects occur. This action proposes a controlled testing environment (sandbox ecosystem) combined with mandatory containment and emergency response plans before wider deployment.

Design

• New bee biotech solutions are first tested in regulated pilot environments with selected partner beekeepers and oversight from authorities.
• Engineered microbes or treatments must include biological containment mechanisms (e.g., limited survival outside managed hives).
• Continuous monitoring tracks spread and bee health impacts.
• Emergency protocols allow rapid withdrawal or containment if problems appear.

Actors involved

• Researchers
• Biotech companies
• Beekeeper networks (monitoring)
• Environmental and agricultural authorities

Assumptions

• Small-scale sandbox ecosystems manage to imitate natural ecosystems.
• Containment mechanisms work reliably in real ecosystems.
• Monitoring detects problems early enough to intervene.
• Beekeepers cooperate in reporting unexpected outcomes.

Risks of Failure & “Success”

Failure Risks

• Containment fails or spread occurs before detection.
• Response measures may be too slow.

Risk of “success”

• Confidence in safe testing could encourage faster or riskier deployments.
• Strict requirements might limit participation by small innovators.

Bügl, H., Danner, J. H., Molinari, R. J., Mulligan, J. T., Park, H., Reichert, B., Roth, D. A., Wagner, R., Budowle, B., Scripp, R. M., Smith, J. A. L., Steele, S. J., Church, G. & Endy, D. (2007). DNA synthesis and biological security. Nature Biotechnology, 25(6), 627–629

Leckenby, E., Dawoud, D., Bouvy, J. & Jónsson, P. (2021). The Sandbox Approach and its Potential for Use in Health Technology Assessment: A Literature Review. Applied Health Economies Health Policy, 19, 857–869. https://doi.org/10.1007/s40258-021-00665-1

4. 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:

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.

Based on the evaluation of the governance options, I would prioritize Transparency Standards, combined with a Secure Testing and Containment Framework.

Transparency is essential for building trust in new biotechnological tools and for ensuring accountability. If projects, testing procedures and releases are openly documented and traceable, it becomes possible to identify where problems arise and address them early. Similar to other industries - for example, the fashion industry, where lack of supply chain transparency hides environmental and social impacts - insufficient transparency in biotechnology makes it difficult to understand risks or intervene effectively when things go wrong.

However, transparency alone is not sufficient. Even if processes are visible, interventions must also be safe in practice. Therefore, I would combine transparency with a Secure Testing and Containment Framework that ensures technologies are tested in controlled environments and include emergency response mechanisms before large-scale deployment. In the case of bee-related biotech applications, unintended spread or ecological effects could impact entire ecosystems. A containment and rapid-response system would help minimize damage if interventions do not behave as expected.

The main trade-off considered is that stronger transparency and safety requirements may slow innovation or increase costs for smaller research groups. There is also uncertainty about whether containment mechanisms will always function reliably in complex natural environments. Nevertheless, given the ecological importance of pollinators and the potential scale of unintended consequences, prioritizing safety and accountability over rapid deployment seems justified.

Part II

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 of Polymerase: 1:10⁸

Length of Human Genome: 3.2 Gbp = 3.2 * 10⁹ base pairs

If the error rate is 1 in 10⁸, copying the whole genome would lead to roughly:

3*10⁹ / 10⁸ ~ 30

That means there are about 30 errors per cell division without additional repair. To deal with this decrepancy biology developed a error correcting polymerase including proofreading mechanisms and mismatch repair systems.

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?

Formula from the Slides:

The complexion for the total number of different ways to arrange N blocks of Q different types (where each type has the same number) is given by:

((20!)/(((20/4)!)⁽⁴⁾)) = 11732745024 ~ 11.7 * 10⁹

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

Phosphoramidite solid-phase synthesis

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

The main problem is stepwise synthesis errors. Each nucleotide addition is not perfect. Typical coupling efficiency: ~99–99.5% per step.

0.995²⁰⁰ ~ 0.37

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

Direct oligonucleotide synthesis adds nucleotides step by step, and each step has a small error rate (≈99–99.5% efficiency). Over thousands of steps, these small errors accumulate, leading to very low yields of full-length, correct DNA. As a result, direct chemical synthesis becomes impractical beyond ~150–200 nucleotides. So companies like Twist Bioscience instead assemble long genes (up to 7kbp) from short oligos and then clone and sequence-verify them.

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

There are 9 essential amino acids: phenylalanine, valine, tryptophan, threonine, isoleucine, methionine, histidine, leucine, and lysine.

However, amino acids such as arginine and histidine may be considered conditionally essential because the body cannot synthesize them sufficiently during specific physiological periods of growth, including pregnancy, adolescent growth or recovery from trauma.¹

As I understand it, the “Lysine Contingency” is derived from Jurassic Park and is fictional. I believe it raises important ethical questions about human intervention in nature. In the movie, the dinosaurs depend on lysine supplements provided by the park’s staff, so they cannot survive or escape without them. This system was intended to prevent the dinosaurs from disrupting the global ecosystem. Although the idea aimed to protect the environment, it also involved engineering organisms to depend on a single nutrient for survival which is questionable. All in all, it is striking to me that the absence of just one essential amino acid could determine life or death.²

1: Lopez, M.J. and Mohiuddin, S.S. (2024) Biochemistry, essential amino acids, National Library of Medicine. Available at: https://www.ncbi.nlm.nih.gov/sites/books/NBK557845/ (Accessed: 10 February 2026).

2: Lysine contingency (no date) Jurassic Park Wiki | fandom. Available at: https://jurassicpark.fandom.com/wiki/Lysine_contingency (Accessed: 10 February 2026).

Other References from Part II: Slides from Lecture 2

Week 2 HW: DNA read/write/edit

Part I: Benchling & In-silico Gel Art

Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks.

Part III: DNA Design Challenge

1. Choose your protein.

Since my project proposal from last week focuses on honeybee health, I searched for relevant proteins in Apis mellifera. During this process, I identified three candidates that seemed particularly interesting: Defensin-1, Hymenoptaecin and Vitellogenin. Working with Twist Bioscience’s codon optimization tool, I learned that the tool only accepts sequences within a specific length range — proteins that are too short or too long cannot be optimized. After several iterations, vitellogenin was the only protein for which I could successfully perform codon optimization. Vg, a phospholipoglycoprotein synthesized and stored in the honey bee fat body, is an ancient reproduction-associated protein that provides nutrients to eggs in most oviparous animals. Honey bee queens, who produce hundreds of eggs each day, have high levels of Vg gene expression. It is involved in nutrient storage, immune regulation and longevity in honeybees. Its expression is closely linked to colony health and higher vitellogenin levels are associated with improved immune responses and tolerance to Varroa destructor infestation. ¹

A bit later, I found that University Münster actually proposed a similar study at iGEM: https://2023.igem.wiki/unimuenster/

Fasta File Text

XP_001122505.3 vitellogenin [Apis mellifera] MLVIILPYLLAARVPSHEATYRDDSDWRRYGPECTYDVLVNMSLSNMDEDARICSVIAFELKCRAKGSDTLNCRFSNGRTARLEDGRGCSNAKRNFAPSTSDRFVDEQPFEIRFNARGIENLVVSRDIARWRLDAMRAIVSQLNVGFELGSGHDRFVAMENSSVGYCEVEVKVSRAGYGGESGGGGLEIALEPERADVAPLSRGSVRIEKVRRPKRCPNRKIYFFGNHRDFSFGSEDIFMDMITSVSRMYISRREMNSFTESTGVMRTSNRPRTMNLHQRIGLSLRNINPARTPIPEIVNPASTSLYAYTNLERIPEYK

1: Amdam, G.V., Fennern, E., Havukainen, H. (2012). Vitellogenin in Honey Bee Behavior and Lifespan. In: Galizia, C., Eisenhardt, D., Giurfa, M. (eds) Honeybee Neurobiology and Behavior. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2099-2_2

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

GTTCTGTCCCGCATGAATCTTACGCTGGCGAAAATGGAGAAGACCAGCAAACCTCTGCCCATGGTTAACAATCCAGAATCGACTGGGAACCTCGTCTACATTTATTCGAACCCGTTTTCAGACGTAGAAGAGCGCCGCGTCAGTAAGACGGCTATGAATAGCAACCAAATTGTGTCGGACAACAGCCTATCAAGTTCTGAAGAAAAATTAAAACAGGATATCCTGAACTTACGGACAGATATCAGCAGCAGCTCCTCATCCATTAGTTCATCTGAGGAAAATGACTTCTGGCAGCCGAAACCCACCCTGGAGGATGCACCGCAGAATAGCTTGCTGCCTAATTTTGTTGGCTATAAAGGTAAACACATCGGTAAATCCGGAAAAGTGGATGTCATAAATGCAGCCAAGGAACTGATTTTCCAAATCGCCAACGAGCTCGAAGACGCTAGTAATATTCCAGTGCATGCGACGCTGGAAAAATTTATGATTCTGTGCAACCTTATGCGTACCATGAATCGTAAACAGATCAGCGAATTGGAATCTAACATGCAGATCTCGCCGAACGAATTAAAACCGAACGATAAATCTCAGGTGGTAAAGCAAAATACCTGGACCGTGTTTCGTGATGCGATTACACAGACCGGCACTGGCCCGGCCTTCCTGACGATTAAA

3. Codon optimization.

I chose E. coli because it is a standard lab organism, grows fast and widely used for protein production.

GTTCTGTCCCGCATGAACCTGACACTTGCAAAGATGGAAAAGACTAGTAAGCCGCTGCCCATGGTTAACAATCCAGAATCGACTGGGAACCTCGTCTACATTTATTCGAACCCGTTTTCAGACGTAGAAGAGCGCCGCGTCAGTAAGACGGCTATGAATAGCAACCAAATTGTGTCGGACAACAGCCTATCAAGTTCTGAAGAAAAATTAAAACAGGATATCCTGAACTTACGGACAGATATCAGCAGCAGCTCCTCATCCATTAGTTCATCTGAGGAAAATGACTTCTGGCAGCCGAAACCCACCCTGGAGGATGCACCGCAGAATAGCTTGCTGCCTAATTTTGTTGGCTATAAAGGTAAACACATCGGTAAATCCGGAAAAGTGGATGTCATAAATGCAGCCAAGGAACTGATTTTCCAAATCGCCAACGAGCTCGAAGACGCTAGTAATATTCCAGTGCATGCGACGCTGGAAAAATTTATGATTCTGTGCAACCTTATGCGTACCATGAATCGTAAACAGATCAGCGAATTGGAATCTAACATGCAGATCTCGCCGAACGAATTAAAACCGAACGATAAATCTCAGGTGGTAAAGCAAAATACCTGGACCGTGTTTCGTGATGCGATTACACAGACCGGCACTGGCCCGGCCTTCCTGACGATTAAA

4. You have a sequence! Now what?

Once the codon-optimized DNA sequence is obtained, it can be used to produce the protein through transcription and translation. In a cell-dependent system, the DNA is cloned into an expression vector, such as pET-21, and introduced into E. coli, where the bacterial machinery transcribes the DNA into mRNA and translates it into the vitellogenin protein. Alternatively, cell-free systems can carry out transcription and translation in vitro, using extracted enzymes and ribosomes without living cells. In both cases, the DNA sequence serves as a template that determines the amino acid sequence of the resulting protein. ²

I would use the cell-free mechanism “PUREexpress”. Vitellogenin is a very large protein, which can be difficult to express in living cells because of size, folding and potential toxicity. A reconstituted, cell‑free system like “PURExpress” provides a clean, RNase‑ and protease‑poor environment, so long mRNAs and large proteins are less likely to be degraded during expression.³

2: Claassens, N. J., Burgener, S., Vögeli, B., Erb, T. J., Bar-Even, A. (2019) A critical comparison of cellular and cell-free bioproduction systems, Current Opinion in Biotechnology, 60 (221-229) 3: Tuckey, C., Asahara, H., Zhou, Y., Chong, S. (2014) Protein synthesis using a reconstituted cell-free system. Curr Protoc Mol Biol, 108, doi: 10.1002/0471142727.mb1631s108

Part IV: Prepare a Twist DNA Synthesis Order

Annotation

supportet by AI - “If you have a DNA strang how do you know which is what to annotate like: task instruction”

Plasmid

Part V: DNA Read/Write/Edit

1. 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).

I would like to sequence Varroa mite DNA because Varroa destructor is a key global parasite of honey bees and a major cause of colony losses. Sequencing its genome and mitochondrial markers would help identify treatment‑resistance mutations, track the spread of different mite lineages between regions, and link mite genotypes to disease outcomes in colonies. This information can directly support better Varroa monitoring, more targeted control strategies, and breeding of honey bees that are more resilient to the specific Varroa populations in their environment, ultimately benefiting pollination, food security, and ecosystem health.⁴

4: Grindrod, I., Martin, SJ. (2021) Parallel evolution of Varroa resistance in honey bees: a common mechanism across continents? Proc Biol Sci, 288(1956), doi: 10.1098/rspb.2021.1375.

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

Chosen sequencing technology

I would use Illumina next-generation sequencing to analyze honeybee genes associated with Varroa mite resistance because it provides highly accurate and cost-efficient sequencing for comparing many samples or studying specific gene regions.⁵

5: Hu, T., Chitnis, N. , Monos, D., Dinh, A. (2021) Next-generation sequencing technologies: An overview, Human Immunology, 82(11), 801-811, https://doi.org/10.1016/j.humimm.2021.02.012.

Generation of technology

This method belongs to the second generation of sequencing technologies because it sequences millions of short DNA fragments in parallel, unlike first-generation Sanger sequencing or third-generation long-read single-molecule sequencing.

Input and preparation steps

• The input is genomic DNA extracted from honeybees or mites. Preparation involves:
• DNA extraction from samples
• DNA fragmentation into short pieces
• Adapter ligation to fragment ends
• PCR amplification of fragments
• Loading fragments onto a sequencing flow cell

How bases are decoded (sequencing principle)

Fragments bind to the flow cell and are amplified into clusters. During sequencing, fluorescently labeled nucleotides are incorporated one base at a time. A camera records the color signal after each cycle, and software converts these signals into DNA base sequences — this process is called base calling.

Output of sequencing

The output consists of millions of short DNA reads, typically stored in FASTQ files containing:

• DNA sequences
• quality scores for each base

These reads are then assembled or mapped to a reference genome to analyze genetic variation related to disease resistance.

3. 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!

For my project, I would like to synthesize DNA that enables the production of a honeybee protein relevant to resistance against Varroa mite infection, specifically a codon-optimized fragment of the vitellogenin gene for expression in E. coli. Producing this protein in a laboratory system would allow further investigation of its structure and function and could support future research on improving honeybee resilience, which is crucial for pollination, biodiversity, and food production.

4. What technology or technologies would you use to perform this DNA synthesis and why? What are the essential steps of your chosen sequencing methods? What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

Modern DNA synthesis relies on chemical oligonucleotide synthesis combined with enzymatic assembly. Short DNA fragments are chemically synthesized and then assembled into longer genes. This method is efficient, scalable, and allows full customization of DNA sequences, including codon optimization and removal of unwanted restriction sites.

Essential steps of DNA synthesis

Digital sequence design of the gene or construct. Chemical synthesis of short DNA oligonucleotides. Assembly of oligos into longer DNA fragments using enzymatic methods. Error correction and amplification of assembled fragments. Cloning into plasmids and propagation in bacteria. Sequence verification to confirm correctness before delivery.

Limitations of this method

• Speed: Gene synthesis can take days to weeks depending on sequence length and complexity.
• Accuracy: Errors can occur during synthesis or assembly, especially in repetitive or GC-rich sequences, requiring verification and correction.
• Scalability: Although modern platforms are highly scalable, very long DNA constructs or entire genomes remain costly and technically challenging.
• Sequence constraints: Certain sequences (e.g., strong repeats or toxic genes) can be difficult to synthesize or maintain in host organisms.

5. 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?

Once again I would want to edit DNA related to honeybee health, specifically genes that contribute to resistance against Varroa mite infestation. Potential edits could focus on genes involved in immune response, grooming behavior, or parasite detection, enhancing bees’ natural ability to remove mites or better tolerate infections transmitted by them. Instead of introducing entirely new traits, the goal would be to support or amplify naturally occurring resistance traits, similar to selective breeding but with greater precision. More broadly, responsible DNA editing could also be applied in agriculture and conservation to help organisms adapt to climate change, reduce pesticide use, and improve resilience in vulnerable ecosystems.

6. What technology or technologies would you use to perform these DNA edits and why? How does your technology of choice edit DNA? What are the essential steps? 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? What are the limitations of your editing methods (if any) in terms of efficiency or precision?

I would use CRISPR–Cas9, which allows precise modification of genes within an organism’s genome. This technology is widely used because it is relatively simple, efficient and adaptable to many organisms.

CRISPR–Cas9 edits DNA by using a guide RNA (gRNA) that directs the Cas9 enzyme to a specific DNA sequence. Cas9 then creates a cut at the targeted location. The cell’s natural DNA repair mechanisms repair this cut, and during repair, scientists can either disable a gene or insert a modified DNA sequence.

• Essential editing steps include:
• Designing a guide RNA targeting the gene of interest.
• Delivering the guide RNA and Cas9 enzyme into cells.
• Cas9 cutting the DNA at the chosen site.
• Cellular repair mechanisms introducing deletions, modifications, or inserting new DNA.
• Screening cells or organisms to confirm successful edits.

Preparation and required inputs

Before editing, we must design the genetic modification and ensure the target gene is well characterized. Required inputs typically include:

• Guide RNA sequences targeting the gene
• Cas9 enzyme or Cas9-encoding plasmid
• A donor DNA template if inserting new sequences
• Delivery system (e.g., plasmids, viral vectors, or microinjection)
• Target cells or embryos to be edited

Limitations

The main limitations of CRISPR/Cas9 relate to delivery, accuracy and ethical concerns. A major challenge is safely delivering the editing system into the correct cells in living organisms, as current delivery vectors have size or efficiency limitations. Another concern is off-target effects, where unintended parts of the genome may be edited, potentially causing harmful consequences such as cancer. Editing efficiency can also vary, meaning not all cells receive the desired modification. Additionally, editing germline cells or embryos raises significant ethical and long-term safety concerns, since changes would be inherited by future generations and their consequences are uncertain. ⁶

6: Redman, M., King, A., Watson, C., King, D. (2016) What is CRISPR/Cas9?, Archives of Diseases in Childhood, 101, 213–215. doi:10.1136/archdischild-2016-310459

Week 3 HW: Lab Automation

Part I: Python Script for Opentrons Artwork

Your task this week is to Create a Python file to run on an Opentrons liquid handling robot.

Firstly, I used Ronan’s Automation Art Interface to translate my logo into a pixelated biological artwork. The software converted the image into a set of coordinate outputs, where each tuple (x, y) represents the precise millimeter offset from the calibrated center of the agar plate. Each of these coordinate pairs defines the placement of a single 1 µL droplet, allowing the robot to reconstruct the digital logo physically on the plate.

I then transferred these coordinates into Google Colab, where I programmed the Opentrons protocol. Before beginning the patterning process, the robot picks up a single 20 µL pipette tip. Since the entire artwork is executed in one color, only one tip is required for the whole procedure, minimizing material use.

The main challenge was preventing the pipette from exceeding its 20 µL capacity. Because each droplet dispenses 1 µL and the artwork consists of many coordinate points, the system must repeatedly refill the pipette. However, simply aspirating 20 µL at fixed intervals can lead to overfilling if residual liquid remains inside the tip.

To solve this, I implemented a volume-tracking mechanism in the code with support of AI (ChatGPT). A variable continuously monitors the remaining liquid in the pipette. The robot only aspirates when the remaining volume reaches zero, and it calculates the exact amount needed—either the full 20 µL capacity or just the remaining volume required to complete the artwork. After each 1 µL dispense, the tracked volume is reduced accordingly. This ensures that the pipette never exceeds its capacity while allowing the artwork to be executed seamlessly and efficiently.

Post-Lab Questions

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

A suitable published paper that utilizes an automation tool for a novel biological application is:

DeRoo, J. B., Jones, A. A., Caroline K. Slaughter, C. K., Ahr, T. W., Stroup, S. M., Thompson, G. B., Snow, C. D. (2025) Automation of protein crystallization scaleup via Opentrons-2 liquid handling. SLAS Technology, 32, https://doi.org/10.1016/j.slast.2025.100268

This study employs the Opentrons OT-2 automated liquid handling robot to optimize protein crystallization workflows. Protein crystallization is a critical step in structural biology, particularly for determining protein structures via X-ray crystallography. Traditionally, crystallization screening requires extensive manual pipetting, which is time-consuming, error-prone, and difficult to standardize.

The novelty of the paper lies in:

Low-cost automation of crystallization screening The researchers adapted the open-source OT-2 robot to perform precise nanoliter- to microliter-scale liquid handling for setting up crystallization trials. This democratizes access to automation, as most conventional crystallization robots are prohibitively expensive.

Workflow optimization and reproducibility The study demonstrates how automated pipetting improves reproducibility and throughput compared to manual methods. It allows systematic variation of crystallization conditions (e.g., precipitant concentration, pH, additives) in a controlled and programmable manner.

Open-source customization A key contribution is the development of customizable protocols that other laboratories can replicate and modify. The paper highlights how open-source hardware and software can accelerate biological research innovation without reliance on proprietary systems.

Biological Impact

By automating crystallization optimization:

• Screening becomes more efficient and scalable.
• Experimental variability is reduced.
• Smaller laboratories gain access to structural biology techniques that were previously limited to well-funded institutions.

Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.

Rather than relying on manual trial-and-error biology, I aim to build programmable experimental pipelines using robotic liquid handling, custom labware, and computational control for my final project. I intend to use modular lab automation tools to develop reproducible, scalable biological systems.

How automation would be used

High-Throughput Construct Assembly

Using a modular liquid handler such as the Opentrons OT-2:

• Automated Golden Gate or Gibson assemblies
• Parallel plasmid construction
• Controlled transformation setups
• Replicate culture inoculations

Example pseudocode:

for construct in plasmid_library: pipette.transfer(2, promoter_plate[construct], assembly_well) pipette.transfer(2, gene_plate[construct], assembly_well) pipette.transfer(6, master_mix, assembly_well)

This would allow rapid screening of different antimicrobial peptide constructs, dsRNA delivery systems, or immune-modulating pathways.

Custom 3D-Printed Bee Microbiome Holders

I would design:

• 3D-printed micro-incubation chambers
• Gut-simulating microfluidic inserts
• Controlled feeding modules

Ginkgo Nebula Integration

Through Ginkgo Bioworks’s Nebula platform, I could:

• Analyze microbiome sequencing data
• Identify candidate symbionts
• Screen gene clusters linked to antimicrobial production
• Compare engineered vs natural strain performance

To put it in a nutshell, lab automation transforms these projects from speculative ideas into experimentally rigorous, iterative engineering systems.

Part III: Final Project Ideas

As explained in this week’s recitation, add 1-3 slides in your Node’s section of this slide deck with 3 ideas you have for an Individual Final Project. Be sure to put your name, city, and country on your slide!