Week 1: Principles and Practices

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

I was reading this review paper, “Engineering bacterial warriors: harnessing microbes to modulate animal physiology”[1]. There’s a section that talks about engineering bacteria that can help corals survive heat stress, like making bacteria that clean up harmful reactive oxygen species (ROS) when water gets too warm [Figure 1]. The paper also discusses transplanting entire communities of beneficial microbes from resilient corals to vulnerable ones (Coral Microbiome Transfer, or CMT) [1,2]. The whole concept of using Beneficial Microorganisms for Corals (BMC) is really promising [3].

Figure 1. Engineered microbes have the capacity to assist corals in alleviating environmental stresses [1].

However, the authors point out major deployment challenges [1, 4]. The effects of introducing new bacteria are unpredictable, and they could outcompete or disrupt the coral’s natural microbiome. Currents could also scatter free bacteria before they even reach the target corals. At the moment, there’s no way to control the dosage or how long the coral is exposed, making the treatment inefficient and potentially wasteful. In essence, it’s difficult to introduce these engineered microbes effectively in the ocean.

The next section of the paper was about human health, discussing how engineered bacteria are being developed for targeted disease therapy [1]. This got me thinking about medical delivery systems, things like timed-release pills, biodegradable implants, or hydrogels that release drugs right where they’re needed in the body. The principle for all these applications is the same, to protect the therapeutic agent and control its release at the target site. So I thought something similar could be done for coral reefs.

I looked into existing coral probiotic delivery systems and came across a smart underwater microbial delivery system for coral reef habitat recovery developed by researchers at KAUST [5]. This system uses a buoy, an FPGA computer, cameras, and AI to monitor coral color and automatically pump probiotics into the water [Figure 2]. However, its complexity, cost, and reliance on pumping microbes into the water column still face some of the core challenges mentioned in the review paper, like dosage control and localization.

Figure 2. Smart underwater microbial delivery system for coral reef habitat (KAUST) [5].

To address the limitations of bulk water delivery, I propose a more targeted approach: a small, biodegradable pod, like a micro-infusion pump specifically for a coral. The pod would be able to attach directly to the coral’s surface to create a localized environment where the release of beneficial bacteria can be controlled. This would allow for a slow and sustained colonization of the coral’s microbiome, making the delivery more efficient and less wasteful than other methods. Diver or robotic systems would be able to precisely deploy the pods which allows for targeted intervention on corals identified as the most at-risk or ecologically valuable for reef recovery.

This idea of targeted, contained delivery isn’t completely new. In agriculture, technologies like coated seeds or biodegradable granules are standard for protecting and delivering beneficial microbes to plant roots [6]. For corals, scientists are already exploring direct methods like “Bacterioplankton” or gels [4]. A small, attachable pod builds on these principles and turns a massive ecological problem into a tangible engineering challenge to deliver a single, effective device.

Sketch of the idea:

References

1. Gao B, Ruiz D, Case H, Jinkerson RE, Sun Q. Engineering bacterial warriors: harnessing microbes to modulate animal physiology. Curr Opin Biotechnol. 2024;87:103113. doi:10.1016/j.copbio.2024.103113.

2. Doering T, Wall M, Putchim L, Rattanawongwan T, Schroeder R, Hentschel U, et al. Towards enhancing coral heat tolerance: a “microbiome transplantation” treatment using inoculations of homogenized coral tissues. Microbiome. 2021;9(1):102. doi:10.1186/s40168-021-01053-6.

3. Peixoto RS, Rosado PM, Leite DCA, Rosado AS, Bourne DG. Beneficial microorganisms for corals (BMC): proposed mechanisms for coral health and resilience. Front Microbiol. 2017;8:341. doi:10.3389/fmicb.2017.00341.

4. Damjanovic K, van Oppen MJH, Menéndez P, Blackall LL. The contribution of microbial biotechnology to mitigating coral reef degradation. Microb Biotechnol. 2017 Jul 11;10(5):1236–1243. doi: 10.1111/1751-7915.12769.

5. Filho J. A smart underwater microbial delivery system for coral reef habitat recovery. InnovateFPGA. 2022 (cited 2026 February 8). Available from: https://www.innovatefpga.com/cgi-bin/innovate/teams.pl?Id=EM043.

6. Bashan Y, de-Bashan LE, Prabhu SR, Hernandez JP. Advances in plant growth-promoting bacterial inoculant technology: formulations and practical perspectives (1998-2013). Plant Soil. 2014;378(1):1-33. doi:10.1007/s11104-013-1956-x.

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.

To ensure the ethical development and deployment of the coral probiotic pod, I propose adapting the governance framework outlined in the Synthetic Genomics: Options for Governance report [7]. The framework focuses on Safety, Security, Responsibility, and Oversight which together provide a clear structure to address the environmental and social challenges of engineering interventions in sensitive ocean ecosystems.

Goal 1: Safety – Establishing a Risk Assessment Protocol

Preventing unintended harm to reef ecosystems requires a phased approach that mirrors established biocontainment principles. This begins with pre-deployment screening in which any engineered microbial strain undergoes standardized laboratory assessment to confirm:

• Non-pathogenic to a panel of key reef organisms (corals, fish, crustaceans)
• Non-toxic in terms of metabolites produced
• Assessed for its competitive impact on the native coral microbiome

Following lab validation, a mandatory phased testing pathway should be implemented. Initial trials would occur in a controlled aquarium system simulating reef conditions. Successful results could then progress to caged in-situ trials, where pods are deployed within permeable enclosures on actual reefs to monitor ecological interactions under natural but contained conditions.

Goal 2: Security – Implementing a Function-Limited Use Framework

To address dual-use concerns inherent to programmable delivery systems, governance should focus on restricting applications to predefined beneficial functions. One approach involves establishing a “Positive List” of approved microbial functions, such as ROS-scavenging or nutrient provision, for coral restoration. An organization like the European Marine Research Network (EuroMarine) could maintain this public, evidence-based registry. Delivery of any agent performing functions not on this list would be prohibited. Complementing this, procedural security measures would include certification training for users in secure handling and deployment protocols, and restricting access to advanced engineered strains to institutions operating under established biosafety and biosecurity frameworks.

Goal 3: Responsibility – Ensuring Equitable Access and Procedural Justice

To align development with principles of environmental justice and accessibility, the pod’s mechanical design should follow an open-source model. 3D printable files and assembly guides should be published under a non-commercial conservation license to democratize access and enable local adaptation. Additionally, formal stakeholder consultation processes should be integrated into deployment planning. Engaging coastal communities, fishery cooperatives, and tourism boards from the start of the project would ensure local ecological knowledge informs implementation and aligns with community-defined priorities.

Goal 4: Oversight – Creating an Adaptive Governance Structure

Effective implementation requires mechanisms for ongoing evaluation and adaptation. A multidisciplinary advisory panel should comprise marine ecologists, synthetic biologists, social scientists, ethicists, and community representatives. They would provide oversight by reviewing field trial proposals and periodically updating the “Positive List” and testing protocols based on emerging research. This could be supplemented by a centralized deployment registry documenting key metadata (location, probiotic function, scale, responsible entity) to enable accountability and long-term impact monitoring.

References

7. Garfinkel MS, Endy D, Epstein GL, Friedman RM. Synthetic Genomics: Options for Governance. J. Craig Venter Institute; 2007. Available from: https://www.jcvi.org/research/synthetic-genomics-options-governance.

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

Action 1: Establishing a Mandatory ‘Reef-Safe’ Biopolymer Certification

• Purpose: Current material standards, such as the EU’s compostability criteria or general marine biodegradation tests, are not designed for devices in direct, sustained contact with sensitive coral reef ecosystems [8]. A new certification is needed to prevent pollution or toxic leaching from pod materials.
• Design: The European Committee for Standardization (CEN) could develop this certification in consultation with groups like the International Coral Reef Initiative (ICRI). It would build on existing marine biodegradation tests (like ISO 18830) but add specific ecotoxicity assays [9,10]. In the UK, Defra could require this certification for Marine Management Organisation (MMO) deployment permits [11].
• Assumptions: This assumes lab tests accurately predict real-world impacts and that certification won’t be too burdensome for conservation projects.
• Risks of Failure & “Success”: The action could fail if perceived as bureaucratic overreach, leading to low adoption or non-compliance. A significant risk of success is ‘greenwashing,’ where achieving material certification creates a false sense of security and diverts attention from necessary ecological monitoring of the probiotic function itself.

Action 2: Creating an Open-Source Hardware Repository Hosted by a Research Consortium

• Purpose: Open-source approaches have worked well in conservation technology. Applying this model to the pod system would prevent proprietary lock-in and encourage adaptation, aligning with funder open-access policies [12,13].
• Design: A consortium like EuroMarine could host the repository. Designs would use licenses like CERN Open Hardware, with governance similar to successful citizen science platforms, allowing vetted contributions while maintaining quality control.
• Assumptions: Without an active community, the repository could stagnate. Success might paradoxically lead to commercial co-option, where companies create proprietary versions that limit equitable access.
• Risks of Failure & “Success”: Failure could stem from repository stagnation without dedicated curation. Additionally, success could lead a for-profit entity to create a proprietary, locked version of the core open design, potentially undermining the goal of equitable access.

Action 3: Implementing Mandatory Pre-Deployment Registration in Existing Public Registers

• Purpose: While transparency is required by EU and UK marine laws, no registry exists for coral interventions. This action would fill that gap by extending existing systems.
• Design: In the UK, the MMO could add a coral intervention module to its Marine Case Management System [11]. For international waters, the UN’s Biosafety Clearing-House (BCH) already tracks living modified organisms and could serve as a model [14]. Funders could require registration as a grant condition.
• Assumptions: This assumes existing systems can be adapted affordably and that transparency itself encourages responsible behavior.
• Risks of Failure & “Success”: Cumbersome processes might reduce compliance. If successful, registry clutter could obscure important projects, and public listings might attract opposition to legitimate research.

References

8. Wei L, McDonald AG, Stark NM. Biodegradable polymers in the marine environment: current status and future perspectives. Environ Sci Technol. 2021;55(9):4203-17.
9. International Organization for Standardization. Plastics — determination of aerobic biodegradation of non-floating plastic materials in a seawater/sandy sediment interface — method by measuring the oxygen demand in closed respirometer. ISO 18830:2018.
10. European Committee for Standardization. Who we are (Internet). Brussels: CEN; 2023 (cited 2026 Feb 8). Available from: https://www.cen.eu.
11. Department for Environment, Food & Rural Affairs. Marine and Coastal Access Act 2009: guidance (Internet). (cited 2026 Feb 8). Available from: https://www.legislation.gov.uk/ukpga/2009/23/contents.
12. European Commission. Horizon Europe programme guide (Internet). Brussels: European Commission; 2021 (cited 2026 Feb 8). Available from: https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/programme-guide_horizon_en.pdf.
13. UK Research and Innovation. UKRI open access policy (Internet). Swindon: UKRI; 2022 (cited 2026 Feb 8). Available from: https://www.ukri.org/publications/ukri-open-access-policy/.
14. Secretariat of the Convention on Biological Diversity. The Biosafety Clearing-House (BCH) (Internet). Montreal: SCBD; 2023 (cited 2026 Feb 8). Available from: https://bch.cbd.int/.

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 implementing Option 2 and Option 3 combined. Looking at the table, Option 2 and Option 3 both score a “1” on all of the “Other considerations” section. That means that they minimize burden, are highly feasible, don’t impede research, and promote good applications. This is important for a new tool meant to address an urgent problem like coral bleaching. If governance is too heavy-handed from the start, it could hinder the innovation and collaboration that’s needed. The trade-off is that Option 2, by itself, scores a “3” on preventing biosecurity incidents. Making designs freely available could, in theory, make misuse easier. This is where Option 3 creates counterbalance because it scores a “1” on helping respond to incidents across biosecurity and environmental protection. By pairing them, it would create a system with the tools to build the coral probiotic pod and help reefs, and it would create transparency and accountability by asking users to outline where, when, and what they’re doing with the tools.

Although Option 1 is undeniably important, it scores low on burden, feasibility and potential to impede research. Strict certification could potentially stall projects, which is why it’s important to begin by requiring the use of characterized and safe materials documented in the open-source repository (Option 2) and logged in the registry (Option 3). As the field matures and the most effective materials become clear, a formal certification like Option 1 can be developed based on real-world data, making it more practical and accepted.

This decision comes with the assumption that the primary users, such as researchers, and conservation NGOs, would be acting in good faith. The governance is primarily designed to support and guide a responsible community, not solely to thwart a malicious one. There’s also uncertainty regarding compliance with the registration system of Option 3. The system would only work if people actually use it, which depends heavily on enforcement tactics like making it a requirement for grant funding from UKRI or Horizon Europe, for example [12,13]. The main trade-off is accepting a potentially higher risk of a small-scale indicent by lowering barriers to entry with Option 2 in exchange for greater capacity for widespread, adaptive learning and rapid scaling. For a crisis like reef degradation, where rapid experimentation is needed, this seems like the necessary and ethical choice.

References

12. European Commission. Horizon Europe programme guide (Internet). Brussels: European Commission; 2021 (cited 2026 Feb 8). Available from: https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/programme-guide_horizon_en.pdf.
13. UK Research and Innovation. UKRI open access policy (Internet). Swindon: UKRI; 2022 (cited 2026 Feb 8). Available from: https://www.ukri.org/publications/ukri-open-access-policy/.

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?

The error rate of DNA polymerase with its intrinsic proofreading function is approximately 1 error per 10⁶ bases incorporated (1:10⁶ or 10⁻⁶). the human genome has approximately 3.2 billion base pairs (3.2 x 10⁹ bp), that means there are about 3,200 errors per genome duplication. To deal with the discrepancy, there’s a third layer of correction called DNA Mismatch Repair (MMR). This is a post-replication system that acts after the polymerase has moved on. Specialized proteins scan newly synthesized DNA, identify mismatches that escaped proofreading, excise the erroneous segment, and resynthesize it correctly. In this way, MMR improves fidelity by an additional 100 to 1000-fold, so that the new error rate is about 1 error per 10⁹ to 10¹⁰ bases. Compared to the human genome, that’s about 0.3 errors per genome duplication.

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 has around 400 amino acids, and each of the 20 amino acids is encoded by 1-6 codons. Using average codon degeneracy (3 codons/amino acid), the possible sequences is around 3^{400, or roughly 10}190 different DNA sequences. In practice all of these codes don’t work to code for the protein for several reasons. First, cells prefer certain codons for efficient translation because rare codons slow down protein production. Also, some sequences form stable structures that hinder ribosome binding or translation initiation, and these sequences could also create unintended splice sites that disrupt mRNA processing. Additionally, sequences like repeats or high GC content can cause recombination or mRNA degradation.

Homework Questions from Dr. LeProust

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

The most commonly used method for oligo synthesis currently is the phophoramidite method developed by Caruthers in 1981.

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

It’s difficult to make oligos longer than 200nt via direct synthesis because of the cumulative stepwise yield. Since the synthesis occurs in a cycle, so it compounds with each added based becoming inefficient and costly. After 200 cycles, even at 99.5% efficiency, only about 37% of the growing chains are the correct full-length product. The other shorter chains would be failed sequences that have to be removed.

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

The yield for a 2000nt strand would be very low which is impractical. Due to the high intrinsic error rate of chemial synthesis, in a 2000bp molecule there would be around 20 random errors which would make the gene non-functional.

Homework Question from George Church

[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”?

The 10 essentiall amino acids in all animals are: Cysteine (Cys), Histidine (His), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Methionine (Met), Phenylalanine (Phe), Threonine (Thr), Tryptophan (Trp), and Valine (Val) [1].

The “Lysine Contingency” is a fictional genetic modification from Jurassic Park that was designed to make engineered dinosaurs dependent on lysine supplements, causing them to die without them [2]. However, knowing the full list of essential amino acids significantly weakens the possibility of the contingency since lysine is not uniquely essential. It’s one of ten amino acids that animals cannot synthesize de novo. A predator deficient in any of these would most like have severe growth impairment, immune dysfunction, and eventually death. Additionally, animals get these essential amino acis from their diet. That means the contingency relies on the assumption that lysine is not available in the natural environment. However, lysine, like all the other essential amino acis, is abundant in protein-rich foods which a carnivorous dinosaur would depend on. Even herbivorous dinosaurs would acquire these amino acis through a varied plant diet. Therefore, the dinosaurs would probably meet their lysine requirement through their normal feeding behavior, which would make the contingency ineffective.

References

1. Hou Y, Wu G. Nutritionally Essential Amino Acids. Advances in Nutrition (Internet). 2018 Sep 15;9(6):849–51. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247364/.
2. Lysine contingency (Internet). Jurassic Park Wiki. Available from: https://jurassicpark.fandom.com/wiki/Lysine_contingency. ‌‌

Week 2: DNA Read, Write, & Edit

Part 3: DNA Design Challenge

1. Choose your protein.

For the Week 1 HW I focused on engineering a biodegradable pod that would control the release of localized beneficial bacteria that could help corals survive heat stress. For this week’s HW, I’ve selected the protein manganese superoxide dismutase (MnSOD) which is the enzyme that helps corals neutralize harmful reactive oxygen species (ROS). When ocean temperatures rise during bleaching events, corals experience oxidative stress, and their cells produce dangerous levels of superoxide radicals (O₂⁻). MnSOD is part of the coral’s natural antioxidant defense system, converting these damaging radicals into hydrogen peroxide (H₂O₂) and oxygen (O₂) [1], which are then further neutralized by other enzymes like catalase.

Protein Selected: Manganese Superoxide Dismutase (MnSOD) from Stylophora pistillata

Accession Numbers:

• UniProt: A3KLM5 (A3KLM5_STYPI)[2]
• NCBI GenBank: AAX99423.1[3]

Organism: Stylophora pistillata (Smooth cauliflower coral)

Figure 1. A photo of Stylophora pistillata [4].

Length: 156 amino acids (partial sequence)

Protein Existence: Evidence at transcript level

Protein Sequence:

AAX99423.1 manganese superoxide dismutase, partial [Stylophora pistillata] YDYDALQPAISAEIMQLHHQKHHATYVNNLNVAAEEKFSEAQAKGDTSAMISLQPALKFNGGGHINHSIFWTNLSPNGGGEPT GALMEAIKEDFGSFENFKERFNAATVAVQGSGWGWLGYSKADKGLVITTCANQDPLQATTGLVPLLGMDVWEHA

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

Using the Central Dogma framework, I worked backwards from the MnSOD protein sequence to find its corresponding DNA sequence. The protein sequence I selected (AAX99423.1) was originally derived from the mRNA record AY916505.1 [2,5]. Because this record includes a /codon_start=3 annotation, I removed the first two nucleotides (which likely represent 5’ UTR sequence) to obtain the actual coding sequence. The resulting 468-nucleotide sequence (156 codons) exactly matches the partial MnSOD protein.

DNA sequence (from NCBI AY916505.1):

MnSOD DNA sequence (partial cds) tacgatgctttacaaccagcaatcagtgcagaaattatgcaacttcatcaccagaaacatcatgcaacatatgtgaacaacttgaatgtagccgaagaaaagttttctgaggcgcaagctaaaggagataccagtgctatgatatcactccagccagccttgaaattcaatggaggaggacatattaatcactcaatattttggacaaatctctctcctaatggtggaggtgaaccaacaggagccttgatggaagctatcaaggaagactttggttcatttgaaaactttaaggaaaggttcaatgcagcaactgtagctgtgcagggctcaggatggggttggctgggttatagcaaggctgacaagggcctggtgatcaccacatgtgccaatcaagaccctctccaggccaccacaggactggtgccacttcttggaatggatgtctgggaacacgca

3. Codon optimization.

Codon optimization is necessary because of codon usage bias. Most amino acids are encoded by multiple codons, however, different organisms have evolved preferences for specific codons based on the availability of matching tRNA molecules in their cells [6,7]. For example, attempting to express the coral MnSOD gene in E. coli could result in low protein yield, truncated proteins, or no expression at all because there would be codons that are rare in bacteria [7]. By codon-optimizing the sequence, the coral gene is essentially translated for the host organism to replace those rare codons with preferred ones but still maintaining the same amino acid sequence. This maximizes translational efficiency and protein yield.

I’ve chosen Escherichia coli (E. coli) as the host organism for codon optimization as it is the most well-characterized expression system, it has well-documented codon usage tables, optimization tools freely available, and it is fast, cheap, and scalable [8]. I used the GenScript GenSmart™ Codon Optimization Tool [9] with the following parameters:

• Host organism: E. coli
• Excluded restriction sites: BsaI, BsmBI, BbsI (to eliminate Type IIs enzyme recognition sequences
• Optimize for high protein expression

Original MnSOD DNA sequence (from 3.2):

tacgatgctttacaaccagcaatcagtgcagaaattatgcaacttcatcaccagaaacatcatgcaacatatgtgaacaacttgaatgtagccgaagaaaagttttctgaggcgcaagctaaaggagataccagtgctatgatatcactccagccagccttgaaattcaatggaggaggacatattaatcactcaatattttggacaaatctctctcctaatggtggaggtgaaccaacaggagccttgatggaagctatcaaggaagactttggttcatttgaaaactttaaggaaaggttcaatgcagcaactgtagctgtgcagggctcaggatggggttggctgggttatagcaaggctgacaagggcctggtgatcaccacatgtgccaatcaagaccctctccaggccaccacaggactggtgccacttcttggaatggatgtctgggaacacgca

Codon-optimized MnSOD sequence for E. coli:

TATGATGCACTACAACCCGCTATATCAGCGGAGATCATGCAACTGCATCACCAGAAGCACCACGCCACGTACGTGAATAACTTAAATGTTGCGGAAGAGAAGTTCAGCGAAGCGCAGGCGAAAGGTGACACCAGCGCAATGATCTCGCTCCAACCGGCTTTGAAATTCAACGGCGGCGGCCATATCAACCACAGCATTTTTTGGACCAACTTGTCCCCGAATGGTGGCGGAGAACCGACTGGTGCACTGATGGAAGCGATTAAAGAGGACTTCGGCTCCTTCGAGAACTTTAAAGAGCGTTTTAACGCCGCTACCGTTGCGGTCCAGGGTTCTGGTTGGGGTTGGCTGGGCTATAGCAAGGCCGATAAGGGCCTGGTTATTACCACGTGCGCTAATCAGGATCCACTGCAAGCGACCACCGGTCTGGTGCCGTTGCTGGGTATGGACGTGTGGGAACATGCG

4. You have a sequence! Now what?

There are two main approaches I could take to produce the MnSOD protein:

1. using living organisms as protein factories (cell-dependent methods)
2. using extracted cellular machinery in a test tube (cell-free methods)

The cell-dependent method, which uses living bacteria, would first require cloning the MnSOD gene into an expression vector [10]. To do that the gene would have to be inserted into a plasmid that contains all the elements needed for expression in E. coli. That would include: a promoter to initiate transcription, a ribosome binding site to start translation, a terminator to stop transcription, and an antibiotic resistance gene to select for bacteria that took up the plasmid. Then, the plasmid would be introduced into competent E. coli cells through heat shock or electroporation. The bacteria that take up the plasmid become antibiotic-resistant, so they can be grown on plates containing that antibiotic. Once there’s a colony of transformed bacteria, they can be grown in liquid culture. When the culture reaches the right density, an inducer like IPTG for a lac promoter would be added to turn on transcription of the MnSOD gene. The bacteria’s RNA polymerase reads the DNA, makes mRNA, and the ribosomes translate that mRNA into protein. Finally, after a few hours of expression, spin down the bacteria, lyse them, and purify the MnSOD protein using techniques like affinity chromatography.

The cell-free methods are essentially protein synthesis in a tube and skips the living organism [11]. For the MnSOD gene, begin by taking E. coli cells, lysing them, and spinning out all the cell debris and genomic DNA. What’s left are all the components needed for transcription and translation, such as ribosomes, tRNAs, amino acids, RNA polymerase, energy regeneration systems, etc. Then, add the purified MnSOD gene with a strong promoter like T7 directly to this extract. The cell-free system does the transcribing and translating of the gene in a few hours. Since there’s no cell wall to break open, purification is simpler. Spin down any precipitates and collect the protein from the supernatant. The cell-free methos is faster, works for toxic proteins that might kill living cells, and allows for easy modification of reaction conditions [12]. The trade-off is cost since it’s more expensive per milligram of protein than living cultures [11].

Whether in cells or in a tube, the fundamental processes of transcription and translation are the same. For MnSOD, the ribosome would link 156 amino acids in exactly the order specified by the optimized codons, and the newly made protein would fold into its functional 3D shape.

In general, the process is the following: Transcription (DNA → RNA) [13] -

• An enzyme called RNA polymerase binds to the promoter region of the gene
• It unwinds the DNA and reads the template strand
• It builds a complementary mRNA molecule, replacing every T with a U
• When it hits the terminator, it releases the finished mRNA

Translation (RNA → Protein) [13]-

• The mRNA binds to a ribosome
• tRNAs bring specific amino acids (each tRNA has an anticodon that matches a codon on the mRNA)
• The ribosome moves along the mRNA, three nucleotides at a time
• Amino acids are linked together in a growing chain
• When the ribosome hits a stop codon (UAA, UAG, or UGA), it releases the finished protein

5. How does it work in nature/biological systems?

In nature, organisms can produce multiple different proteins from a single gene through several mechanisms. The two most common are alternative splicing in eukaryotes, and alternative translation initiation in both prokaryotes and eukaryotes. Alternative splicing happens after transcription but before translation. When a gene is transcribed, the initial RNA (pre-mRNA) contains both exons and introns. The spliceosome removes the introns and joins the exons together. By choosing different combinations of exons, a single gene can produce multiple different mRNA variants, each encoding a different protein isoform [14]. This is very common in humans, with over 95% of our genes undergoing alternative splicing, which is part of why we can have around 20,000 genes but make thousands of different proteins.

Alternative translation initiation happens when a single mRNA has multiple start codons (AUG) in different reading frames or at different positions. Ribosomes can start translation at these different sites, producing proteins with different N-termini from the same transcript. This is more common in viruses and bacteria but happens in eukaryotes too. For the MnSOD gene, it is just one protein, but the central dogma still applies, information flows from DNA to RNA to protein.

To see how this work, I’ve taken a small section of the MnSOD gene and traced it through transcription and translation. I used the first 15 amino acids of the protein to create the alignment. This is the DNA sequence segment from the optimized gene:

TATGATGCACTACAACCCGCTATATCAGCGGAGATCATGCAACTGCATCACCAGAAG

During transcription, an enzyme called RNA polymerase reads the DNA template strand and builds a complementary mRNA copy. Every T in the DNA becomes a U in the RNA. Here’s what the RNA looks likes:

AUACUACGUGAUGUUGGGCGAUAUAGUCGCCUCUAGUACGUUGACGUAGUGGUCUUC

The ribosome reads the mRNA in groups of three nucleotides and matches each codon to an amino acid. Transfer RNAs (tRNAs) bring the right amino acids based on the codon sequence. This is where the genetic code gets translated into protein.

Figure 2. Snapshot from Benchling of MnSOD from DNA to Protein.

References

1. Demicheli V, Moreno DM, Radi R. Human Mn-superoxide dismutase inactivation by peroxynitrite: a paradigm of metal-catalyzed tyrosine nitration in vitro and in vivo. Metallomics. 2018 May 23;10(5):679-695. doi: 10.1039/c7mt00348j.
2. National Center for Biotechnology Information. Manganese superoxide dismutase, partial [Stylophora pistillata]. GenBank: AAX99423.1 [Internet]. 2005 [cited 2026 Feb 17]. Available from: https://www.ncbi.nlm.nih.gov/protein/AAX99423.1.
3. UniProt Consortium. Superoxide dismutase (Fragment) OS=Stylophora pistillata OX=50429 GN=AWC38_SpisGene15259 PE=4 SV=1. UniProtKB A3KLM5 [Internet]. 2007 [cited 2026 Feb 17]. Available from: https://www.uniprot.org/uniprotkb/A3KLM5/entry.
4. Rusconi G. Stylophora pistillata [image]. In: Sealifebase [Internet]. 2005 [cited 2026 Feb 17]. Available from: https://www.sealifebase.ca/summary/Stylophora-pistillata.html.
5. Furla P, Richier S, Allemand D. Stylophora pistillata manganese superoxide dismutase mRNA, partial cds [Internet]. GenBank: AY916505.1; 2005 [cited 2026 Feb 17]. Available from: https://www.ncbi.nlm.nih.gov/nuccore/AY916505.1.
6. Plotkin JB, Kudla G. Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet. 2011 Jan;12(1):32-42. doi: 10.1038/nrg2899.
7. Gustafsson C, Govindarajan S, Minshull J. Codon bias and heterologous protein expression. Trends Biotechnol. 2004 Jul;22(7):346-53. doi: 10.1016/j.tibtech.2004.04.006.
8. Rosano GL, Ceccarelli EA. Recombinant protein expression in Escherichia coli: advances and challenges. Front Microbiol. 2014 Apr 17;5:172. doi: 10.3389/fmicb.2014.00172.
9. GenScript. GenSmart™ Codon Optimization [Internet]. Piscataway (NJ): GenScript; 2026 [cited 2026 Feb 17]. Available from: https://www.genscript.com/gensmart-free-gene-codon-optimization.html.
10. Green MR, Sambrook J. Molecular Cloning: A Laboratory Manual. 4th ed. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2012.
11. Gregorio NE, Levine MZ, Oza JP. A User’s Guide to Cell-Free Protein Synthesis. Methods Protoc. 2019 Mar 20;2(1):24. doi: 10.3390/mps2010024.
12. Silverman AD, Karim AS, Jewett MC. Cell-free gene expression: an expanded repertoire of applications. Nat Rev Genet. 2020 Mar;21(3):151-170. doi: 10.1038/s41576-019-0186-3.
13. Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell. 6th ed. New York: Garland Science; 2014. Chapter 6: How Cells Read the Genome: From DNA to Protein.
14. Black DL. Mechanisms of alternative pre-messenger RNA splicing. Annu Rev Biochem. 2003;72:291-336. doi: 10.1146/annurev.biochem.72.121801.161720.

Part 5: DNA Read/Write/Edit

DNA Read

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

I would want to sequence environmental DNA (eDNA) from coral reef water samples around deployment sites of the biodegradable pods. If the MnSOD-producing bacteria where deployed on a reef, various factors would have to be monitored. The first factor would be whether the strain persists in the environment which eDNA sequencing from water samples could detect using the MnSOD gene itself [1]. Water samples collected at various distances from deployment sites would tell if containment is working by showing whether or not it has spread beyond the target area [1]. Additionally, coral-associated samples would reveal if introducing the probiotic is disrupting the natural microbial community [2].

(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 Illumina next-generation sequencing (NGS). [3,4] For eDNA metagenomics, the high throughput is essential for capturing the full diversity of microbial communities. Illumina’s short reads have very low error rates (around 0.1-1%) [5], which matters when confirming the gene sequence. Illumina also offers the best balance of data quality and price considering the scale of monitoring required in this specific case. This is a second-generation sequencing technology, and it’s defined by massively parallel sequencing, i.e. reading millions of short DNA fragments simultaneously instead of one at a time like Sanger sequencing [3,4]. The main difference between Sanger, which is first-generation, and Illumina, is the number of fragments read at one time. Sanger reads one fragment at a time [6,7], whilst Illumina can read millions of fragments in parallel [3,4]. Other distinctions include long reads up to 900 bp, slow and expensive but accurate for each base for Sanger [7] vs short reads of up to 300 bp, high accuracy and lower cost per base for Illumina [4]. Third generation methods like PacBio and Nanopore can achieve very long reads up to 100 kb or more but they have higher error rates [8,9].

The input for Illumina NGS would be the DNA extracted from filtered seawater samples or coral mucus swabs. The following are the preparation steps [1,10]:

1. Filter large volumes of seawater to capture microbial biomass
2. Extract total DNA (including human, fish, microbial, and free DNA)
3. Quantify and check quality
4. Fragmentation (mechanical or enzymatic)
5. Adapter ligation with barcodes (to multiplex many samples)
6. PCR amplification
7. Library pooling

The first essential step of Illumina sequencing is the library preparation during which DNA is fragmented, and adapters are ligated to both ends [3,4]. Then, the library fragments must be attached to a flow cell surface. Bridge amplification creates thousands of identical copies of each fragment in tight clusters. Fluorescently labeled nucleotides would then be added one at a time. As each nucleotide incorporates, a camera takes an image of the flow cell. The color tells you which base (A, T, C, or G) was added. Then, a software analyzes the images, identifying which base was added to each cluster in each cycle. The sequence of colors across cycles gives you the DNA sequence for each fragment [3,4]. Finally, the millions of short reads are assembled and annotated. The output is then millions of short sequencing reads in FASTQ format, which is a text file containing a read identifier, the nucleotide sequence (A, T, C, G), and quality scores for each base (Phred scores) indicating the confidence in each call [11]. This would provide the community composition data which tells you which microbial species are present and in what relative abundances.

DNA Write

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

I want to synthesize a heat-sensing genetic circuit that could eventually be used to monitor coral reef health. The idea is to create bacteria that glow when the water gets too warm providing an early warning sign before corals start to bleach. The DNA I’d synthesize is a single construct containing two parts: a heat-shock promoter from a coral or its symbiotic algae, and a fluorescent protein gene (mCherry) that makes bacteria turn red. When the temperature rises, the promoter turns on, the bacteria produce mCherry, and they start to glow. If it’s possible to make bacteria that glow when the water changes temperature, then it would be possible to swap the mCherry gene for the MnSOD gene later and have bacteria that actually protect corals instead of just reporting on them [2].

The mCherry sequence I’m referring to is the standard version developed by Shaner et al. [12] and codon-optimized for E. coli expression based on the iGEM Parts Registry [13]. I have to find the exact promoter sequence from a coral database separately but the reporter part, the mCherry sequence, is the following:

ATGGTGAGCAAGGGCGAGGAGGATAACATGGCCATCATCAAGGAGTTCATGCGCTTCAAGGTGCACATGGAG GGCTCCGTGAACGGCCACGAGTTCGAGATCGAGGGCGAGGGCGAGGGCCGCCCCTACGAGGGCACCCAGACC GCCAAGCTGAAGGTGACCAAGGGTGGCCCCCTGCCCTTCGCCTGGGACATCCTGTCCCCTCAGTTCATGTAC GGCTCCAAGGCCTACGTGAAGCACCCCGCCGACATCCCCGACTACTTGAAGCTGTCCTTCCCCGAGGGCTTC AAGTGGGAGCGCGTGATGAACTTCGAGGACGGCGGCGTGGTGACCGTGACCCAGGACTCCTCCCTGCAGGAC GGCGAGTTCATCTACAAGGTGAAGCTGCGCGGCACCAACTTCCCCTCCGACGGCCCCGTAATGCAGAAGAAG ACCATGGGCTGGGAGGCCTCCTCCGAGCGGATGTACCCCGAGGACGGCGCCCTGAAGGGCGAGATCAAGCAG AGGCTGAAGCTGAAGGACGGCGGCCACTACGACGCTGAGGTCAAGACCACCTACAAGGCCAAGAAGCCCGTG CAGCTGCCCGGCGCCTACAACGTCAACATCAAGTTGGACATCACCTCCCACAACGAGGACTACACCATCGTG GAACAGTACGAACGCGCCGAGGGCCGCCACTCCACCGGCGGCATGGACGAGCTGTACAAGTAA

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

I would use Twist Bioscience’s silicon-based DNA synthesis platform [14]. Twist synthesizes DNA by building short fragments on silicon chips and then assembling them into longer pieces. Their method is accurate, reliable, and perfect for a project like this where I only need one construct to start testing. First, I would design my sequence and upload it to Twist’s portal. Inside their machine, they synthesize short overlapping oligonucleotides adding one nucleotide at a time in a series of chemical cycles. Then they assemble those fragments into the full-length gene using PCR assembly, where the overlapping ends act like puzzle pieces that only fit together one way. Once the full gene is made, they insert it into a plasmid and transform it into E. coli. They grow up the bacteria, sequence the DNA to make sure it’s 100% correct, and then send me either the purified DNA or a tube of bacteria with my gene inside [14].

In terms of speed, it takes about 5-10 business days to get the DNA [14]. That’s fine for planning experiments but not something you’d use in an emergency. For accuracy, the chemistry isn’t perfect since each coupling step is about 99.5% efficient, meaning that for a 1000 bp gene, only about 1% of full-length products might be error-free. That’s why Twist sequences everything before shipping, so they only send out perfect clones. For scalability, Twist can make thousands of genes at once on their silicon chips but for a single gene like in my case, that doesn’t really matter. The main limitation for me would be cost.

DNA Edit

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

Building on my project to engineer beneficial bacteria for coral reefs, I would want to edit two different targets: the MnSOD gene itself to create improved versions, and the genome of my engineered E. coli chassis to add biosafety features.

First, I would edit the MnSOD gene to test variants that might have higher activity or better stability. By introducing targeted mutations at specific amino acid positions, I could potentially create a superoxide dismutase enzyme that works more efficiently at the higher temperatures corals experience during bleaching events. This kind of protein engineering through gene editing is a common approach to improve enzyme function. Second, and more importantly for my project’s real-world application, I would edit the genome of my engineered E. coli strain to add a kill switch. In my Week 1 governance section, I discussed the importance of containment and preventing engineered bacteria from spreading uncontrollably in the environment. A kill switch is a genetic circuit designed to cause cell death under specific conditions, for example, if the bacteria escape the coral reef environment or if a certain time period has passed. This would address the ethical concerns I raised about disrupting native microbiomes and would make the whole approach much safer.

The specific edit would involve inserting a synthetic gene circuit into a neutral site in the E. coli chromosome. This circuit could be designed so that the bacteria require a synthetic amino acid or a specific chemical signal to survive. Alternatively, it could be a temperature-sensitive switch that kills the bacteria if they leave the warm reef waters. These kinds of biocontainment strategies are actively being developed in synthetic biology to address exactly the safety concerns I outlined in my earlier work [15].

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

I would use CRISPR-Cas9 for both types of edits [16]. CRISPR is the most versatile and precise genome editing tool available, and it works well in E. coli which is my chassis organism. For editing the MnSOD gene on a plasmid, CRISPR-Cas9 would allow me to introduce specific point mutations efficiently. For the more complex task of inserting a kill switch into the bacterial chromosome, CRISPR is ideal because it can target a specific genomic location with high precision [17]. CRISPR-Cas9 works through a simple two-component system. The Cas9 protein is a nuclease that cuts DNA, and a guide RNA (gRNA) directs it to the right location [16].

There are five essential steps to CRISPR-Cas9. First, there needs to be a guide RNA with a 20-nucleotide sequence that matches my target site in the E. coli genome. The target site must be next to a PAM sequence (NGG for the standard Cas9 from Streptococcus pyogenes) which is required for Cas9 to bind. For the kill switch insertion, there would also need to be a donor DNA template which is the kill switch gene sequence flanked by homology arms that match the genomic target site [18]. Then, three components would be introduced into the E. coli cells: a plasmid expressing Cas9, a plasmid expressing the guide RNA, and the donor DNA template for insertion edits. This is typically done through electroporation, which uses an electric field to make cells temporarily permeable to DNA. Inside the cell, the guide RNA binds to Cas9 and directs it to the matching genomic sequence. Cas9 checks for the PAM sequence, unwinds the DNA, and if the match is good, it makes a double-strand break about three nucleotides upstream of the PAM [17]. Next, the cell’s natural repair systems kick in. Without a donor template, the break is repaired by non-homologous end joining (NHEJ), which is error-prone and often disrupts the gene. With a donor template present, the cell can use homology-directed repair (HDR) to copy the donor sequence into the genome, which is how I would insert my kill switch [18]. After editing, I would sequence the target region to confirm the edit worked correctly and that there were no off-target mutations.

For the kill switch insertion, the preparation steps would include:

• Designing the kill switch genetic circuit (promoter, toxin gene, regulator elements)
• Designing guide RNAs targeting a neutral integration site in the E. coli genome (sites like attTn7 that are known to tolerate insertions without disrupting essential genes)
• Designing and ordering the donor DNA template (the kill switch flanked by 500-1000 bp homology arms)
• Cloning the guide RNA into a suitable expression plasmid
• Preparing the Cas9 expression plasmid (or using a strain that already expresses Cas9)
• Preparing competent E. coli cells for transformation

The input materials would be: the two plasmids (Cas9 and gRNA), the donor DNA template (either as linear DNA or on a separate plasmid), and competent cells of my target strain [18].

CRISPR-Cas9 is powerful but has limitations. In terms of efficiency, homology-directed repair (HDR) is much less efficient than non-homologous end joining (NHEJ), especially in bacteria. For inserting a kill switch, only a small percentage of cells that take up the DNA will undergo the correct HDR event. This means I would need a good screening method to find the correct clones [17]. In terms of precision, CRISPR can sometimes cut at off-target sites that are similar but not identical to the intended sequence. This could introduce unwanted mutations elsewhere in the genome. Using high-fidelity Cas9 variants and carefully designing guide RNAs with unique targets can minimize this risk [16]. Another limitation is delivery. Getting all the components into cells efficiently can be challenging, especially for non-model organisms. For E. coli, electroporation works well, but for editing coral symbionts directly, delivery would be much harder. Finally, there’s the PAM requirement. Cas9 can only target sequences next to a PAM, which limits where I can edit. For my kill switch integration site, I would need to find a safe genomic location that also has a suitable PAM sequence nearby. Despite these limitations, CRISPR-Cas9 is still the best choice because it is precise, programmable, and has a huge research community developing improved versions and methods.

References

1. Liles MR, Williamson LL, Rodbumrer J, Torsvik V, Goodman RM, Handelsman J. Isolation and Cloning of High-Molecular-Weight Metagenomic DNA from Soil Microorganisms. Cold Spring Harb Protoc. 2009;2009(8):pdb.prot5271.
2. Gao B, Ruiz D, Case H, Jinkerson RE, Sun Q. Engineering bacterial warriors: harnessing microbes to modulate animal physiology. Curr Opin Biotechnol. 2024;87:103113.
3. Illumina Inc. An Introduction to Next-Generation Sequencing Technology [Internet]. San Diego: Illumina; 2021 [cited 2026 Feb 17]. Available from: https://www.illumina.com/technology/next-generation-sequencing.html.
4. Illumina Inc. HiSeq 4000 Sequencing System [Internet]. San Diego: Illumina; 2024 [cited 2026 Feb 17]. Available from: https://www.biocompare.com/23967-Next-Generation-Sequencers/11182181-HiSeq-4000-Sequencing-System/.
5. De-Kloet RE, Jansen HJ, Groenen MAM, Megens HJ. Comparison of Pacific Biosciences (PacBio), Oxford Nanopore Technologies (ONT), Illumina (IL), 10× Genomics linked-read sequencing on the Illumina platform (10×), RNA sequencing on the Illumina platform (RNA-seq), BioNano Genomics (BNG) and the genome-wide chromatin conformation capture protocol Hi–C (Hi–C). [Table 3] In: Comprehensive comparison of long-read and short-read sequencing technologies for de novo genome assembly. 2020 [cited 2026 Feb 17]. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7925608/table/Tab3/.
6. Thermo Fisher Scientific. How to Conduct Sanger Sequencing [Internet]. Waltham (MA): Thermo Fisher Scientific; 2025 [cited 2026 Feb 17]. Available from: https://www.thermofisher.com/hk/en/home/life-science/sequencing/sequencing-learning-center/capillary-electrophoresis-information/how-conduct-fragment-analysis0.html.
7. University of British Columbia Sequencing and Bioinformatics Consortium. Sanger FAQ [Internet]. Vancouver: UBC; 2025 [cited 2026 Feb 17]. Available from: https://sequencing.ubc.ca/our-services-equipment/sanger-sequencing/sanger-faq.
8. Yale School of Medicine. PacBio Single Molecule Real-Time (SMRT) Sequencing System [Internet]. New Haven: Yale University; 2024 [cited 2026 Feb 17]. Available from: https://dev.medicine.yale.edu/keck/microarray/services/long-reads-sequencing/pacbio/.
9. Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol. 2021 Nov 8;39(11):1348-1365. doi: 10.1038/s41587-021-01108-x.
10. Peabody MA, Hullahalli K, Sistu H, Pritchard JR, Walker S. Preparation of functional metagenomic libraries from low biomass samples using METa assembly and their application to capture antibiotic resistance genes. mSystems. 2025 Oct 21;10(10):e01039-25. doi: 10.1128/msystems.01039-25.
11. Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2010 Apr;38(6):1767-71. doi: 10.1093/nar/gkp1137.
12. Shaner NC, Campbell RE, Steinbach PA, Giepmans BN, Palmer AE, Tsien RY. Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat Biotechnol. 2004 Dec;22(12):1567-72.
13. iGEM Foundation. mCherry coding sequence (BBa_J06504) [Internet]. 2022 [cited 2026 Feb 17]. Available from: http://parts.igem.org/Part:BBa_J06504.
14. Twist Bioscience. DNA Synthesis Technology [Internet]. San Francisco: Twist Bioscience; 2026 [cited 2026 Feb 17]. Available from: https://www.twistbioscience.com/.
15. Chan CTY, Lee JW, Cameron DE, Bashor CJ, Collins JJ. ‘Deadman’ and ‘Passcode’ microbial kill switches for bacterial containment. Nat Chem Biol. 2016 Feb;12(2):82-6. doi:10.1038/nchembio.1979.
16. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012 Aug 17;337(6096):816-21. doi: 10.1126/science.1225829.
17. Jiang W, Bikard D, Cox D, Zhang F, Marraffini LA. RNA-guided editing of bacterial genomes using CRISPR-Cas systems. Nat Biotechnol. 2013 Mar;31(3):233-9. doi: 10.1038/nbt.2508.
18. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering using the CRISPR-Cas9 system. Nat Protoc. 2013 Nov;8(11):2281-2308. doi: 10.1038/nprot.2013.143.

Week 3: Lab Automation

Python Script for Opentrons Artwork

Using the GUI at opentrons-art.rcdonovan.com I was able to generate a pattern to simulate the skyline at night in a desert (see Figure 1). LifeFabs Opentrons only has access to Blue, Pink, and Purple pigment which is why I decided to use these 3 colors only.

Figure 1. Night sky in a desert (Automation Art Interference GUI).

The colors were achieved using eqFP578 (blue), TagRFP (pink), and mCherry (purple.)

Then, using the coordinates from the GUI, I followed the instructions in the HTGAA26 Opentrons Colab to write my own Python script with the assistance of Claude (Anthropic) which helped me structure the pipetting logic. This is the code:

from opentrons import types

metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
    'author': 'Karol Duque',
    'protocolName': 'Fluorescent Protein Scene',
    'description': 'Three-color artistic scene using fluorescent bacteria: '
                   'mCherry (purple under UV) in A1, '
                   'TagRFP (pink under UV) in B1, '
                   'EqFP578 (blue under UV) in C1. '
                   'Coordinates generated at opentrons-art.rcdonovan.com. '
                   'AI assistance: Claude (Anthropic) helped structure the pipetting logic.',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

# Using CSS color names that closely match actual fluorescence appearance:
#   mCherry  → 'darkorchid'  (fluoresces purple)
#   TagRFP   → 'hotpink'       (fluoresces pink)
#   EqFP578  → 'dodgerblue'    (fluoresces blue)
#
# Confirm physical well positions with your TA before running!
well_colors = {
    'A1' : 'darkorchid',  # mCherry
    'B1' : 'hotpink',       # TagRFP
    'C1' : 'navy',    # EqFP578
}


def run(protocol):
  ##############################################################################
  ###   Load labware, modules and pipettes
  ##############################################################################

  tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')
  pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])
  temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
  temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 'Cold Plate')
  color_plate = temperature_plate
  agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')  ## TA MUST CALIBRATE EACH PLATE!
  center_location = agar_plate['A1'].top()
  pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

  ##############################################################################
  ###   Patterning
  ##############################################################################

  def location_of_color(color_string):
    for well, color in well_colors.items():
      if color.lower() == color_string.lower():
        return color_plate[well]
    raise ValueError(f"No well found with color {color_string}")

  def dispense_and_detach(pipette, volume, location):
      assert(isinstance(volume, (int, float)))
      above_location = location.move(types.Point(z=location.point.z + 5))
      pipette.move_to(above_location)
      pipette.dispense(volume, location)
      pipette.move_to(above_location)

  ###
  ### YOUR CODE HERE to create your design
  ###

  # Coordinates from opentrons-art.rcdonovan.com
  # mCherry  → 'darkorchid'  well A1  (fluoresces purple)
  # TagRFP   → 'hotpink'       well B1  (fluoresces pink)
  # EqFP578  → 'navy'    well C1  (fluoresces blue)

  mcherry_points = [(-5, 25),(5, 25),(-20, 20),(10, 20),(-12.5, 17.5),(-27.5, 15),(-25, 15),(-30, 12.5),(-27.5, 12.5),(-25, 12.5),(-22.5, 12.5),(27.5, 12.5),(-30, 10),(-27.5, 10),(-25, 10),(-22.5, 10),(-20, 10),(-17.5, 10),(-15, 10),(25, 10),(27.5, 10),(-32.5, 7.5),(-30, 7.5),(-27.5, 7.5),(-25, 7.5),(-22.5, 7.5),(-17.5, 7.5),(-15, 7.5),(-12.5, 7.5),(-10, 7.5),(0, 7.5),(2.5, 7.5),(5, 7.5),(7.5, 7.5),(10, 7.5),(12.5, 7.5),(22.5, 7.5),(25, 7.5),(27.5, 7.5),(30, 7.5),(-32.5, 5),(-27.5, 5),(-25, 5),(-22.5, 5),(-20, 5),(-17.5, 5),(-15, 5),(-12.5, 5),(-10, 5),(-5, 5),(-2.5, 5),(0, 5),(2.5, 5),(10, 5),(12.5, 5),(15, 5),(17.5, 5),(20, 5),(22.5, 5),(27.5, 5),(30, 5),(-32.5, 2.5),(-30, 2.5),(-27.5, 2.5),(-25, 2.5),(-22.5, 2.5),(-17.5, 2.5),(-15, 2.5),(-12.5, 2.5),(-10, 2.5),(-7.5, 2.5),(-5, 2.5),(-2.5, 2.5),(2.5, 2.5),(12.5, 2.5),(15, 2.5),(17.5, 2.5),(20, 2.5),(22.5, 2.5),(25, 2.5),(27.5, 2.5),(30, 2.5),(-32.5, 0),(-30, 0),(-27.5, 0),(-25, 0),(-22.5, 0),(-20, 0),(-17.5, 0),(-15, 0),(-12.5, 0),(-10, 0),(-5, 0),(-2.5, 0),(2.5, 0),(10, 0),(15, 0),(17.5, 0),(20, 0),(22.5, 0),(25, 0),(-20, -2.5),(-17.5, -2.5),(-12.5, -2.5),(-10, -2.5),(-7.5, -2.5),(-5, -2.5),(-2.5, -2.5),(2.5, -2.5),(17.5, -2.5),(-7.5, -5),(-5, -5)]

  tagrfp_points = [(-10, 15),(10, 15),(-7.5, 12.5),(12.5, 12.5),(-20, 7.5),(-30, 5),(25, 5),(-20, 2.5),(10, 2.5),(-7.5, 0),(27.5, 0),(30, 0),(-32.5, -2.5),(-30, -2.5),(-27.5, -2.5),(-25, -2.5),(-22.5, -2.5),(-15, -2.5),(10, -2.5),(15, -2.5),(20, -2.5),(22.5, -2.5),(25, -2.5),(27.5, -2.5),(30, -2.5),(-32.5, -5),(-30, -5),(-27.5, -5),(-25, -5),(-22.5, -5),(-20, -5),(-17.5, -5),(-15, -5),(-12.5, -5),(-10, -5),(-2.5, -5),(10, -5),(15, -5),(17.5, -5),(22.5, -5),(25, -5),(27.5, -5),(30, -5),(-32.5, -7.5),(-30, -7.5),(-27.5, -7.5),(-22.5, -7.5),(-20, -7.5),(-17.5, -7.5),(-12.5, -7.5),(-10, -7.5),(-7.5, -7.5),(-5, -7.5),(-2.5, -7.5),(15, -7.5),(17.5, -7.5),(25, -7.5),(27.5, -7.5),(30, -7.5),(-32.5, -10),(-30, -10),(-20, -10),(-17.5, -10),(-12.5, -10),(-7.5, -10),(-5, -10),(-2.5, -10),(0, -10),(2.5, -10),(15, -10),(22.5, -10),(25, -10),(27.5, -10),(30, -10),(-30, -12.5),(-20, -12.5),(-12.5, -12.5),(-7.5, -12.5),(-5, -12.5),(-2.5, -12.5),(0, -12.5),(2.5, -12.5),(12.5, -12.5),(15, -12.5),(17.5, -12.5),(22.5, -12.5),(27.5, -12.5),(-30, -15),(-27.5, -15),(-22.5, -15),(-20, -15),(-17.5, -15),(-7.5, -15),(-5, -15),(-2.5, -15),(0, -15),(2.5, -15),(10, -15),(12.5, -15),(15, -15),(17.5, -15),(-20, -17.5),(-17.5, -17.5),(-10, -17.5),(-7.5, -17.5),(-5, -17.5),(0, -17.5),(2.5, -17.5),(10, -17.5),(-17.5, -20),(-12.5, -20),(-10, -20),(-7.5, -20),(-10, -22.5)]

  eqfp578_points = [(-10, 30),(-7.5, 30),(-5, 30),(-2.5, 30),(0, 30),(2.5, 30),(5, 30),(7.5, 30),(-15, 27.5),(-12.5, 27.5),(-10, 27.5),(-7.5, 27.5),(-5, 27.5),(-2.5, 27.5),(0, 27.5),(2.5, 27.5),(5, 27.5),(7.5, 27.5),(10, 27.5),(12.5, 27.5),(-20, 25),(-17.5, 25),(-15, 25),(-12.5, 25),(-10, 25),(-7.5, 25),(-2.5, 25),(0, 25),(2.5, 25),(7.5, 25),(10, 25),(12.5, 25),(15, 25),(17.5, 25),(-22.5, 22.5),(-20, 22.5),(-17.5, 22.5),(-15, 22.5),(-12.5, 22.5),(-10, 22.5),(-7.5, 22.5),(-5, 22.5),(-2.5, 22.5),(0, 22.5),(2.5, 22.5),(5, 22.5),(7.5, 22.5),(10, 22.5),(12.5, 22.5),(15, 22.5),(17.5, 22.5),(20, 22.5),(-25, 20),(-22.5, 20),(-17.5, 20),(-15, 20),(-12.5, 20),(-10, 20),(-7.5, 20),(-5, 20),(-2.5, 20),(0, 20),(2.5, 20),(5, 20),(7.5, 20),(12.5, 20),(15, 20),(17.5, 20),(20, 20),(22.5, 20),(-27.5, 17.5),(-25, 17.5),(-22.5, 17.5),(-20, 17.5),(-17.5, 17.5),(-15, 17.5),(-10, 17.5),(-7.5, 17.5),(-5, 17.5),(-2.5, 17.5),(0, 17.5),(2.5, 17.5),(5, 17.5),(7.5, 17.5),(10, 17.5),(12.5, 17.5),(15, 17.5),(17.5, 17.5),(20, 17.5),(22.5, 17.5),(25, 17.5),(-22.5, 15),(-20, 15),(-17.5, 15),(-15, 15),(-12.5, 15),(-7.5, 15),(-5, 15),(-2.5, 15),(0, 15),(2.5, 15),(5, 15),(7.5, 15),(12.5, 15),(15, 15),(17.5, 15),(20, 15),(22.5, 15),(25, 15),(-20, 12.5),(-17.5, 12.5),(-15, 12.5),(-12.5, 12.5),(-10, 12.5),(-5, 12.5),(-2.5, 12.5),(0, 12.5),(2.5, 12.5),(5, 12.5),(7.5, 12.5),(10, 12.5),(15, 12.5),(17.5, 12.5),(20, 12.5),(22.5, 12.5),(25, 12.5),(-12.5, 10),(-10, 10),(-7.5, 10),(-5, 10),(-2.5, 10),(0, 10),(2.5, 10),(5, 10),(7.5, 10),(10, 10),(12.5, 10),(15, 10),(17.5, 10),(20, 10),(22.5, 10),(-7.5, 7.5),(-5, 7.5),(-2.5, 7.5),(15, 7.5),(17.5, 7.5),(20, 7.5),(-7.5, 5),(5, 5),(7.5, 5),(0, 2.5),(5, 2.5),(7.5, 2.5),(0, 0),(5, 0),(7.5, 0),(12.5, 0),(0, -2.5),(5, -2.5),(7.5, -2.5),(12.5, -2.5),(0, -5),(2.5, -5),(5, -5),(7.5, -5),(12.5, -5),(20, -5),(-25, -7.5),(-15, -7.5),(0, -7.5),(2.5, -7.5),(5, -7.5),(7.5, -7.5),(10, -7.5),(12.5, -7.5),(20, -7.5),(22.5, -7.5),(-27.5, -10),(-25, -10),(-22.5, -10),(-15, -10),(-10, -10),(5, -10),(7.5, -10),(10, -10),(12.5, -10),(17.5, -10),(20, -10),(-27.5, -12.5),(-25, -12.5),(-22.5, -12.5),(-17.5, -12.5),(-15, -12.5),(-10, -12.5),(5, -12.5),(7.5, -12.5),(10, -12.5),(20, -12.5),(25, -12.5),(-25, -15),(-15, -15),(-12.5, -15),(-10, -15),(5, -15),(7.5, -15),(20, -15),(22.5, -15),(25, -15),(27.5, -15),(-27.5, -17.5),(-25, -17.5),(-22.5, -17.5),(-15, -17.5),(-12.5, -17.5),(-2.5, -17.5),(5, -17.5),(7.5, -17.5),(12.5, -17.5),(15, -17.5),(17.5, -17.5),(20, -17.5),(22.5, -17.5),(25, -17.5),(-27.5, -20),(-25, -20),(-22.5, -20),(-20, -20),(-15, -20),(-5, -20),(-2.5, -20),(0, -20),(2.5, -20),(5, -20),(7.5, -20),(10, -20),(12.5, -20),(15, -20),(17.5, -20),(20, -20),(22.5, -20),(25, -20),(-25, -22.5),(-22.5, -22.5),(-20, -22.5),(-17.5, -22.5),(-15, -22.5),(-12.5, -22.5),(-7.5, -22.5),(-5, -22.5),(-2.5, -22.5),(0, -22.5),(2.5, -22.5),(5, -22.5),(7.5, -22.5),(10, -22.5),(12.5, -22.5),(15, -22.5),(17.5, -22.5),(20, -22.5),(22.5, -22.5),(-22.5, -25),(-20, -25),(-17.5, -25),(-15, -25),(-12.5, -25),(-10, -25),(-7.5, -25),(-5, -25),(-2.5, -25),(0, -25),(2.5, -25),(5, -25),(7.5, -25),(10, -25),(12.5, -25),(15, -25),(17.5, -25),(20, -25),(-20, -27.5),(-17.5, -27.5),(-15, -27.5),(-12.5, -27.5),(-10, -27.5),(-7.5, -27.5),(-5, -27.5),(-2.5, -27.5),(0, -27.5),(2.5, -27.5),(5, -27.5),(7.5, -27.5),(10, -27.5),(12.5, -27.5),(15, -27.5),(17.5, -27.5),(-15, -30),(-12.5, -30),(-10, -30),(-7.5, -30),(-5, -30),(-2.5, -30),(0, -30),(2.5, -30),(5, -30),(7.5, -30),(10, -30),(12.5, -30),(-10, -32.5),(-7.5, -32.5),(-5, -32.5),(-2.5, -32.5),(0, -32.5),(2.5, -32.5),(5, -32.5),(7.5, -32.5)]

  def draw_points(points, color_string):
      """One tip per color. Aspirates in batches of 20uL, dispenses 1uL per dot."""
      pipette_20ul.pick_up_tip()
      i = 0
      while i < len(points):
          batch = min(20, len(points) - i)
          pipette_20ul.aspirate(batch, location_of_color(color_string))
          for _ in range(batch):
              x, y = points[i]
              loc = center_location.move(types.Point(x=x, y=y))
              dispense_and_detach(pipette_20ul, 1, loc)
              i += 1
      pipette_20ul.drop_tip()

  # Draw order: blue background first, then purple (mCherry), then pink (TagRFP) on top
  draw_points(eqfp578_points, 'navy')   # EqFP578 → blue 
  draw_points(mcherry_points,  'darkorchid') # mCherry → purple
  draw_points(tagrfp_points,   'hotpink')      # TagRFP  → pink

  # drop_tip() is called inside draw_points() for each color

The output is the following:

Figure 2. Night sky in a desert (Opentrons Colab).

Post-Lab Questions

1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
2. 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.

Final Project Ideas

This idea is ba continuation on the research I did for the homework in Weeks 1 and 2.

This idea came about after reading about the recent wildfires in Patagonia, Argentina and further researching recent wildfires across the world including Chile, Spain and Portugal. I started thinking about the ways that already exist to prevent wildfires and thought if there were any methods that use modified living organisms.

Because my background is in biomedical engineering I thought I should have at least one project idea that was related healthcare. Before starting my degree I did some research into bandaids made with hydrogels which act like stitches and prevent infection. Last summer, I did an internship which used microfluidics for blood glucose monitoring which made me think if there was any way I could combine microfluidics and something to improve wound care. That led me to chronic wounds and I began researching monitoring methods for these wounds that led me to biosensors for chronic wound management.