Week 1 HW: Principles and Practices

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

Throughout my previous research as a new biodesigner, I have been particularly drawn to two themes: the intricate relationship between the gut microbiota and its effects on human physical and mental health, and the fascinating world of fungi and their broader implications for planetary health. I have deepened my research into psilocybin and related tryptamine alkaloids (substances that activate serotonin 5-HT receptors), which are already being investigated clinically for neuropsychiatric conditions (Xi et al., 2023), but what especially interests me is emerging evidence that some of their effects may be mediated by the gut microbiota rather than the brain alone (Caspani et al., 2024). There is still a lack of research on the precise impact that serotonergic psychedelics have on the structure and composition of the gut microbiota, even though animal studies suggest possible links (Császár-Nagy et al., 2022). A survey of wild‑type rats has shown changes in the abundance of spore‑forming gut bacteria following oral administration of serotonin, suggesting that serotonergic compounds such as psilocybin could directly or indirectly reshape microbial communities (Fung et al., 2019).

Beyond psilocybin, the traditional use of other psychoactive organisms also points to underexplored antimicrobial and immunomodulatory effects. For instance, the peyote cactus (Lophophora williamsii) has been reported to display activity against multiple penicillin‑resistant strains of Staphylococcus aureus (McCleary et al., 1960). Such examples raise broader questions about how these organisms interact with microbes and the host’s physiology. These kinds of plants, fungi, and compounds have long histories in traditional medicine yet remain only partially understood within Western biomedical frameworks (Doesburg-van Kleffens et al., 2023). I believe that the intersection of psychedelics, microbiota, and immune function is a promising area for synthetic biology yet to be further explored, with potential not only for already studied mental health conditions but also for inflammatory bowel diseases (IBD) (Qureshi et al., 2025). Rather than viewing these compounds solely through a brain‑centric lens, it would be interesting to investigate their therapeutic potential through the gut microbiota as a primary interface rather than a secondary effect.

Psychedelic‑assisted therapy is currently highly regulated, expensive, and accessible to very few people (Rea et Wallace, 2021). From a biodesign and synthetic biology perspective, biosynthesising psilocybin in a bacterial host such as Escherichia coli through recombinant DNA technology (Keller et al., 2025) could potentially provide a more controllable, scalable, and eventually more affordable route to producing psilocybin or related analogues.

In the context of the HTGAA course, I therefore propose to focus on a minimal and safe fragment of this broader concept, with the following objectives:

• Investigate how natural serotonergic psychedelics and related natural compounds might interact with the gut microbiota- immune axis, with a focus on IBD and gut inflammations.
• Design a conceptual framework for microbial psilocybin production in E. coli (Adams et al., 2019) as a platform for future microbiome‑targeted therapies.
• Prototype an E. coli experimental system expressing a heterologous lyase enzyme from a psilocybin-related tryptamine biosynthetic pathway (Abrahms et al., 2025), assembled via PCR-based cloning methods.
• Apply CRISPR‑based editing to modulate the expression of the heterologous enzyme and characterise the resulting change in a readout (through colour or fluorescence) to determine if the right sequence in E.Coli is being targeted.
• Develop a simple in silico pathway model to explore where metabolic bottlenecks might occur in a full psilocybin biosynthesis pathway in E. coli (Irvine et al., 2023).
• Reflect on ethical, regulatory, and accessibility questions around engineering psychoactive compounds and further document how these considerations shape the design of the project.

Reference List:

• Abrahms, Z.N., Sen, A.K. and Jones, J.A. (2025).Pathway engineering for the biosynthesis of psychedelics. Current Opinion in Biotechnology, 94, pp.103314–103314. doi:https://doi.org/10.1016/j.copbio.2025.103314.
• Adams, A.M., Kaplan, N.A., Wei, Z., Brinton, J.D., Monnier, C.S., Enacopol, A.L., Ramelot, T.A. and Jones, J.A. (2019). In vivo production of psilocybin in E. coli. Metabolic Engineering, [online] 56, pp.111–119. doi:https://doi.org/10.1016/j.ymben.2019.09.009.
• Caspani, G., Ruffell, S.G.D., Tsang, W., Netzband, N., Rohani-Shukla, C., Swann, J.R. and Jefferies, W.A. (2024). Mind over matter: the microbial mindscapes of psychedelics and the gut-brain axis. Pharmacological Research, 207, p.107338. doi:https://doi.org/10.1016/j.phrs.2024.107338.
• Császár-Nagy, N., Bob, P. and Bókkon, I. (2022). A Multidisciplinary Hypothesis about Serotonergic Psychedelics. Is it Possible that a Portion of Brain Serotonin Comes From the Gut? Journal of Integrative Neuroscience, 21(5), p.148. doi:https://doi.org/10.31083/j.jin2105148.
• Doesburg-van Kleffens, M., Zimmermann-Klemd, A.M. and Gründemann, C. (2023). An Overview on the Hallucinogenic Peyote and Its Alkaloid Mescaline: The Importance of Context, Ceremony and Culture. Molecules, [online] 28(24), p.7942. doi:https://doi.org/10.3390/molecules28247942.
• Fung, T.C., Vuong, H.E., Luna, C.D.G., Pronovost, G.N., Aleksandrova, A.A., Riley, N.G., Vavilina, A., McGinn, J., Rendon, T., Forrest, L.R. and Hsiao, E.Y. (2019). Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nature Microbiology. doi:https://doi.org/10.1038/s41564-019-0540-4.
• Keller, M.R., McKinney, M.G., Sen, A.K., Guagliardo, F.G., Hellwarth, E.B., Islam, K.N., Kaplan, N.A., Gibbons, W.J., Kemmerly, G.E., Meers, C., Wang, X. and Jones, J.A. (2025). Psilocybin biosynthesis enhancement through gene source optimization. Metabolic Engineering, [online] 91, pp.119–129. doi:https://doi.org/10.1016/j.ymben.2025.04.003.
• McCleary, J.A., Sypherd, P.S. and Walkington, D.L. (1960). Antibiotic activity of an extract of peyote (Lophophora Williamii (Lemaire) Coulter). Economic Botany, 14(3), pp.247–249. doi:https://doi.org/10.1007/bf02907956.
• Qureshi, O., Cowley, J., Pegg, A., Cooper, A.J., Gordon, J., Brady, C.A., Belli, A., Butterworth, S., Upthegrove, R., Andrews, N. and Barnes, N.M. (2025). Are we hallucinating or can psychedelic drugs modulate the immune system to control inflammation? British journal of pharmacology, [online] p.10.1111/bph.70138. doi:https://doi.org/10.1111/bph.70138.
• Xi, D., Berger, A., Shurtleff, D., Zia, F.Z. and Belouin, S. (2023). National Institutes of Health psilocybin research speaker series: State of the science, regulatory and policy landscape, research gaps, and opportunities. Neuropharmacology, [online] 230, p.109467. doi:https://doi.org/10.1016/j.neuropharm.2023.109467.

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.

Policies Goal: To ensure microbial psilocybin production for microbiome-targeted IBD therapy/medication prioritises safety, equitable access despite microbiome variability, respect for traditional knowledge and transparency to build public trust and prevent harm.

• Risk assessment for gut-specific harm: to safely develop this kind of medication or therapy, it will require a longitudinal patient gut-microbome monitoring prior to and post-dosing to refine safety protocols and the detection of any adverse shifts.
• Accessibility whilst respecting indviduality of the gut microbiome: creating an accessible medication targeting something so unique to the individual will be a challenge. There are now affordable baseline microbiome profiling kits (e.g. Feel Gut - Microbiome Test Kit - 250£) to screen high-risk patients pre-treatment, while developing 2–3 engineered E. coli strains producing dosage variants (low/medium/high) based on symptom severity or gut sensitivity.
• Value of traditional knowledge: crediting and collaborating with communities that have historically used psychedelic organisms for medical approaches through benefit-sharing agreements (BSAs).
• Transparency and accountability: Create traceable audit trails for strains and products to ensure responsibility if misuse occurs and build public trust through open data sharing.

Sources:

• DiliTrust (2025). Understanding Audit Trails: Implementation, Types, and Best Practices. [online] Dilitrust. Available at: https://www.dilitrust.com/audit-trail/ [Accessed 7 Feb. 2026].
• FeelGut (2026). Gut Microbiome Health Test. [online] Feel Gut. Available at: https://feelgut.co.uk/products/gut-microbiome-health-test?gad_source=1&gad_campaignid=21790867659&gbraid=0AAAAAqF2ctBqNuD8W2LgDdGB8xmh-dJkI&gclid=Cj0KCQiAhaHMBhD2ARIsAPAU_D4EnlJcLWOuT0Ol-Gz0qDHbGOnE0a_08EaWu5OmccHvfelHGAJLgeUaAmI0EALw_wcB [Accessed 7 Feb. 2026].
• Ogilvy.com.au (2024). Shared Success: What is benefit sharing and why does it matter? - Insight - MinterEllison. [online] Minterellison.com. Available at: https://www.minterellison.com/articles/what-is-benefit-sharing-and-why-does-it-matter [Accessed 7 Feb. 2026].
• Secretariat of the Convention on Biological Diversity (2011). Convention on Biological Diversity: ABS Theme Access and benefit-sharing. [online] Available at: https://www.cbd.int/abs/infokit/revised/web/factsheet-abs-en.pdf [Accessed 7 Feb. 2026].

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

To meet these policy goals, I propose the following three potential governance actions:

1. COLLECTIVE DATABASE: Call for the development of a collective database where all biologists who have worked on the E. Coli psiloyibin strains would share their protocol and CRISPR history. This calls for the collaboration of universities and researchers worldwide for future research to optimise time by limiting repetitions and consequently reducing costs. The development of controlled access will also be necessary to ensure the information in the database is used ethically and for the correct reasons.
2. PATIENT GUT-MICROBIOME SCREENING: Call for the pre-screening of patients’ gut microbiome to ensure appropriate candidates. This also calls for collaboration with a pharmaceutical partner to deliver these kits directly to potential patients or to hospitals and institutions where the samples will be analysed to determine whether they are suitable for treatment of the patient's gut microbiome. This will ensure safety and a reduction of side effects, and better results by than accordingly selecting which strain would benefit the patient the most. There are still some risks considering ‘false negative’ testing, which could therefore make it difficult to fully confirm the appropriate treatment, as well as the potential increase in price of the treatment if the gut-microbiome data were to become a Pharma IP monopoly, limiting access due to often increasing prices (Dosi et al., 2023).
3. CROSS COLLABORATION Call for the implementation of royalty funds for indigenous cultures that have contributed to the development of this project with their knowledge on the medical application of psychedelics to ensure an ethical and transparent development of the project. Furthermore, developing a wider web of collaboration with governments, scientists, hospitals, psychologists, and doctors to meet and discuss/review strains, trials and dosages. This complex cross-collaboration would only be possible with an FDA approval of the usage of such psychedelics to begin with, which would require a long time, many trials and solid evidence of success at a high success rate level (Lamkin, 2022).

References:

• Dosi, G., Marengo, L., Staccioli, J. and Virgillito, M.E. (2023). Big Pharma and Monopoly capitalism: a long-term View.Structural Change and Economic Dynamics, [online] 65, pp.15–35. doi:https://doi.org/10.1016/j.strueco.2023.01.004.
• Lamkin, M. (2021). Prescription Psychedelics: The Road from FDA Approval to Clinical Practice. The American Journal of Medicine, 135(1). doi:https://doi.org/10.1016/j.amjmed.2021.07.033.

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:

Scale: 1-3 (1: Most effective, 2: Moderately effective, 3: Least effective, or N/A)

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.

I propose the prioritisation of the strain database, as well as the microbiome pre-screening, as the foundational governance actions for the development of microbial psilocybin. Given the strict regulations surrounding the use of psychedelic drugs in the medical world, transparency, feasability and safety are essential for governmental approval. Strategically, developing this project in countries like Switzerland or the Netherlands would offer optimal conditions. In Zurich and Geneva (Switzerland), research on the medical use of psychedelic compounds, such as psilocybin or LSD, in clinical psychiatric trials is continuously growing (Elçi, 2025). Additionally, their biotechnological infrastructure would allow for an ideal environment to conduct the development of such a project. On the other hand, the legal use of psilocybin truffles in the Netherlands should also be considered as a place of interest. Overlooking this as phases, the initial phase should consist of the launch of the strain database for safety and optimisation, followed by mandating affordable gut-microbiome screening. Lastly, validating via in-silico computational models the prediction of human gut-microbiome-psilocybin interactions through pharmacokinetic data (Grogan and Preuss, 2023) to build towards the potential commercialisation of such a product.

List of References:

• Grogan, S. and Preuss, C. (2023). Pharmacokinetics. [online] PubMed. Available at: https://www.ncbi.nlm.nih.gov/books/NBK557744/ [Accessed 9 Feb. 2026].
• Aylin Elçi (2025). Switzerland is home to Europe’s only psychedelics treatment. [online] SWI swissinfo.ch. Available at: https://www.swissinfo.ch/eng/multinational-companies/switzerland-is-home-to-europes-only-psychedelics-treatment/89195943 [Accessed 7 Feb. 2026].

Assignment (Week 2 Lecture Prep)

Homework Questions from Professor Jacobson:

1)Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

The error rate ratio of polymerase is 1:10^6 base pairs, equivalent to one mistake every million bases. In comparison, the human genome consists of approximately 3 billion base pairs (3 x 10^9) (National Human Genome Research Institute, 2026), meaning each replication introduces around 3000 errors (3x10^9 x 10^6), leading to genetic mutations. In synthetic biology, high-fidelity DNA polymerases have proven to have much lower error rates for PCR cloning of long biosynthetic pathways. These polymerases possess 3’ to 5’ exonuclease proofreading domains that detect mismatches through structural perturbations and immediately remove them before continuing synthesis. (Clent Life Science, 2024).

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?

There are astronomically many possible ways to code for an average human protein. For example, a 300-amino-acid protein may have 10^100 different synonymous codon combinations (Rajbanshi and Guruacharya, 2025). However, most fail in practice because cells prefer specific codons (codon usage bias), which causes rare codons to slow down the tRNA process. Consequently, this creates nonuniform ribosome decoding rates on mRNAs and in turn disrupts the contranslational protein folding process, which is essential for proper protein function (Liu et al., 2021).

References:

• Bates, S. (2019). Base Pair. [online] Genome.gov. Available at: https://www.genome.gov/genetics-glossary/Base-Pair [Accessed 8 Feb. 2026].
• Clent Life Science (2024). High-fidelity DNA Polymerases & When to use them. [online] Clent Life Science. Available at: https://clentlifescience.co.uk/high-fidelity-dna-polymerases-and-when-to-use-them/ [Accessed 8 Feb. 2026].
• Liu, Y., Yang, Q. and Zhao, F. (2021). Synonymous but Not Silent: The Codon Usage Code for Gene Expression and Protein Folding. Annual Review of Biochemistry, 90(1), pp.375–401. doi:https://doi.org/10.1146/annurev-biochem-071320-112701.
• Rajbanshi, B. and Guruacharya, A. (2025). codonGPT: reinforcement learning on a generative language model enables scalable mRNA design. Nucleic Acids Research, [online] 53(22). doi:https://doi.org/10.1093/nar/gkaf1345.

Homework Questions from Dr. LeProust:

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

Solid-phase phosphoramidite is currently the most commonly used method for oligo synthesis. It is a cyclical four-step phosphoramidite synthesis method (McLaughlin, 2025), which was developed in 1981 by Marvin Caruthers.

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

Making oligos longer than 200 nucleotides via direct synthesis is difficult due to cumulative yield losses. During this synthesising method, one nucleotide is added at a time. Although there is a high success rate (99%), each added nucleotide will contribute to yield loss (ATDBio Ltd, 2005). At the 200th nucleotide added (0.99^200), the overall yield production will have a new success rate of 0.135%, resulting in extremely low yield and consequently resulting in a waste of expensive reagents and purification time (Mühlegger,2025).

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

As mentioned in the last question, chemical DNA synthesis adds only one nucleotide at a time, at a 99% success rate. Taking into consideration that we only use one strand of DNA for the synthesis, 0.99^2000 would reduce the success rate by 0.0002% yield for a 2000bp gene. Considering this extremely low amount, it would make it practically impossible for the yield to be detected for the purification process.

References:

• ATDBio Ltd (2005). ATDBio - Solid-phase oligonucleotide synthesis. [online] atdbio.com. Available at: https://atdbio.com/nucleic-acids-book/Solid-phase-oligonucleotide-synthesis [Accessed 8 Feb. 2026].
• Glen Research (2026). Glen Report 21.211 - TECHNICAL BRIEF – Synthesis of Long Oligonucleotides. [online] Glenresearch.com. Available at: https://www.glenresearch.com/reports/gr21-211 [Accessed 8 Feb. 2026].
• McLaughlin, L. (2025). What Is Oligonucleotide Synthesis? Phosphoramidite oligonucleotide synthesis. [online] Biotechnologyreviews.com. Available at: https://www.biotechnologyreviews.com/p/what-is-oligonucleotide-synthesis [Accessed 8 Feb. 2026].
• Michael Mühlegger (2025). Oligonucleotide manufacturing – challenges & solutions. [online] Single Use Support: Pionneering Biopharma. Available at: https://www.susupport.com/blogs/biopharmaceutical-products/oligonucleotide-manufacturing-challenges-solutions [Accessed 8 Feb. 2026].

Homework Question from George Church:

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

The essential amino acids are: Histidine (His), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Methionine (Met), Phenylalanine (Phe), Threonine (Thr), Tryptophan (Trp), Valine (Val), and Arginine (ARG). (Lopez and Mohiuddin, 2024).

The Lysine Contingency was a genetically engineered fail‑safe created by Dr Henry Wu in the late 1980s to prevent Jurassic Park’s dinosaurs from synthesising lysine. The idea was that park staff would have to supplement the animals with lysine to keep them alive, and if any dinosaur escaped Isla Nublar, it would die without this dietary supply. This was intended as a strategy to protect outside ecosystems. However, this design was fundamentally flawed. In 1997, Dr Sarah Harding showed that the dinosaurs could obtain enough lysine from their environment through their normal diet, making the contingency ineffective in practice. Since all animals already depend on dietary lysine in the first place, the Contingency never provided the precise control the park claimed (Jurassic-Pedia, 2024).

I agree with Dr Sorkin’s view that, even though these dinosaurs were cloned, they still deserved rights as living animals. Humans chose to recreate this extinct species, so it seems ethically wrong that we should also reserve the right to let them die for our convenience or as a simplistic safety measure. This raises a wider question about how much power humans should have over organisms they create or modify. In my view, these ethical discussions should happen before such cloning work begins, not after problems appear. Overall, the Lysine Contingency feels poorly thought through and aimed at solving a problem that should never have been framed that way in the first place.

References:

• Lopez, M.J. and Mohiuddin, S.S. (2024). Biochemistry, Essential Amino Acids. [online] PubMed. Available at: https://www.ncbi.nlm.nih.gov/books/NBK557845/ [Accessed 8 Feb. 2026].
• Jurassic-Pedia (2024). Lysine Contingency (S/F) / (S/F-T/G) – Jurassic-Pedia. [online] Jurassic-Pedia. Available at: https://www.jurassic-pedia.com/lysine-contingency-sf/ [Accessed 9 Feb. 2026].

Week 2 HW: Dna-Read-Write-and-Edit

Part 1: Benchling & In-silico Gel Art

I simulated the Restriction Enzyme Digestion in Benchling to create a design. I found it initially difficult to visualise patterns or images with the 7 restriction enzymes. I therefore decided to mix certain enzymes in the same wells to generate more DNA fragments and explore shapes further.

Part 3: DNA Design Challenge

Name of protein: psiH (tryptamine 4-monooxygenase)

I chose this specific protein as it relates to my project idea from homework 1. PsiH catalyses the 4-hydroxylation of tryptamine to 4-hydroxytryptamine, which is an essential and unique part of psilocybin biosynthesis that allows for the production of psilocin (the active therapeutic metabolite). This P450 enzyme (psiH) acts as a critical rate‐limiting step of psilocybin production. Furthermore, it needs exact heme binding and substrate fit, which is rare in nature and tough to engineer in E. coli (Huang et al., 2025). This makes PsiH the technical core of my biosynthetic pathway design from Homework 1, where engineered E. coli would produce psilocybin locally to activate gut serotonin signalling for IBD treatment (Robinson et al., 2023).

Refrences:

Amino Acid Protein Sequence:

MIAVLFSFVIAGCIYYIVSRRVRRSRLPPGPPGIPIPFIGNMFD MPEESPWLTFLQWGRDYNTDILYVDAGGTEMVILNTLETITDLLEKRGSIYSGRLEST MVNELMGWEFDLGFITYGDRWREERRMFAKEFSEKGIKQFRHAQVKAAHQLVQQLTKT PDRWAQHIRHQIAAMSLDIGYGIDLAEDDPWLEATHLANEGLAIASVPGKFWVDSFPS LKYLPAWFPGAVFKRKAKVWREAADHMVDMPYETMRKLAPQGLTRPSYASARLQAMDL NGDLEHQEHVIKNTAAEVNVGGGDTTVSAMSAFILAMVKYPEVQRKVQAELDALTNNG QIPDYDEEDDSLPYLTACIKELFRWNQIAPLAIPHKLMKDDVYRGYLIPKNTLVFANT WAVLNDPEVYPDPSVFRPERYLGPDGKPDNTVRDPRKAAFGYGRRNCPGIHLAQSTVW IAGATLLSAFNIERPVDQNGKPIDIPADFTTGFFRHPVPFQCRFVPRTEQVSQSVSGP

3.2 DNA Reverse Translation:

atgattgcggtgctgtttagctttgtgattgcgggctgcatttattatattgtgagccgc cgcgtgcgccgcagccgcctgccgccgggcccgccgggcattccgattccgtttattggc aacatgtttgatatgccggaagaaagcccgtggctgacctttctgcagtggggccgcgat tataacaccgatattctgtatgtggatgcgggcggcaccgaaatggtgattctgaacacc ctggaaaccattaccgatctgctggaaaaacgcggcagcatttatagcggccgcctggaa agcaccatggtgaacgaactgatgggctgggaatttgatctgggctttattacctatggc gatcgctggcgcgaagaacgccgcatgtttgcgaaagaatttagcgaaaaaggcattaaa cagtttcgccatgcgcaggtgaaagcggcgcatcagctggtgcagcagctgaccaaaacc ccggatcgctgggcgcagcatattcgccatcagattgcggcgatgagcctggatattggc tatggcattgatctggcggaagatgatccgtggctggaagcgacccatctggcgaacgaa ggcctggcgattgcgagcgtgccgggcaaattttgggtggatagctttccgagcctgaaa tatctgccggcgtggtttccgggcgcggtgtttaaacgcaaagcgaaagtgtggcgcgaa gcggcggatcatatggtggatatgccgtatgaaaccatgcgcaaactggcgccgcagggc ctgacccgcccgagctatgcgagcgcgcgcctgcaggcgatggatctgaacggcgatctg gaacatcaggaacatgtgattaaaaacaccgcggcggaagtgaacgtgggcggcggcgat accaccgtgagcgcgatgagcgcgtttattctggcgatggtgaaatatccggaagtgcag cgcaaagtgcaggcggaactggatgcgctgaccaacaacggccagattccggattatgat gaagaagatgatagcctgccgtatctgaccgcgtgcattaaagaactgtttcgctggaac cagattgcgccgctggcgattccgcataaactgatgaaagatgatgtgtatcgcggctat ctgattccgaaaaacaccctggtgtttgcgaacacctgggcggtgctgaacgatccggaa gtgtatccggatccgagcgtgtttcgcccggaacgctatctgggcccggatggcaaaccg gataacaccgtgcgcgatccgcgcaaagcggcgtttggctatggccgccgcaactgcccg ggcattcatctggcgcagagcaccgtgtggattgcgggcgcgaccctgctgagcgcgttt aacattgaacgcccggtggatcagaacggcaaaccgattgatattccggcggattttacc accggcttttttcgccatccggtgccgtttcagtgccgctttgtgccgcgcaccgaacag gtgagccagagcgtgagcggcccg

Source

3.3 Codon optimisation

In your own words, describe why you need to optimise codon usage. Which organism have you chosen to optimise the codon sequence for and why?

Although different codons can code for the same amino acid, each species/organism has a bias for its codon preferences. This is done by changing/optimising the DNA codon sequence (not the amino-acid sequence) of the protein in order to match the codon preferences of the host organism (Cheema et al., 2022). If I were to take a human gene and insert it into a bacterium, it might use certain codons that the bacterium wouldn’t/would rarely use. This, in turn, makes the translation process slower or incomplete, resulting in a low protein yield (Creative BioLabs,2025). Therefore, by optimising the sequence with a specific host, I can make the translation process faster and more reliable.

For this specific exercise, I chose to optimise the psiH protein for E. Coli. I made this choice because E. coli is one of the most commonly used hosts for genetic engineering due to its rapid culture rate, simple nutritional needs and well-understood genetics (Adamczyk and Reed, 2017). Additionaly it is relatively cheap to culture (Francis and Page,2010). In relation to Homework 1, during the time of this course, E. coli is an appropriate host for prototyping the psilocybin pathway to conceptually extend toward microbiome-targeted therapies.

sources

What technologies could be used to produce this protein from your DNA? Describe in your own words how the DNA sequence can be transcribed and translated into a protein.

A variety of technologies could be used to produce the psiH protein from its DNA. One of the most suitable that we have discussed in lectures would be Gibson Assembly. It would allow us to stitch together multiple DNA fragments (promoter, Ribosome binding site, my optimised DNA sequence of psiH, terminator…) inside the plasmid of the E. Coli without the use of restriction enzymes. You may also use external platforms like Twist Bioscience to order your full plasmid by giving them the appropriate optimised sequence.

The DNA sequence may be transcribed and translated into a protein with the following steps:

Sources:

Part 4: Prepare a Twist DNA Synthesis Order

Building my DNA Insert Sequence

I began by optimising my psiH translated protein DNA sequence in Benchling with a linear topology and optimising it for E. coli. I then added the reading direction (forward), and the given DNA sequences highlighted in the homework (Promoter, RBS, Coding Sequence, 7x His Tag, Stop Codon, Terminator).

Linear Map of the entire sequence:

Final Sequence Benchling link!

Building my Full Plasmid Sequence

After downloading my insert sequence (expression cassette) as a FASTA file and uploading it into my Twist account, selecting an appropriate vector (pTwist Amp High Copy), I was able to download the full plasmid sequence (GenBank). I then imported the GenBank file of my plasmid back into Benchling.

Part 5: DNA Read/Write/Edit

5.1 DNA Read

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

I want to sequence the 3955 bp E. coli plasmid containing the codon-optimised PsiH gene (tryptamine 4-monooxygenase from Psilocybe cubensis) that I developed with Twist Bioscience technology for exercise 4. This would allow for the verification of the construct (Gibson Assembly) of the plasmid, to verify that there are no mutations in the critical heme-binding site essential for the following steps of psilocibin synthesis. Ultimately, the goal would be to create a baseline sequence for future IBD therapy-scale engineering (Adams et al., 2019).

Sources

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

I initially considered Sanger sequencing due to its high accuracy (~99.9%) and effectiveness for targeted validation of small DNA regions. However, my E. coli psiH plasmid has 3955 bp, which exceeds Sanger’s read length of ~800 bp per reaction. Full coverage would require multiple reads; consequently, needing the development of multiple primers, which creates a time-consuming and costly process (AAT Bioquest, 2024).

I therefore continued my research and thought Oxford Nanopore Technologies' MinION device was a more appropriate fit (Oxford Nanopore Technologies, 2024). This technology generates long reads (>20 kb) capable of sequencing my entire 3955 bp plasmid in 1-2 reads with >99% accuracy (Brown et al., 2023). This approach would allow for the verification of the complete PsiH integration in the plasmid, promoter/RBS/terminator junctions and detect assembly errors.

Sources

1) Is your method first-, second-, or third-generation or other? How so?

Oxford Nanopore Technologies' MinION is a 3rd generaation sequencer. This means that it can read much longer sequences than any 1st or 2nd generation sequencing technologies (Hilt and Ferrieri, 2022). Additionally, it is one of the rare sequencing technologies that allows for real-time analysis. (Oxford Nanopore Technologies,2021)

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2) What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.

Input: Purified plasmid DNA from my engineered E. coli (3955 bp PsiH construct)

Preparation

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3) What are the essential steps of your chosen sequencing technology? How does it decode the bases of your DNA sample (base calling)?

Image: Instructions from Monarch Spin Plasmid Miniprep Kit, 2026.

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4) What is the output of your chosen sequencing technology?

Output: A FASTQ file containing the complete 3955 bp sequence of my optimised E.coli PsiH plasmid.

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5.2 DNA Write

I will synthesise the recombinant DNA of the engineered E. Coli as it contains the introduced plasmid from the psilocybin producing fungi.

5.2 DNA Write

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

I would like to synthesise the codon-optimised PsiH gene. This is an essential step for the development of this project, as fungal codons usually don’t express well in bacteria (Naqvi et al., 2016). Synthesising it will allow for a higher level of psiH with the ultimate goal of psilocin production.

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

I would use the services of Twist Bioscience to ensure a correct synthesis with no PCR errors, and be ready for the Gibson Assembly of my plasmid.

1) What are the essential steps of your sequencing methods?

Twist uses a Phosphoramidite synthesis method. These are the four essential steps:

Image: Twist Bioscience Website, 2026.

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2) What are the limitations of your sequencing method (if any) in terms of speed, accuracy, and scalability?

Phosphoramidite synthesis makes short DNA pieces (200-1500 bp) (Hughes and Ellington,2017), but Twist Bioscience assembles them into my full 2155 bp PsiH gene with error correction anyway, so it wouldn’t affect the actual synthesis process of my DNA. However, another issue I would like to raise is the use of hazardous reagents in reactions, washing and purification processes of this type of synthesis, which raises significant safety and environmental issues which shouldn’t be undermined (Gao et al., 2025).

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5.3 DNA Edit

What DNA would you want to edit and why?

I’d like to edit the PsiH gene via mutagenesis. This would allow for the optimisation of tryptamine binding, which in turn will enhance enzyme activity in E. coli without needing host genome changes (Huang et al., 2025).

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

I would use the site-directed mutagenesis method to examine the relationship between function and structure of my selected protein (Zhang et al., 2009). To do so, I would amplify my Twist PsiH plasmid with mutant primers in order to enhance the tryptamine binding in E.coli host.

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1) How does your technology of choice edit DNA? What are the essential steps?

PCR site-directed mutagenesis edits PsiH using custom primers carrying my mutation (one base change to improve tryptamine binding) to amplify the full Twist plasmid during PCR, producing nicked circular copies with the incorporated alteration.

2) What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?

image: From Addgene.org

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3) What are the limitations of your editing methods (if any) in terms of efficiency or precision?

Considering that I am only editing a small part of my DNA, using this method has a relatively high precision rate for a single alteration. Prior steps, though, like a mistake in the primer preparation, which will cause wrong mutations (Alvarez, 2024), as well as a very large plasmid, will cause a drop in accuracy (Jacobs et al., 2011).

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Week 3 HW: Lab Automation

Assignment: Python Script for Opentrons Artwork

My OpenTron design is inspired by the nudibranch (sea slug) from the Mollusca phylum. Image source: Siewert, I. (2014). Nudibranch - Marine Life in Thailand. [online] Diving in Phuket Thailand. Available at: https://www.diving-thailand-phuket.com/nudibranch-marine-life-thailand/.

Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons.

After following the instructions, I downloaded the Python script from the GUI website to insert it into my Google Drive code. The code seemed to be all correct, but I encountered an error when it came to the proteins available on the GUI website and the actual colours in the code. I therefore used the Gemini function available in Google Colab to debug my code when it couldn’t recognise some of the colours. Gemini helped identify missing arguments in the load_labware function and suggested remapping custom protein names to standard colours for visualisation. This is why my final image colours don't match those on the OpenTrons art website.

Final image:

Post Lab-Questions

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

I found a 2025 paper named 'Real‑time AI‑driven quality control for laboratory automation: a novel computer vision solution for the opentrons OT‑2 liquid handling robot'that was

I found a 2025 paper named: Real‑time AI‑driven quality control for laboratory automation: a novel computer vision solution for the Opentrons OT‑2 liquid handling robot (Khan et al., 2025), which I found particularly interesting.

This paper explores a novel AI-driven computer vision model called YOLOv8 (object detection model), created for the enhancement of quality that limits the accuracy and reliability of liquid handling robots like Opentrons OT-2. Combining these two systems would allow for the precise detection of pippette tips as well as liquid volumes, consequently providing real-time feedback on potential errors like incorrect placement or the absence of pipette tips as well as liquid levels. The paper highlights that the results obtained with their model present an effective, accessible and affordable solution for the improvement of laboratory automation for both academic and research laboratories.

In the introduction, the authors discuss the advantages ( enhancing reproducibility, efficiency and safety) but also disadvantages (high costs, protocol variability and limited expertise) of automation as well as how using it in life sciences remains limited compared to its high success rates in other industries like manufacturing and food production. The paper, therefore, addresses this gap through their YOLOv8 model in combination with the Opentrons OT-2 liquid handling robot.

The paper continues into the Methodology section, which was divided into 4 sections:

The following are the experimental results and analysis, where they began by evaluating the YOLOv8 performance and concluding that the trend indicates the model had progressively enhanced its ability to recognise and, in turn, categorise objects more accurately throughout the training. They continued by looking at the readings from the detected tips and liquids by testing the YOLOv8 system through the conduction of 50 experiments. The experiments consisted of running the OT-2 robot in real time, and intentionall exclude certain pipette tips. The tests resulted in a 98% accuracy of identifying missing tips and their exact location. In addition, the authors tested the measurement of liquids within the pipette tips by conducting 100 experiments using a variety of liquid quantities. Their results showed a 95% accuracy in the assessment of liquid within the tips, which suggested that it was an effective approach but that there is still room for improvement. The authors suggest the implementation of liquid segmentation techniques to do so.

In the discussion section, the authors mainly highlight the potential of their technology as an affordable alternative to costly proprietary solutions and how it aids in the improvement of the quality of produced results, which consequently increases the throughput of the laboratory.

Lastly, in the conclusion and future direction section, the paper describes the potential of the YOLOv8 coupled with OT-2 in the advamncement in life science laboratory automation. The authors also state the limitations of their study, such as their experiment focusing on specific pipette types and liquid volumes. They suggest that future works should address these by expanding the dataset to include a wider range of experimental conditions as well as additional liquid handling tasks. Furthermore, they aim to further develop the YOLOv8 AI computer to detect challenges such as air bubbles, which also affect liquid handling accuracy.

Paper Reference:

Khan, S.U., Møller, V.K., Frandsen, R.J.N. and Mansourvar, M. (2025). Real-time AI-driven quality control for laboratory automation: a novel computer vision solution for the Opentrons OT-2 liquid handling robot. Applied Intelligence, 55(6). doi:https://doi.org/10.1007/s10489-025-06334-3.

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

For my final project (if I continue the direction of my initial project proposition), I would use a Python script to automate E. coli transformation for screening Psilocybe-derived psiH pathway variants.(Bryant, 2022)

The automation tools I would need/ want to use apart from Python:

As a biodesign student, I want to combine industrial automation (OT-2) with hands-on fabrication challenges. The 3D modelling is a new territory for me, but pushing these design boundaries alongside molecular automation is core to biodesign practice. Additionally, since lab access through my node is uncertain and equipment availability unknown, I'll prioritise developing the Python script and 3D printed components, as these I can prototype independently while planning for potential laboratory access.

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