Week 1 HW: Principles and Practices

–

1. Biological Engineering Application

Proposed application: Engineered microbial biofactories for small-molecule drug production.

Having a background in Biomedical Science and current training in translational physiology and pharmacology, I am particularly interested in small-molecule drug development. This project proposes engineering commercially viable cells capable of producing small-molecule drugs that are difficult or costly to synthesize using traditional chemical methods.

Inspired by microbial production of compounds such as penicillin, this approach would use bacterial or alternative host cells to generate either full drug molecules or high-value intermediates, depending on chemical feasibility. Using CRISPR or prime editing, metabolic pathways would be modified to enhance yield and specificity, similar in concept to the work by Paddon et al. (Nature, 2013).

As a proof of concept, the kappa opioid receptor (KOR) is selected as the biological target, with Salvinorin A as the compound of interest. Chemical synthesis of Salvinorin A suffers from extremely low yields (~0.15–5%), making it expensive and impractical for large-scale research. Improving yield through microbial biosynthesis would reduce costs, accelerate KOR research, and support the development of novel analgesics.

2. Governance & Policy Goals

Goal 1: Safety

• 1a. Prevent misuse of engineered microbes to produce psychoactive or harmful substances
• 1b. Prevent harmful exposure to laboratory personnel

Goal 2: Equal Opportunity

• 2a. Maintain low production costs to ensure global accessibility
• 2b. Avoid monopolization of the technology and promote open access

Goal 3: Ethical Innovation

• 3a. Encourage transparent reporting of methods, yields, and failures
• 3b. Align research incentives with public health goals, particularly analgesic development

3. Governance Actions

Action 1: Biosafety Review (DURC)

• Purpose: Identify misuse and safety risks in engineered microbes producing bioactive compounds
• Design: Mandatory dual-use and toxicity assessments by IBCs; compliance tied to funding and regulatory approval
• Assumptions: Honest reporting; predictable risks
• Risks & Success:
  • Failure: Bureaucratic burden slows research
  • Success: Improved biosecurity and public trust

Action 2: Genetic Kill Switches

• Purpose: Prevent environmental escape or uncontrolled proliferation
• Design: Engineered auxotrophy and kill-switch mechanisms; incentives via funding and approvals
• Assumptions: Stability and affordability of safeguards
• Risks & Success:
  • Failure: Mutation or safeguard failure
  • Success: Reduced environmental risk

Action 3: Pharmacovigilance

• Purpose: Monitor production and use of KOR-targeted molecules
• Design: Controlled distribution; adverse-event reporting by clinicians
• Assumptions: Reliable detection and reporting
• Risks & Success:
  • Failure: Under-reporting or diversion
  • Success: Safe translation without blocking research

4. Governance Scoring Matrix

5. Prioritization & Ethical Reflection

Based on the scoring, Options 1 (DURC biosafety review) and 2 (genetic kill switches) are the highest priorities. These address the most immediate ethical risks associated with misuse, environmental contamination, and accidental exposure.

While pharmacovigilance is important, it becomes more relevant at later translational stages. Trade-offs include increased upfront costs and longer development timelines; however, these are justified by improved safety, transparency, and public trust.

The primary ethical concern identified during this week’s coursework is dual-use misuse of engineered microbes, including unauthorized production or environmental release. Strong oversight, transparency, and adherence to biosafety protocols should sufficiently mitigate these risks.

Homework – Lecture 2 Questions

George Church Question

Question: What are the 10 essential amino acids in all animals, and how does this affect the “Lysine Contingency”?

Answer:
The 10 essential amino acids in animals are:

• Histidine
• Isoleucine
• Leucine
• Lysine
• Methionine
• Phenylalanine
• Threonine
• Tryptophan
• Valine
• Arginine

Animals cannot synthesize lysine endogenously and must obtain it from their environment. This weakens the concept of the lysine contingency as a biosafety mechanism for engineered organisms. Many natural environments already contain lysine, meaning deprivation is unreliable. Additionally, organisms can evolve around this dependency, making lysine-based containment a fragile and insufficient safety strategy on its own.

Week 2 HW: DNA Read, Write, & Edit

Part 1: Benchling & In-silico Gel Art

This is the Lambda Sequence

This is the Lambda sequence with the cuts

First, I tried to use Ronan’s website to get a template, and then I made it in Benchling

Template:

Benchling:

3.1. Choose your protein.

I decided to use the Kappa Opioid GPCR, as this is the target for my biofactory, which is for my final project. It comes from the OPKR1 gene, and UniProt entry P41145 (OPRK_HUMAN) lists the canonical human kappa opioid receptor as 380 amino acids with this sequence:

MDSPIQIFRGEPGPTCAPSACLPPNSSAWFPGWAEPDSNGSAGSEDAQLEPAHISPAIPVIITAVYSVVFVVGLVGNSLVMFVIIRYTKMKTATNIYIFNLALADALVTTTMPFQSTVYLMNSWPFGDVLCKIVISIDYYNMFTSIFTLTMMSVDRYIAVCHPVKALDFRTPLKAKIINICIWLLSSSVGISAIVLGGTKVREDVDVIECSLQFPDDDYSWWDLFMKICVFIFAFVIPVLIIIVCYTLMILRLKSVRLLSGSREKDRNLRRITRLVLVVVAVFVVCWTPIHIFILVEALGSTSHSTAALSSYYFCIALGYTNSSLNPILYAFLDENFKRCFRDFCFPLKMRMERQSTSRVRNTVQDPAYLRDIDGMNKPV

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

A convenient “answer key” for the corresponding human CDS is provided in KEGG for hsa:4986 (OPRK1), showing a 1143 nt coding region (380 aa + stop) https://www.genome.jp/dbget-bin/www_bget?hsa:4986

3.3. Codon optimisation.

Codon optimisation is done to match the host codon usage to improve the translation efficiency and protein yield. For the sake of this protein, I chose E. coli for simplicity so I can practice this according to the homework

https://en.vectorbuilder.com/tool/codon-optimization/c1dd076b-99f7-469f-84a3-c205c444346c.html

ATGGATAGCCCGATTCAGATTTTTCGCGGCGAACCGGGCCCGACGTGCGCCCCGAGCGCGTGTCTGCCGCCGAACAGCAGCGCGTGGTTTCCGGGCTGGGCGGAACCGGATAGCAATGGCAGCGCGGGTAGCGAAGATGCGCAGCTGGAACCGGCGCATATTAGTCCGGCGATCCCGGTGATTATTACCGCCGTGTATAGCGTGGTCTTTGTGGTGGGTCTGGTGGGCAACAGCCTGGTGATGTTTGTTATTATTCGCTATACCAAAATGAAAACCGCAACCAACATCTACATCTTCAACCTGGCACTGGCGGATGCGCTGGTGACCACCACCATGCCGTTTCAGAGCACCGTGTATCTGATGAATAGCTGGCCGTTCGGCGACGTGCTGTGTAAAATTGTGATTAGCATCGATTACTATAATATGTTTACCAGCATTTTTACCCTCACCATGATGAGCGTGGATCGTTACATTGCCGTGTGCCATCCGGTGAAAGCGCTGGATTTTCGTACGCCGCTGAAAGCGAAAATTATTAATATTTGCATTTGGCTGCTGAGCAGCAGCGTGGGCATTAGCGCGATTGTGCTGGGCGGCACCAAAGTGCGTGAAGATGTGGATGTGATCGAATGCAGCCTGCAGTTTCCGGATGACGATTATTCATGGTGGGATCTGTTTATGAAAATCTGCGTATTTATTTTTGCCTTTGTGATCCCTGTGCTGATTATTATTGTGTGCTACACCCTGATGATTCTGCGTCTGAAATCTGTGCGCCTGCTGAGCGGCAGCCGCGAAAAAGATCGTAATCTGCGCCGCATTACCCGCCTGGTGCTGGTGGTGGTGGCCGTGTTTGTGGTGTGCTGGACCCCGATCCACATTTTTATCCTGGTGGAAGCGCTGGGCTCGACGTCACATAGCACCGCGGCGCTGAGCAGCTATTACTTTTGCATTGCCCTGGGCTATACCAACAGCAGCCTGAATCCGATTCTGTATGCCTTTCTGGACGAAAATTTTAAACGCTGCTTTCGCGATTTTTGTTTTCCGCTGAAAATGCGCATGGAACGCCAGAGTACCAGCCGCGTGCGCAACACCGTGCAGGATCCGGCGTACCTGCGCGACATTGATGGTATGAACAAACCGGTGTAA

3.4. You have a sequence! Now what?

Technology 1: Clone codon-optimized CDS into a bacterial plasmid (T7/lac promoter), transform into E. coli. Codon optimization is used to match host codon bias to improve expression.

Technology 2: Induce expression; proteinproduction follows the same central dogma (DNA to RNA to protein), but membrane insertion/folding for 7TM proteins is a key challenge in bacteria.

Part 4: Prepare a Twist DNA Synthesis Order

This is my Benchling sequence link https://benchling.com/s/seq-pBSv19pNJ2bIdI5DcYzM?m=slm-17Elwj2xG3ryxk0DBJrw

This is the full OPRK1 Sequence:

Finally, this is my proposed vector:

5.1 DNA Read (i) What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank). I think it could be interesting to sequence small genetic regions related to caffeine metabolism and sensitivity; CYP1A2, AHR, ADORA2A. This is relevant as many individuals experience coffee and caffene differently and do not enjoy it as much as others, or experience more abhorrent side effects than others. By analysing the metabolism of caffeine from CYP1A2 and investigating the adenosine receptors, a specialised suggestion of caffeine intake, bean type, and coffee type can be permutated to give people the best experience with minimal side effects.

(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? Also answer the following questions:

1. Is your method first-, second- or third-generation or other? How so? Sanger sequencing is 1st-generation sequencing. It reads DNA by creating DNA fragments terminated by special nucleotides (ddNTPs) and separating them by capillary electrophoresis to infer the base order
2. What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps. The input would be genomic DNA from a cheek swab or saliva sample. The steps to prepare them would be to Extract DNA from the sample, PCR amplify the short region(s) containing the SNP(s), to purify the PCR product and then set up Sanger sequencing reaction
3. What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)? In Sanger sequencing, you first make many DNA copies, but the copying sometimes stops when a special “terminator” base (a fluorescent ddNTP) is added. This creates lots of DNA fragments of different lengths, each ending in a colored base. The fragments are then separated by capillary electrophoresis, and a detector reads the color signal as fragments pass by. The sequencing software converts the color peaks into A, C, G, T letters
4. What is the output of your chosen sequencing technology? The output of Sanger sequencing is usually a chromatogram/trace file (often .ab1) showing colored peaks, plus a text DNA sequence that the software called from those peaks

5.2 DNA Write (i) What DNA would you want to synthesize (e.g., write) and why? These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :) I want to synthesize plasmid DNA vectors that turn bacteria into biofactories for making human-useful therapeutics, specifically: a therapeutic hormone for metabolic disease (example: human insulin) a small-molecule product relevant to cardiovascular/metabolic health (example: Coenzyme Q10 (CoQ10) as a medically used antioxidant supplement with cardiovascular relevance)

(ii) What technology or technologies would you use to perform this DNA synthesis and why? Also answer the following questions:

1. What are the essential steps of your chosen sequencing methods? The essential steps are: (1) computational design of the DNA construct (promoters/RBS/genes/terminators plus plasmid features), (2) chemical DNA writing using cyclic solid-phase phosphoramidite synthesis to generate oligos, (3) assembly of oligos into longer gene-length fragments when needed, (4) cloning/packaging into a plasmid backbone for bacterial expression, and (5) sequence verification/quality control so the final plasmid matches the intended design. These steps reflect the standard phosphoramidite cycle used for oligo construction and the gene/plasmid workflow offered by commercial synthesis providers
2. What are the limitations of your writing method (if any) in terms of speed, accuracy, scalability? A key limitation is that errors accumulate as DNA length increases, because each chemical base-addition step is not perfectly efficient; this means long constructs are more likely to contain substitutions or deletions and often require assembly from shorter pieces plus verification. In addition, although high-throughput synthesis platforms scale very well for many sequences in parallel, overall cost and turnaround time can still be bottlenecks for very large libraries or long, complex constructs, and certain sequence patterns (like repeats or extreme GC content) can reduce synthesis success and increase the need for troubleshooting or redesign

5.3 DNA Edit (i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why? I would want to edit human DNA SNPs that cause metabolic/cardiovascular disease, especially those that lead to very high LDL cholesterol and early heart disease risk, such as variants involved in familial hypercholesterolemia (FH). FH is commonly linked to harmful variants in LDLR (and sometimes related genes like APOB or PCSK9), and editing these could lower lifelong LDL exposure and reduce cardiovascular

(ii) What technology or technologies would you use to perform these DNA edits and why? Also answer the following questions:

1. How does your technology of choice edit DNA? What are the essential steps? In prime editing, a Cas9 nickase fused to a reverse transcriptase is guided to a specific DNA site by a pegRNA that also contains the template for the desired change; the system nicks DNA and then “writes” the corrected sequence, which cellular repair processes finalize into a stable edit. This avoids relying on the same double-strand-break repair competition that often makes precise HDR edits difficult
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? The main preparation is selecting the exact SNP to fix (e.g., an LDLR pathogenic variant) and designing the appropriate guide/pegRNA to target it. The key inputs are the editor components (prime editor or base editor), the guide RNA(s), and the target human cells/tissue context (for cholesterol disorders this is often discussed in relation to the liver because it controls LDL metabolism).
3. What are the limitations of your editing methods (if any) in terms of efficiency or precision? Major limitations include variable editing efficiency, possible off-target changes or unintended byproducts, and the practical challenge of safe, effective delivery of the editing system to the correct tissue in humans.

Week 3 HW: Lab Automation

Homework Submission

Design Explanation

For the art, I first sketched ideas on my tablet and then tried to upload them as an image on OpenTrons. The image with birds flying through the sky was pleasing to me aesthetically, but there were limited colours available to us. I chose the following palm tree and adapted some of the colours.

Thus, I settled on this design: https://opentrons-art.rcdonovan.com/?id=4p046971r45mo4c

After using Microsoft Copilot to help me code in OpenColab, I settled on the following code and image: https://colab.research.google.com/drive/1K-qcZjtwZKHQ8mIlfLnY42qBpRsIZFts#scrollTo=pczDLwsq64mk&uniqifier=1

–

Homework Questions

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

Norton-Baker B, Denton MCR, Murphy NP, Fram B, Lim S, Erickson E, et al. Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline. Sci Rep. 2024;14(1):14449. http://dx.doi.org/10.1038/s41598-024-64938-0

This article by Baker et al. describes a robot-assisted pipeline for enzyme engineering. The article explains how, with the advances of AI and genome data, it is extremely taxing to manually test every permutation. There is simply too much to test using the standard laboratory methods. The main bottleneck they have found is the production, purification and characterisation of the proteins. The author’s attempt to make a generalisable protocol that is cost-effective and high-throughput to streamline enzyme discovery. Their idea is to use robot-assisted pipelines using the opentrons OT-2 liquid handling robot on a 96-well plate to scale to hundreds of proteins per week.

The workflow specifically uses the OT-2 to automate time-consuming steps like transforming E. coli with plasmids, inoculation, lysing, and protein purification using Ni-charged magnetic beads. The workflow also cleaves proteins with a protease instead of eluting to make it easier to use in downstream assays. The authors did this for PET-degrading proteins to screen for enzymes to degrade plastics. They did this using 23 published hydrolases, which were expressed and purified and then measured their thermostability and activity.

Using this pipeline, they were able to have repeatable results after doing 3 separate trials. Across replicate wells and runs, they obtained reproducible enzyme yields reaching up to 400 μg for some proteins, and verified that the samples were sufficiently pure and correctly sized using SDS-PAGE. Finally, the authors use the purified enzymes to generate a benchmark dataset by testing stability (via DSF melting temperatures) and activity across a large matrix of conditions (including different pH values, temperatures, substrates, and timepoints), which lets them rank enzyme performance in a standardised way. In their side-by-side benchmark, LCC-A2 consistently generated the largest amounts of PET breakdown products confirmed by UV-Vis and HPLC ratios, making it the strongest overall performer under their assay setup.

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

For my final project involving decaffeination using synthetic bacteria, automation could help in testing many enzymes and/or bacterial strains simultaneously. Similar to the article I described in the previous question, it can be helpful to use automation, such as OpenTrons, to identify the best enzyme rather than manually testing each prospective enzyme. Similar to the article by Baker et al., a 96-well screen could be done to test a different enzyme/strain condition (dose, pH, temperature, time). The OT-2 would automate all pipetting by adding tea/coffee, buffers/cofactors, enzyme/strain inputs, and pulling timed samples into a quench plate making screening faster.To ensure this doesn’t create unwanted flavour-related byproducts, I’d measure not only caffeine reduction but also methylxanthine byproducts, which can vary depending on the enzyme/strain used.

Furthermore, as for my idea regarding the biological synthesis of Salvinorin A or Paclitaxel, a common class of molecules among them is terpenes. This is a type of molecule usually derived via plant extraction. Thus, this could be a good target to screen using automation. The principle is similar to decaffeination, where an array of enzymes and pathways could be tested at the same time with an OT-2 workflow with P450 enzymes, followed by analytical tools like HPLC readouts to examine successful synthesis.