Week 1 HW: Principles and Practices

Governance Assignment

Biological Engineering Application

Immunotherapies are a promising avenue in cancer treatment as they leverage the immune system’s innate ability to recognize and target non-self structures. However, traditional immunotherapies often result in on-target off-tumor effects, particularly in solid tumors. Synthetic biology has enabled new avenues of discovery to minimize this immunotherapy-related toxicity: engineering immune cells to target tumor-associated antigens (TAAs) or engineering genetic circuits to detect cancer disease signatures (Zhu et al., 2024). For example, modifying the traditional Chimeric Antigen Receptor (CAR) T-cell immunotherapy approach with a synthetic Notch (synNotch) receptor has demonstrated the ability to suppress off-target cytotoxicity related to organ rejection (Reddy et al., 2024) and selectively target cancerous cells in the central nervous system of mice rather than elsewhere in the body (Simic et al., 2024). Yet, while synNotch-modified CAR-T therapies show promise in their ability to reduce immunotherapy-related toxicity, additional research is needed to effectively administer these bioengineered cell systems in patients beyond pre-clinical experimentation.

Governance Goals

Goal 1: Perform rigorous pre-clinical testing to ensure new immunotherapies meet safety and efficacy standards before introducing into human patients.

Goal 2: Design ethical clinical trials with standardized eligibility, safety, and efficacy protocols with consideration for unique patterns of patient progression or response to treatment.

Goal 3: Create policies and organizations to promote equitable access to cancer prevention, screening, diagnosis, and immunotherapy resources to patients from diverse backgrounds and socioeconomic statuses across the globe.

Governance Actions

Option 1: Create an international organization to create global standards for immunotherapy clinical trial design and safety measures

1. Purpose: Immunotherapy safety regulations are regulated at the national level, so efforts to promote global administration of novel cancer immunotherapies may experience roadblocks if national standards do not align. Establishing international safety and efficacy standards for immmunotherapy clinical trials will promote efficient administration of immunotherapy treatments across global lines.
2. Design: An international healthcare organization, such as the WHO, must establish and agree upon an international standard for efficacy and safety in immunotherapy clinical trials. Then, all countries that participate in this organization must agree to the international standards to ensure ease of treatment deployment across global lines.
3. Assumptions: This option assumes that international clinical trial standards will supercede any national guidelines, and that countries will be willing to adopt the international standard and/or deploy their immunotherapies in other countries.
4. Risks of Failure and “Successes”: Establishing international immunotherapy clinical trial safety and efficacy guidelines may “fail” if the organization lacks the power to enforce the adoption of these guidelines across its participating countries. However, “success” of this option may delay the time it takes to put immunotherapies into clinical trials if international standards are incredibly restrictive and difficult to meet.

Option 2: Establish an international database to upload immunotherapy pre-clinical and clinical trial data

1. Purpose: To create a centralized, accurate database with the safety and efficacy data for immunotherapies in pre-clinical and clinical trials adminsitered to diverse patient populations. This database will ultimately promote safer and more effective administration of immunotherapies as a large dataset is available for review.
2. Design: Either an international healthcare organization (ex. WHO) or an independent organization would oversee the funding for the database and ensure the uploaded data is both reliable and accurate.
3. Assumptions: This option would assume that immunotherapy administration can be standardized across diverse healthcare settings, particularly on the global scale. Additionally, creating a comprehensive and accessible database assumes that uploaded data is not subject to reportability bias.
4. Risks of Failure and “Successes”: This measure would fail if the uploaded data is skewed toward well-funded research programs and not local healthcare systems with patients who experience barriers to immunotherapy access because this would not be a comprehensive dataset. However, if this measure is “successful”, immunotherapies with lower overall efficacy according to database metrics but high efficacy in a small patient population may be deprioritized or defunded.

Option 3: Establish an independent organization to increase access to preventative cancer screenings, diagnostic tools, and long-term care

1. Purpose: Inadequate access to preventative cancer screenings (i.e. mammograms, pap smears, colonoscopies, etc.) as a result of financial, geographic, or other barriers leads to later diagnosis and poorer progonosis. As immunotherapies are most effective when treatment is begun at earlier stages of cancer progression, inadequate preventative measures undermine the innovative bioengineering design of novel immunotherapies. Establishing an organization to ensure equitable access to cancer screenings and diagnostic tools without financial barriers, both within the United States and abroad, will allow the advances of innovative immunotherapies to benefit more patients than just those with easy access to preventative measures.
2. Design: As this organization would be independent of government funding, it would require public or philanthropic funding to decrease the cost barriers to preventative cancer screenings for patients with financial concerns. Additionally, the efforts of this organization would need to be integrated with healthcare systems on both the local and international scales to establish sites to receive preventative screenings and the capability to receive long-term follow-up care.
3. Assumptions: This option relies on the assumption that patients whose cancer is detected by increased preventative measures will then be able to access the immunotherapy treatments that target their cancer.
4. Risks of Failure and “Successes”: Without sufficient funding, this independent organization could ultimately shut down and fail in its mission to increase equiable access to preventative cancer screenings. Additionally, if the organization does not have reliable connections to local healthcare systems, its efforts to reduce cost barriers for patients seeking cancer screenings will not be realized. If this organization were to be “successful”, the influx of patients who are identified by screening measures may cause strain on the healthcare system.

Scoring Governance Actions

Prioritization

I believe Option 3 should be prioritized. While innovative bioengineering applications to cancer immunotherapies are a promising avenue for decreasing cancer mortality both nationally and internationally, these efforts are in vain if they cannot be administered effectively in a large patient population. Increasing early detection of cancer by promoting equitable access to cancer screenings and diagnostic testing will ultimately increase the use of bioengineered immunotherapies as cancers detected at earlier stages are better candidates to be treated by immunotherapy approaches.

Week 2 Preparation

Professor Jacobson

1. The error rate for a polymerase is 1 in 10⁶ (1,000,000) bases. The human genome is 3x10⁹ (3,000,000,000) base pairs in length, leading to an estimated 3,000 errors per replication of a human cell. Biology has processes in place, such as mismatch repair, capable of recognizing and excising these errors, then synthesizing the correct nucleotide.
2. The genetic code is comprised of 64 distinct codons that encode 20 naturally-occuring amino acids. However, not all codons are used equally because each tRNA with an anticodon specified for a particular amino acid are not equally expressed in every organism.

Dr. LeProust

1. Currently, the most commonly used method for oligo synthesis is phosphoramidite chemistry.
2. Oligos longer than 200 bp have high error rates and high rates of decay.
3. As a result of the high error rate and rates of decay, a 2000 bp gene synthesized via direct oligo synthesis would no longer resemble the desired sequence and would not encode the desired protein.

George Church

1. The 10 essential amino acides are arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. These amino acids are not naturally synthesized in the human body and therefore must be consumed through the diet. The “Lysine Contingency” from Jurassic Park describes a genetic mutation introduced into the dinosaur enzyme for lysine synthesis as a means of protection from dinosaurs getting off the island (if the dinosaurs cannot synthesize an essential amino acid like lysine, they will not survive long). However, considering lysine is one of the amino acids that is solely ingested through the diet, the “Lysine Contingency” would not have any affect on dinosaurs because they should not have an enzyme responsible for lysine synthesis.

Website Preparation

I completed the setup of my personal HTGAA website, including adding my biography, contact information, and cover photo to my homepage.

Week 2 HW: DNA Read, Write, and Edit

Part 1: Benchling and In-silico Gel Art

Virtual restriction enzyme digest designed with DNA from the bacteriophage Kampy (isolated at W&M!) and the restriction enzymes BstXI, KpnI, and SfiI to resemble two bacteriophages. The chosen restriction enzymes were selected because they were in stock at William & Mary, had multiple cut sites in the Kampy DNA, and could be combined to make a design resembling a bacteriophage.

Part 2: Restriction Digests and Gel Electrophoresis

The imaged gel resembles the band lengths predicted by the virtual digest! While interpreting the gel image as two bacteriophages does require a bit of imagination, the digest was successful in that the true gel resembled the in silico prediction.

Part 3: DNA Design Challenge

3.1. Choose Your Protein

I chose the protein TTYH3 (tweety homolog 3) because it is the subject of my honors thesis research. ttyh3 encodes a calcium-dependent chloride channel and is a member of the tweety gene family- consisting of members ttyh1, ttyh2, and ttyh3- that is highly conserved across eukaryotes. ttyh3 is the least-characterized member of the tweety gene family, making it an intriguing subject of research. During neural development, the gene ttyh3 is primarily expressed in post-mitotic neurons. However, its precise function and role in neural development remain unknown. In my research, I aim to provide greater insight into the role of TTYH3 in neural development by overexpressing and knocking-out the ttyh3 gene in X. laevis and observing changes in expression of the downstream neural marker genes Sox2 and tubb2b.

Sequence from UniProt: MAGVSYAAPWWVSLLHRLPHFDLSWEATSSQFRPEDTDYQQALLLLGAAALACLALDLLFLLFYSFWLCCRRRKSEEHLDADCCCTAWCVIIATLVCSAGIAVGFYGNGETSDGIHRATYSLRHANRTVAGVQDRVWDTAVGLNHTAEPSLQTLERQLAGRPEPLRAVQRLQGLLETLLGYTAAIPFWRNTAVSLEVLAEQVDLYDWYRWLGYLGLLLLDVIICLLVLVGLIRSSKGILVGVCLLGVLALVISWGALGLELAVSVGSSDFCVDPDAYVTKMVEEYSVLSGDILQYYLACSPRAANPFQQKLSGSHKALVEMQDVVAELLRTVPWEQPATKDPLLRVQEVLNGTEVNLQHLTALVDCRSLHLDYVQALTGFCYDGVEGLIYLALFSFVTALMFSSIVCSVPHTWQQKRGPDEDGEEEAAPGPRQAHDSLYRVHMPSLYSCGSSYGSETSIPAAAHTVSNAPVTEYMSQNANFQNPRCENTPLIGRESPPPSYTSSMRAKYLATSQPRPDSSGSH

3.2. Reverse Translate: Protein Sequence to DNA Sequence

Using the “reverse translate” tool on bioinformatics.org, I generated the following nucleotide sequence from the TTYH3 amino acid sequence: atggcgggcgtgagctatgcggcgccgtggtgggtgagcctgctgcatcgcctgccgcattttgatctgagctgggaagcgaccagcagccagtttcgcccggaagataccgattatcagcaggcgctgctgctgctgggcgcggcggcgctggcgtgcctggcgctggatctgctgtttctgctgttttatagcttttggctgtgctgccgccgccgcaaaagcgaagaacatctggatgcggattgctgctgcaccgcgtggtgcgtgattattgcgaccctggtgtgcagcgcgggcattgcggtgggcttttatggcaacggcgaaaccagcgatggcattcatcgcgcgacctatagcctgcgccatgcgaaccgcaccgtggcgggcgtgcaggatcgcgtgtgggataccgcggtgggcctgaaccataccgcggaaccgagcctgcagaccctggaacgccagctggcgggccgcccggaaccgctgcgcgcggtgcagcgcctgcagggcctgctggaaaccctgctgggctataccgcggcgattccgttttggcgcaacaccgcggtgagcctggaagtgctggcggaacaggtggatctgtatgattggtatcgctggctgggctatctgggcctgctgctgctggatgtgattatttgcctgctggtgctggtgggcctgattcgcagcagcaaaggcattctggtgggcgtgtgcctgctgggcgtgctggcgctggtgattagctggggcgcgctgggcctggaactggcggtgagcgtgggcagcagcgatttttgcgtggatccggatgcgtatgtgaccaaaatggtggaagaatatagcgtgctgagcggcgatattctgcagtattatctggcgtgcagcccgcgcgcggcgaacccgtttcagcagaaactgagcggcagccataaagcgctggtggaaatgcaggatgtggtggcggaactgctgcgcaccgtgccgtgggaacagccggcgaccaaagatccgctgctgcgcgtgcaggaagtgctgaacggcaccgaagtgaacctgcagcatctgaccgcgctggtggattgccgcagcctgcatctggattatgtgcaggcgctgaccggcttttgctatgatggcgtggaaggcctgatttatctggcgctgtttagctttgtgaccgcgctgatgtttagcagcattgtgtgcagcgtgccgcatacctggcagcagaaacgcggcccggatgaagatggcgaagaagaagcggcgccgggcccgcgccaggcgcatgatagcctgtatcgcgtgcatatgccgagcctgtatagctgcggcagcagctatggcagcgaaaccagcattccggcggcggcgcataccgtgagcaacgcgccggtgaccgaatatatgagccagaacgcgaactttcagaacccgcgctgcgaaaacaccccgctgattggccgcgaaagcccgccgccgagctataccagcagcatgcgcgcgaaatatctggcgaccagccagccgcgcccggatagcagcggcagccat

3.3. Codon Optimization

Codon optimization is necessary because not all organisms use each codon with the same frequency due to differences in the abundance of various tRNAs, so optimizing the codons in the designed sequence will ideally increase the translational yield of my protein. I chose to optimize my sequence for X. laevis because that is the model organism in which I study the role of ttyh3 in neural development.

Using the Twist Bioscience Codon Optimization tool, I generated the following codon-optimized sequence: ATGGCTGGTGTGTCTTATGCTGCTCCTTGGTGGGTCTCTTTATTACATCGGTTGCCACACTTCGACCTCTCCTGGGAAGCCACATCTAGTCAATTCCGACCAGAGGACACAGACTACCAACAAGCACTATTATTGCTAGGGGCTGCCGCTTTAGCTTGTTTGGCTCTTGACCTTCTCTTCCTTTTGTTCTACTCTTTCTGGTTATGTTGTAGAAGAAGGAAGTCAGAGGAGCACCTCGACGCAGACTGTTGTTGTACTGCTTGGTGTGTCATAATCGCTACTCTTGTATGTTCAGCAGGTATAGCAGTAGGATTCTACGGGAATGGTGAGACATCCGACGGAATCCACCGGGCAACTTACTCCCTCAGACACGCTAATAGAACTGTTGCTGGTGTACAAGACCGGGTATGGGACACTGCAGTAGGGTTGAATCACACAGCAGAGCCTTCATTACAAACTTTAGAGAGACAACTTGCTGGAAGACCTGAGCCACTTAGAGCTGTTCAAAGATTACAAGGATTGTTAGAGACGCTCCTAGGGTACACTGCAGCCATCCCATTCTGGCGAAATACTGCCGTATCCTTAGAGGTACTCGCAGAGCAAGTTGACCTCTACGACTGGTACCGATGGCTTGGATACCTTGGGTTGTTGTTGTTGGACGTTATCATATGTTTACTCGTATTAGTTGGACTCATCAGGTCATCTAAGGGAATACTTGTTGGGGTTTGTTTACTTGGGGTTCTTGCTCTCGTCATCTCTTGGGGAGCATTGGGTCTTGAGCTTGCTGTTTCAGTAGGGTCAAGTGACTTCTGTGTAGACCCCGACGCCTACGTCACAAAGATGGTCGAGGAGTACTCAGTTCTTAGTGGAGACATCTTACAATACTACCTCGCTTGTTCACCAAGGGCAGCTAATCCCTTCCAACAAAAGCTTTCAGGTTCTCACAAGGCACTCGTAGAGATGCAAGACGTTGTCGCAGAGTTGCTTAGAACAGTTCCTTGGGAGCAACCAGCAACAAAGGACCCATTGCTCAGAGTCCAAGAGGTCCTTAATGGAACTGAGGTTAATCTCCAACACCTAACAGCCCTTGTAGACTGTCGATCACTCCACTTGGACTACGTCCAAGCTTTGACAGGTTTCTGTTACGACGGAGTTGAGGGTCTAATATACCTCGCCCTTTTCTCCTTCGTTACAGCTCTAATGTTCTCCAGTATCGTTTGTTCTGTTCCCCACACTTGGCAACAAAAGAGAGGACCCGACGAGGACGGAGAGGAAGAGGCAGCACCCGGTCCCAGACAAGCACACGACTCTTTGTACCGGGTCCACATGCCAAGTTTGTACTCATGTGGGTCTTCTTACGGTAGTGAGACAAGTATACCAGCCGCTGCCCACACTGTTTCTAATGCCCCCGTTACAGAGTACATGTCCCAAAATGCAAATTTCCAAAATCCCCGATGTGAGAATACGCCTTTGATAGGACGGGAGAGTCCCCCACCTTCATACACATCATCAATGAGGGCAAAGTACCTTGCAACATCACAACCCCGACCCGACTCCAGTGGATCACAC

3.4. You have a sequence! Now what?

To synthesize the ttyh3 mRNA and protein, I would use an in vitro transcription and translation kit (such as the ones produced by ThermoFischer) to synthesize ttyh3 mRNA and TTYH3 protein, respectively.

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

Single genes can produce multiple proteins at the transcriptional level through the process of alternative splicing. In eukaryotic cells, transcription and mRNA processing occurs in the nucleus whereas translation occurs in the cytoplasm. After mRNA is synthesized via transcription, mRNA processing (5’ cap addition, poly(A) tail addition, and intron splicing) occurs and exons (which make the coding region of the protein) are annealed together after introns are removed. However, the combinations of different exons (alternative splicing) can produce different proteins from the same primary (immature) mRNA transcript.

Part 4: Prepare a Twist DNA Synthesis Order

Part 5: DNA Read/Write/Edit

5.1. DNA Read

• What DNA would you want to sequence (e.g., read) and why? I would like to sequence the DNA of a tumor sample from a cancer in which ttyh3 is observed to be misregulated (i.e. HCC, genitourinary cancers, colorectal cancer, etc.). I would be interested to observe which mutations in ttyh3 gene lead to functional changes that promote tumorigenesis, tumor invasiveness, etc. and which changes have little to no impact.

• 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 whole-genome sequencing (WGS), a second-generation sequencing method, because it is a relatively quick method of obtaining a base-by-base view of any single nucleotide variants (SNVs) in the genome which is essential information for understanding how mutations in the ttyh3 gene increase tumor proliferation, invaseiness, or aggression. The input for this sequencing is gDNA extracted from a tumor biopsy and prepared using the Illumina DNA Library Prep Kit. Following DNA extraction, the sample is sequenced using the “sequencing by synthesis” (SBS) technology which utilizes dNTPs bound to fluorescently-labeled reversible terminators to decode each base. The output of Illumina sequencing is a FASTQ file with quality scores.

5.2. DNA Write

• What DNA would you want to synthesize (e.g., write) and why? Completely unrelated to my work with X. laevis, I think it would be fascinating to design a phage with a novel gene (or perhaps an entirely synthetic genome!). The protein structure could be predicted with a tool like AlphaFold, then the amino acid sequence could be reverse transcribed into cDNA for the gene of interest and integrated into a plasmid. As advancements in phage therapy show promise in combating antibiotic resistance, it would be fascinating to design a protein that would increase phage DNA replication, phage entry, etc. to increase the efficacy of the treatment.

• What technology or technologies would you use to perform this DNA synthesis and why? First, and I don’t know if this is possible, I would create the desired 3D protein structure with a tool like AlphaFold and obtain the amino acid sequence. Then, similarly to this homework assignment, I would generate cDNA by reverse transcribing the AA sequence and order a plasmid from Twist (or elsewhere) with the gene of interest.

5.3. DNA Edit

• What DNA would you want to edit and why? I would like to develop a technology to perform somatic editing of the DNA of individuals with genetic diseases that result from mutations in a single gene (i.e. Huntington’s, cystic fibrosis, etc.). While many similar technologies already exist and some clinical trials have shown promise (i.e. sickle cell anemia!), I would like to expand these technologies to other genetic diseases.

• What technology or technologies would you use to perform these DNA edits and why? I would use CRISPR/Cas9-mediated homlogy directed repair to excise the mutated gene and replace it with a nonmutated, functional gene. This would involve design of the nonmutated gene and the sgRNA to direct the Cas9 enzyme to the appropriate cut site. The limitations of CRISPR/Cas9 technology are possible off-target effects (low precision) or the construct not being effectively delivered to all cells (low efficiency).

Week 3 HW: Lab Automation

Python Script for Opentrons Artwork

My Opentrons design is meant to resemble a frog because I use Xenopus laevis as my model organism in my honors thesis research at William & Mary.

Post-Lab Questions

1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications. In Sanders et al., 2022, the researchers use an Opentron robot to optimize a bacterial whole-genome sequencing (WGS) protocol for gut microbiota samples. The Opentron was used for DNA extraction and library preparation steps, reducing the overall cost of WGS by ~$10 per genome and eliminating the need for 16S rRNA gene-based screening.

2. Write a description about what you intend to do with automation tools for your final project. My final project will likely involve the design of a genetic circuit whose expression is regulated by a synNotch system. As such, I could use the Opentron to automatize construction of my genetic circuit.

Final Project Ideas

For my final project, I am interested in engineering a synNotch-regulated circuit into Xenopus laevis, either to control cell-fate decisions or to control expression of a fluorescent protein to observe cell-cell communication during embryonic development. See my slide in the Committed Listener slide deck linked here.

Week 4 HW: Protein Design Part I

Part A: Conceptual Questions

Part B: Protein Analysis and Visualization

Part C: Using ML-Based Protein Design Tools

C1: Protein Language Modeling

C2: Protein Folding

C3: Protein Generation

Part D: Group Brainstorm on Bacteriophage Engineering

I collaborated with Heather Qian on this assignment!

Computational Goal: We will attempt to increase the titer of the L protein expressed by MS2 in the E. coli host.

Overall solution: create a new transcription factor that binds very tightly to the promoter, increasing expression of the L protein

Inverse protein folding using ProteinMPNN

• Use structure of a transcription factor (a PDB file) that binds with very high affinity to a promoter that is highly homologous to the L protein promoter
• Input the structure of the aforementioned transcription factor into ProteinMPNN along with the amino acid sequence of a native transcription factor that binds to the L protein promoter
• This will theoretically generate the sequence of a protein that is structurally similar to a transcription factor with high DNA-binding affinity but is specific to the L protein promoter

Confirm the binding affinity between our designed transcription factor and the MS2 L protein promoter with a ligand-binding AI model

Why will these tools accomplish our computational goal?

• Protein MPNN is an inverse-folding algorithm. The sequence of the L protein promoter and other transcription factors that bind to this promoter are known. However, to generate a transcription factor with higher affinity for this promoter sequence than the native L protein transcription factors, we will model our inverse-folding after an existing transcription factor that behaves in the manner we envision (binds with high affinity) for our engineered transcription factor. As discussed in this week’s lecture, existing AI algorithms are good at designing structures similar to previously-characterized structures but less good at designing different (novel) structures. As such, our inverse-folding approach hinges on the existence of another high affinity transcription factor for a homologous promoter
• Using a ligand-binding AI model will provide a computational indication of the success of our engineering without the need to use an in vitro or in vivo model

Possible pitfalls:

• We are unable to find a transcription factor that binds to a highly homologous promoter sequence with high affinity :(
• Inaccuracies in AI predictions :(

Schematic:

Week 5 HW: Protein Design Part II

Part A: SOD1 Binder Peptide Design

Part 1: Generate Binders with PepMLM

SOD1_A4V Sequence: MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Part 2: Evaluate Binders with AlphaFold3

Binder	ipTM Score	Binding Site	Evaluation
1	0.71		Binder 1 associates near the β barrel at the surface of the protein.
2	0.44		Like Binder 1, Binder 2 associates near the β barrel at the surface of the protein. However, this binder has a much lower ipTM score, meaning the confidence in this generated structure is much lower.
3	0.31		Binder 3 associates across the β barrel and disordered region at the surface of the SOD1 protein.
4	0.30		Binder 4 associates at the surface of the disordered region.
5	0.39		Binder 5 also associates at the surface of the disordered region.

Binders 1 and 2 have stronger ipTM values than Binder 5, the known binder provided for this exercise. All binders associate near the surface of the SOD1 protein, not integrating into the protein interior. The ipTM value for Binder 1 is relatively strong (0.71), meaning there is high confidence in that association between SOD1 and the binder.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

Binder 1, the binder with the highest ipTM value, also has the strongest binding affinity. None of the binders are predicted to be hemolytic. Unsurprisingly, Binder 1 appears to have the best balance of predicted binding and therapeutic properties. I will use Binder 1 to complete Part 4.

Part 4: Generate Optimized Peptides with moPPIt

Unfortunately, although I attempted to run motif 17-23 with multiple times and consulted ChatGPT for guidance, I was unable to make the moPPit program work without errors.

Part B: RD4 Drug Discovery Platform Tutorial

Will come back and do this later :)

Part C: Final Project: L-Protein Mutants

It appears that the L-protein mutants that result in a functional MS2 (1 in the “Lysis” column of the spreadsheet) correlate with the positive log likelihood ratio mutations on the heat map.

Proposed Mutations:

Mutation	Domain	Reasoning	L-protein Multimer	Evaluation
F -> S at position 5	Soluble Domain	When L-protein sequences was input into BLASTp, the F -> S mutation was observed in the third top hit in Emesvirus zinderi. Additionally, the F -> S mutation in position 5 appeared a yellowish-green on the heat map, indicating a “positive” mutation.		The mutant octomer appears to form a pore-like quaternary structure. The ipTM score is 0.13, indicating low confidence that this is an accurate assembly of the mutant multimer.
R -> S at position 19	Soluble Domain	In the table of L-protein mutants, this mutation has a score of 1 in the Lysis column, providing experimental evidence of a functional lysis protein. Additionally, mutations in the R amino acid at position 19 were identified in multiple top BLASTp hits.		Like the previous mutant, the Mutant 2 octomer forms a pore-like quaternary structure with an ipTM score of 0.14, indicating low confidence in this assembly.
R -> I at position 31	Soluble Domain	This mutation is associated with a functional L protein on the “L-protein mutant” spreadsheet (1 in lysis column) and appears to have a positive log likelihood ratio on the heat map.		The pore generated for this octomer appears to be tighter than Mutants 1 and 2, and although the ipTM score of 0.16 still indicates low confidence in the structure, it is slightly stronger than the previous two mutants.
A -> P at position 45	Transmembrane Domain	Once again, this mutation was identified on the “L-protein mutant” spreadsheet as resulting in a function lysis protein. Although this mutation occurs in the transmembrane domain and therefore is less likely to result in a functional advantage to the MS2 phage, the functional lysis protein and low negative score on the heat map suggest it may be a better choice than other mutations in the transmembrane domain.
E -> S at position 61	Transmembrane Domain	This mutation has a positive log likelihood ratio on the heat map, and a mutation in the E protein was identified in a BLASTp alignment with the L-protein for the MS12 phage.

Week 6 HW: Genetic Circuits Part I

DNA Assembly

1. What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?

• DNA polymerase: uses dNTP monomers to synthesize new DNA strands
• dNTPs: monomers of new DNA strand
• Buffer: stabilizes the pH of the reaction for optimal enzymatic function
• MgCl2: Cofactor for the polymerase; optimizes enzymatic function and primer annealing

2. What are some factors that determine primer annealing temperature during PCR?

• GC content (higher GC content = more hydrogen bonds = stronger primer annealing = higher annealing temperature)
• Primer length (longer primers = more hydrogen bonds = stronger primer annealing = higher annealing temperature)

3. There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other.

• PCR:
  • Enzyme: DNA polymerase
    • DNA polymerases bind to primers and synthesize complementary DNA to the template strand
  • Purpose: amplify!
    • Polymerases synthesize DNA by recognizing primers (designed to flank the specific region of interest) and incorporating dNTPs into a novel DNA strand
  • When to use:
    • To detect a specific sequence within a mixed sample
    • To create more of a specific DNA sequence (amplify)
• Restriction enzyme:
  • Enzyme: restriction endonuclease
    • Restriction endonucleases recognize specific nucleotide sequences (4-8 bp) in double-stranded DNA and cut in a specific pattern (blunt or sticky ends, depending on the enzyme)
  • Purpose: cut!
    • Restriction enzymes cut existing template DNA, they do NOT amplify DNA fragments
  • When to use:
    • To linearize bacterial plasmids (ex. for in vitro transcription of capped mRNA for microinjection into X. laevis!)
    • To cut DNA fragments to assemble/ligate together and transform into a plasmid

4. How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?

• Primer design!
  • The 5’ tail overhang of each primer should be identical to the adjacent DNA fragment
  • the 3’ end of the primer should be complementary to the DNA fragment to which it will anneal
  • Restriction Digest:
    • Restriction enzyme cut sites should be avoided
  • PCR:
    • Primers should be designed that flank the overhang regions

5. How does the plasmid DNA enter the E. coli cells during transformation?

• Heat shock transformation - briefly raising temperature increases the permeability of the bacterial cell wall, allowing the plasmid DNA to enter into the E. coli cell
  • E. coli cell sample placed on ice for ~15-30 minutes
  • Sample heated to 42ºC for ~15-30 seconds
  • Sample then returned to ice for ~5 minutes

6. Describe Golden Gate Assembly Golden Gate Assembly utilizes Type IIS restriction enzymes (such as BsaI) which recognize non-pallindromic sequences, cut outside the recognition site (to avoid damaging the DNA sequence of interest), and create sticky ends of variable lengths. First, template DNA is amplified using PCR with primers specified to include the TIIS recognition and cut sites. Next, a restriction digest with the TIIS enzyme is performed to create DNA fragments with sticky ends. The sticky ends of adjacent fragments should be complementary so the sequences can be ligated in the appropriate order. Following ligation of DNA fragments with a plasmid, the engineered plasmid can be transformed into a bacterial cell (ex. E. coli), and bacterial colonies containing the plasmid can be screened for furhter use.

Image generated by ChatGPT

Asimov Kernel

Repressilator

The protein concentrations generated from this recreation of the Repressilator meet my expectations in that they oscillate over time. However, unlike the in vivo system, the oscillations do not decay over time in this computational model.

Construct 1: Negative Feedback Loop

I have designed a LacI genetic circuit in which the LacI gene should bind to its own promoter and inhibit its own expression. I would expect the RNA and protein concentration to fluctate slightly, then reach steady state.

My system immediately reaches steady state, which is not what I predicted. However, I was unable to add an operator sequence in this circuit, so perhaps that is my issue.

Construct 2: Toggle Switch

Modeled after the classical toggle switch genetic circuit of mutual repression, I expect this construct to reach a bistable state. If I were able to add an inducer to this system, I would expect to be able to switch which bistable state is selected by the system. However, in this instance, my system reached a single steady state.

My system behaves the way I predicted it would!

Construct 3: Dual Repressors

My final circuit contains two repressors, LacI and TetR, controlled by the QacR promoter. Additionally, the expression of QacR is controlled by the expression of either LacI or tetR (two separate QacR genes). I expect the system to reach a single steady state. If I was able to design an inducible promoter, I could be able to switch between stable states.

My system behaved as predicted :)

Week 7 HW: Genetic Circuits Part II

Intracellular Artificial Neural Networks (IANNs)

What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions?

Variables input into Boolean functions are binary (either ON or OFF), whereas the variables input into IANNs can be continuous. Additionally, IANNs can input multiple variables at a time while Boolean functions (like a two-layer IANN) only integrate two variables.

Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal.

You could use an IANN to model cell fate decisions during embryonic development. The network could consider intracellular factors—such as gene expression—and extracellular influences—such as cell density and external signaling from morphogens or canonical signaling pathways—to predict the cell fate of a pluripotent or hemipotent cell during development. IANNs would provide a more representative in silico cell fate model than a Boolean function because, unlike the Boolean function, the IANN would be able to incorporate multiple influences (mentioned previously) that influence fate rather than one factor at a time. One limitation of the IANN in this application might be treating all cells in a developing embryo as homogeneous rather than incorporating noise and stochasticity into its fate predictions.

Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2.

[[ADD]]

Fungal Materials

What are some examples of existing fungal materials and what are they used for? What are their advantages and disadvantages over traditional counterparts?

Hinneberg et al. (2025) provide a recent review of existing fungal materials and exciting avenues for future innovation. Some examples of fungal material from the article include biodegradable packing, leather alternatives, and various strucutral components. Fungal materials are a sustainable alternative to many manufactured goods; however, the scalability of fungal material production is limited, and the process is time-consuming.

What might you want to genetically engineer fungi to do and why? What are the advantages of doing synthetic biology in fungi as opposed to bacteria?

I would be interested in engineering a fungus to detect pH changes or other environmental changes in the water of the tanks where I keep my Xenopus laevis frogs for my research here at William & Mary. It would be interesting to have a biosensor that can demonstrate when the environment the frogs are kept in is outside of the standard physiological range so it can be adjusted for their health and well-being.

Unlike bacteria, fungi are eukaryotic, meaning they are a better model of the intracellular complexity of human cells than bacteria. In particular, protein folding in fungi follows the same processing (chromatin remodeling, endomembrane system, RNA processing such as post-translational modifications, etc.) as proteins in human cells. Additionally, as fungi are eukaryotic (multicellular), they are capable of responding to signaling from neighboring cells or external influences in a similar manner to human cells.

Final Project!

Project Title

Engineering a small molecule to target the βγ subunits of pre-Bötzinger Complex μ opioid receptors

Final Project Description

Renarcotization is a delayed consequence of naloxone administration after opioid consumption that occurs when an opioid re-binds to a receptor following release of naloxone from the μ opioid receptors (MOR) active site, resulting in delayed respiratory depression. Existing literature has identified the βγ subunits of the MOR as responsible for regulating the respiratory depression associated with opioid binding to MOR. In this project, I aim to design a small molecule that will bind to the βγ subunits of the MORs in the pre-Bötzinger complex (pre-BötzC) of M. musculus to target the respiratory depression associated with opioid administration without disrupting the analgesic effects associted with activation of the MOR α subunit.

Final Project Aim 1

The first aim of my final project is to design a small molecule capable of binding to βγ subunits of pre-BötzC MORs by utilizing the Boltz Lab Drug Discovery Platform introduced in Part B of the Week 5 homework assignment. Existing literature has identified Gallein as a small molecule capable of effectively binding to the MOR βγ subunit, but it was ineffective when applied in vivo. My first objective will be to modify this structure (then perhaps pursue alternative structures) to optimize targeting the specific MOR expressed in the pre-BötzC to prevent lethal off-target effects.

Week 1 Lab: Pipetting

Week 2 Lab: DNA Gel Art

Benchling Gel Design

Restriction Digest Protocol

Gel Preparation Protocol

Gel Run and Imaging Protocol

Final Gel Image

Week 3 Lab: Opentrons Art

Opentrons Design

Opentrons CoLab Code

 ### Green
  cursor = center_location.move(types.Point(x=-18, y = 12))

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(15, location_of_color('Green'))
  dispense_and_detach(pipette_20ul, 3, cursor)

  cursor = cursor.move(types.Point(x=-4, y=4))

  for i in range(4):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=3:
      cursor = cursor.move(types.Point(y=4))

  pipette_20ul.aspirate(15, location_of_color('Green'))

  for i in range(2):
    cursor = cursor.move(types.Point(x=4, y=4))
    dispense_and_detach(pipette_20ul, 3, cursor)

  cursor = cursor.move(types.Point(x=4))

  for i in range(3):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=2:
      cursor = cursor.move(types.Point(x=4, y=-4))

  pipette_20ul.aspirate(18, location_of_color('Green'))

  cursor = cursor.move(types.Point(x=4))

  for i in range(3):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=2:
      cursor = cursor.move(types.Point(x=4, y=4))

  cursor = cursor.move(types.Point(x=4))

  for i in range(3):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=2:
      cursor = cursor.move(types.Point(x=4, y=-4))

  pipette_20ul.aspirate(15, location_of_color('Green'))

  cursor = cursor.move(types.Point(y=-4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(y=-4))

  cursor = cursor.move(types.Point(y=-4))

  for i in range(3):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=2:
      cursor = cursor.move(types.Point(x=-4, y=-4))

  cursor = cursor.move(types.Point(x=-4))

  pipette_20ul.aspirate(18, location_of_color('Green'))

  for i in range(6):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=5:
      cursor = cursor.move(types.Point(x=-4))

  cursor = cursor.move(types.Point(x=-4))

  pipette_20ul.aspirate(15, location_of_color('Green'))

  for i in range(3):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=2:
      cursor = cursor.move(types.Point(x=-4, y=-4))

  cursor = cursor.move(types.Point(y=-4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(y=-4))

  cursor = cursor.move(types.Point(y=-4))

  pipette_20ul.aspirate(15, location_of_color('Green'))

  for i in range(3):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=2:
      cursor = cursor.move(types.Point(x=4, y=-4))

  cursor = cursor.move(types.Point(x=-12, y=16))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=-4))

  cursor = cursor.move(types.Point(x=-4, y=-4))

  pipette_20ul.aspirate(9, location_of_color('Green'))

  dispense_and_detach(pipette_20ul, 3, cursor)

  cursor = cursor.move(types.Point(y=-4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=4, y=-4))

  cursor = cursor.move(types.Point(x=-4, y=-4))

  pipette_20ul.aspirate(12, location_of_color('Green'))

  for i in range(4):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=3:
      cursor = cursor.move(types.Point(x=4))

  cursor = cursor.move(types.Point(x=12))

  pipette_20ul.aspirate(18, location_of_color('Green'))

  for i in range(6):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=5:
      cursor = cursor.move(types.Point(x=4))

  cursor = cursor.move(types.Point(x=4))

  pipette_20ul.aspirate(15, location_of_color('Green'))

  for i in range(3):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=2:
      cursor = cursor.move(types.Point(x=4, y=4))

  cursor = cursor.move(types.Point(y=4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(y=4))

  cursor = cursor.move(types.Point(y=4))

  pipette_20ul.aspirate(15, location_of_color('Green'))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=-4, y=4))

  cursor = cursor.move(types.Point(x=8, y=-8))

  dispense_and_detach(pipette_20ul, 3, cursor)

  cursor = cursor.move(types.Point(x=4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=4, y=-4))

  cursor = cursor.move(types.Point(y=-4))

  pipette_20ul.aspirate(18, location_of_color('Green'))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=-4, y=-4))

  cursor = cursor.move(types.Point(x=4, y=-4))

  for i in range(4):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=3:
      cursor = cursor.move(types.Point(x=-4))

  pipette_20ul.drop_tip()
  pipette_20ul.pick_up_tip()

  ### Red
  cursor = cursor.move(types.Point(x=-8, y=44))

  pipette_20ul.aspirate(12, location_of_color('Red'))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=-4))

  cursor = cursor.move(types.Point(y=4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=4))

  cursor = cursor.move(types.Point(x=-24))

  pipette_20ul.aspirate(12, location_of_color('Red'))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=-4))

  cursor = cursor.move(types.Point(y=-4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=4))

  cursor = cursor.move(types.Point(x=4, y=-8))

  pipette_20ul.aspirate(18, location_of_color('Red'))

  for i in range(4):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=3:
      cursor = cursor.move(types.Point(x=4))

  cursor = cursor.move(types.Point(x=-4, y=-4))

  for i in range(2):
    dispense_and_detach(pipette_20ul, 3, cursor)
    if i!=1:
      cursor = cursor.move(types.Point(x=-4))

  pipette_20ul.drop_tip()

Individual Final Project

Group Final Project