Individual Final Project

Notebook

This page logs the notes, thoughts, brainstorming, and planning associated with my individual final project.

Links to section

• Idea brainstorming
• Quorum-sensing based killswitch
• PHA synthase enzyme engineering

Feb 24, 2026

Brainstorming:

• Identification of PhaC analog in Cyanobacterium aponium UTEX 3222 and overproducing or engineering for increased efficiency
  • BLAST/align with known PHA-synthases
  • Compare efficiency / mutations that improved turnover in other PhaC - test analogous mutations (aligned location, similar or different AAs). improved substrate specificity?
  • Site-specific saturation mutagenesis? Would be good use for automation
• Quorum sensing based killswitch (i.e. cell dies if it escapes bioreactor)
  • Has to have some kind of inducible element or won’t grow after initial transformation
  • What’s good at quorum sensing already?
• Something else??? Something in E coli that can be done on Opentron
  • Because it’s more convenient for a final project to be executed in Victoria remotely
• Cyanobacterial expression plasmid across multiple cyano species
  • needs to include E coli machinery for manipulation and production (and conjugation, for relevant species)

Ideas:

1. PhaC protein engineering
   1. Short term aim: Design small library of PhaC variants with expected improvement
   2. Medium term aim: Generate library and test in chassis strain
   3. Long term aim: Develop PHB bio-manufacturing cyanobacterial strain for carbon-neutral/carbon-negative plastic (depending on biodegradation).
2. Quorum sensing based circuit for biocontainment
   1. Short term aim: Design killswitch with genetic circuit to trigger based on quorum sensing.
   2. Medium term aim: Build genetic circuit with expression based on quorum sensing with a measureable output; test circuit in E. coli.
   3. Long term aim: Optimize circuit sensitivity and test with killswitch expression; integrate into bio-manufacturing chassis strains for population-linked biocontainment.
3. Broad cyanobacterial expression plasmid
   1. Short term aim: Design plasmid backbone based off native cyanobacterial plasmids and established E. coli machinery.
   2. Medium term aim: Test expression in multiple cyanobacterial strains (including some previously considered genetically intractable with classic broad-host-range vectors).
   3. Long term aim: Establish protocol for domestication of newly prospected, wild-type cyanobacterial strains using the cyanobacterial plasmid.

Mar 31, 2026

Leaning towards quorum sensing killswitch because it’s more aligned with my prior experience and knowledge, so i think it will take less research on my behalf. since i’m already falling behind on homeworks, i’m worried about how much time it would take to optimize a protein since i have no prior machine learning experience.

Quorum sensing notes:

• auto-inducer: triggers expression
• keep in mind phenolic compounds and other naturally occurring quorum quenching
• also keep in mind the potential for auto-inducer production from other related bacteria; like if biomanufacturing strain escapes bioreactor but lands in soil with existing microbiome - we still want the escaped cells to die
  • maybe we could make a synthetic quorum sensing system for orthogonality: would require biosynthetic pathway for auto-inducer, auto-inducer recognition (inducible promoter, transcription factor, riboswitch, etc.), auto-inducer export pathway to preferentially or rapidly diffuse out of the cell
• for killswitch activation at low population (cells escaped from bioreactor), maybe consider secondary/back-up activation from an environmental signal
• potentially test circuit with fluorescence or colorimetric output first before killswitch/toxin-antitoxin genes

References

1. Miguel, CMTS; Santos, CA; Lima, EMF; et al. Quorum Sensing in Bacteria: From Mechanisms to Applications in Foods. 2026. Current Opinion in Food Science: 101394. DOI: 10.1016/j.cofs.2026.101394.
2. Ream, MJ; Prather, KLJ. Orthogonal quorum sensing circuits enable dynamic regulation in Escherichia coli. 2026. Metabolic Engineering, 96: 104-112. DOI: 10.1016/j.ymben.2026.03.009.

Circuits:

• killswitch trigger for population high:
  • low constitutive expression of toxin
  • autoinducer-triggered expression of antitoxin
  • maybe autoinducer can also trigger expression of toxin repressor
• killswitch trigger for population low (like Paula’s idea for targeted drug delivery):
  • low constitutive expression of antitoxin
  • autoinducer-triggered expression of toxin
  • maybe autoinducer can also trigger expression of antitoxin repressor

Apr 2, 2026

In Victoria node recitation last night, Derek suggested a possibility for cell-free testing of the system to be able to use the Gingko cloud lab instead. Originally i had figured that because i want it to be a killswitch system, that it needs to be in a living cell. Also because traditional quorum sensing systems are dependent on autoinducer concentration within vs outside the cell membrane. But Derek suggested considering instead how to design a quorum sensing system that would be cell-free like on a paper biosensor: that triggers when at a minimum concentration, rather than triggering at the first cell it sees.

Phrasing it that way made me think of the analog computing of the neuromorphic circuits: where inputs are additive (positive or negative). So to reach a minimum concentration rather than the very first thing present, there has to be a counter-actor to the sensed thing present; something that degrades or binds to the autoinducer or signalling metabolite that is expressed constitutively at a low level. So when the signalling molecule concentration is low (low population), it will be all used up by the counter-actor before it can trigger expression of the QS-controlled genes. When the signalling molecule concentration is high (high population), it will outnumber the counter-actor, so it can still trigger the QS-controlled genes.

For example, a riboswitch that recognizes a small molecule metabolite. The metabolite is produced and exported by cells, and when present, activates the gene of interest (in my switch a killswitch or fluorescent protein). We’d also want to express the riboswitch as an aptamer that is unconnected to the gene of interest to bind the metabolite at low concentrations, until a high concentration of the metabolite is reached and the metabolite outnumbers the loose aptamer and can trigger the riboswitch to activate gene expression.

Apr 3, 2026

Brainstorming and design

Drafts

Title: Population-dependent killswitch to prevent bioreactor escape

Short description: Biocontainment is essential for safe biomanufacturing, but most strains with biocontainment have bespoke systems designed for that particular strain. A population-dependent killswitch would kill any cells that escape the bioreactor where they are being cultivated or harvested. My initial idea is a toxin-antitoxin system expressed under control of a quorum-sensing circuit. Future considerations: safeguards against biocontainment escape through mutation, multiple levels of regulation.

Aims:

1. Design a genetic circuit that controls expression dependent upon cell population density in E. coli. The circuit will be designed with the intent for a final use with a killswitch, but fluorescent or colorimetric outputs might be used for initial design and validation. Validate the circuit with a simulation in Asimov Kernel.
2. Test circuit in E. coli with a measurable output (such as fluorescence).
3. Test circuit with killswitch; integrate into a biomanufacturing chassis strain for population-linked biocontainment.

Companies: Asimov - I plan on using Kernel to design and simulate my genetic circuit.
Basecamp Research - Maybe their AI can help me design overlapping genes to prevent killswitch escape via toxin gene mutation.
Cultivarium - If successful, quorum-based biocontainment could be a useful genetic tool to port to new potential chassis microbes.

Project idea slide

References:

1. Leonard SP; Halvorsen TM; Lim B; et al. Synthetic overlapping genes stabilize genetic systems. 2026. mBio, 17(3):e0272525. DOI: 10.1128/mbio.02725-25.
2. Blazejewski, T; Ho, H-I; Wang, HH. Synthetic sequence entanglement augments stability and containment of genetic information in cells. 2019. Science, 365(6453): 595-598. DOI: 10.1126/science.aav5477
3. Ream, MJ; Prather, KLJ. Orthogonal quorum sensing circuits enable dynamic regulation in Escherichia coli. 2026. Metabolic Engineering, 96: 104-112. DOI: 10.1016/j.ymben.2026.03.009

Apr 7, 2026

Last night in the Victoria node recitation, Derek was really talking about how cool the Kernel-Twist-Nebula-Waters pipeline is, and he mentioned that he was a little disappointed that not many projects seemed like they were fully utilizing it. Especially since he still isn’t back in Victoria, it seems like the only way i’ll be able to get any actual lab data unfortunately, so that’s different from my original plan with the quorum sensing. i ended up messaging Derek on Discourse to ask if it was too late to change my mind, and he said that while technically yes, since no one had signed up on my slide as a mentor yet, i could change it. So i went ahead and came up with a new slide and replaced my original response.

Description:

Companies:

References

These are for using mass spectrometry for PHA analysis, for the Waters step of the pipeline.

1. Khang, TU; Kim, M-J; Yoo, JI; et al. Rapid analysis of polyhydroxyalkanoate contents and its monomer compositions by pyrolysis-gas chromatography combined with mass spectrometry (Py-GC/MS). 2021. International Journal of Biological Macromolecules, 174: 449-456. DOI: 10.1016/j.ijbiomac.2021.01.108
2. Johnston, B; Radecka, I; Chiellini, E; et al. Mass spectrometry reveals molecular structure of polyhydroxyalkanoates attained by bioconversion of oxidized polypropylene waste fragments. 2019. Polymers, 11(10):1580. DOI: 10.3390/polym11101580
3. Conners, EM; Bose, A. State-of-the-art methods for quantifying microbial polyhydroxyalkanoates. 2025. ASM Applied and Environmental Microbiology, 91(9):e00274-25. DOI: 10.1128/aem.00274-25 These are for machine learning for enzyme engineering.
4. Landwehr, GM; Bogart, JW; Magalhaes, C; et al. Accelerated enzyme engineering by machine-learning guided cell-free expression. 2025. Nature Communications, 16: 865(2025). DOI: 10.1038/s41467-024-55399-0
5. Qiu, S; Saeed, H; Leonard, W; et al. Machine learning for enzyme catalytic activity: current progress and future horizons. 2026. Briefings in Bioinformatics, 27(1): bbag002. DOI: 10.1093/bib/bbag002
6. Cui, H; Su, Y; Dean, TJ; et al. Enzyme specificity prediction using cross-attention graph neural networks. 2025. Nature, 647: 639-647. DOI: 10.1038/s41586-025-09697-2
7. Link to sci comm article about Paper6: https://chbe.illinois.edu/news/stories/new-AI-tool-helps-match-enzymes-substrates This last one is about cell-free PHB synthesis.
8. Satoh, Y; Tajima, K; Tannai, H; et al. Enzyme-catalyzed poly(3-hydroxybutyrate) synthesis from acetate with CoA recycling and NADPH regeneration in Vitro. 2003. Journal of Bioscience and Bioengineering, 95(4): 335-341. DOI: 10.1016/S1389-1723(03)80064-6

Apr 9, 2026

Talking to Derek about the timeline in our Victoria node recitation last night, he suggested that everything for lab work will probably need to be ordered in the next two weeks to get data in time for final project presentations. He also said that if we want to run anything on the Ginkgo Nebula cloud lab, we need to talk with Ronan and see if he has the capacity for it. Given this timeline, i am almost definitely not going to be able to figure out any ML-guided protein engineering before the final ordering. What i’m thinking instead is to design initial constructs of PhaC from C. necator, PhaC from UTEX 3222, and a rational design for a UTEX 3222 PhaC mutant, all designed for cell-free expression. The reaction will probably include the monomer, since it’s simpler than using the full 5-enzyme cell free system from Satoh et al (reference 8 above) that used acetate as the feedstock, but I will need to double check the energy and CoA regeneration.

Apr 10, 2026

Enzyme sequence choices

PhaC enzyme from Cupriavidus necator was chosen as my wild-type. I used the amino acid sequence from Uniprot and codon-optimized it in Benchling for Escherichia coli. This was from homework 2 i think, and i just used that one.

For the mutant: I found a review paper (Ref1) that identified Ala510 in PhaC_Cnecator as having a role in substrate specificity: with A510M, A510Q, and A510C all increasing promiscuity (M, C both sulfur-containing residues; Q, C both polar residues; M, Q both larger residues); a related PhaC from Chromobacterium sp. USM2 found that changing the analogous A to M/W/V (all non-polar residues, larger than A) increased promiscuity; and the same PhaC from Chromobacterium sp. USM2 found that changing the analogous A to S (similar size, but polar) increased substrate specificity (towards short-chain-length PHAs, like PHB). It’s surprising that A->S had the oppposite effect as A->C, for these two different PhaC variants from different bacteria. But since I didn’t have time to read a lot more, I figured A510S was a good construct to test against the PhaC_Cnecator wild type to start with.

I tried to identify the PhaC sequence from UTEX 3222 to test as well, but I was unable to, as of yet. While the paper in which UTEX 3222 was prospected said the authors identified the genes encoding the PHA biosynthesis enzymes (Ref2), the genes weren’t annotated on the full genome sequence assembly, and I got no results BLASTing either C. necator PhaC or cyanobacterial PhaC from Synechocystis sp. PCC 6803 or the more closely relatated Microcystis aeruginosa sp. PCC 7608SL. I also tried BLASTing PhaE (another PHA biosynthetic enzyme) from a few different cyanobacterial strains as well, with still no results. All BLAST searches were tBLASTn to search for the nucleotide gene sequence within the genome assembly from the amino acid sequences from the various known PhaC protein sequences. The genes were also not listed amongst the biosynthetic genes listed in the Supplementary Information from the UTEX 3222 paper. I was out of ideas at this point, and on a time constraint, so to get constructs added to the order list today, I decided to move forward just with the PhaC_Cnwt and PhaC_CnA510S for now. If/when I get my ML design program working, maybe I can email George Church (or whoever on the author list did the genome annotation) to try to run it through, but I can definitely start with the C. necator one.

Construct design

Derek sent me a message asking me to order constructs today. So I went into Kernel to design PhaC_Cnecator with T7 promoter, RBS and terminator for cell-free expression because the E. coli based cell-free expression kits I found online from both NEB and Thermo Fisher both used T7 polymerase. In Kernel, I used a T7 promoter, T7 RBS, and T7 terminator from the iGEM repository. I chose promoter Bba_Z0251 from the many options because it had a lot of documentation on its iGEM registry page, and matched the full consensus sequence (from the T7 promoters iGEM page). I chose RBS Bba_Z0261 from the many options because it was analyzed by the same iGEM team as the promoter I used. I used T7 terminator Bba_K731721 from the many options because it most closely matched a quick google search for the T7 terminator sequence. While Kernel did have a genetic part for PhaC_Cnecator in the Uniprot repository, it actually didn’t have a nucleotide sequence associated with it. So i copied the promoter, RBS, and terminator sequences from Kernel into Benchling, where I used the previously codon-optimized PhaC_Cnecator sequence from homework 2. I used Benchling’s translation tool to identify the Ala at position 510, and changed a single nucleotide to change A510 to A510S (Ala: GCC; Ser: TCC) for the mutant.

Then I exported the FASTA files for both constructs and uploaded them into the Twist portal for clonal genes. I couldn’t remember if it mattered using linear gene fragments or clonal genes within a plasmid, but I went with clonal plasmid because previous experience with linear fragment orders from Twist were pretty low concentration. I remembered from lecture that Ronan preferred us to use chloramphenicol for an antibiotic marker if needed, so I decided to use the pTwist-Chlor-HighCopy cloning vector. Twist’s interface found both genes to be complex, so I used its internal codon optimization to fix this issue: I identified the organism as E. coli, did not omit any restriction enzyme recognition sites, and selected the promoter, RBS, and terminator regions as sequences that should not be changed. To my surprise, these sequences were not identical except the one point mutation; they were optimized differently, but I suppose it doesn’t really matter. Then I exported the full constructs (including plasmid) GenBank files from Twist and re-uploaded into Benchling to generate the link for adding the spreadsheet.

After meeting with Derek to explain, he suggested using linear gene fragments instead of clonal, so I re-did the Twist ordering bit to generate prices and optimized fragment GenBank files. I elected to leave the adaptors on because I assume those will give long enough arms, but I’m not really sure. Derek said he’d check with Ronan.

Experimental design

I checked that Millipore Sigma does in fact carry my substrate, I think. The substrate being the PHB monomer: 3-hydroxy-butyryl-CoA. However, it’s very expensive, so I’ll see about also ordering the DNA and cheaper substrates for the 5-enzyme biosynthetic pathway with CoA recycling that was in one of the papers I found (Ref3). After comparing the even just of all the substrates I’d still need for the full pathway, it’s cheaper just to order the original substrate (since I’d still need at least a little bit of CoA, which is still expensive on its own) then to also get a bunch of additional DNA. Derek mentioned that I need to figure out what kind of purification is needed for Waters to analyze my PHB product at the end of the reaction.

Reference

1. Chek, MF; Hiroe, A; Hakoshima, T; et al. PHA synthase (PhaC): interpreting the functions of bioplastic-producing enzyme from a structural perspective. 2018. Applied Microbiology and Biotechnology, 103: 1131-1141. DOI: 10.1007/s00253-018-9538-8
2. Schubert, MG; Tang, T-C; Goodchild-Michelman, IM; et al. Cyanobacteria newly isolated from marine volcanic seeps display rapid sinking and robust, high-density growth. 2024. ASM Applied and Environmental Microbiology, 90: e00841-24. DOI: 10.1128/aem.00841-24
3. Satoh, Y; Tajima, K; Tannai, H; et al. Enzyme-catalyzed poly(3-hydroxybutyrate) synthesis fro macetate with CoA recycling and NADPH regeneration in Vitro. 2003. Journal of Bioscience and Bioengineering, 95(4): 335-341. DOI: 10.1016/S1389-1723(03)80064-6

Apr 13, 2026

After Derek talked to Ronan, he suggested going back to clonal genes. I used my original PhaC_Cn construct, but then I decided to just have the point mutation for the mutant and have the rest of the sequence be identical. So I copied the PhaC_Cn-pTwist construct into a new DNA sequence in Benchling, and made the point mutation (GCA->TCA), and then verified quickly in Twist that this sequence is still simple and the same price.

Group Final Project