Week 1 HW: Final Project Ideas

1. What would you like to biomanufacture and why? I would like to make a microbial inoculant for a traditional milpa, or Three Sisters system, of corn, beans, and squash. The inoculant would contain beneficial bacteria and fungi that could help the plants get nutrients from the soil while also potentially helping them survive periods of drought or flooding. I became interested in this idea because of my background and interest in agriculture, especially Indigenous agricultural systems. A lot of modern agriculture is based around growing one crop at a time, while a milpa grows multiple crops together. Corn, beans, and squash can benefit each other in different ways, so I thought it would be interesting to see if beneficial microorganisms could also be selected specifically for the three crops growing together instead of for only one crop. I am especially interested in making the inoculant help with water stress. Drought is an obvious problem for agriculture, but too much water can also be damaging. Flooding can leave soil saturated with water and reduce the amount of oxygen available to plant roots. I have seen examples of both problems affecting Indigenous farming communities. A few years ago, severe flooding in Oaxaca damaged agricultural fields, while more recently a Mam woman I follow who grows traditional crops with her family in Georgia described their crops struggling with drought. My idea would be to find bacteria and fungi that already have useful relationships with plants and combine several of them into one product. Some might help plants obtain nutrients, while others might help plants deal with drought or very wet soil. The goal would not necessarily be to make the plants completely drought- or flood-proof. Instead, I would want to see whether the inoculant could make the Three Sisters more resilient so that they have a better chance of surviving stressful growing conditions and still producing a crop.

2. What living system would you use? I would use beneficial bacteria and fungi associated with plants and soil. My first approach would be to look for microorganisms that already have useful traits instead of immediately trying to genetically modify them. For example, microorganisms could potentially be collected from the roots or surrounding soil of plants that are doing well under drought or very wet conditions. I would then want to test which microorganisms benefit corn, beans, and squash. Some bacteria or fungi could help the plants access nutrients such as nitrogen or phosphorus. Others could potentially affect root growth or help plants respond to water stress. I do not think one microorganism would necessarily need to do everything. The final inoculant could contain several different microorganisms that each provide a different benefit. For example, it might contain: bacteria that help make nutrients available to plants; fungi that help roots access water and nutrients; microorganisms associated with better drought tolerance; microorganisms associated with better tolerance of waterlogged soil. These organisms could be grown separately and eventually combined into one inoculant. If naturally occurring microorganisms were not enough, it might eventually be possible to modify bacteria or fungi to improve certain traits. However, I would first want to see what could be accomplished using microorganisms that already exist.

3. How would you use automation? I think automation would be most useful for testing lots of different microorganisms and combinations of microorganisms. There could potentially be dozens of bacteria and fungi worth testing. Doing every combination by hand would take a very long time. A liquid-handling robot could help prepare different microbial combinations in small experiments so that the same amounts are used each time. The plants could then be grown under different conditions. Some could receive normal watering, some could experience drought, and others could experience very wet or waterlogged soil. I could compare things such as plant survival, growth, root growth, and final biomass. More detailed experiments could also measure how much water the plants are using or how stressed they are. Automation could help narrow a large number of possible microbial combinations down to a smaller number that would then be worth testing in a greenhouse or field. If the inoculant eventually became a real product, the microorganisms would probably need to be grown using much larger equipment than what was used during these early experiments.

4. How much would you need to make, and how long would it take? A. How much? For this project, I would define a meaningful first scale as producing enough inoculant for one hectare of a Three Sisters field. I do not yet know what the correct amount of microorganisms per hectare would be. This would depend on how concentrated the inoculant was and whether it was applied to the seeds, roots, or soil. For a rough estimate, I will assume that I need 10 liters of concentrated microbial inoculant per hectare. That would mean: 1 hectare × 10 L/hectare = 10 L of inoculant If it were eventually used on 100 hectares: 100 hectares × 10 L/hectare = 1,000 L of inoculant This is only an initial estimate. I would need to research existing microbial agricultural products to determine a more realistic application rate. B. How long? The amount of time needed would depend on which microorganisms are eventually selected. For a rough estimate, I will assume that the bacteria could be grown over approximately 1–2 days. Additional time would then be needed to prepare the original cultures, combine the microorganisms, and make sure the finished inoculant contains the organisms that it is supposed to. I would therefore roughly estimate several days to produce one batch. Producing enough for one hectare might be possible using relatively small equipment. Producing enough for hundreds or thousands of hectares would require much larger tanks or multiple tanks operating at the same time. The fungi might also be more difficult to produce than the bacteria. Some beneficial fungi depend on plants to grow, so I would need to research how those organisms are commercially produced before I could make a good estimate for them.

5. What don’t you know yet? There are several major things I would need to learn before knowing whether this idea could actually work.

6. Can the same microorganisms benefit corn, beans, and squash? Because these are three different plants, a microorganism that works well with one might not work with the others. I would need to test whether one community of microorganisms could work with all three crops growing together.

7. Can one inoculant help with both drought and flooding? Drought and flooding are almost opposite problems. During drought there is too little water, while during flooding the soil can contain so much water that the roots do not receive enough oxygen. I do not know whether microorganisms that help during drought would also survive and remain useful during flooding. It may require different microorganisms working together.

8. Would it actually work in a field? Something that works in a controlled laboratory or greenhouse experiment may not work the same way in agricultural soil. There are already many microorganisms living in soil, and temperature, rainfall, soil type, and other factors could affect the introduced microorganisms. I would eventually want to compare Three Sisters plots with and without the inoculant under normal watering, drought, and waterlogged conditions. This would show whether the inoculant actually improves plant survival, growth, or yield under realistic conditions.

Week 2 HW: DNA DESIGN

Part 1 — Define your expression target What molecule are you expressing? For this assignment, I would focus on expressing ACC deaminase, an enzyme encoded by the acdS gene. My larger project from Homework #1 was a microbial inoculant for corn, beans, and squash that could potentially help the plants with nutrient availability and environmental stresses such as drought and flooding. A complete inoculant would probably involve several microorganisms and biological functions, which would be too complicated to design as one genetic construct. For this homework, I am therefore focusing on one possible part of that system. ACC deaminase breaks down ACC (1-aminocyclopropane-1-carboxylate), which is involved in the plant’s production of the stress-related hormone ethylene. My idea is that a plant-associated bacterium expressing ACC deaminase could potentially help moderate plant stress responses. I do not expect this single enzyme to make corn, beans, or squash drought- or flood-resistant by itself. Instead, it would be one possible function in the larger microbial community proposed in Homework #1. What is the sequence? I would use the acdS gene encoding ACC deaminase from Pseudomonas putida UW4. The protein is 338 amino acids long, and its reviewed UniProt accession is Q5PWZ8. The corresponding genomic sequence is associated with GenBank accession AF047710. UniProt For Kernel, I would import the natural acdS sequence and then use Kernel’s codon-optimization tool for my chosen host rather than trying to design the protein myself. What host or chassis? For the assignment, I would use Pseudomonas putida as the bacterial host. I chose a bacterial host because the final product from Homework #1 is supposed to be a microbial soil/root inoculant. Pseudomonas species are common in plant-associated microbial research, and the original acdS gene I selected already comes from a P. putida strain. This also keeps my first genetic design relatively simple rather than trying to engineer the plant itself. Where does the product need to go? I would keep ACC deaminase inside the bacterial cell (cytoplasm). Because of this, I would not initially add a secretion signal peptide. The bacteria would colonize the area around or near plant roots and express the enzyme themselves.

Part 2 — Design the genetic parts This is where I’d keep your design intentionally basic. The assignment’s table on page 2 says that a prokaryotic design needs a promoter → RBS → coding sequence → terminator, along with a suitable vector backbone. HTBAA Homework #2_ DNA Design Your preliminary parts could therefore be: Part What I would use Why Promoter Constitutive bacterial promoter available in Kernel Keeps acdS expression relatively simple for this first design. RBS Standard bacterial RBS available in Kernel Allows the bacterial ribosome to begin translating the protein. CDS acdS, based on P. putida UW4 Q5PWZ8 Produces the ACC deaminase enzyme that is the focus of my design. Signal peptide None I currently intend the enzyme to remain inside the bacterial cell. Terminator Standard bacterial terminator available in Kernel Provides a clear end to transcription. Backbone Pseudomonas-compatible/broad-host-range plasmid available in Kernel Allows the construct to be maintained in the bacterial host.

I wouldn’t invent specific promoter/backbone names yet. Look at what’s actually available in your Kernel workspace first. The homework specifically allows you to reuse an existing backbone. HTBAA Homework #2_ DNA Design Once you show me what Kernel offers, we can choose among the real parts rather than making your paper sound more knowledgeable than you actually were when doing the exercise. Part 3 — Assemble the construct My basic construct would be: Promoter → RBS → acdS CDS → Terminator inside a Pseudomonas-compatible plasmid backbone. I would check in Kernel that all of the parts face the same direction, that the coding sequence has the expected start and stop codons, and that there are no unexpected stop codons within the gene. The purpose of this construct would be to make the bacterial host produce ACC deaminase. This represents only one possible stress-related component of the larger microbial inoculant I proposed in Homework #1. You’ll then insert the Kernel schematic/screenshot here, because that’s explicitly part of the deliverable. HTBAA Homework #2_ DNA Design Part 4 — Optimize and quality-check We can’t honestly finish this section until you actually run Kernel because the assignment asks what each tool returned. HTBAA Homework #2_ DNA Design Once you run them, your responses can be simple: Codon optimization: I used Kernel to codon-optimize the acdS coding sequence for Pseudomonas putida. The amino acid sequence should remain the same, but some DNA codons were changed to ones better suited to the host. [ADD YOUR KERNEL RESULTS: number/percentage of bases changed, GC changes, etc.] Sequence search/BLAST: I searched the coding sequence against known sequences. The closest match was [RESULT]. This was [expected/unexpected] because my sequence was based on the naturally occurring acdS gene from P. putida UW4. Safety screen: I ran Kernel’s safety screen on the finished construct. The tool checks the sequence for [COPY/SUMMARIZE WHAT KERNEL SAYS IT CHECKS]. My construct [did/did not] produce a warning. [EXPLAIN RESULT.] Don’t fabricate those results before running it. Part 5 — From design to build For a first prototype, I would probably order the acdS coding sequence as synthetic DNA and assemble it into an existing plasmid backbone, rather than trying to synthesize an entire plasmid from scratch. One possible assembly method would be Gibson assembly. Gibson et al. demonstrated that DNA fragments with overlapping ends can be assembled together in a single reaction, including much larger DNA molecules than the construct I am proposing. This would allow the synthetic acdS sequence to be joined with an existing vector containing the other necessary parts. DNA synthesis is not perfect, however. Errors can occur during synthesis, particularly as DNA designs become longer or more complicated. Carr et al. demonstrated methods for detecting and correcting errors in synthetic DNA. Therefore, after assembling my construct, I would want the finished sequence to be checked before assuming that it exactly matches my computer design. For an early experiment I would only need a small number of DNA constructs to test different versions of the design. If this eventually became an agricultural product, however, I would not need to manufacture a new plasmid for every plant or every dose of inoculant. Once the desired bacterial strain had the genetic construct, the bacteria themselves would be grown in larger quantities. The DNA-design stage is therefore mainly important for creating and testing the microbial strain rather than producing each final dose of inoculant. Those two assigned papers are particularly appropriate because the homework explicitly asks you to ground this section in at least two readings, and both Gibson assembly and DNA-synthesis error correction are included in the assigned reading list. HTBAA Homework #2_ DNA Design HTBAA Homework #2_ DNA Design Part 6 — What don’t you know yet?

1. Will producing more ACC deaminase actually improve stress tolerance in all three crops? Corn, beans, and squash may respond differently. I would test plants inoculated with the engineered bacteria against plants inoculated with the unmodified bacteria under normal, drought, and waterlogged conditions. Measurements could include survival, growth, root growth, and final biomass.
2. How much ACC deaminase should the bacteria produce? More expression is not automatically better. A very strong promoter could put unnecessary stress on the bacterium, while a weak promoter might not produce enough enzyme to affect the plant. I could compare constructs using promoters of different strengths and measure both bacterial growth and ACC-deaminase activity.
3. Would the engineered bacteria successfully interact with plants and other microorganisms in a real milpa? My original idea depends on a community of microorganisms interacting with three different crops. Even if this single engineered bacterium works in a controlled experiment, it may behave differently in actual soil. I would eventually test it first with each crop individually and then with corn, beans, and squash grown together.

Week 3 HW: Cell-Free Protocols

Part A — Understanding the CFPS Formulation

1. Why are potassium glutamate and magnesium glutamate used at different concentrations? Which is more sensitive? Potassium glutamate and magnesium glutamate are both salts, but they have different jobs in a cell-free protein synthesis reaction. Potassium glutamate is used at a much higher concentration to help create an ionic environment that is similar to the inside of an E. coli cell. Magnesium is needed at a much lower concentration because it directly affects important parts of protein synthesis, including ribosomes and nucleotides. Because of this, magnesium is the one I would expect to be more sensitive to small concentration changes. The PANOx-SP work also re-optimized magnesium when the energy system and reaction environment were changed, which helped me understand why it is treated as an important variable.
2. What is the difference between a PEP + NTP system and a Ribose + NMP system? A PEP + NTP formulation supplies the reaction with nucleoside triphosphates such as ATP, GTP, CTP, and UTP directly, while phosphoenolpyruvate (PEP) helps provide energy for the reaction. This is a more established approach, but some of these reagents can be expensive. A Ribose + NMP approach starts with cheaper or less energy-rich building blocks and depends more on the metabolism that is still active in the cell lysate to build or regenerate the molecules needed for transcription and translation. Olsen et al. (2026) showed that an optimized low-cost cell-free formulation could even produce NTPs from nitrogenous bases and ribose. The main reason to explore this type of formulation is to lower the cost while still producing useful amounts of protein.
3. Comparison of two literature CFPS compositions I compared the PANOx-SP/Cytomim approach described by Jewett and Swartz (2004) with the newer low-cost formulation developed by Olsen et al. (2026). The older system uses a fairly large set of added reagents, including potassium, magnesium, and ammonium glutamate; amino acids; ATP, GTP, CTP, and UTP; PEP; NAD; CoA; spermidine; putrescine; folinic acid; and other components. Published versions of this approach commonly use roughly 130–175 mM potassium glutamate, around 8–12 mM magnesium glutamate, about 10 mM ammonium glutamate, and around 30–33 mM PEP, although exact recipes can vary. Olsen et al. (2026) instead screened 1,231 different reagent formulations and developed an optimized 12-component formulation. Their goal was not only high protein production, but also lowering the number and cost of reagents. They reported about 2.4 g/L protein at the 15 µL scale and about 3.7 g/L at the 4 mL scale with oxygen supplementation, with an average estimated reagent-cost reduction of about 95% compared with previous formulations. To me, the main difference is that PANOx-SP is a useful established starting point, while the newer work shows that some of the traditional ingredients can be simplified or replaced when cost is part of the optimization goal. Part B — My Reaction Compositions For the designer portion, I started from the provided PEP reference recipes instead of trying to create completely new reactions from scratch. I changed one main salt variable for each protein so that the result would be easier to interpret. The three proteins also have different folding needs, so I kept the provided redox/folding components when they were important. Protein Reference Main change Reasoning sfGFP sfGFP PEP K(Glu) lowered from about 280 mM to about 250 mM sfGFP is the simplest target and does not require disulfide-bond folding. I used a moderate potassium-glutamate change as a simple one-variable test of the ionic environment. PETase PETase PEP Mg(Glu) increased from about 8.2 mM to 10.1 mM Magnesium affects the translation machinery and was one of the salt variables highlighted in the plate design. I kept DsbC and the existing redox components because this PETase has one disulfide bond. Reteplase Reteplase PEP K(Glu) increased from 200 mM to the designer maximum of about 224 mM Reteplase is the hardest target because it has nine disulfide bonds. I changed a salt variable but left the oxidizing system, GSSG/GSH, and DsbC alone so I was not also changing the folding conditions.

Submitted-design notes sfGFP: I lowered potassium glutamate while keeping the rest of the PEP reference formulation as constant as possible. This gives a straightforward test of whether a somewhat lower salt concentration changes folded sfGFP production. PETase: I increased magnesium glutamate to approximately 10.1 mM. PETase activity is measured using p-nitrophenol release rather than as a direct protein yield, so the useful comparison will be whether the new formulation increases enzyme activity. Reteplase: I increased potassium glutamate to approximately 224 mM, which was the maximum the designer allowed. I did not remove GSSG, GSH, or DsbC because Reteplase contains nine disulfide bonds and needs an oxidizing/folding-support formulation. Its readout is also enzyme activity rather than a direct physical protein yield. Community Reagent For the community-reagent portion, I would upvote FkpA, an E. coli folding chaperone that was already suggested by another student. I chose it because the difficult part of producing Reteplase is not just making the protein chain, but getting a protein with many disulfide bonds to fold into an active form. The community suggestion describes FkpA as a possible additional folding support alongside DsbC. Since this idea is already on the class page, upvoting it also avoids duplicating an existing suggestion. AI Use I used ChatGPT (OpenAI, GPT-5.6 Sol) while working through this homework. I used it mainly to help me understand what the Cell-Free Reaction Designer was showing, compare the reference formulations, choose simple one-variable changes for the three proteins, check that I had selected the correct protein template before submitting, and organize my notes into this write-up. I checked the final reaction settings in the class designer before submitting them. Examples of prompts/questions I used: • “k glu maxes out at 224” References Jewett, M. C., & Swartz, J. R. (2004). Mimicking the Escherichia coli cytoplasmic environment activates long-lived and efficient cell-free protein synthesis. Biotechnology and Bioengineering, 86(1), 19–26. https://doi.org/10.1002/bit.20026 Olsen, M. L., Copeland, C. E., Sundberg, C. A., et al. (2026). Design-driven optimization of low-cost reagent formulations for reproducible and high-yielding cell-free gene expression. Nature Communications, 17, 3478. https://doi.org/10.1038/s41467-026-69605-8

Final Project