Week 1 HW: Principles and Practices

1. Biological Engineering Application: A Distributed, Low Cost Environmental Biosensing Platform For my project, I’ve been imagining a simple, low cost environmental biosensing system that anyone could use — something like disposable test strips with engineered microbes or cell free components that change color when they detect contaminants in water or soil. Think of it as an open source, modular “bio test strip” that communities could use to check for things like heavy metals, PFAS, or harmful bacteria without needing a lab. What draws me to this idea is how practical and empowering it could be. A lot of communities don’t have access to reliable water testing, and waiting for official reports can take weeks or months. A cheap, easy to use biosensor could help people catch problems early, respond faster, and feel more in control of their environment. It also has a nice educational angle — something schools, citizen science groups, or community labs could use to learn about biology while doing something meaningful. There are already tools in synthetic biology that point in this direction (like cell free biosensors or engineered yeast reporters), but I’m interested in pushing the idea toward something more distributed and democratized — something that doesn’t require a lab coat or a research budget to use.
2. Governance / Policy Goals for an Ethical Future Because this kind of biosensor could end up in the hands of a lot of different people — community groups, teachers, students, DIYbio hobbyists, or folks in low resource settings — I want to think carefully about how to make sure it’s used safely and responsibly. That’s where my governance goals come in. Main Goal: Prevent Harm (Non Malfeasance) The first and most obvious goal is making sure the tool doesn’t accidentally cause harm — biologically, socially, or environmentally. If the biosensor uses engineered microbes, I need to think about the possibility of accidental release. Even “safe” strains can behave unpredictably in the wild, so containment and design safeguards matter. There’s also the social side. Environmental data can be sensitive. A false positive could cause unnecessary panic; a false negative could give people a false sense of security. So part of this goal is making sure the results are easy to interpret and hard to misuse. Side Goal 1: Promote Security and Prevent Malicious Use Because the platform is meant to be low cost, open source, and easy to distribute, it has all the qualities that make a tool empowering — but also potentially vulnerable. Someone could try to repurpose the biosensor to detect things it was never meant to detect, like human biomarkers or pathogens in ways that violate privacy. There’s also the supply chain angle: if the components can be tampered with, someone could alter the biosensor to give misleading results. So part of the governance plan is making sure the system can’t be easily weaponized or misused. Side Goal 2: Promote Equity and Autonomy This one feels especially important. Communities that deal with pollution often have complicated relationships with outside researchers or government agencies. They may lack access to testing, or they may distrust the people who usually control environmental data. I want this tool to do the opposite — to give communities more control, not less. That means thinking about access, affordability, and who gets to decide how the data is used. The goal is to support autonomy, not create new dependencies or power imbalances.
3. Governance Actions Below are three governance actions, each analyzed through the required four aspects. Option 1: Community Biolab “Safety‑by‑Design” Toolkit Purpose Right now, community labs vary widely in safety practices. This toolkit standardizes risk assessment, training, and experiment planning. Design • Developed by community labs + biosafety experts • Includes checklists, training modules, and a risk‑flagging app • Incentives: badges, access to shared equipment, recognition Assumptions • Community members will voluntarily adopt the toolkit • Training can be made simple and engaging • Labs have enough resources to implement it

Risks • Over‑reliance on checklists instead of real understanding • Labs may treat badges as performative rather than meaningful • Could create a divide between “certified” and “uncertified” labs Option 2: DNA Synthesis “Pre Check” Assistant Purpose Small labs often lack tools to screen DNA orders. This assistant helps them avoid ordering risky sequences. Design • Web app that screens sequences against risk lists • Built with input from synthesis companies and regulators • Provides explanations, not just warnings Assumptions • Users will run sequences through the tool • Risk lists are accurate and up to date • False positives won’t frustrate users Risks • False negatives could create a false sense of security • Malicious users could probe the system to find “safe” variants • Could burden small labs if too strict

Option 3: Genetic Kill Switch Design Library Purpose Provide standardized, vetted kill switch designs for engineered microbes. Design • Curated library with documentation • Includes failure modes and testing guidelines • Could be maintained by a consortium (iGEM, ABSA, NIH) Assumptions • Kill switches work reliably across contexts • Users will implement them correctly • Regulators will accept standardized designs Risks • Overtrust in kill switch reliability • Dual use: knowledge could help people defeat containment • False sense of safety could encourage riskier deployments

5. Which governance combination works together?

Prioritization and Recommendation Looking back at the scoring, the option that clearly rises to the top for me is Option 1: the Community Biolab Safety by Design Toolkit. It’s the most practical, the most immediately useful, and honestly the one that feels closest to the spirit of HTGAA — empowering people to do biology safely, confidently, and creatively. Community labs are incredibly diverse in experience and resources, so giving them a simple, friendly toolkit that makes safety feel approachable (rather than intimidating) seems like the biggest win with the least friction. I would pair this with Option 3: the Genetic Kill Switch Design Library, especially for any projects that involve engineered organisms leaving the bench. Even if my own biosensing project ends up being cell free, I’ve realized how often people underestimate environmental risks. A well documented, vetted kill switch library gives people a starting point that’s safer than whatever they might cobble together on their own. It’s not perfect — kill switches can fail, and people can over trust them — but it’s still a meaningful step toward responsible design. Option 2, the DNA Synthesis Pre Check Assistant, is valuable, but it feels more specialized. It’s great for labs that regularly order DNA, but it doesn’t help the broader community as much as the other two. It also introduces some workflow friction, and I can imagine small labs getting frustrated if the tool flags things too aggressively. So I see it as a useful add on rather than a core priority. Trade offs, Assumptions, and Uncertainties There are a few trade offs I had to think through: • Option 1 relies on people actually using it. Tools and checklists only work if people adopt them, and community labs can be very independent. I’m assuming that if the toolkit is well designed, friendly, and not preachy, people will actually want to use it. • Option 3 assumes kill switches are reliable enough to matter. In reality, biology is messy. Even the best kill switches can fail in unexpected environments. I’m assuming that a curated library with clear documentation will reduce misuse and overconfidence. • Option 2 assumes people want to screen their own DNA orders. Some labs might see this as extra work or feel like they’re being policed. I’m assuming the tool can be designed in a way that feels helpful rather than punitive. There’s also uncertainty around how these tools would be maintained long term. Community resources often start strong and then fade unless someone takes ownership.

Who I Would Recommend This To? If I had to choose an audience, I’d direct this recommendation to leaders of community biology labs, DIYbio networks, and local makerspaces. These are the groups that would benefit the most from Option 1 and Option 3, and they’re also the ones who can adopt new norms quickly without waiting for national regulations. I’d also share it with organizations like iGEM, ABSA, and the Global Community Bio Summit, since they already play a role in shaping norms and could help maintain shared resources like the kill switch library. If you want, I can help you craft the final ethical reflection section in the same human tone, so your whole assignment feels cohesive and personal.

Homework Questions from Professor Jacobson Cells copy their DNA using an enzyme called DNA polymerase. It does a pretty good job, but it’s not flawless—on average, it makes about one mistake per million base pairs. When you compare that to the size of the human genome (about 4.2 billion base pairs), you end up with roughly 3,200 potential errors every time a cell divides. That sounds alarming, but cells have several built‑in proofreading and repair systems. These teams of proteins constantly scan the DNA, spot errors, and fix them. They dramatically cut down the number of mistakes, though they can’t eliminate them entirely. Another topic is how many different DNA sequences could encode a typical human protein—and why some sequences work better than others. A “typical” protein is about 345 amino acids long, which corresponds to around 1,036 base pairs of DNA. Because the genetic code is redundant, multiple codons can specify the same amino acid. But in real organisms, not all codons are treated equally. Some are translated faster or more accurately, depending on the cell’s machinery. And translation speed matters: if the ribosome moves too quickly or too slowly, the protein may not fold correctly, which can affect its function.

Homework Questions from Dr. LeProust The most widely used technique for making synthetic DNA fragments (oligonucleotides) is solid‑phase phosphoramidite synthesis. It works extremely well for short sequences, but the longer the oligo, the more problems arise. Once you get past about 200 nucleotides, errors start piling up and the chemistry becomes less efficient. This is why you can’t just “chemically synthesize” a full 2,000‑base‑pair gene in one go—the error rate becomes too high over such a long stretch. Instead, long genes are usually built by assembling many shorter, more reliable pieces.

Homework Question from George Church Animals need ten essential amino acids that they can’t make themselves and must obtain from their diet: arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. This ties into the “Lysine Contingency” from Jurassic Park. In the story, the dinosaurs are engineered so they can’t produce lysine, meaning they would die without special supplements from the lab. The issue is that this wouldn’t actually work—no animals can make lysine, not even modern ones. They all get it from food. So if the dinosaurs escaped, they could simply eat plants or animals that contain lysine and survive just fine. It’s a clever narrative idea, but biologically unrealistic. A more plausible failsafe would involve engineering a dependency on something that doesn’t already exist in nature or that only the lab can provide.

Professor Jacobson

1. DNA Polymerase Error Rate and How Cells Deal With It DNA polymerase is the enzyme responsible for copying DNA, but it isn’t flawless. On its own, it makes about one mistake per million bases it copies. Considering the human genome is roughly 3 billion base pairs, that would add up to around 3,000 errors every time a cell divides if nothing else helped. Fortunately, cells have several layers of quality control. DNA polymerase can actually proofread as it goes, using a 3′→5′ exonuclease activity to remove incorrectly added bases. This proofreading step alone improves accuracy dramatically—down to about one error per billion bases. After replication finishes, additional mismatch repair systems sweep through the DNA to catch and fix leftover mistakes. Another reason this error rate doesn’t cause constant problems is that most of the human genome doesn’t code for proteins. Many mutations land in non‑coding regions and have no effect. And if a cell does accumulate harmful mutations—something that often happens in cancer-prone cells—it can trigger apoptosis, a built‑in self‑destruct program that prevents damaged cells from continuing to divide.

2. How Many Ways You Can Code a Protein—and Why Most Sequences Fail In theory, there are an astronomical number of DNA sequences that could encode a typical human protein. A protein of about 345 amino acids (roughly 1,036 base pairs) can be encoded in many different ways because the genetic code is redundant. When you do the math, you get around 10¹⁵⁷ possible DNA sequences that would all produce the same amino acid chain. But in real cells, most of those sequences would perform poorly. One major reason is codon bias. Different organisms prefer certain codons over others, and using rare codons can slow translation because the matching tRNAs are scarce. The DNA sequence also affects how the mRNA folds, and if the mRNA forms very stable structures, ribosomes can stall or stop prematurely. That can reduce protein production or even change how the protein folds, which affects its function.

Dr. LeProust – Oligonucleotide Synthesis

1. The Current Method Today, the standard way to make synthetic DNA fragments (oligonucleotides) is solid‑phase phosphoramidite chemical synthesis. It’s reliable, efficient, and widely used.
2. Why Oligos Get Hard to Make Beyond ~200 Nucleotides The challenge with long oligos is that each chemical step has a small chance of failing. Even with a high stepwise efficiency—say, around 99.5%—those tiny losses add up. As the sequence gets longer, the probability of ending up with a perfectly correct strand drops sharply. By the time you reach 200 nucleotides, the amount of full‑length, error‑free product becomes very small. Other issues, like side reactions and physical crowding on the solid support, also make long sequences harder to synthesize cleanly.
3. Why You Can’t Directly Synthesize a 2,000 bp Gene Trying to chemically synthesize a 2,000 base pair gene in one piece just isn’t practical. The accumulated errors would leave you with almost no usable product. Instead, scientists synthesize shorter oligos and then assemble them into full genes using enzymatic or biological methods.

George Church

1. The Ten Essential Amino Acids Most animals—including humans—can’t make ten of the amino acids they need. These essential amino acids must come from the diet: arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. Arginine is sometimes considered “semi‑essential,” especially during periods of growth.
2. Why the Jurassic Park “Lysine Contingency” Doesn’t Work In Jurassic Park, the dinosaurs are engineered so they supposedly can’t make lysine, meaning they’d die without supplements from the lab. The problem is that no vertebrate can make lysine anyway—all animals already depend on dietary lysine. And lysine is easy to find in nature. Plants, animals, insects—almost everything in a natural ecosystem contains lysine. So escaped dinosaurs could simply eat lysine‑rich food and survive. It’s a fun plot device, but biologically it doesn’t hold up. A real failsafe would need to involve a dependency on something not found in the wild.

Week 2 HW: DNA read, write and edit

Part 1

Here is the image that I managed to make using electrophoresis gel art:

vs

It’s not really the same but if you imagine the emoji having long hair you can sort of see it.

A friend of mine said that it reminds it of Frisks face from undertale and ever since I’ve started seeing it aswell.

Part 3

I chose fibroin, which is one of the two main proteins found in silk. Silk in its raw state consists of two main proteins, sericin and fibroin, with a glue-like layer of sericin coating two singular filaments of fibroin called brins. I used uniprot to find a sequence for fibroin that is produced by the Aliatypus thompsoni, also know as the trapdoor spider: SQ SEQUENCE 193 AA; 18898 MW; 94DFC97AA25F9796 CRC64; ASSASGASSS IGVASSKGVA SSSKTATKAR ISAGSSGSST STKSSSSAST AVPTNLSGSR SHALSSSNSG QDNTVGDDFG LGYISGGILP VNTPALNFPS DLGSLTSGLL SSLDGPVLPS VEYRITSLTS SVLSLLSTSG GAFNYSSFAK NLAILAYQIS VSNPGLSVSQ VVSETLLESV GALIHILVSS QVG

After I reverse translated, the protein looks like this (using https://www.genecorner.ugent.be/rev_trans.html): GCGAGCAGCGCGAGCGGCGCGAGCAGCAGCATTGGCGTGGCGAGCAGCAAAGGCGTGGCG AGCAGCAGCAAAACCGCGACCAAAGCGCGCATTAGCGCGGGCAGCAGCGGCAGCAGCACC AGCACCAAAAGCAGCAGCAGCGCGAGCACCGCGGTGCCGACCAACCTGAGCGGCAGCCGC AGCCATGCGCTGAGCAGCAGCAACAGCGGCCAGGATAACACCGTGGGCGATGATTTTGGC CTGGGCTATATTAGCGGCGGCATTCTGCCGGTGAACACCCCGGCGCTGAACTTTCCGAGC GATCTGGGCAGCCTGACCAGCGGCCTGCTGAGCAGCCTGGATGGCCCGGTGCTGCCGAGC GTGGAATATCGCATTACCAGCCTGACCAGCAGCGTGCTGAGCCTGCTGAGCACCAGCGGC GGCGCGTTTAACTATAGCAGCTTTGCGAAAAACCTGGCGATTCTGGCGTATCAGATTAGC GTGAGCAACCCGGGCCTGAGCGTGAGCCAGGTGGTGAGCGAAACCCTGCTGGAAAGCGTG GGCGCGCTGATTCATATTCTGGTGAGCAGCCAGGTGGGC

Afterwards I had to optimize the codon sequence, which results in this: GCATCTAGCGCGTCTGGTGCGAGCAGCTCTATTGGTGTGGCAAGCTCTAAGGGCGTTGCGTCTAGCAGCAAAACCGCGACCAAAGCGCGTATTAGCGCGGGTAGCAGCGGCAGCTCTACCAGCACCAAGTCTAGCAGCTCTGCAAGCACTGCGGTTCCGACCAACTTATCTGGCTCTCGTAGCCATGCATTAAGCTCTTCTAACAGCGGCCAGGACAACACTGTTGGTGATGATTTTGGCCTGGGCTATATTAGCGGTGGCATCCTGCCAGTTAACACCCCAGCGCTGAATTTTCCAAGCGATTTAGGCTCTTTAACTAGCGGCCTGCTGAGCTCTTTAGATGGCCCAGTGTTACCGTCTGTTGAGTATCGTATCACTTCTTTAACTTCTTCTGTTTTAAGCCTGCTGAGCACTAGCGGCGGTGCGTTTAACTACTCTAGCTTCGCGAAAAACCTGGCAATCCTGGCGTACCAAATCAGCGTTAGCAACCCGGGTCTGAGCGTGTCTCAGGTGGTTTCTGAGACCCTGTTAGAATCTGTTGGCGCACTGATCCATATCCTGGTGAGCAGCCAGGTGGGT

The question still stands… How can I produce this protein using my DNA?

Well obvioulsy I can’t make the sequence straight from my DNA since my body doesn’t produce silk. What I can do instead is use my DNA as a backbone (which I can just extract from my cells) and edit it so it has the fibroin sequence using CRIPS-based editing or PCR-based gene assembly. After I have the right sequence for fibroin production, it will be insedrted into a plasmid and then transported into E-coli cells. Inside a cell, RNA polymerase reads the DNA sequence and it produces mRNA copy of it. Ribosomes translates it into the polypeptide chain which is then folded into it’s desired shape (in our case silk fibers).

Part 4

4.2

This is the given example with the E-coli glowing phosphorescent green under UV light.

4.3-6

Part 5

5.1 DNA READ

i) Since I’m a computer engineer at base, I would love to be able to make a synthetic DNA data storage. Other than my personal preference for this conjucture of amazing subjects, DNA data storage is the most efficient way of storing information since it doesn’t need any energy after creation and can store insane ammounts of information (~215 petabytes in 1 gram of DNA) for long periods of time in minimal spaces. However sequencing is critical due to the errors that can occur during encoding. Since DNA stores information passively, sequencing is crucial when decoding aswell.

ii) I would most likely need the Third-generation sequencing mostly because the DNA sequences that are supposed to be stored are long. Not only that, but since ONT can read continious sequences of DNA molecules and give me real-time answers clearly makes it the superior option. In addition, it does not require PCR amplification to create a signal, meaning it bypasses the “vicious” cycle of amplification biases that can sometimes lead to errors in GC-rich regions of the genome.

5.2 DNA write

i) In my opinion, it would be a dream to be able to produce a synthetic genetic construct that allows a host organism to efficintly produce spider silk proteins. The production of silk should most likely be regulated so I know exactly the length of each strand, or why not make them arbitrarily. Why you may ask? Silk is one of the strongest biodegradable materials known to man, and it also has a lot of uses in medicine, textile industry and engineering. For some kids out there maybe the “I want to become Spiderman” wish might come true one day.

ii) So what technologies would I need?

If I wanted to synthesize a spider‑silk gene, I’d start by ordering commercially made oligonucleotides and then assemble them using methods like Gibson Assembly or overlap‑extension PCR. These approaches are well‑suited for building long, repetitive genes and let me incorporate codon optimization along with any regulatory elements I need, such as promoters, ribosome‑binding sites, and terminators.

To verify the construct, I’d rely on a combination of Sanger sequencing and long‑read next‑generation sequencing (like PacBio or Oxford Nanopore). Sanger is extremely accurate (around 99.99%) but it’s slow and not ideal for very long or highly repetitive sequences, so I’d use it mainly to confirm individual repeat units. Long‑read NGS, on the other hand, can capture the entire gene in one go and handles repetitive regions well, though each read is a bit less accurate and requires some bioinformatic assembly.

Using both methods together gives a high level of confidence that the full‑length gene was synthesized correctly, striking a good balance between accuracy, speed, and scalability.

5.3 DNA edit

i) When I think about valuable DNA edits, I’m most interested in changes that improve health, resilience, and environmental sustainability, rather than enhancements for their own sake. In humans, this could mean correcting disease-causing mutations, like inherited metabolic disorders or certain forms of blindness, where fixing a single faulty gene could prevent suffering without changing a person’s identity. Another promising area is reducing age-related decline, helping people stay healthier as they get older rather than trying to extend lifespan indefinitely. To me, the worst part about aging isn’t my appearance changing, but me losing my senses (poorer eyesight and hearing), so something of great interest to me would be to manage to slow down something like eyesight loss.

I’m cautious about human enhancement, such as boosting intelligence or physical ability, because of ethical concerns around fairness and consent. But using DNA editing to prevent disease, strengthen ecosystems, and support sustainable agriculture seems like a direction with clear benefits and manageable risks.

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

If the goal is to reduce eyesight degradation, particularly conditions driven by inherited mutations or age‑related cellular decline—the most appropriate technologies would be those that offer high precision and minimal disruption to the genome. In this context, CRISPR‑based tools remain the leading candidates, but not the traditional cut‑and‑repair versions. Instead, I would look toward base editing and prime editing, which are designed to make extremely targeted changes without creating double‑strand breaks. That matters because retinal cells are delicate, slow‑dividing, and difficult to replace, so any editing approach needs to be as gentle and predictable as possible.

Base editors are useful when eyesight degradation stems from a single incorrect DNA letter, because they can convert one nucleotide into another with high specificity. Prime editors go a step further, allowing slightly larger corrections or small insertions and deletions, which could be valuable for conditions where the underlying mutation is more complex. Both technologies aim to correct the genetic cause of degeneration before it leads to irreversible damage.

For age‑related vision loss, where the problem isn’t a single mutation but a gradual decline in cellular function, the focus would shift toward editing pathways that support cellular resilience, mitochondrial health, or protective responses to oxidative stress. Again, precision tools like prime editing would be preferable, because they allow subtle adjustments to gene regulation without fundamentally altering the identity or behavior of retinal cells.

Across all of these possibilities, the priority is safety: technologies that minimize off‑target effects, avoid unnecessary DNA breaks, and allow careful control over the extent of the edit. The retina is one of the most sensitive tissues in the body, so any intervention must be both scientifically justified and ethically grounded

Week 3 HW: Lab automation

Part 1:

Here is the reference image that I managed to make using the opentrons art GUI:

I tried my best to recreate it on my own (no ai) in colab but it didn’t work quite well:

I will most likely have fun with the python code later aswell! Maybe I’ll design an even better looking cat.

Part 2:

I found a very interesting article related to protein crystallization that uses Opentrons-2 liquid handling (you can find the whole article here: https://www.sciencedirect.com/science/article/pii/S2472630325000263)

This paper looks at how a research team used the Opentrons OT-2—an affordable liquid-handling robot—to take over one of the most tedious parts of protein research: setting up crystallization experiments. Growing protein crystals is famously slow, delicate work, and when scientists need lots of crystals that all behave the same way—for studying molecular structures or making biomaterials—the manual effort quickly becomes exhausting. The researchers wanted to know if a general-purpose robot like the OT-2 could handle this large-scale setup reliably.

A difficult part about protein crystalization automation comes from the preparation of the samples. To speed up the process, they programmed the robot in Python and modified it to work with larger 24-well crystallization plates. This wasn’t plug-and-play—they had to design and 3D-print a custom adapter and create a matching software definition so the robot would know how to handle the plates. They then put the system through three tests: mixing colored liquids to check accuracy, growing crystals of lysozyme, and crystallizing a more complex protein from Campylobacter jejuni that their lab studies as a biomaterial.

The results were better than expected. The robot could mix solutions, fill reservoirs, and set up crystallization drops accurately and consistently. When compared with plates prepared by human researchers, the robot’s plates actually produced crystals more reliably—although the robot worked a bit more slowly. Just as importantly, it spared scientists from hours of repetitive pipetting, reduced experiment-to-experiment variation, and managed most tricky steps with only small tweaks for thicker liquids or mixing needs.

In short, the study shows that a relatively inexpensive, flexible robot can take over a big chunk of protein crystallization work. That makes experiments more consistent and far less labor-intensive. For labs that don’t have the budget for specialized crystallization machines, this approach could be a practical and accessible alternative.

I’m not yet 100% sure what my final project, so for the sake of answering the question regarding lab automation in my project let’s take the fungal-material growth project. The project consists of a small automated setup for growing fungal mycelium in a controlled environment. Lab automation is important because it reduces the ammount of work keeping the conditions (like humidity, temperature, airflow, and moisture) of a certain test ideal, that would be absolute hell to do by hand. Mycellium is very sensitive, automating them should make the growth process more consistent and easier to reproduce.

The setup will include sensors that track humidity and temperature, along with a microcontroller that adjusts a humidifier, heater, and small fans to keep conditions stable. I also want to automate substrate hydration using a simple pump system, since moisture levels strongly influence how the material develops. A camera will record the growth over time, allowing me to monitor progress and analyze how different conditions affect the final material. The camera is very important since it can help me detect contamination and also measure the are covered by the fungi

All data from the sensors and the camera will be logged automatically, giving me a clear picture of how the fungus responds to different environments. By combining these elements, the project becomes a small “smart” fungal farm that can grow mycelium materials in a controlled, repeatable way.

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project