Week 1 HW: Principles and Practices

Bio-Hybrid Fusion Blanket

Research Context:
I am currently a research assistant investigating Magnetohydrodynamics (MHD), specifically focusing on the complex interactions between magnetic fields and 150-million-degree plasma. My work involves optimizing plasma confinement within Tokamak reactors. At these extreme temperatures, the behaviour of the plasmas is governed by a delicate balance of magnetic pressure and fluid dynamics, creating an environment that is incredibly hostile to the physical structures surrounding it.

Physics Problem:
In a Deuterium-Tritium fusion reactor, the blanket is a critical component that lies in the interior of the reactor. It captures high energy neutrons released by fusion converting their kinetic energy into heat which generated electricity. It also contains lithium which when struck by those neutrons breeds tritium, the fuel we can recycle in the reactor.

Currently, these blankets are limited by severe material degradation. High-energy neutron bombardment causes metals to swell, become brittle, and crack from the inside out as waste reactants accumulate. Plasma is also a volatile fluid that is difficult to control with magnets; sudden disruptions can dump massive thermal loads onto the reactor walls. Since current materials are rigid and static, they cannot absorb or repair these shocks, leading to surface melting and catastrophic structural failures.

The proposal:
The idea is to develop a bio-hybrid, self-healing blanket for the fusion reactor replacing rigid metal walls with a dynamic system where biology acts as both the architect and the maintenance crew.

One idea could be utilizing synthetic biology to grow the initial reactor structure. By using biology as a 3D template, we can grow a reactor structure that places lithium atoms with perfect accuracy. This creates a more efficient fuel-making system inside a heat-shield wall filled with tiny, vein-like cooling channels that traditional machines simply can’t build.

Another idea involves using the reactor’s downtime as a biological recovery phase. Once the system is cooled, the network of vascular channels becomes a highway for bespoke, bio-engineered cells designed to seek out and clear trapped helium waste. These cellular workers then secrete new mineral precursors to “re-grow” the scaffolding at the site of neutron-induced cracks, allowing the blanket to rejuvenate its structural integrity like a self-maintaining organ.

One goal would be to ensure the bio-hybrid blanket is easy to clean up. We want to avoid creating bio-nuclear waste that is harder to handle than regular blanket material.

Sub goal 1: Easy deconstruction – the biological structure should be non-toxic and easy to recycle and dissolve away after use; we should be able to filter out and recycle expensive metals used such as lithium.

Sub goal 2: Chemical safety – the maintenance cells must be engineered so they don’t produce harmful chemicals while they work, so the reactor process doesn’t require hazardous waste treatment.

Action 1: Digital DNA Registry (Technical Strategy)
Actor: Researchers
Purpose: Move away from secret, proprietary cell design to a shared public database of genetic blueprints.
Design: Researchers must upload their genetic code of their cells to a registry so other people know how to handle and recycle them.
Assumptions: Assumes labs will share blueprints, and that a global standard for DNA data would work.
Risk of Failure: Bad actors could learn how to destabilise or reverse engineer cells since it’s public.
Success: Any country could build and recycle their reactors and blankets.

Action 2: Green Fusion Tax Credits (Financial Incentive)
Purpose: Reward reactors that prove they are highly recyclable.
Design: The government would give extra funding or tax breaks to companies whose bio blankets leave minimal toxic waste behind.
Assumptions: Assumes money is the biggest motivator for companies to prioritize over speed of reactor development.
Risk of Failures: Companies might greenwash their data to get money without being clean.
Success: Low waste reactors become the most profitable way to run and becomes industry standard.

Action 3: Biological Security (New Rule)
Purpose: Prevent technology from being turned into a biological weapon that can survive extreme environments.
Design: Require fusion labs to store biological material in high security facilities with background checks like those used for handling nuclear fuel.
Assumptions: Assumes these bio engineered cells are dangerous.
Risk of Failure: Expensive security could slow down science, so we never get to clean fusion energy.
Success: Only good actors working on building innovative materials to help achieve clean energy get access to these biomaterials.

I would prioritize Action 3: Biological Security as the main requirement addressed to the U.S. Department of Energy and Defense. This is because we first and foremost should address the immediate risk of creating bioweapons that can withstand radiation and high temperatures. This ensures that the foundation of the industry is built on containment and control before scaling or commercialization. Once the technology is regulated in a similar manner to nuclear fuel, Action 1 should be incentivized serving as a long-term safety net, providing a transparent repair manual for materials once they are safely deployed.

Trade-offs and Uncertainties:
Innovation vs. Security: The primary trade-off is that high security increases costs and can slow down academic research. There is a risk that over-regulating early-stage biology could delay clean fusion energy development.
Assumption of Risk: This plan assumes these bio-engineered cells are dangerous enough to warrant military-grade security. If the cells are actually fragile outside the reactor, the security measures might be unnecessary.

Error rate for polymerase is 1 in 10⁶ bases. The human genome length is 3.2 × 10⁹ bases. Biology deals with the discrepancy using the MutS Repair system.

Average Human Protein: 1036bp = 345 amino acids
Each amino acid can have 61 sense codons – so that’s 61^345 = huge number of different ways. Most codes don’t work in practice because differences in codon bias, mRNA and translation efficiency can disrupt expression, stability, or correct protein production.

Phosphonamidite DNA Synthesis.

Due to the high error rate – 1 in 10^2 per base so errors and truncated products accumulate exponentially with each base addition cycle.

After 2000 chemical synthesis cycles, errors and incomplete couplings accumulate at each step, and because the process has no proofreading, nearly all strands become truncated or mutated, leaving virtually no correct full-length product.

10 essential amino acids which can’t be synthesized in the body:
Phenylalanine
Valine
Threonine
Tryptophan
Isoleucine
Methionine
Histidine
Arginine
Leucine
Lysine

Since Lysine is one of the amino acids which can’t be synthesised, lysine contingency as a strategy for bio containment exploits this natural dependency to control.

Sources:

https://nutrenaworld.com/blog/horses/what-are-essential-amino-acids-in-protein-and-why-do-they-matter/

Ai prompt – What is Lysine Contingency:

Lysine Contingency is a biocontainment strategy where an engineered organism is made unable to synthesize lysine, so it can only survive if lysine is externally supplied.

Week 2 HW: DNA Design Challenge

I chose the ATP synthase beta subunit because it’s essentially a biological motor and connects to my broader interest in energy systems:

Protons flow down their gradient across the mitochondrial membrane, almost like current moving through a circuit, and that flow physically spins part of the protein like a tiny turbine. That rotation drives changes in the beta subunits, which catalyze the formation of ATP from ADP and phosphate.

So it’s literally energy stored in a gradient being converted into mechanical motion and then into chemical energy. I find that idea really compelling, it’s molecular thermodynamics in action, where fundamental physics laws become something tangible inside living cells.

From NCBI I obtained the protein sequence:

https://www.ncbi.nlm.nih.gov/protein/NP_001677.2/

https://www.ncbi.nlm.nih.gov/protein/NP_001677.2?report=fasta

We know we go from 3 DNA bases → RNA → 1 Codon → 1 Amino Acid → 1 Protein letter

We can find the nucleotide record

https://www.ncbi.nlm.nih.gov/nuccore/NM_001686.4
https://www.ncbi.nlm.nih.gov/nuccore/NM_001686.4?report=fasta

Multiple codons can code for the same amino acid, but different organisms prefer certain codons over others. So we have to optimize codon usage for that specific organism otherwise translation might be inefficient, we want to use tRNA’s that are plentiful – which bind to that specific codon attaching the specific amino acid.

I have chosen E.coli as the organism to optimize the protein sequence for. Since we use them in the fluorescent bacteria artwork lab!

Above is the entire mRNA sequence, but we need the coding sequence (CDS) – the mRNA sequence has additional information like a start and end codon and untranslated regions. We can go to the CDS record instead, obtain the coding sequence and then use our codon optimization on it.

https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi?REQUEST=CCDS&DATA=CCDS8924.1
https://www.idtdna.com/CodonOpt

We get this result:

We can use cell dependent expressions, like cloning the optimized DNA sequence into a plasmid vector and introducing it into a host organism such as E.coli. Once inside the promoter recruits RNA polymerase and transcribes the DNA sequence into mRNA. The ribosomes then binds to the mRNA and tRNA’s match codons and deliver amino acids. The amino acids are then linked together to form the protein. The bacteria would then produce ATP synthase beta subunit as part of their cellular machinery.

Expression Cassette

https://benchling.com/s/seq-QDGibA4g7TjoTuX3lb5A?m=slm-Gx8zqXYh9sr4lxSK0Xqu

We can view the full plasmid sequence for our clonal genes (circular dna) and pTwist Amp High Copy cloning vector in Benchling:

https://benchling.com/s/seq-wsl9w63Z5DcxN7rlp5cG?m=slm-ndl9y5U2FSsJgNYW6z7

I would choose to sequence the DNA of extremophiles that thrive in high-radiation or high-temperature environments. By sequencing genes involved in radiation resistance, DNA repair, and protein stabilization, we could better understand the molecular mechanisms that allow biological systems to survive under extreme stress. This knowledge could help inform the engineering of radiation-resistant biological materials or bio-hybrid systems designed to operate in harsh energy environments. Studying these organisms connects molecular biology with broader challenges in advanced energy systems.

I would use Illumina sequencing to sequence the DNA since it provides high accuracy and high throughput and is well suited for whole-genome sequencing and variant detection. It’s a second generation technique, sequencing millions of short DNA fragments in parallel using sequencing-by-synthesis. Illumina sequencing reads DNA by copying it one base at a time and taking a picture after each base is added.

The input would be the extracted genomic DNA.

Preparation steps:

1. Fragment DNA into short pieces
2. Ligate sequencing adapters
3. PCR amplify fragments
4. Load onto flow cell for cluster amplification

Essential sequencing steps:

1. DNA fragments bind to flow cell
2. Bridge amplification forms clusters
3. Fluorescently labeled nucleotides are added one at a time
4. A camera detects the fluorescent signal for each incorporated base
5. The color signal determines the base

The output would be millions of short sequence reads containing nucleotide sequences and quality scores which can be assembled into a genome or aligned to a reference.

I would want to synthesize a cluster of genes involved in enhanced DNA repair and protein stabilization from extremophiles and express them in a model organism. By combining multiple protective pathways, we could engineer cells with improved resistance to radiation and thermal stress. The idea would to use this to develop radiation-resistant biomaterials or biological components for extreme energy environments. We could build a genetic circuit that enables engineered bacteria to sense and respond to radiation stress. This circuit could include radiation response promoters, DNA repair genes and protective protein pathways that activate under high oxidative or ionizing radiation conditions.

To synthesize this genetic circuit, we could use Twist combined with phosphoramidite solid-phase DNA synthesis and Gibson Assembly for multi-fragment assembly.

Essential steps:

1. Design optimized DNA sequence computationally
2. Chemically synthesize short oligonucleotides (base-by-base addition)
3. Cleave and purify oligos
4. Assemble fragments into full-length gene (e.g., Gibson Assembly)
5. Clone into plasmid backbone
6. Sequence-verify construct

Limitations:

Length limits: Direct chemical synthesis is reliable only for short fragments with longer genes requiring assembly. There’s also base errors so we would need to do sequencing validation and it can be very expensive for large gene clusters and take a large amount of time.

I would edit the genomes of photosynthetic microorganisms such as algae to improve their efficiency in converting light energy into chemical fuels. I could target genes involved in photosystem efficiency, carbon fixation pathways, and hydrogen production.

Photosynthesis is essentially a natural solar energy conversion system, but it is quite inefficient. We could modify regulatory genes to reduce energy losses or redirect metabolic pathways toward hydrogen or biofuel production, so we could have biological systems that convert sunlight into storable chemical energy more efficiently.

I am interested as it connects directly to large-scale energy systems and treating living cells as programmable energy conversion platforms, similar to designing more efficient reactors or turbines.

WE could use CRISPR-Cas12a for genome editing in cyanobacteria.

How does it edit dna

1. Design guide RNAs targeting specific genes.
2. Deliver Cas12a and guide RNAs into the cells.
3. Cas12a cuts the DNA at precise locations.
4. The cell repairs the cut using a donor DNA template to insert optimized sequence

Design:

• Identify metabolic bottlenecks in photosynthesis or fuel production.
• Design guide RNAs.
• Design donor DNA templates if inserting new sequences.

Inputs:

• Cas enzyme
• Guide RNAs
• Donor DNA (if needed)
• Host cells (e.g., cyanobacteria)

Limitations

• Off-target edits may occur.
• Large pathway rewiring is complex.
• Efficiency gains may be modest due to thermodynamic constraints.

Week 1 Lab: Pipetting

First time in a wet lab very exciting!

I learned about the different pipette ranges: P20 1–20 µL, P200 20–200 µL, P1000 100–1000 µL and when to use each one appropriately.

I also practiced proper pipetting technique holding the pipette vertically identifying the first and second stops when pressing the plunger and carefully controlling the release to ensure accurate and precise liquid handling.

I got a sneak peek at gel electrophoresis and how it can be used to separate DNA fragments.

Week 2 Lab: DNA Read Write Edit

In Lab 2 I learned how restriction enzymes can be used to cut DNA at very specific sequences, almost like precise molecular scissors. These enzymes recognize short DNA sequences called restriction sites and cleave the DNA at or near those locations, allowing us to deliberately fragment genetic material in a controlled way.

I also learned that restriction enzymes, or endonucleases, naturally come from bacteria. In their original context, they act as a defense mechanism by cutting up invading viral DNA, protecting the bacterial cell from infection. It was interesting to see how a biological immune strategy becomes a foundational lab tool.

We then discussed how CRISPR can be thought of as a generalized or programmable restriction enzyme. Instead of being limited to one fixed recognition site, CRISPR systems can be guided to almost any DNA sequence, making them far more flexible and powerful for gene editing.

I also learned how to use Benchling to simulate restriction enzyme digests in silico. We uploaded DNA sequences and tested different enzyme combinations to see how the DNA would be cut and what fragment sizes we would expect before actually running the gel.

To run a virtual digest, the DNA sequence has to be uploaded in a standard format, usually either FASTA or GenBank.

I learned in more detail how gel electrophoresis works and why DNA moves through the gel the way it does. Because DNA has a negatively charged phosphate backbone, it migrates toward the positive electrode when an electric field is applied. The agarose gel acts like a molecular sieve, so smaller DNA fragments move faster and travel further than larger ones, allowing the fragments to separate by size.

I weighed out agarose and mixed it with 1x TAE buffer to make a 1 percent solution. I microwaved it in short bursts until it fully dissolved, let it cool slightly, added SYBR Safe stain, poured it into the gel tray with a comb inserted, and allowed it to solidify to form wells.

I prepared the DNA digestion mixture by combining lambda DNA, the correct enzyme buffer, the chosen restriction enzyme or enzymes, and nuclease free water. I then incubated the tubes at 37 degrees Celsius so the enzymes could cut the DNA into fragments.

After the gel set, I removed the comb, filled the gel box with 1x TAE buffer, and mixed my DNA samples with loading dye. I carefully loaded each well without puncturing the gel and ran the gel at around 80 to 115 volts for about 45 minutes to separate the DNA fragments by size.

Once the run was complete, I transferred the gel to a blue light transilluminator, and captured an image of the separated DNA bands to analyze the pattern of fragments - there was a lot of noise but the experiment was fun nonetheless.

Week 3 Lab Automation

In Lab 3 I learnt about Opentrons and how lab automation can turn biology into something creative and visual. We used the Opentrons OT-2 pipetting robot to precisely deposit genetically engineered E. coli onto black charcoal agar plates. These bacteria were engineered to express fluorescent proteins in different colors, so when the plates were placed under UV light, the patterns we programmed glowed brightly.

It was a cool mix of automation and biology. Instead of manually pipetting, we let the robot handle the precise liquid handling, which made it possible to create detailed, glowing bio-art designs. It felt like combining coding, synthetic biology, and art into one project, and it gave a glimpse of how automation can scale up much more serious biological experiments too.

We learned how to use the Opentrons Python API to write a protocol, essentially a set of instructions that controls the robot’s pipettes. Instead of manually pipetting, we defined coordinates, volumes, and movement steps in code so the robot could deposit liquid precisely into specific wells to create a defined pattern.

Also we could simulate the protocol before running it on the actual robot. This let us preview how the design would look, check for mistakes, and adjust the pattern in software first.

https://opentrons-art.rcdonovan.com/

One of the coolest parts of this lab was using Opentrons Art, a tool built by TA Ronan that turns lab automation into a creative platform. Instead of writing everything from scratch in Python, this interface dramatically simplifies the workflow for creating agar-based designs. You can literally paint directly onto a virtual plate or upload an image, and the tool converts it into a protocol-ready layout for the robot.

What makes it profound is it’s become a living archive of art created by HTGAA students over time. It transforms a liquid-handling robot into a medium for expression, blending synthetic biology, automation, and visual design!

1. Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode or Python scripts, procedures you may need to automate, 3D printed holders you may need, and more.

I want to use the Opentrons to prototype my bio-self healing blanket idea by automating two core parts of the project. First, I could screen different conditions that encourage biological mineralization or coating formation on scaffold materials. Second, I could test simplified self-healing systems where engineered cells or cell-free reactions deposit repair material in response to specific chemical damage signals. The robot is useful because it can run large combinatorial matrices of pH, ions, nutrients, and precursor concentrations with precision and consistency, and it can repeat dosing, media swaps, and sampling over time without constant manual pipetting.

In the first automated pipeline, I would distribute different mineralization conditions across a multiwell plate containing scaffold coupons. At set times, the Opentrons would refresh media, add precursor doses, and take small aliquots for downstream measurements. In the second pipeline, I would generate gradients of damage cues such as ionic strength or pH and then introduce cells plus repair precursors to see whether deposition localizes to the most damaged regions. This becomes a fast, reproducible way to test both the “architect” build phase and the “maintenance crew” repair phase of the concept.

2. Find and describe a published paper that utilizes the Opentrons or similar automation tools to achieve novel biological applications (eg automated PACE)

The paper DNA-BOT: a low-cost, automated DNA assembly platform for synthetic biology shows how researchers used an Opentrons OT-2 robot to automatically assemble DNA instead of doing everything by hand. They built 88 different genetic constructs in parallel, mixing and matching promoters and genes to explore lots of combinations quickly and cheaply. The big takeaway is that you don’t need an expensive biofoundry anymore a relatively affordable lab robot can handle high-throughput DNA building for everyday research labs.

https://academic.oup.com/synbio/article/5/1/ysaa010/5869449

Individual Final Project

Group Final Project