Week 1 HW: Principles and Practices

THEME MUSIC🎵

(listen as you read this :))

1. First, describe a biological engineering application or tool you want to develop and why.

I want to develop a biological engineering system for smarter nutrient liberation in agricultural soils, especially for sugarcane. The current nutrient management system depends heavily on chemical fertilizers that release nutrients in poorly timed pulses, leading to massive nutrient losses, soil degradation, and environmental damage. I do not want to sugarcoat it: the situation is bad. I am Brazilian, which also means I come from the country that exports the most sugar in the world, largely through intensive sugarcane monoculture. My goal is to explore how engineered soil microorganisms could sense plant-derived signals and release nutrients only when they are biologically needed, turning the soil from a passive substrate into an active, responsive system.

2. Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals.

The sugar industry is a highly profitable one: the global sugar market was estimated to be valued at $46.4 billion in 2023. That being said, profitability also creates incentives for misuse. If a biological system significantly improves nutrient efficiency, it could be exploited to further intensify monoculture, expand cultivation into fragile ecosystems, or concentrate power in the hands of large agribusinesses rather than improving sustainability. A central governance goal, therefore, is to prevent technological gains from amplifying extractive or environmentally harmful practices. To do that, we can break down into sub-goals such as regulating deployment to avoid land-use expansion and establishing public or open-access frameworks that limit exclusive corporate ownership of engineered soil systems. This is important because it is very important that small and medium-scale farmers also have access to this technology.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”).

Action 1: Establishment of a biosafety and ecological oversight committee; Purpose: Ensure that engineered soil microorganisms are biologically safe, ecologically contained, and function as intended. This includes preventing unintended persistence in the environment, horizontal gene transfer, or disruption of native soil microbiomes. Design: Development of standardized biosafety evaluation protocols, including genetic safeguards (such as kill-switches or nutrient dependencies), controlled soil microcosm experiments, and long-term monitoring of microbial survival and gene stability. Ecological impact assessments would be required before any field deployment. Risk of failure and “success”: Failure: engineered microorganisms persist uncontrollably in the environment, transfer genetic material to native species, or alter soil ecosystems in harmful ways.
Success: the system demonstrates predictable behavior, effective containment, and minimal ecological disruption under real soil conditions.

Action 2: Phased field trials and agronomic validation Purpose: Test whether the engineered microorganisms actually improve nutrient synchronization with plant demand without causing harm, ensuring that efficiency gains are real, reproducible, and context-dependent rather than theoretical. Design: Multi-phase field trials beginning with small, controlled plots and gradually expanding to larger agricultural settings. Always havinf a control group, including runnning comparative studies against conventional fertilization practices, measuring nutrient use efficiency, crop yield, soil health indicators, and environmental runoff. Risk of failure and “success”: Failure: the system fails to improve nutrient efficiency, behaves inconsistently across environments, or introduces new agronomic risks.
Success: nutrient release becomes better synchronized with plant needs, fertilizer input is reduced, and soil quality is maintained or improved over multiple growing cycles.

Action 3: Governance frameworks to prevent extractive or monopolistic use Purpose: Prevent efficiency gains from being exploited to intensify monoculture, expand cultivation into ecologically sensitive areas, or concentrate control within large agribusiness corporations. Design: Creation of policy frameworks that tie deployment of engineered soil systems to sustainability metrics, land-use regulations, and open or publicly governed licensing models. This includes transparency requirements, public-sector involvement, and safeguards against exclusive proprietary ownership of essential soil biotechnologies. Risk of failure and “success”: Failure: the technology is captured by profit-driven actors and used to accelerate environmental degradation or deepen agricultural inequality. Success: the system is deployed in ways that promote long-term soil resilience, equitable access for small and medium-scale farmers, and environmentally responsible agricultural practices.

4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

5. Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties

I would prioritize a combination of Option 1 (biosafety and ecological oversight) and Option 2 (phased field trials and agronomic validation) as the foundation for responsible governance. Because before thinking about the politics of how and by whom the technology will be used (although these questions should be considered from the beginning) we need to make sure it is actually safe to deploy. Establishing biological safety and ecological predictability should be (ALWAYS!) a prerequisite for any ethical discussion about scale, access, or commercialization.

FINAL REFLECTION

One ethical question that really made me stop and think during this project was whether making biological systems more efficient automatically makes them more sustainable. Engineering soil microorganisms to release nutrients in a smarter way could reduce fertilizer use and environmental damage, but it could also be used to push monoculture systems even further or expand agriculture into already fragile ecosystems (as I see it so frequently happening in Brazil :Brazil:). And honestly, I’m not completely sure where the safety limits of genetic modification are, especially in complex environments like soil. That uncertainty is part of what excites me - and I’m eager to explore, question, and learn more about this during HTGA :D

Week 2 lecture preparation

Homework Questions from Professor Jacobson:

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

DNA polymerase makes about one mistake every one million bases copied (an error rate of ~10⁻⁶) after its built-in proofreading activity. The human genome is about three billion base pairs long, so if DNA were copied using only polymerase proofreading, each cell division would introduce thousands of errors 🤯. Biology solves this problem by adding extra layers of quality control after replication, especially mismatch repair and other DNA repair pathways, which reduce the final error rate to roughly one mistake per billion to ten billion bases.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

An average human protein (~1036 bp, ~345 amino acids) can be encoded in astronomically many ways because most amino acids are specified by multiple synonymous codons; in theory this corresponds to roughly to 1 followed by 150 zeros (🤯) different DNA sequences that encode the same protein.

In practice, most of these sequences fail because of codon bias, effects on mRNA stability and secondary structure (as shown by studies linking codon usage to mRNA half-life and folding), translation speed and co-translational folding (as revealed by ribosome-profiling and folding-kinetics experiments), GC-content constraints, and embedded regulatory or splicing signals within coding regions. We can think of this problem as

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently?

The typical method for Oligonucleotide synthesis is the phosphoramidite method

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?

Chemical oligo synthesis accumulates errors because each nucleotide coupling step is slightly imperfect. Altoug there exists papers on

3. Why can’t you make a 2000bp gene via direct oligo synthesis?

A 2000 bp gene would require thousands of sequential coupling steps, causing error rates and truncation products to completely dominate over correct full-length molecules. The yield of an error-free sequence would be essentially zero, so long genes must instead be assembled from shorter, high-quality oligos using enzymatic methods like PCR or Gibson assembly (ChatGPT helped me aswer. My prompt: The question + so what is usually done when a 2000bp+ gene is needed?.

Homework Questions from George Church:

1. What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

The ten essential amino acids in animals are histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine

Week 2 HW: DNA r/w/e

Part 1: Benchling & In-silico Gel Art:

• Simulate Restriction Enzyme Digestion with the following Enzymes:

  • EcoRI
  • HindIII
  • BamHI
  • KpnI
  • EcoRV
  • SacI
  • SalI
  
  • Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks.
    It’s a smiley face!! :)
  

  Part 3: DNA Design Challenge:

3.1 Chose your protein
Alkaline phosphatase. I chose this protein because it plays because it is important for nutrient sensing, as it is naturally activated under conditions of phosphate limitation. In Escherichia coli, alkaline phosphatase (PhoA) hydrolyzes organic phosphate compounds to release inorganic phosphate, directly linking environmental signals to nutrient availability. Additionally, it is a well-characterized enzyme with a resolved structure and extensively documented sequence information, making it an ideal model protein for computational and experimental analysis.

Sequence: (P00634)
MKQSTIALALLPLLFTPVTKARTPEMPVLENRAAQGDITAPGGARRLTGDQTAALRDSLSDKPAKNIILLIGDGMGDSEITAARNYAEGAGGFFKGIDALPLTGQYTHYALNKKTGKPDYVTDSAASATAWSTGVKTYNGALGVDIHEKDHPTILEMAKAAGLATGNVSTAELQDATPAALVAHVTSRKCYGPSATSEKCPGNALEKGGKGSITEQLLNARADVTLGGGAKTFAETATAGEWQGKTLREQAQARGYQLVSDAASLNSVTEANQQKPLLGLFADGNMPVRWLGPKATYHGNIDKPAVTCTPNPQRNDSVPTLAQMTDKAIELLSKNEKGFFLQVEGASIDKQDHAANPCGQIGETVDLDEAVQRALEFAKKEGNTLVIVTADHAHASQIVAPDTKAPGLTQALNTKDGAVMVMSYGNSEEDSQEHTGSQLRIAAYGPHAANVVGLTDQTDLFYTMKAALGLK

3.2 Reverse Translate:
atgaaacagagcaccattgcgctggcgctgctgccgctgctgtttaccccggtgaccaaa gcgcgcaccccggaaatgccggtgctggaaaaccgcgcggcgcagggcgatattaccgcg ccgggcggcgcgcgccgcctgaccggcgatcagaccgcggcgctgcgcgatagcctgagc gataaaccggcgaaaaacattattctgctgattggcgatggcatgggcgatagcgaaatt accgcggcgcgcaactatgcggaaggcgcgggcggcttttttaaaggcattgatgcgctg ccgctgaccggccagtatacccattatgcgctgaacaaaaaaaccggcaaaccggattat gtgaccgatagcgcggcgagcgcgaccgcgtggagcaccggcgtgaaaacctataacggc gcgctgggcgtggatattcatgaaaaagatcatccgaccattctggaaatggcgaaagcg gcgggcctggcgaccggcaacgtgagcaccgcggaactgcaggatgcgaccccggcggcg ctggtggcgcatgtgaccagccgcaaatgctatggcccgagcgcgaccagcgaaaaatgc ccgggcaacgcgctggaaaaaggcggcaaaggcagcattaccgaacagctgctgaacgcg cgcgcggatgtgaccctgggcggcggcgcgaaaacctttgcggaaaccgcgaccgcgggc gaatggcagggcaaaaccctgcgcgaacaggcgcaggcgcgcggctatcagctggtgagc gatgcggcgagcctgaacagcgtgaccgaagcgaaccagcagaaaccgctgctgggcctg tttgcggatggcaacatgccggtgcgctggctgggcccgaaagcgacctatcatggcaac attgataaaccggcggtgacctgcaccccgaacccgcagcgcaacgatagcgtgccgacc ctggcgcagatgaccgataaagcgattgaactgctgagcaaaaacgaaaaaggctttttt ctgcaggtggaaggcgcgagcattgataaacaggatcatgcggcgaacccgtgcggccag attggcgaaaccgtggatctggatgaagcggtgcagcgcgcgctggaatttgcgaaaaaa gaaggcaacaccctggtgattgtgaccgcggatcatgcgcatgcgagccagattgtggcg ccggataccaaagcgccgggcctgacccaggcgctgaacaccaaagatggcgcggtgatg gtgatgagctatggcaacagcgaagaagatagccaggaacataccggcagccagctgcgc attgcggcgtatggcccgcatgcggcgaacgtggtgggcctgaccgatcagaccgatctg ttttataccatgaaagcggcgctgggcctgaaa

3.3 Codon optimization
Codon optimization is important because organisms differ on the way the use certain codons. We know the genetic code is universal and degenerate, meaning that one aminoacid can be encoded by multiple codons, but different organisms do not use these synonymous codons with the same frequency. This phenomenon, known as codon usage bias, affects how efficiently a gene is translated. If a DNA sequence contains codons that are rare in the host organism, the corresponding tRNAs may be scarce, which can slow down translation, reduce protein yield, or even affect proper folding. Therefore codon optimization adjust the sequence to match the codons preferred by the organism, therefore improving the yield and overall expression.
For this project, I chose to optimize the codon sequence for Escherichia coli because it is one of the most widely used host organisms for recombinant protein expression. It grows rapidly, is inexpensive to culture, has well-characterized genetics, and many expression vectors are available. Since alkaline phosphatase (PhoA) is naturally found in E. coli, optimizing for this host ensures efficient production while maintaining compatibility with standard molecular biology workflows.
I used *VectorBuilder’s Codon Optimization Tool.
Result: Pasted Sequence: GC=61.29%, CAI=1.00 // Improved DNA[1]: GC=59.38%, CAI=0.93
Sequence: ATGAAACAGAGCACCATTGCGCTGGCGCTGCTGCCGCTGCTGTTTACCCCGGTCACGAAAGCGCGCACCCCGGAAATGCCGGTGCTGGAAAACCGCGCAGCACAGGGTGATATCACCGCGCCGGGCGGTGCGCGCCGCCTGACCGGCGATCAGACCGCGGCCCTGCGCGATAGCCTGAGCGATAAACCGGCGAAAAATATTATTCTGCTGATTGGCGACGGTATGGGCGATAGCGAAATCACCGCCGCGCGCAATTATGCGGAAGGCGCCGGTGGCTTTTTTAAAGGTATCGATGCGCTGCCGCTGACCGGCCAGTACACCCACTACGCGCTGAATAAAAAAACTGGTAAACCGGATTATGTCACCGATAGTGCGGCCAGCGCAACCGCGTGGAGCACCGGCGTGAAAACCTACAATGGCGCGCTGGGCGTGGATATTCATGAAAAAGATCACCCGACGATTCTGGAAATGGCGAAAGCGGCCGGCCTGGCGACCGGCAATGTGAGCACCGCGGAACTGCAGGATGCCACCCCGGCGGCGCTGGTGGCCCATGTGACCAGCCGTAAATGCTATGGTCCGAGCGCGACCAGCGAAAAATGTCCGGGCAACGCGCTGGAAAAAGGTGGCAAAGGCAGCATTACCGAACAGCTGCTGAACGCGCGTGCCGATGTGACCCTGGGCGGAGGCGCAAAAACCTTTGCCGAAACCGCGACCGCGGGCGAATGGCAGGGCAAAACCCTGCGCGAACAGGCGCAGGCCCGCGGTTATCAGCTGGTTAGCGATGCGGCCAGCCTGAATAGCGTGACCGAAGCGAACCAGCAGAAACCGCTGCTGGGCCTGTTTGCGGATGGTAATATGCCGGTGCGCTGGCTGGGTCCGAAAGCGACCTATCACGGTAACATTGATAAACCGGCGGTGACCTGCACCCCGAACCCGCAGCGCAACGATAGCGTGCCGACCCTGGCACAGATGACCGATAAAGCCATTGAACTGCTGAGCAAAAATGAAAAAGGCTTTTTCCTGCAGGTGGAAGGCGCGTCCATTGATAAACAGGATCACGCAGCCAACCCGTGTGGCCAGATTGGCGAAACCGTGGATCTGGATGAAGCGGTGCAGCGTGCCCTGGAATTTGCGAAAAAAGAAGGTAACACCCTGGTGATTGTGACCGCAGACCATGCGCATGCGAGCCAGATCGTGGCGCCGGATACCAAAGCGCCGGGTCTGACCCAAGCGTTGAATACCAAAGATGGTGCGGTGATGGTGATGAGCTATGGCAACAGCGAGGAAGATAGCCAGGAACACACCGGCAGTCAGCTGCGTATTGCCGCATACGGCCCGCATGCGGCGAACGTGGTGGGCCTGACCGATCAGACCGACCTGTTTTACACCATGAAAGCGGCACTGGGCCTGAAA

3.4. You have a sequence! Now what?
Once the optimized DNA sequence is obtained, it can be cloned into an expression plasmid containing a promoter, ribosome binding site, and selection marker, and then introduced into a host such as Escherichia coli through transformation. Inside the cell, the promoter drives transcription of the DNA into mRNA by RNA polymerase, and ribosomes bind to the mRNA to translate its codons into the corresponding amino acid sequence, producing the alkaline phosphatase protein, which then folds into its functional structure. This is the cell-dependent method.

Part 4: Prepare a Twist DNA Synthesis Order:

I attempted to place the Twist DNA synthesis order, but the platform did not function properly. Although I was able to log in, a message stating “Contact your Distributor” repeatedly appeared, and the system automatically logged me out, preventing me from proceeding with the order. I am aware that other students in my region (Brazil/Latin America) experienced similar difficulties accessing the platform.

Part 5: DNA Read/Write/Edit:

DNA Read

(i) What DNA would you want to sequence (e.g., read) and why?
I would sequence soil metagenomic DNA from the sugarcane rhizosphere (and bulk soil as a control) to understand which microbial communities and genes are present that control nutrient cycling. In particular, I’d look for taxa and functional genes involved in phosphorus solubilization/mineralization (e.g., phosphatases), nitrogen transformations (nitrification/denitrification), and other pathways that determine whether nutrients are retained, lost, or made bioavailable. This would let me link nutrient status (low/high P or N) to shifts in microbial composition and metabolic potential.

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why? I would use Illumina shotgun metagenomic sequencing, which is a second-generation technology because it performs massively parallel sequencing of millions of short DNA fragments using sequencing-by-synthesis chemistry. The input would be total DNA extracted from soil samples, which would be fragmented, end-repaired, ligated to sequencing adapters (with barcodes if multiplexing), PCR-amplified if necessary, and loaded onto a flow cell to generate clusters. During sequencing, fluorescently labeled nucleotides are incorporated one at a time, and imaging after each cycle detects the emitted fluorescence to determine the identity of each base (base calling). The output consists of millions of short reads in FASTQ format, containing both nucleotide sequences and quality scores, which can then be analyzed to identify microbial taxa and nutrient-cycling genes.

DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why?
I would like to synthesize a nutrient-responsive genetic circuit for soil bacteria that turns nutrient mobilization “on” only when the environment is nutrient-limited—for example, a phosphate-starvation–responsive promoter (Pho regulon) driving a codon-optimized alkaline phosphatase (PhoA) module to help mineralize organic phosphate into bioavailable phosphate. Concretely, the construct would look like [P_Pho]–RBS–phoA–terminator, where the PhoA protein sequence can be sourced from UniProt P00634 and the Pho regulon is sourced from the well-characterized PhoB/PhoR phosphate-sensing regulatory system in bacteria.

(ii) What technology or technologies would you use to perform this DNA synthesis and why?
I would use commercial gene synthesis based On sdlid-phase phosphoramidite oligonucleotide synthesis followed by DNA assembly (like Gibson Assembly,as I think we are going to learn in HTGAA) because this is the most reliable way to “write” a custom genetic circuit with a good sequence control. In practice, short oligos are chemically synthesized cycle-by-cycle, then cleaved/deprotected and purified, and multiple fragments are assembled into the full construct (they are oftten sequneced afterward, to check if everything is ok). The main limitations are that error rates and cost increase with sequence length, as long constructs require multi-fragment assembly and verification.

DNA Edit

(i) What DNA would you want to edit and why?
I would want to edit the genomes of plant-associated soil bacteria to enhance their ability to sense nutrient limitation and respond in a controlled, beneficial way. Specifically, I would modify regulatory regions of genes involved in phosphate metabolismto fine-tune their sensitivity and dynamic range.

(ii) What technology or technologies would you use to perform these DNA edits and why?
I would use CRISPR-Cas9 genome editing because it is the standard technique and it allows precise modification of specific DNA sequences in bacteria. CRISPR edits DNA by using a designed guide RNA (gRNA) that directs the Cas9 nuclease to a complementary target sequence, where Cas9 introduces a double-strand break; the cell then repairs this break either through non-homologous end joining (introducing small insertions or deletions) or through homology-directed repair if a donor DNA template is provided, enabling precise edits. Preparation involves designing the guide RNA sequence, constructing or obtaining a plasmid encoding Cas9 and the gRNA, optionally designing a repair template with homology arms, and delivering these components into the target bacterial cells (e.g., via transformation or electroporation). Limitations include variable editing efficiency depending on the organism, potential off-target effects if guide design is imperfect, lower efficiency of homology-directed repair in some bacteria, and challenges in delivering editing machinery into non-model environmental strains.

Week 3 HW: Lab Automation

Part 1: Python Script for Opentrons Artwork

I used the amazing Donovan’s tool to create this, then used the coordinates… It’s a homage to my cat Nico. He is a crazy cute orange cat.

Part 2: Post-Lab Questions

• Question 1
  I chose this paper: Automation of protein crystallization scaleup via Opentrons-2 liquid handling by DeRoo et al. I’m really into structural biology, and protein crystallization is a critical step in it, because it enables structure determination via X-ray crystallography. However, traditional crystallization trials are highly manual, repetitive, and sensitive to small variations in liquid handling. This paper demonstrates how the Opentrons-2 liquid handling robot can be used to automate protein crystallization experiments, specifically sitting-drop crystallization in 24-well plates.

• Question 2
  I intend to use the Opentrons robot to automate the fabrication, assembly, and testing of paper-based biosensors that detect environmental signals (such as carbon monoxide). The core idea is to use automation to precisely deposit reagents onto paper substrates in reproducible patterns, enabling scalable production and systematic testing of biosensor designs. I do need to research more to undderstand exactly what is possible or not with OpenTrons.

Part 3: Final Project Ideas

It’s all on the slides!

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project