Week 2 HW:DNA Read, Write, & Edit
Here’s your complete assignment — all parts (Part 1 through Part 5) joined together exactly as we built them, with no changes to the text, just cleanly organized and with clear instructions in Spanish (between parentheses) for where to insert screenshots/images.
Copy-paste this entire block into your document (Google Docs, Notion, etc.), then add the images in the marked spots.
Part 1: Benchling & In-silico Gel Art
My Journey – From Frustration to Gel Art Success
I created my free Benchling account on February 16, 2026 (see screenshot of login with email sabrslian@gmail.com). I imported the full Lambda phage genome (NC_001416.1, 48,502 bp) and began experimenting with fragments to simulate restriction digests using the seven required enzymes: EcoRI, HindIII, BamHI, KpnI, EcoRV, SacI, and SalI.
First attempt (failure)
I started with a 2,590 bp fragment (positions 1–2,590). After running single and double digests, the results were extremely disappointing: almost every lane showed only 1–3 large bands (>2 kb) with almost no small fragments. It was impossible to create any artistic pattern — the gels looked nearly identical to uncut DNA.
What I learned from the failure
The initial region simply did not contain enough recognition sites for the required enzymes. Gel art, like Paul Vanouse’s Latent Figure Protocol, requires dense, strategically placed cut sites to generate varied band sizes that can form shapes.
Solution: change the DNA region
I scanned the Lambda genome and selected a new, richer fragment (positions ~20,000–22,240 bp, ~2,240 bp total). This region has many more sites for the seven enzymes. Single digests now produced a beautiful range of band sizes (300 bp to ~2 kb), giving me the raw material needed for art.
Final artistic design
Using Ronan’s Gel Art tool and Benchling, I iterated dozens of combinations. My favorite design repeats enzymes across multiple lanes to create a deliberate pattern:
- Lane 1–2: EcoRI
- Lane 3–6: KpnI (x4)
- Lane 7–9: EcoRV (x3)
When visualized together, the bands form a clear pair of “gafas” (sunglasses) — a playful, cool-looking figure that reminds me of the Plants vs. Zombies Peashooter wearing shades.
This is my final Latent Figure Protocol-style artwork: a cheeky, recognizable shape made purely from DNA fragments separated on a gel.
Total time spent: ~4 hours.
The failures were frustrating, but they taught me more than a perfect first try ever could. Choosing the right DNA region is half the art in gel art.
Part 2: Gel Art – Restriction Digests and Gel Electrophoresis (Wet Lab)
As a Committed Listener located in Santa Cruz de la Sierra, Bolivia, I do not have access to the MIT/Harvard teaching labs. Therefore, I was unable to perform the physical restriction digests and run the real agarose gel.
I completed the entire experiment in silico (as described in Part 1), including the final “gafas / Peashooter-with-sunglasses” pattern, which would have been the exact design I would have taken to the wet lab if I had access.
I’m really proud of the virtual result and can’t wait to one day run this exact digest for real!
Part 3: DNA Design Challenge
3.1. Choose your protein.
I chose sfGFP (superfolder Green Fluorescent Protein) as my protein for this challenge.
There are two main reasons for my choice. First, I think it would be funny and amazing to walk through the Salar de Uyuni at night and see bacteria glowing green under the starry sky — it would turn a beautiful natural landscape into something almost magical and sci-fi. Second, sfGFP could be a simple and fast way to visualize and measure the size of bacterial colonies, especially extremophiles growing in their natural environment (even if they are hard to culture in the lab). The green fluorescence is easy to detect under UV light, it’s hard to miss, and it would allow quick observation without destroying or losing the colony.
I obtained the protein sequence from NCBI/UniProt (reference accession: P42212 for the original GFP, with superfolder mutations commonly used in synthetic biology).
Here is the amino acid sequence in FASTA format:
3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.
To reverse translate the sfGFP amino acid sequence into a possible DNA sequence, I went directly to NCBI and downloaded the nucleotide sequence corresponding to a commonly used sfGFP coding region (accession example: commonly referenced in Addgene plasmids like pQE9-sfGFP or similar constructs). I chose this approach for practicality instead of using a generic translation tool, as the NCBI sequence is already validated and matches real lab use.
Here is the original (non-optimized) DNA sequence I used (coding region only):
I loaded both the amino acid sequence and this nucleotide sequence into Benchling to verify they align correctly (the DNA translates exactly to the protein sequence without frameshifts).
3.3. Codon optimization.
Codon optimization is necessary because different organisms have different preferences (codon bias) for which codons they use to encode the same amino acid. There are 64 possible codons, but only 20 amino acids + 3 stop codons, so most amino acids have multiple codons. If you use rare codons in a host organism, translation can be slow, inefficient, or cause misfolding because the cell has fewer tRNAs for those codons. Optimization replaces rare codons with the host’s preferred ones while keeping the exact same amino acid sequence.
For this task, I optimized the sfGFP sequence for Escherichia coli (specifically E. coli K-12 or BL21 strains). I chose E. coli for simplicity in this exercise: it is the easiest organism to work with in synthetic biology homework, has well-known codon tables, fast growth, cheap culture, and many tools (like Twist Bioscience) support it directly. Although my long-term idea is to use extremophiles, E. coli is the practical choice here to complete the task quickly and correctly.
I used the Twist Bioscience Codon Optimization Tool, selected “E. coli” as the organism, kept default parameters (avoided Type IIS restriction sites like BsaI, BsmBI, BbsI for future cloning compatibility), and obtained the optimized sequence below:
3.4. You have a sequence! Now what?
With this optimized DNA sequence, I can produce the sfGFP protein using cell-dependent methods (I chose not to use cell-free for this exercise).
The DNA would be transcribed into mRNA by RNA polymerase (from a promoter like T7 or J23106) and translated into sfGFP protein by ribosomes.
Step-by-step process:
- Insert the optimized sfGFP DNA into a plasmid (e.g., pTwist Amp High Copy from Twist, adding a C-terminal His-tag for purification).
- Transform competent E. coli (e.g., BL21(DE3)) using heat shock or electroporation, select on antibiotic plates.
- Grow cells in LB, induce expression with IPTG (0.1–1 mM), incubate 4–16 hours at 16–37°C.
- Lyse cells, purify His-tagged sfGFP using Ni-NTA chromatography, and check fluorescence under UV.
This reliable method produces glowing protein in hours and is ideal for imaging or bioart.
(Aquí inserta Imagen 11 – opcional: captura de Benchling con cassette o esquema del proceso de expresión en E. coli)
Part 4: Prepare a Twist DNA Synthesis Order
(This is a practice exercise, not a real order.)
4.1. Create a Twist account and a Benchling account
I already have accounts on both platforms. Benchling email: sabrslian@gmail.com.
4.2. Build Your DNA Insert Sequence
In Benchling, I created a new linear DNA sequence named “sfGFP_expression_cassette”. I concatenated the following parts in order, annotating each one by right-clicking and selecting “Create annotation”:
- Promoter (BBa_J23106): TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC
Annotation: “Promoter_BBa_J23106” - RBS (BBa_B0034 with spacer): CATTAAAGAGGAGAAAGGTACC
Annotation: “RBS_BBa_B0034” - Coding sequence: codon-optimized sfGFP from Part 3.3
Annotation: “CDS_sfGFP_optimized” - 7x His Tag: CATCACCATCACCATCAC
Annotation: “His7_tag” - Stop Codon: TAA
Annotation: “Stop_codon” - Terminator (BBa_B0015): CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA
Annotation: “Terminator_BBa_B0015”
I reviewed the linear map to confirm all sections are correctly annotated and in the right order. The total length is approximately 850 bp.
(Aquí inserta Imagen principal: captura del Linear Map de tu secuencia en Benchling – link: https://benchling.com/s/seq-GimDj0IV4i7XlbZMOPaE?m=slm-jnG5Isbh4S3lAfPOnDVY – muestra todas las anotaciones visibles)
I exported the insert as FASTA for use in Twist.
(Aquí inserta Imagen opcional: captura de la ventana de exportación FASTA en Benchling)
4.3. On Twist, Select The “Genes” Option
4.4. Select “Clonal Genes” option
4.5. Import your sequence
4.6. Choose Your Vector
On Twist Bioscience, I selected Genes > Clonal Genes (circular DNA, faster for direct transformation into E. coli without assembly steps).
I chose Nucleotide Sequence > Upload Sequence File and uploaded my FASTA file from Benchling containing the sfGFP expression cassette (promoter, RBS, optimized CDS, His-tag, stop, terminator).
I selected the vector pTwist Amp High Copy (Ampicillin resistance, high copy number, ideal for E. coli expression at MIT/Harvard labs).
I reviewed the construct preview (full plasmid with my cassette inserted). I downloaded the complete construct as GenBank (.gb) file.
(Aquí inserta Imagen 1: captura de Twist mostrando menú “Genes” seleccionado)
(Aquí inserta Imagen 2: captura de selección “Clonal Genes”)
(Aquí inserta Imagen 3: captura de subida del FASTA en Twist – muestra archivo y nombre)
(Aquí inserta Imagen 4: captura de elección de vector pTwist Amp High Copy + preview del construct)
I imported the downloaded GenBank back into Benchling as a new circular sequence named “sfGFP in pTwist Amp High Copy”. This is my complete simulated plasmid ready for transformation.
(Aquí inserta Imagen 5: captura del mapa circular/plasmid view en Benchling del plásmido completo importado desde Twist)
Congratulations — I built and simulated my first synthetic plasmid!
Important for final projects:
- Fully annotated Benchling insert fragment (see link: https://benchling.com/s/seq-GimDj0IV4i7XlbZMOPaE?m=slm-jnG5Isbh4S3lAfPOnDVY)
- Desired Twist cloning vector: pTwist Amp High Copy
Part 5: DNA Read/Write/Edit
5.1 DNA Read
(i) What DNA would you want to sequence (e.g., read) and why?
I would sequence DNA from extremophiles to discover genes that enable resistance to extreme salinity, UV radiation, and temperature changes, and explore whether those traits can be pushed to the limit for synthetic biology applications (e.g., glowing sfGFP microbes in harsh conditions).
(ii) What technology or technologies would you use to perform sequencing on your DNA and why?
I would use Oxford Nanopore sequencing because it is portable, generates long reads (ideal for complex metagenomes), handles difficult DNA (high GC/modified bases), and requires minimal preparation.
Is your method first-, second- or third-generation or other? How so?
Third-generation. Nanopore reads single molecules in real time without amplification.What is your input? How do you prepare your input? List the essential steps.
Input: extracted genomic/metagenomic DNA.
Steps:- Extract DNA (lysis + purification)
- Optional: shear to ~10–20 kb
- Add adapters (ligation kit)
- Load onto flow cell
What are the essential steps of your chosen sequencing technology, how does it decode the bases (base calling)?
Steps:- DNA passes through nanopore
- Ionic current changes per base
- Signal recorded in real time
- Base calling: neural network software (Guppy/Bonito) converts signal to sequence
What is the output of your chosen sequencing technology?
FAST5 (raw signal) → FASTQ (sequences + quality scores) → long reads (10–100 kb average, up to >1 Mb)
5.2 DNA Write
(i) What DNA would you want to synthesize (e.g., write) and why?
I would synthesize a genetic circuit combining codon-optimized sfGFP with stress-response promoters (e.g., salt/UV-inducible) to create bacteria that glow only under extreme conditions — a simple biosensor for monitoring stress in harsh environments or bioart applications. Twist could synthesize the ~1–2 kb cassette.
(ii) What technology or technologies would you use to perform this DNA synthesis and why?
I would use Twist Bioscience silicon-based synthesis — accurate for gene-length constructs, cost-effective, fast (7–14 days), and Benchling-compatible.
What are the essential steps of your chosen synthesis method?
- Design sequence in Benchling
- Export FASTA and upload to Twist
- Select Clonal Genes + vector (pTwist Amp High Copy)
- Twist synthesizes, assembles, clones, verifies, ships
What are the limitations of your synthesis method (if any) in terms of speed, accuracy, scalability?
- Speed: 7–14 days
- Accuracy: ~99.9%, but errors possible >3 kb (needs verification)
- Scalability: good for genes, expensive for genomes
5.3 DNA Edit
(i) What DNA would you want to edit and why?
I would edit extremophile bacterial genomes to enhance resistance traits (e.g., improve UV/salt/desiccation genes) while keeping sfGFP for visual monitoring. This could advance understanding of life in extreme conditions and inspire climate-resilient biotech.
(ii) What technology or technologies would you use to perform these DNA edits and why?
I would use CRISPR-Cas9 — precise, affordable, widely used, and effective for targeted bacterial edits.
How does your technology of choice edit DNA? What are the essential steps?
Creates double-strand break; cell repairs via homology-directed repair or NHEJ.
Steps:- Design gRNA + repair template
- Deliver Cas9 + gRNA + template
- Cas9 cuts
- Cell repairs (with edit if template provided)
What preparation do you need to do (e.g. design steps) and what is the input for the editing?
Preparation: design gRNA (Benchling/CRISPR tools) + donor DNA.
Input: bacterial cells, Cas9 + gRNA plasmid, donor template, electroporation reagents.What are the limitations of your editing method (if any) in terms of efficiency or precision?
Efficiency: 10–90% (many cells unedited).
Precision: high with good design, but off-target cuts possible — requires sequencing validation.
Most of the information above was obtained from AI (Grok) to answer quickly, but I will take the time to research it myself to understand it well and explain it in my own words.