Week 1 HW: Principles and Practices

cover imaagen

1.First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

I aim to develop extremophile bacteria specialized in surviving environments with extreme living conditions beyond Earth’s atmosphere, such as the Moon and Mars. This proposal is based on the study of extremophile bacterial colonies from Bolivian deserts that exhibit extreme environmental conditions, such as the Salar de Uyuni. Additionally, the development of this project would be supported by previous research conducted both locally and in other countries with analogous environments, such as Chile and the Atacama Desert, using these findings as a foundation for the design and biological engineering of these microorganisms. The objective of this project is to support future research by providing the scientific community with the capacity to develop and manage this technology, as well as its possible variants, for applications in biomining studies on the Moon or Mars, astrobiology research, and space sample collection. In this way, the project seeks to reduce the time required to explore alternative data collection methods and to overcome the limitations associated with the inability of conventional bacterial colonies to survive in extreme space environments.

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. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

This project would include governance measures to ensure ethical and safe use of extremophile bacteria in space research. First, controlled scientific use and safety protocols would require researchers to submit experimental plans with containment strategies and activation controls before deploying bacteria, ensuring they are used only for biomining, astrobiology, and space sample collection. Second, ethical oversight and data sharing guidelines would promote responsible collaboration by establishing rules for proper attribution, controlled access to sensitive biological information, and compliance with international research standards. These measures aim to reduce risks of misuse while supporting scientific progress.

3.Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.).

Purpose: What is done now and what changes are you proposing?

Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)

Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?

Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

Governance Action 1:

Mandatory International Regulations for the Use of Space Biotechnology. Purpose: Currently, space agencies like NASA have planetary protection standards, but these do not fully cover synthetic bacteria for use on non-terrestrial celestial bodies. I propose new global standards to ensure that these colonies are used only for beneficial research, such as terraforming and biomining, and to prevent harm such as contamination. Design: Actors: UN (e.g., UNOOSA), NASA, ESA, and national space agencies. They create a committee to approve projects; researchers must submit plans with safety tests (e.g., kill-switches in the bacteria). Assumptions: Countries will cooperate, and the standards will not stifle innovation. Risks of Failure and “Success”: Failure: Excessive bureaucracy slows down research, or if other research organizations do not respect this committee. Unintended Consequences of Success: The rules could exclude small countries like Bolivia from participating.

Governance Action 2:

Grants and Incentives for Ethical Space Microbiology Projects. Purpose: Currently, space biotechnology is dominated by large countries; I propose incentives to support researchers in developing countries, such as those in South America, Eastern Europe, or Asia, to develop these bacteria safely. Design: Actors: International funds (e.g., World Bank, NASA grants), universities, and biotechnology companies. They offer funding for projects that include ethical reviews and data sharing; Bolivia could get special funding for the Uyuni samples. Assumptions: The money will incentivize good behavior, and the funds will be distributed fairly. Risks of Failure and “Success”: Failure: Corruption or false “ethical” claims. Unintended consequences of success: Over-funding could lead to rushed experiments with risks.

Governance Action 3:

Secure Global Database for Extremophile Bacteria Data. Purpose: Currently, data on bacteria like those from the Uyuni is scattered; I propose a shared platform to promote beneficial research while controlling access to prevent misuse. Design: Actors: Academic researchers, the UN, and technology companies (e.g., Google for AI security). They build an online database with password-protected access for verified users; include tools to detect dual-use risks. Assumptions: People will share data honestly, and technology can prevent cyberattacks. Risks of Failure and “Success”: Failure: Low participation if it is difficult to use. Unintended consequences of success: Hackers could steal data for malicious purposes.

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. For this, you can choose one or more relevant audiences for your recommendation, which could range from the very local (e.g. to MIT leadership or Cambridge Mayoral Office) to the national (e.g. to President Biden or the head of a Federal Agency) to the international (e.g. to the United Nations Office of the Secretary-General, or the leadership of a multinational firm or industry consortia). These could also be one of the “actor” groups in your matrix}

Based on the scoring results, I would prioritize a combination of Option 1: Mandatory International Regulations and Option 2: Grants and Incentives for Ethical Projects. Option 1 provides the strongest protection for biosecurity, laboratory safety, and environmental protection, ensuring that extremophile bacteria are used responsibly and reducing the risk of contamination or misuse. Option 2 complements this by facilitating research through funding and ethical incentives, especially for developing countries such as Bolivia, making participation feasible and equitable.

The main trade-off is that Option 1 may slow research due to strict international standards, whereas Option 2 accelerates innovation but is less enforceable. By combining them, we balance safety and biosecurity with research efficiency and inclusivity. Key assumptions include international cooperation on regulations, fair distribution of funding, and researchers’ adherence to ethical standards.

The recommended audience for this combined governance approach includes international scientific governance bodies, such as the United Nations Office for Outer Space Affairs (UNOOSA), and national space agencies, ensuring broad compliance while supporting global collaboration in space biotechnology.

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:

>sfGFP (superfolder variant)
MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITLGMDELYK

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):

ATGAGCAAAGGAGAAGAACTTTTCATTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTGGAGAGGGTGAAGGTGATGCTACAAACGGAAAACTCACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCATGGCCAACACTTGTCACTACTCTGACTTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCACATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGAACTACAAGACGCGTGCTGAAGTCAAGTTTGAGGGAGATACCCTTGTTAACTAGAATCGAGTTAAAAGGTATTGATTTTAAAGAAGATGGAAACATTCTTGGACACAAATTGGAATACAACTATAACTCACACAACGTATACATCATGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACGTTGAAGATGGTAGCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGTCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAATAA

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:

ATGTCTAAAGGTGAAGAACTGTTCACCGGTGTGGTGCCGATCCTGGTGGAACTGGACGGCGACGTGAACGGCCACAAGTTCTCCGTGTCCGGCGAAGGCGAGGGCGACGCCACCTACGGCAAACTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCGACCCTGGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAGGGCTACGTGCAGGAACGCACCATCAGCTTCAAAGACGACGGCAACTACAAGACCCGCGCCGAAGTGAAGTTTGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCAACATCCTGGGCCACAAGCTGGAGTACAACTACAACAGCCACAACGTGTACATCATGGCCGACAAGCAGAAGAACGGCATCAAGGTGAACTTCAAGATCCGCCACAACGTGGAAGACGGCTCCGTGCAGCTGGCCGACCACTACCAGCAGAATACCCCCATCGGCGACGGCCCCGTGCTGCCCGACAACCACTACCTGAGCACCCAGTCCGCGCTGTCCAAGGACCCCAACGAGAAGCGCGACCACATGGTGCTGCTGGAGTTCGTGACCGCCGCTGGCATCAC CCTGGGCATGGACGAGCTGTACAAG

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:

1. Insert the optimized sfGFP DNA into a plasmid (e.g., pTwist Amp High Copy from Twist, adding a C-terminal His-tag for purification).
2. Transform competent E. coli (e.g., BL21(DE3)) using heat shock or electroporation, select on antibiotic plates.
3. Grow cells in LB, induce expression with IPTG (0.1–1 mM), incubate 4–16 hours at 16–37°C.
4. 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.

1. Is your method first-, second- or third-generation or other? How so?
   Third-generation. Nanopore reads single molecules in real time without amplification.

2. 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

3. 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

4. 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.

1. 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

2. 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.

1. 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)

2. 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.

3. 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.

Week 3 HW:Lab Automation

from opentrons import types

metadata = { # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata ‘author’: ‘Sebastian Rios’, ‘protocolName’: ‘Say My Name’, ‘description’: ‘I am not in danger, I am the DANGER ‘, ‘source’: ‘HTGAA 2026 Opentrons Lab’, ‘apiLevel’: ‘2.20’ }

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Robot deck setup constants - don’t change these

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TIP_RACK_DECK_SLOT = 9 COLORS_DECK_SLOT = 6 AGAR_DECK_SLOT = 5 PIPETTE_STARTING_TIP_WELL = ‘A1’

well_colors = { ‘A1’ : ‘Red’, ‘B1’ : ‘Green1’, ‘D1’ : ‘Green2’, ‘C1’ : ‘Orange’ }

def run(protocol): ##############################################################################

Load labware, modules and pipettes

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Tips

tips_20ul = protocol.load_labware(‘opentrons_96_tiprack_20ul’, TIP_RACK_DECK_SLOT, ‘Opentrons 20uL Tips’)

Pipettes

pipette_20ul = protocol.load_instrument(“p20_single_gen2”, “right”, [tips_20ul])

Modules

temperature_module = protocol.load_module(’temperature module gen2’, COLORS_DECK_SLOT)

Temperature Module Plate

temperature_plate = temperature_module.load_labware(‘opentrons_96_aluminumblock_generic_pcr_strip_200ul’, ‘Cold Plate’)

Choose where to take the colors from

color_plate = temperature_plate

Agar Plate

agar_plate = protocol.load_labware(‘htgaa_agar_plate’, AGAR_DECK_SLOT, ‘Agar Plate’) ## TA MUST CALIBRATE EACH PLATE!

Get the top-center of the plate, make sure the plate was calibrated before running this

center_location = agar_plate[‘A1’].top()

pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

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Patterning

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Helper functions for this lab

pass this e.g. ‘Red’ and get back a Location which can be passed to aspirate()

def location_of_color(color_string): for well,color in well_colors.items(): if color.lower() == color_string.lower(): return color_plate[well] raise ValueError(f"No well found with color {color_string}")

For this lab, instead of calling pipette.dispense(1, loc) use this: dispense_and_detach(pipette, 1, loc)

def dispense_and_detach(pipette, volume, location): """ Move laterally 5mm above the plate (to avoid smearing a drop); then drop down to the plate, dispense, move back up 5mm to detach drop, and stay high to be ready for next lateral move. 5mm because a 4uL drop is 2mm diameter; and a 2deg tilt in the agar pour is >3mm difference across a plate. """ assert(isinstance(volume, (int, float))) above_location = location.move(types.Point(z=location.point.z + 5)) # 5mm above pipette.move_to(above_location) # Go to 5mm above the dispensing location pipette.dispense(volume, location) # Go straight downwards and dispense pipette.move_to(above_location) # Go straight up to detach drop and stay high

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Fluorescent pattern

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sfgfp_points = [(-16.5, 31.9),(-14.3, 31.9),(-12.1, 31.9),(-9.9, 31.9),(-7.7, 31.9),(-5.5, 31.9),(-3.3, 31.9),(-1.1, 31.9),(1.1, 31.9),(3.3, 31.9),(5.5, 31.9),(7.7, 31.9),(9.9, 31.9),(12.1, 31.9),(14.3, 31.9),(16.5, 31.9),(-18.7, 29.7),(-16.5, 29.7),(-14.3, 29.7),(-12.1, 29.7),(-9.9, 29.7),(-7.7, 29.7),(-5.5, 29.7),(-3.3, 29.7),(-1.1, 29.7),(1.1, 29.7),(3.3, 29.7),(5.5, 29.7),(7.7, 29.7),(9.9, 29.7),(12.1, 29.7),(14.3, 29.7),(16.5, 29.7),(-18.7, 27.5),(-16.5, 27.5),(-14.3, 27.5),(-12.1, 27.5),(-9.9, 27.5),(-7.7, 27.5),(-5.5, 27.5),(-3.3, 27.5),(-1.1, 27.5),(1.1, 27.5),(3.3, 27.5),(5.5, 27.5),(7.7, 27.5),(9.9, 27.5),(12.1, 27.5),(14.3, 27.5),(16.5, 27.5),(18.7, 27.5),(-20.9, 25.3),(-18.7, 25.3),(-16.5, 25.3),(-14.3, 25.3),(-12.1, 25.3),(-9.9, 25.3),(-7.7, 25.3),(-5.5, 25.3),(-3.3, 25.3),(-1.1, 25.3),(1.1, 25.3),(3.3, 25.3),(5.5, 25.3),(7.7, 25.3),(9.9, 25.3),(12.1, 25.3),(14.3, 25.3),(16.5, 25.3),(18.7, 25.3),(20.9, 25.3),(-20.9, 23.1),(-18.7, 23.1),(-16.5, 23.1),(-14.3, 23.1),(-12.1, 23.1),(-9.9, 23.1),(-7.7, 23.1),(-5.5, 23.1),(-3.3, 23.1),(-1.1, 23.1),(1.1, 23.1),(3.3, 23.1),(5.5, 23.1),(7.7, 23.1),(9.9, 23.1),(12.1, 23.1),(14.3, 23.1),(16.5, 23.1),(18.7, 23.1),(20.9, 23.1),(-23.1, 20.9),(-20.9, 20.9),(-18.7, 20.9),(-16.5, 20.9),(-14.3, 20.9),(-12.1, 20.9),(-9.9, 20.9),(-7.7, 20.9),(-5.5, 20.9),(-3.3, 20.9),(-1.1, 20.9),(1.1, 20.9),(3.3, 20.9),(5.5, 20.9),(7.7, 20.9),(9.9, 20.9),(12.1, 20.9),(14.3, 20.9),(16.5, 20.9),(18.7, 20.9),(20.9, 20.9),(-34.1, 18.7),(-31.9, 18.7),(-23.1, 18.7),(-20.9, 18.7),(-18.7, 18.7),(-16.5, 18.7),(-14.3, 18.7),(-12.1, 18.7),(-9.9, 18.7),(-7.7, 18.7),(-5.5, 18.7),(-3.3, 18.7),(-1.1, 18.7),(1.1, 18.7),(3.3, 18.7),(5.5, 18.7),(7.7, 18.7),(9.9, 18.7),(12.1, 18.7),(14.3, 18.7),(16.5, 18.7),(18.7, 18.7),(20.9, 18.7),(23.1, 18.7),(34.1, 18.7),(-34.1, 16.5),(-31.9, 16.5),(-29.7, 16.5),(-27.5, 16.5),(-25.3, 16.5),(-23.1, 16.5),(-20.9, 16.5),(-18.7, 16.5),(-16.5, 16.5),(-14.3, 16.5),(-12.1, 16.5),(-9.9, 16.5),(-7.7, 16.5),(-5.5, 16.5),(-3.3, 16.5),(-1.1, 16.5),(1.1, 16.5),(3.3, 16.5),(5.5, 16.5),(7.7, 16.5),(9.9, 16.5),(12.1, 16.5),(14.3, 16.5),(16.5, 16.5),(18.7, 16.5),(20.9, 16.5),(23.1, 16.5),(25.3, 16.5),(27.5, 16.5),(29.7, 16.5),(31.9, 16.5),(34.1, 16.5),(-34.1, 14.3),(-31.9, 14.3),(-29.7, 14.3),(-27.5, 14.3),(-25.3, 14.3),(-23.1, 14.3),(-20.9, 14.3),(-18.7, 14.3),(-16.5, 14.3),(-14.3, 14.3),(-12.1, 14.3),(-9.9, 14.3),(-7.7, 14.3),(-5.5, 14.3),(-3.3, 14.3),(-1.1, 14.3),(1.1, 14.3),(3.3, 14.3),(5.5, 14.3),(7.7, 14.3),(9.9, 14.3),(12.1, 14.3),(14.3, 14.3),(16.5, 14.3),(18.7, 14.3),(20.9, 14.3),(23.1, 14.3),(25.3, 14.3),(27.5, 14.3),(29.7, 14.3),(31.9, 14.3),(34.1, 14.3),(-29.7, 12.1),(-27.5, 12.1),(-25.3, 12.1),(-23.1, 12.1),(-20.9, 12.1),(-18.7, 12.1),(-16.5, 12.1),(-14.3, 12.1),(-12.1, 12.1),(-9.9, 12.1),(-7.7, 12.1),(-5.5, 12.1),(-3.3, 12.1),(-1.1, 12.1),(1.1, 12.1),(3.3, 12.1),(5.5, 12.1),(7.7, 12.1),(9.9, 12.1),(12.1, 12.1),(14.3, 12.1),(16.5, 12.1),(18.7, 12.1),(20.9, 12.1),(23.1, 12.1),(25.3, 12.1),(27.5, 12.1),(29.7, 12.1),(31.9, 12.1),(34.1, 12.1),(-23.1, 9.9),(-20.9, 9.9),(20.9, 9.9),(23.1, 9.9),(-23.1, 7.7),(23.1, 7.7),(-23.1, 5.5),(-20.9, 5.5),(20.9, 5.5),(23.1, 5.5),(-25.3, -5.5),(-23.1, -7.7),(20.9, -7.7),(-23.1, -9.9),(-20.9, -9.9),(20.9, -9.9),(-23.1, -12.1),(20.9, -12.1),(-20.9, -14.3),(20.9, -14.3),(-20.9, -16.5),(-7.7, -16.5),(7.7, -16.5),(20.9, -16.5),(-9.9, -18.7),(9.9, -18.7),(18.7, -18.7),(-18.7, -20.9),(14.3, -20.9),(18.7, -20.9),(-16.5, -23.1),(-12.1, -23.1),(14.3, -23.1),(-14.3, -25.3),(-9.9, -25.3),(12.1, -25.3),(16.5, -25.3),(-12.1, -27.5),(-7.7, -27.5),(-12.1, -29.7),(-9.9, -31.9),(9.9, -31.9),(12.1, -31.9),(-7.7, -34.1),(-5.5, -34.1),(-3.3, -34.1),(5.5, -34.1),(7.7, -34.1),(-1.1, -36.3),(1.1, -36.3),(3.3, -36.3)] # Green1 mwasabi_points = [(-14.3, 9.9),(-12.1, 7.7),(-9.9, 7.7),(-7.7, 7.7),(-5.5, 7.7),(-3.3, 7.7),(-1.1, 7.7),(1.1, 7.7),(3.3, 7.7),(5.5, 7.7),(7.7, 7.7),(9.9, 7.7),(9.9, 5.5),(12.1, 5.5),(-1.1, -1.1),(1.1, -1.1),(-1.1, -3.3),(1.1, -3.3),(-1.1, -5.5),(1.1, -5.5),(-3.3, -7.7),(3.3, -7.7),(-3.3, -9.9),(3.3, -9.9),(-5.5, -12.1),(5.5, -12.1),(-5.5, -14.3),(-3.3, -14.3),(3.3, -14.3),(5.5, -14.3),(-1.1, -16.5),(1.1, -16.5),(-7.7, -18.7),(-5.5, -18.7),(-3.3, -18.7),(3.3, -18.7),(5.5, -18.7),(7.7, -18.7),(-9.9, -20.9),(-7.7, -20.9),(-5.5, -20.9),(-3.3, -20.9),(-1.1, -20.9),(1.1, -20.9),(3.3, -20.9),(5.5, -20.9),(7.7, -20.9),(9.9, -20.9),(-5.5, -25.3),(-3.3, -25.3),(-1.1, -25.3),(1.1, -25.3),(3.3, -25.3),(5.5, -25.3)] # Red azurite_points = [(-25.3, 3.3),(-23.1, 3.3),(-20.9, 3.3),(-18.7, 3.3),(-16.5, 3.3),(-14.3, 3.3),(-12.1, 3.3),(-9.9, 3.3),(-7.7, 3.3),(-5.5, 3.3),(-3.3, 3.3),(-1.1, 3.3),(1.1, 3.3),(3.3, 3.3),(5.5, 3.3),(7.7, 3.3),(9.9, 3.3),(12.1, 3.3),(14.3, 3.3),(16.5, 3.3),(18.7, 3.3),(20.9, 3.3),(23.1, 3.3),(25.3, 3.3),(-25.3, 1.1),(-23.1, 1.1),(-20.9, 1.1),(-18.7, 1.1),(-16.5, 1.1),(-14.3, 1.1),(-12.1, 1.1),(-9.9, 1.1),(-7.7, 1.1),(-5.5, 1.1),(-3.3, 1.1),(-1.1, 1.1),(1.1, 1.1),(3.3, 1.1),(5.5, 1.1),(7.7, 1.1),(9.9, 1.1),(12.1, 1.1),(14.3, 1.1),(16.5, 1.1),(18.7, 1.1),(20.9, 1.1),(23.1, 1.1),(25.3, 1.1),(-27.5, -1.1),(-25.3, -1.1),(-23.1, -1.1),(-20.9, -1.1),(-18.7, -1.1),(-16.5, -1.1),(-14.3, -1.1),(-12.1, -1.1),(-9.9, -1.1),(-7.7, -1.1),(-5.5, -1.1),(-3.3, -1.1),(3.3, -1.1),(5.5, -1.1),(7.7, -1.1),(9.9, -1.1),(12.1, -1.1),(14.3, -1.1),(16.5, -1.1),(18.7, -1.1),(20.9, -1.1),(23.1, -1.1),(-25.3, -3.3),(-23.1, -3.3),(-20.9, -3.3),(-18.7, -3.3),(-16.5, -3.3),(-14.3, -3.3),(-12.1, -3.3),(-9.9, -3.3),(-7.7, -3.3),(-5.5, -3.3),(-3.3, -3.3),(3.3, -3.3),(5.5, -3.3),(7.7, -3.3),(9.9, -3.3),(12.1, -3.3),(14.3, -3.3),(16.5, -3.3),(18.7, -3.3),(20.9, -3.3),(23.1, -3.3),(-23.1, -5.5),(-20.9, -5.5),(-18.7, -5.5),(-16.5, -5.5),(-14.3, -5.5),(-12.1, -5.5),(-9.9, -5.5),(-7.7, -5.5),(-5.5, -5.5),(-3.3, -5.5),(5.5, -5.5),(7.7, -5.5),(9.9, -5.5),(12.1, -5.5),(14.3, -5.5),(16.5, -5.5),(18.7, -5.5),(20.9, -5.5),(-20.9, -7.7),(-18.7, -7.7),(-16.5, -7.7),(-14.3, -7.7),(-12.1, -7.7),(-9.9, -7.7),(-7.7, -7.7),(-5.5, -7.7),(7.7, -7.7),(9.9, -7.7),(12.1, -7.7),(14.3, -7.7),(16.5, -7.7),(18.7, -7.7),(-18.7, -9.9),(-16.5, -9.9),(-14.3, -9.9),(-12.1, -9.9),(-9.9, -9.9),(-7.7, -9.9),(7.7, -9.9),(9.9, -9.9),(12.1, -9.9),(14.3, -9.9),(16.5, -9.9)] # Orange mclover3_points = [(-5.5, -9.9),(-18.7, -18.7),(18.7, -23.1),(16.5, -27.5),(14.3, -29.7)] # Green2

pattern_map = [ (“Green1”, sfgfp_points), (“Red”, mwasabi_points), (“Orange”, azurite_points), (“Green2”, mclover3_points) ]

pipette_20ul.pick_up_tip()

for color_name, points in pattern_map: pipette_20ul.pick_up_tip() for (x_offset, y_offset) in points:

      pipette_20ul.aspirate(1, location_of_color(color_name))

      target_location = center_location.move(
          types.Point(x=x_offset, y=y_offset)
      )

      dispense_and_detach(pipette_20ul, 1, target_location)

pipette_20ul.drop_tip()