I’m a computer engineering student interested in the intersection of biology, technology, and medicine. I enjoy exploring how emerging tools can be designed and used to benefit both people and the environment.
Instagram: @domenicavizcaino
Week 1 HW: Principles and Practices
Week 2 HW: DNA Read, Write, & Edit
Part 0: Basics of Gel Electrophoresis Completed Part 1: Benchling & In-silico Gel Art After opening my Benchling account and joining the HTGAA group in Benchling, I imported the Lambda DNA. A restriction enzyme digestion was simulated in Benchling with the following enzymes:
Python Script for Opentrons Artwork For this lab, I generated an artistic design using the GUI at opentrons-art.rcdonovan.com. I decided to design some undersea animals because I really like being underwater and observing their nature. Given that in my node’s lab there are only fluorescent green and red, I decided to draw a red crab, a green turtle, and a red and green fish.
One biological engineering tool I would like to develop is a programmable biological computing platform based on synthetic genetic circuits that can sense biological inputs, perform simple computational operations, and produce interpretable outputs such as a visible color change or measurable signal. This idea comes from my interest in using synthetic biology as a medium for computation that could complement or eventually reduce reliance on energy-intensive electronic systems.
Rather than using biological systems only as sensors, I am interested in engineering circuits that can process multiple inputs and perform basic forms of computation within living or cell-free systems. For example, a biological circuit could detect a combination of biological markers and compute a response based on their relationships, producing an output that reflects the processed information rather than a single input. These types of systems could act as early models for more advanced biological computation and could eventually contribute to sustainable technologies that merge biology and information processing.
I am also interested in how neural network concepts and quantum computing approaches might inform the design of such biological circuits. Neural networks provide a framework for pattern recognition and adaptive systems, while quantum and quantum-inspired optimization methods may help solve complex design problems involved in assembling reliable biological circuits. Although these approaches are still emerging, integrating them with synthetic biology could help advance a future in which biological systems play a role in computation, diagnostics, and personalized medicine.
In the long term, platforms like this could support more personalized and environmentally sustainable medical technologies by allowing biological systems to process information locally and respond early to complex biological states. However, because tools that combine sensing, computation, and biological data processing could be misused or misinterpreted, it is important to consider governance and ethical safeguards alongside technical development.
Main goal
Enable beneficial innovation in biological computing while preventing unsafe deployment, privacy violations, and misleading medical use.
Sub-goal 1: Reduce dual-use and misuse risk (non-malfeasance)
Because programmable biocircuits and design workflows can lower barriers to engineering biology, governance should reduce the chance they’re repurposed for harm (intentional misuse) and reduce accidental risks (lab accidents, unintended releases). This is the core concern in synthetic genomics governance options and WHO dual-use governance.
Sub-goal 2: “Trustworthy outputs” for health contexts (avoid harm from errors)
If biocircuits are ever used to inform health decisions (even as screening tools), governance should prevent harm from false positives/negatives, unclear limitations, and overconfident marketing. The Bioethics Commission stresses ongoing risk analysis, transparency, and public trust; this fits that logic.
Sub-goal 3: Protect autonomy, privacy, and consent
Biological systems that sense/compute on biological states could enable non-consensual testing or sensitive inference. Governance should promote consent norms, privacy-preserving design, and clear boundaries for acceptable uses—consistent with ethics frameworks emphasizing transparency, stewardship, and justice/fairness.
Sub-goal 4: Promote equitable access and responsible innovation
Ensure benefits (diagnostics, sustainability, safer computing) are broadly shared and not limited to wealthy settings; align incentives so safety and equity are not afterthoughts. This aligns with “societal goals” framing in the White House biotech R&D goals report.
Action 1 — “Architecture/code” + industry norm: sequence screening + provenance for risky designs
Actors: DNA synthesis & gene/genome services, protein design platforms, funders, and journals.
Purpose: Today, many safeguards rely on institutional biosafety review and some DNA-order screening, but AI/protein design and synthesis are moving fast. A proposed change is to strengthen screening and add provenance/traceability for high-risk designed sequences so dangerous designs are harder to order and easier to flag. This mirrors the biosecurity direction argued in the Baker & Church Science editorial and fits WHO-style biorisk mitigation.
Design:
Require/expand sequence screening for orders (DNA and other relevant synthesis inputs) using updated threat databases and better detection of modified/obfuscated sequences.
Store minimal audit logs (provenance) for flagged/high-risk categories with clear governance on who can access logs and under what conditions.
Tie compliance to funding/journal policies (e.g., “must use screened providers”) and to procurement requirements. (This is similar in spirit to governance options discussed for providers/users in synthetic genomics governance thinking.)
Assumptions:
Assumes screening algorithms keep up with rapid design methods.
Assumes there’s agreement on what counts as “high-risk” and how to avoid overblocking legitimate research.
Assumes audit logging can be done without creating new privacy/security problems.
Risks of failure & “success”:
Failure: adversaries route around regulated providers; screening misses novel threats; smaller labs get locked out.
Success: more surveillance infrastructure than necessary; chilling effect on open science; inequity if only wealthy groups can access compliant supply chains.
Action 2 — “Norms + law” in research & community biology: tiered biosafety/biosecurity readiness + external support
Actor: universities, community labs (DIYbio spaces), iGEM-like education programs, local regulators, biosafety professionals.
Purpose: Right now, safety practices vary widely across institutions and community spaces. The change is a tiered, “right-sized” readiness system: clear requirements for training, risk assessment, and project review that scale with risk—especially for projects involving novel circuits, pathogen-adjacent work, or anything that could be misused. This aligns with the Bioethics Commission’s call for ethics education and governance that anticipates DIY contexts, and with WHO’s emphasis on governance tools/mechanisms for biorisk and dual-use.
Design:
Assumptions:
Risks of failure & “success”:
Failure: becomes box-checking; pushes experimentation underground; inconsistent enforcement.
Success risk: could reduce accessibility for low-resource groups unless paired with funding/support; may centralize power in institutions and exclude community innovation.
Action 3 — “Market + regulation” for health-facing use: pre-market validation + truth-in-marketing + post-market monitoring
Actor: companies (including startups), healthcare regulators, clinical partners, insurers, app platforms.
Purpose: Today, the jump from “cool demo” to “health claim” is where harm happens. For biocircuits that might be used in personalized medicine pathways, require that products can’t be marketed as diagnostic/therapeutic guidance without evidence standards and clear labeling of limitations. This supports the Bioethics Commission’s emphasis on transparency, ongoing risk analysis, and public trust.
Design:
Assumptions:
Risks of failure & “success”:
Failure: regulatory gaps lead to misleading products; over-regulation slows beneficial innovation.
Success risk: big companies with money for trials dominate; smaller innovators struggle unless there are grants/partnership pathways.
| Does the option: | Option 1 | Option 2 | Option 3 |
|---|---|---|---|
| Enhance Biosecurity | |||
| • By preventing incidents | 1 | 2 | 3 |
| • By helping respond | 2 | 1 | 3 |
| Foster Lab Safety | |||
| • By preventing incident | 2 | 1 | 3 |
| • By helping respond | 2 | 1 | 3 |
| Protect the environment | |||
| • By preventing incidents | 1 | 2 | 3 |
| • By helping respond | 2 | 1 | 3 |
| Other considerations | |||
| • Minimizing costs and burdens to stakeholders | 2 | 2 | 3 |
| • Feasibility? | 2 | 1 | 2 |
| • Not impede research | 2 | 1 | 3 |
| • Promote constructive applications | 2 | 1 | 1 |
Based on the scoring, I would prioritize a combination of Option 2 and Option 1, while treating Option 3 as a later-stage priority once the technology begins to move toward health-facing applications. Option 2, which proposes a tiered biosafety and responsibility framework for academic and community laboratories, stands out as the most feasible and immediately impactful starting point. It scored highest for fostering lab safety, improving response capacity, and not impeding research, because similar training and review systems already exist in many research environments and can be expanded without excessive cost. Strengthening education, risk assessment, and oversight in a tiered way allows governance to scale with the level of risk rather than applying overly strict requirements to all projects. This aligns with governance approaches that emphasize responsible stewardship, ethics training, and adaptive oversight while still supporting innovation. Option 1, which involves sequence screening and provenance tracking for higher-risk designs, complements this by addressing upstream biosecurity risks that cannot be fully mitigated through local lab training alone. Screening and traceability can help prevent misuse and enable investigation if something goes wrong, making it an important second priority that strengthens prevention at the supply-chain level.
Option 3, focused on pre-market validation and clear rules for health-related claims, becomes essential once biological computing tools begin to be used in medical or personalized medicine contexts. Although it scored lower on feasibility and cost because regulatory validation can be burdensome and slow, it is critical for preventing harm from inaccurate or misleading outputs and for maintaining public trust. For that reason, I would treat Option 3 as a second-phase priority: not the first governance action for early-stage research, but something that should be planned early so that clear evidence standards exist before technologies are deployed in real health settings.
Several trade-offs influenced this prioritization. Strong screening and regulatory measures can improve safety and security but may also increase costs, slow research, or concentrate power in larger institutions that can more easily comply with requirements. Conversely, focusing only on education and local oversight could leave gaps in system-wide protection if risky materials can still be obtained without screening. The combined approach of Option 2 and Option 1 balances these concerns by strengthening safety culture and responsibility at the research level while also adding upstream safeguards to reduce misuse. However, this approach assumes that risk categories can be defined clearly, that screening systems will keep pace with advances in design technologies, and that enough biosafety expertise exists to support tiered review processes. There is also uncertainty about how regulators will classify and evaluate emerging biological computing tools that blur boundaries between research, diagnostics, and wellness technologies.
My recommendation is directed primarily toward university leadership, biosafety offices, research funders, and community biology networks, because these actors are well positioned to implement tiered safety frameworks and require the use of screened synthesis providers. A secondary audience includes biotechnology companies and synthesis provider consortia, whose cooperation would make screening and traceability more consistent across the field. Finally, as the technology matures toward personalized medicine applications, health regulators and clinical partners would become key audiences for implementing validation and marketing standards. Overall, prioritizing Option 2 and Option 1 provides a practical and balanced foundation for responsible development, while Option 3 ensures that future clinical uses are introduced in a safe and trustworthy way.
Homework Questions from Professor Jacobson
QUESTION 1
From the slide comparing chemical vs biological DNA synthesis, the error-correcting polymerase is shown with an error rate ~ 1 in 10⁶ (10⁻⁶) per base. The slides list the human genome length as ~3.2 Gbp. If you multiply that out: 3.2×10⁹ bases × 10⁻⁶ ≈ 3.2×10³, so ~3,200 errors per genome copy at that raw polymerase error rate. That’s a big discrepancy. Biology reduces discrepancy in layers, including:
Proofreading exonuclease activity (noted on the polymerase slide as proofreading/error-correcting exonuclease functions).
Post-replication mismatch repair, e.g., the slide explicitly mentions the MutS repair system as an “Error Correction” mechanism.
Plus broader cellular responses (conceptually): damage repair pathways, cell-cycle checkpoints, and removal of heavily damaged cells.
QUESTION 2
The slides give an average human protein coding length of around 1036 bp. Because the genetic code is redundant since most amino acids have multiple codons, the number of distinct DNA sequences that could encode the same protein is very large. In practice, many of these sequences don’t work well because the same protein does not equal the same expression.
Some reasons why not all codes work in practice are:
Homework Questions from Dr. LeProust
QUESTION 1
The standard workhorse method is solid-phase phosphoramidite chemical DNA synthesis, shown as the “Phosphoramidite DNA Synthesis Cycle” in the slides.
QUESTION 2
Because direct chemical synthesis is stepwise: each added base has <100% coupling efficiency, so the yield of full-length product drops exponentially with length. It also accumulates deletions or substitutions, and longer products are harder to purify away from the huge mixture of truncated failure products.
QUESTION 3
The compounded stepwise yield becomes extremely low, and the error burden becomes too high to get enough correct full-length molecules. Instead, many shorter oligos can be synthesized, then assembled into longer DNA using Gibson Assembly, and later the sequence gets verified and corrected if needed.
Homework Question from George Church
The 10 essential amino acids for animals are:
(Google search)
Lysine contingency is a scientific procedure created for Jurassic Park, which consists of using lysine dependence and availability as a safety control. The fact that lysine is essential for animals doesn’t automatically make it a strong containment strategy. Animals need lysine from diet, but lysine is still common in many real-world environments because it’s present in food, biomass, and is produced by microbes/plants. So lysine dependence alone could be a weak containment barrier outside carefully controlled settings. This makes me think lysine-based containment could work as one layer in a broader safety plan, but it likely shouldn’t be the only line of defense.
Completed
After opening my Benchling account and joining the HTGAA group in Benchling, I imported the Lambda DNA.
A restriction enzyme digestion was simulated in Benchling with the following enzymes:
The image I tried to recreate in the style of Paul Vanouse’s latent Figure Protocol was a bird flying as seen below.
I do not have lab access.
3.1. Choose your protein
I chose bacteriorhodopsin because it is a light-activated membrane protein that can convert light into electrochemical signals. This makes it interesting for synthetic biology applications in bio-electronics and biological computing which is what I would like to do my final project on. In particular, bacteriorhodopsin has been used in optical memory systems and protein-based neural networks, where its light-induced conformational changes allow it to function similarly to a switch or synaptic element. I am interested in future applications where biological molecules are integrated with electronic or neuromorphic systems for sensing, computing, or personalized medical technologies, so bacteriorhodopsin is a useful model protein to study.
The following information was taken from UniProt:
UniProt entry: P02945 Organism: Halobacterium salinarum Protein: bacteriorhodopsin Organism taxonomy ID: 64091 Protein existence evidence: 1 Sequence version: 2
UniProt Format:
sp|P02945|BACR_HALSA Bacteriorhodopsin OS=Halobacterium salinarum OX=64091 PE=1 SV=2 MLELLPTAVEGVSQAQITGRPEWIWLALGTALMGLGTLYFLVKGMGVSDPDAKKFYAITTLVPAIAFTMYLSMLLGYGLTMVPFGGEQNPIYWARYADWLFTTPLLLLDLALLVDADQGTILALVGADGIMIGTGLVGALTKVYSYRFVWWAISTAAMLYILYVLFFGFTSKAESMRPEVASTFKVLRNVTVVLWSAYPVVWLIGSEGAGIVPLNIETLLFMVLDVSAKVGFGLILLRSRAIFGEAEAPEPSAGDGAAATSD
This protein has 262 amino acids.
3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence
This sequence includes a stop codon at the end TAA
ATGTTAGAATTATTACCTACTGCTGTTGAAGGTGTTTCTCAAGCTCAAATTACTGGTCGTCCTGAATGGATTTGGTTAGCTTTAGGTACTGCTTTAATGGGTTTAGGTACTTTATATTTTTTAGTTAAAGGTATGGGTGTTTCTGATCCTGATGCTAAAAAATTTTATGCTATTACTACTTTAGTTCCTGCTATTGCTTTTACTATGTATTTATCTATGTTATTAGGTTATGGTTTAACTATGGTTCCTTTTGGTGGTGAACAAAATCCTATTTATTGGGCTCGTTATGCTGATTGGTTATTTACTACTCCTTTATTATTATTAGATTTAGCTTTATTAGTTGATGCTGATCAAGGTACTATTTTAGCTTTAGTTGGTGCTGATGGTATTATGATTGGTACTGGTTTAGTTGGTGCTTTAACTAAAGTTTATTCTTATCGTTTTGTTTGGTGGGCTATTTCTACTGCTGCTATGTTATATATTTTATATGTTTTATTTTTTGGTTTTACTTCTAAAGCTGAATCTATGCGTCCTGAAGTTGCTTCTACTTTTAAAGTTTTACGTAATGTTACTGTTGTTTTATGGTCTGCTTATCCTGTTGTTTGGTTAATTGGTTCTGAAGGTGCTGGTATTGTTCCTTTAAATATTGAAACTTTATTATTTATGGTTTTAGATGTTTCTGCTAAAGTTGGTTTTGGTTTAATTTTATTACGTTCTCGTGCTATTTTTGGTGAAGCTGAAGCTCCTGAACCTTCTGCTGGTGATGGTGCTGCTGCTACTTCTGATTAA
3.3. Codon optimization
Although many different DNA sequences can encode the same protein, organisms prefer certain codons over others. This is called codon bias. If a gene uses codons that are rare in the host organism, the protein may be expressed poorly because the necessary tRNAs are less abundant. Codon optimization adjusts the DNA sequence so that it uses codons preferred by the host organism while still encoding the same amino-acid sequence. This improves translation efficiency, protein yield, and overall stability of expression.
Codon optimization can also remove problematic sequences such as restriction enzyme sites, repeats, or regions that form strong secondary structures, which can interfere with cloning or gene synthesis.
I optimized the bacteriorhodopsin coding sequence for Escherichia coli expression. E. coli is one of the most commonly used organisms in synthetic biology because it grows quickly, is easy to genetically engineer, and has well-characterized expression systems. Optimizing the gene for E. coli would make it easier to clone and express bacteriorhodopsin in a laboratory setting for research or synthetic biology applications.
The sequence was codon optimized using the IDT codon optimization tool, avoiding Type IIs enzyme recognition sites BsaI, BsmBI, and BbsI. These were avoided because restriction enzymes like BsaI, BsmBI, and BbsI are commonly used in synthetic biology cloning methods (especially Golden Gate assembly). These enzymes recognize very specific DNA sequences and cut at those sites. If the gene contains one of those recognition sequences internally, the enzyme will cut the gene in the middle when trying to clone it. That would break the construct and prevent proper assembly.
ATG TTG GAA CTG TTA CCG ACC GCG GTT GAA GGA GTC TCT CAG GCG CAG ATT ACG GGG CGT CCA GAA TGG ATC TGG TTG GCA CTG GGA ACA GCT TTA ATG GGC TTA GGA ACT CTG TAT TTT TTA GTC AAG GGA ATG GGC GTT TCC GAC CCT GAT GCT AAA AAA TTT TAT GCA ATT ACC ACA CTG GTG CCA GCT ATT GCC TTC ACC ATG TAC TTG TCT ATG CTT CTG GGG TAT GGA CTT ACA ATG GTT CCG TTT GGC GGT GAG CAA AAT CCG ATT TAT TGG GCA CGT TAC GCG GAC TGG CTT TTT ACG ACG CCG TTA CTG TTA TTG GAT CTT GCA CTT CTG GTG GAC GCG GAT CAA GGT ACC ATT CTT GCA TTG GTG GGG GCT GAT GGG ATA ATG ATA GGC ACC GGT TTA GTG GGT GCA CTG ACA AAA GTA TAT TCA TAT CGC TTC GTG TGG TGG GCC ATC TCA ACA GCC GCC ATG CTT TAC ATA TTG TAT GTA TTG TTT TTT GGC TTT ACC TCG AAA GCA GAG AGT ATG CGT CCG GAA GTA GCG TCT ACA TTC AAA GTG TTG CGC AAT GTT ACG GTG GTG TTA TGG TCA GCC TAC CCC GTA GTA TGG CTG ATT GGT AGC GAG GGA GCG GGT ATT GTA CCG CTT AAT ATC GAA ACC CTG CTG TTC ATG GTA CTG GAC GTG TCG GCC AAG GTG GGC TTC GGC CTG ATA CTG TTA CGT AGC AGA GCA ATT TTC GGA GAA GCT GAA GCT CCA GAG CCT AGT GCA GGT GAT GGT GCA GCC GCA ACG TCA GAT TAA
3.4. You have a sequence! Now what?
The bacteriorhodopsin protein can be produced by first introducing the codon-optimized DNA sequence into an expression system. This can be done using either cell-dependent (living cells) or cell-free protein expression technologies.
Cell-dependent
One common method is to insert the codon-optimized gene into a plasmid vector and transform it into a host organism such as Escherichia coli. Inside the cell, the DNA is first transcribed into messenger RNA (mRNA) by RNA polymerase. The mRNA is then translated by ribosomes, which read the codons in the mRNA and assemble the corresponding amino acids into the bacteriorhodopsin protein. Transfer RNAs (tRNAs) bring amino acids to the ribosome based on codon-anticodon pairing, allowing the polypeptide chain to form. After translation, the protein folds into its functional structure and can be integrated into membranes or purified for further use.
Cell-free
The gene can be expressed using a cell-free protein synthesis system, which contains ribosomes, enzymes, nucleotides, and tRNAs without living cells. In this system, the DNA template is added directly to the reaction mixture, where it is transcribed into mRNA and translated into protein in vitro. Cell-free systems allow rapid protein production and easier control of conditions, which can be useful for synthetic biology and protein engineering experiments.
After opening my Twist and Benchling accounts, I followed the steps mentioned and obtained the following plasmid.
5.1 DNA Read
(i) What DNA would you want to sequence (e.g., read) and why?
I would want to sequence DNA used in synthetic biological computing systems, particularly DNA strands that encode digital information or act as molecular logic circuits. DNA can be used to store data, perform computations, and respond to biological signals. Sequencing these DNA molecules would allow us to read stored information, verify computational outputs, and detect mutations or errors that occur over time.
In the long term, sequencing DNA-based computing systems could enable personalized medicine applications. For example, DNA circuits inside cells could record molecular events such as exposure to drugs, stress signals, or disease biomarkers. Sequencing these DNA records would allow clinicians to understand how a patient’s cells responded to treatment and adjust therapies accordingly. Accurate DNA reading is therefore essential for both bio-computing and medical diagnostics.
(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 a combination of Illumina sequencing (second-generation) and Oxford Nanopore sequencing (third-generation).
Illumina sequencing provides high accuracy, which is useful for verifying computational DNA outputs and detecting small errors. Nanopore sequencing allows long reads and real-time sequencing, which is useful for quickly decoding DNA circuits or recording information from living cells. Input would be synthetic DNA strands or DNA extracted from engineered cells.
Preparation steps:
Illumina sequencing uses fluorescently labeled nucleotides. As each base is incorporated during synthesis, a fluorescent signal is detected and recorded to determine the sequence. Nanopore sequencing detects changes in electrical current as DNA passes through a nanopore, and software converts those signals into base calls.
The output is digital sequence data (FASTQ files) that can be decoded into stored information or analyzed to determine how the DNA circuit functioned.
5.2 DNA Write
(i) What DNA would you want to synthesize (e.g., write) and why?
I would synthesize DNA sequences that function as biological computing circuits. These sequences could encode logic operations, data storage elements, or sensing modules that respond to cellular signals. For example, a DNA circuit could detect inflammation markers in a patient and record that information into DNA for later sequencing.
Such systems could eventually be used in personalized medicine, where engineered cells monitor patient health and record molecular events. The synthesized DNA could include logic gates, memory elements, and protein-coding sequences that allow cells to process information and respond to disease states.
(ii) What DNA synthesis technology would you use and why?
I would use phosphoramidite-based oligonucleotide synthesis followed by DNA assembly methods such as Gibson Assembly or Golden Gate assembly.
Essential steps
Limitations
Despite these limitations, modern DNA synthesis allows rapid construction of custom genetic circuits for research and therapeutic applications.
DNA Edit
(i) What DNA would you want to edit and why?
I would want to edit DNA in engineered cells used for biological computing and personalized medicine. For example, cells could be edited to contain DNA circuits that sense disease markers, process information, and respond by producing therapeutic molecules. Editing could also be used to improve stability and accuracy of DNA-based memory systems. In medicine, editing patient-derived cells could allow personalized treatments tailored to an individual’s genetic and molecular profile.
(ii) What editing technology would you use and why?
I would use CRISPR-Cas9 and base editing technologies to make precise modifications to DNA. A guide RNA directs the Cas9 enzyme to a specific DNA sequence. Cas9 creates a cut at that location, and the cell’s repair machinery introduces the desired edit using a provided template or repair process.
For this lab, I generated an artistic design using the GUI at opentrons-art.rcdonovan.com. I decided to design some undersea animals because I really like being underwater and observing their nature. Given that in my node’s lab there are only fluorescent green and red, I decided to draw a red crab, a green turtle, and a red and green fish.
The coordinates given are the following: mrfp1_points = [(6.6,28.6), (8.8,28.6), (11,28.6), (13.2,28.6), (15.4,28.6), (17.6,28.6), (19.8,28.6), (6.6,26.4), (8.8,26.4), (11,26.4), (13.2,26.4), (15.4,26.4), (17.6,26.4), (19.8,26.4), (22,26.4), (24.2,26.4), (26.4,26.4), (6.6,24.2), (8.8,24.2), (11,24.2), (13.2,24.2), (15.4,24.2), (17.6,24.2), (19.8,24.2), (22,24.2), (24.2,24.2), (26.4,24.2), (8.8,22), (11,22), (13.2,22), (15.4,22), (17.6,22), (19.8,22), (22,22), (24.2,22), (26.4,22), (28.6,22), (11,19.8), (13.2,19.8), (15.4,19.8), (17.6,19.8), (19.8,19.8), (22,19.8), (24.2,19.8), (26.4,19.8), (28.6,19.8), (30.8,19.8), (17.6,17.6), (19.8,17.6), (22,17.6), (24.2,17.6), (26.4,17.6), (28.6,17.6), (30.8,17.6), (17.6,15.4), (19.8,15.4), (22,15.4), (24.2,15.4), (26.4,15.4), (28.6,15.4), (30.8,15.4), (33,15.4), (15.4,13.2), (17.6,13.2), (19.8,13.2), (22,13.2), (24.2,13.2), (26.4,13.2), (28.6,13.2), (30.8,13.2), (33,13.2), (35.2,13.2), (15.4,11), (17.6,11), (26.4,11), (28.6,11), (30.8,11), (33,11), (35.2,11), (15.4,8.8), (17.6,8.8), (28.6,8.8), (30.8,8.8), (33,8.8), (35.2,8.8), (15.4,6.6), (33,6.6), (35.2,6.6), (35.2,4.4), (37.4,4.4), (35.2,2.2), (37.4,2.2), (35.2,0), (37.4,0), (37.4,-2.2), (35.2,-4.4), (37.4,-4.4), (35.2,-6.6), (33,-8.8), (35.2,-8.8), (33,-11), (-6.6,-13.2), (0,-13.2), (6.6,-13.2), (13.2,-13.2), (19.8,-13.2), (26.4,-13.2), (33,-13.2), (-6.6,-15.4), (-4.4,-15.4), (-2.2,-15.4), (0,-15.4), (6.6,-15.4), (13.2,-15.4), (19.8,-15.4), (22,-15.4), (24.2,-15.4), (26.4,-15.4), (33,-15.4), (-4.4,-17.6), (-2.2,-17.6), (4.4,-17.6), (6.6,-17.6), (8.8,-17.6), (11,-17.6), (13.2,-17.6), (15.4,-17.6), (22,-17.6), (24.2,-17.6), (-4.4,-19.8), (-2.2,-19.8), (2.2,-19.8), (4.4,-19.8), (6.6,-19.8), (8.8,-19.8), (11,-19.8), (13.2,-19.8), (15.4,-19.8), (17.6,-19.8), (22,-19.8), (24.2,-19.8), (-2.2,-22), (0,-22), (2.2,-22), (4.4,-22), (6.6,-22), (8.8,-22), (11,-22), (13.2,-22), (15.4,-22), (17.6,-22), (19.8,-22), (22,-22), (0,-24.2), (2.2,-24.2), (4.4,-24.2), (6.6,-24.2), (8.8,-24.2), (11,-24.2), (13.2,-24.2), (15.4,-24.2), (17.6,-24.2), (19.8,-24.2), (-2.2,-26.4), (0,-26.4), (2.2,-26.4), (4.4,-26.4), (6.6,-26.4), (8.8,-26.4), (11,-26.4), (13.2,-26.4), (15.4,-26.4), (17.6,-26.4), (19.8,-26.4), (22,-26.4), (-2.2,-28.6), (2.2,-28.6), (4.4,-28.6), (6.6,-28.6), (8.8,-28.6), (11,-28.6), (13.2,-28.6), (15.4,-28.6), (17.6,-28.6), (22,-28.6), (-2.2,-30.8), (4.4,-30.8), (6.6,-30.8), (8.8,-30.8), (11,-30.8), (13.2,-30.8), (15.4,-30.8), (22,-30.8)] sfgfp_points = [(6.6,30.8), (8.8,30.8), (11,30.8), (13.2,30.8), (15.4,30.8), (17.6,30.8), (19.8,30.8), (4.4,28.6), (22,28.6), (24.2,28.6), (26.4,28.6), (-6.6,26.4), (-4.4,26.4), (-2.2,26.4), (0,26.4), (4.4,26.4), (28.6,26.4), (-8.8,24.2), (-6.6,24.2), (-4.4,24.2), (-2.2,24.2), (0,24.2), (4.4,24.2), (28.6,24.2), (-8.8,22), (-6.6,22), (-4.4,22), (-2.2,22), (0,22), (6.6,22), (30.8,22), (-8.8,19.8), (-6.6,19.8), (-4.4,19.8), (-2.2,19.8), (0,19.8), (8.8,19.8), (33,19.8), (-17.6,17.6), (-15.4,17.6), (-13.2,17.6), (-11,17.6), (-6.6,17.6), (-4.4,17.6), (-2.2,17.6), (11,17.6), (13.2,17.6), (15.4,17.6), (33,17.6), (-24.2,15.4), (-22,15.4), (-19.8,15.4), (-17.6,15.4), (-15.4,15.4), (-13.2,15.4), (-11,15.4), (-6.6,15.4), (-4.4,15.4), (-2.2,15.4), (15.4,15.4), (35.2,15.4), (-26.4,13.2), (-24.2,13.2), (-22,13.2), (-19.8,13.2), (-17.6,13.2), (-15.4,13.2), (-13.2,13.2), (-11,13.2), (-6.6,13.2), (-4.4,13.2), (-2.2,13.2), (13.2,13.2), (37.4,13.2), (-28.6,11), (-26.4,11), (-24.2,11), (-22,11), (-19.8,11), (-17.6,11), (-15.4,11), (-13.2,11), (-11,11), (-6.6,11), (-4.4,11), (-2.2,11), (13.2,11), (19.8,11), (37.4,11), (-30.8,8.8), (-28.6,8.8), (-26.4,8.8), (-24.2,8.8), (-22,8.8), (-19.8,8.8), (-17.6,8.8), (-15.4,8.8), (-13.2,8.8), (-11,8.8), (-6.6,8.8), (-4.4,8.8), (13.2,8.8), (19.8,8.8), (37.4,8.8), (-30.8,6.6), (-28.6,6.6), (-26.4,6.6), (-24.2,6.6), (-22,6.6), (-19.8,6.6), (-17.6,6.6), (-15.4,6.6), (-13.2,6.6), (-11,6.6), (-6.6,6.6), (-4.4,6.6), (13.2,6.6), (17.6,6.6), (37.4,6.6), (-30.8,4.4), (-28.6,4.4), (-26.4,4.4), (-24.2,4.4), (-22,4.4), (-19.8,4.4), (-17.6,4.4), (-15.4,4.4), (-13.2,4.4), (-11,4.4), (-6.6,4.4), (-4.4,4.4), (15.4,4.4), (39.6,4.4), (-33,2.2), (-30.8,2.2), (-28.6,2.2), (-26.4,2.2), (-24.2,2.2), (-22,2.2), (-19.8,2.2), (-17.6,2.2), (-15.4,2.2), (-13.2,2.2), (-8.8,2.2), (-6.6,2.2), (-4.4,2.2), (39.6,2.2), (-35.2,0), (-33,0), (-30.8,0), (-28.6,0), (-26.4,0), (-24.2,0), (-22,0), (-19.8,0), (-17.6,0), (-15.4,0), (-11,0), (-8.8,0), (-6.6,0), (-4.4,0), (39.6,0), (-35.2,-2.2), (-33,-2.2), (-30.8,-2.2), (-28.6,-2.2), (-26.4,-2.2), (-24.2,-2.2), (-22,-2.2), (-19.8,-2.2), (-17.6,-2.2), (-13.2,-2.2), (-11,-2.2), (-8.8,-2.2), (-6.6,-2.2), (-4.4,-2.2), (39.6,-2.2), (-35.2,-4.4), (-33,-4.4), (-30.8,-4.4), (-28.6,-4.4), (-26.4,-4.4), (-24.2,-4.4), (-22,-4.4), (-19.8,-4.4), (-15.4,-4.4), (-13.2,-4.4), (-8.8,-4.4), (-6.6,-4.4), (39.6,-4.4), (-33,-6.6), (-30.8,-6.6), (-28.6,-6.6), (-26.4,-6.6), (-24.2,-6.6), (-22,-6.6), (-17.6,-6.6), (-15.4,-6.6), (-8.8,-6.6), (37.4,-6.6), (-19.8,-8.8), (-17.6,-8.8), (37.4,-8.8), (-33,-11), (-30.8,-11), (-28.6,-11), (-26.4,-11), (-24.2,-11), (-22,-11), (-19.8,-11), (30.8,-11), (35.2,-11), (-24.2,-13.2), (-22,-13.2), (-19.8,-13.2), (30.8,-13.2), (35.2,-13.2), (-24.2,-15.4), (-22,-15.4), (-19.8,-15.4), (30.8,-15.4), (35.2,-15.4), (-24.2,-17.6), (-22,-17.6), (33,-17.6), (35.2,-17.6), (-24.2,-19.8), (33,-19.8)]
Later, I simulated the design in the Opentrons Colab. This protocol programs the Opentrons robot to create an artistic pattern on an agar plate using two fluorescent proteins (mRFP1 in red and sfGFP in green).
First, the robot loads the necessary labware: a 20 µL tip rack, a temperature module holding the color plate (agar plate with dyes), and the agar plate where the design will be drawn. The pipette is initialized and set to start from a specific tip.
The script defines helper functions:
location_of_color() finds the well containing a specific color.
dispense_and_detach() carefully deposits a droplet onto the agar surface by approaching from above, dispensing, and lifting back up to avoid smearing.
Two lists of (x, y) coordinates define the artwork. These coordinates represent positions relative to the center of the agar plate.
The code: Download my Opentron Python script
The simulation:
After doing the simulation, I filled out the Google form and signed up for a timeslot to run the code on the robot:
Paper: BOTany Methods: Accessible Automation for Plant Synthetic Biology
The paper I found describes how researchers used automated liquid handling systems to process biological samples in a standardized way. Instead of manually pipetting every reaction, they used robotic tools to precisely mix reagents, prepare samples, and run assays with much less human intervention. This allowed them to reduce variability, increase reproducibility, and process many samples at the same time. Automation was especially important because small pipetting differences can affect biological results. By using robotics, the researchers were able to make their workflow more scalable and reliable, which is critical in biomedical research where consistency matters.
For my final project, I would like to use an Opentrons robot to automate the testing of DNA-based logic circuits using cell-free protein synthesis. The robot would transfer DNA constructs into a well plate, add the expression mix, dispense cofactors, and incubate the reactions before measuring fluorescence output. This would allow me to test many different circuit designs at once and compare their outputs automatically. I could also design a small 3D-printed holder to support custom plates or patterned substrates. Overall, automation would help me prototype biological computation systems faster and more accurately, while reducing human error and making the experiments more scalable.
In slides for committed listeners.
