DIY Electroporation Project: BioVolt - First rolled out at DEFCON 32- Now revisted from END to END

Project Overview: BioVolt - DIY Electroporation Device & Full Transformation Pipeline

Biological engineering application/tool to develop:
BioVolt is a portable, ultra-low-cost DIY electroporation device (~$10-20 in parts) that uses a piezoelectric crystal from a barbecue lighter to generate ~2,000 V pulses for temporary cell membrane permeabilization. This enables DNA/RNA uptake in bacteria (e.g., E. coli), yeast, plant protoplasts, or even stem cells for genetic transformation. Inspired by the DEFCON 32 talk “You got a lighter I need to do some Electroporation” (presented by Dr. James Utley (Me), Phil Rhodes, and Josh Hill from Viva Securus/Syndicate Laboratories), it builds on frugal biohacking principles: piezoelectric trigger pulsing, custom microfluidic cuvettes from aluminum tape/magnets/glass slides, and simple high-voltage testing.

DEFCON 32 Presentation — Where It Started for me

At DEFCON 32 the talk I presented focused on the device itself — proving that a barbecue lighter’s piezoelectric crystal could generate sufficient voltage to temporarily permeabilize cell membranes for DNA uptake. The talk covered design details, demos, troubleshooting (e.g., arc gap tuning with Post-it notes), and the biohacking ethos behind building a ~$10 electroporator.

Key highlights from the talk: ~2,000 V pulses via lighter clicks, high cell mortality (50-70%) but viable transformants, GFP reporter demos, open protocols encouraged.

Next Phase: End-to-End Pipeline with Efficiency Focus

The next phase of BioVolt moves beyond the device and brings the entire workflow end to end, with a focus on efficiency and frugal validation. The goal: take a piezoelectric electroporator built from a barbecue lighter and prove — through a full pipeline — that it actually works. The pipeline includes:

1. Plasmid amplification via thermal cycling — Before electroporation, the initial plasmid source will be amplified using the MJ Research PTC-100 thermal cycler (Peltier-effect programmable controller) available in the lab. This ensures sufficient plasmid DNA concentration for transformation.

2. DNA concentration measurement — Using the Rodeo open colorimeter (visible light version for OD600 cell density measurements) and, if possible, the UV version for DNA concentration quantification. This provides pre- and post-transformation metrics.

3. Electroporation — Transformation of cells with the amplified plasmid DNA using the BioVolt piezoelectric device, followed by recovery and plating.

4. Post-transformation PCR verification — For good measure, PCR will be run after transformation using the same thermal cycler to check whether the insert is present in the recovered cells. This triangulates and correlates with plating results to provide a hasty “close enough” frugal validation.

5. Gel electrophoresis confirmation — Agarose gel electrophoresis to visualise PCR products and verify successful transformation (e.g., presence of reporter genes like GFP via band patterns under UV).

The aim is to triangulate multiple data points — plasmid amplification, colorimetric/UV measurement, transformation plating, and post-transformation PCR — to build confidence that the piezo electroporator from a lighter actually delivers. Fingers crossed, this provides a credible, frugal, end-to-end validation of a DIY electroporation workflow.

This democratizes synthetic biology for education, citizen science, and personal biohacking in resource-limited settings.

Lab Setup & Tools in Action - You can see I got some goods to work with!

My biohacker lab integrates the device with the full verification pipeline.

On to the assignement - Interactive Governance Assessment Form

An interactive Python application (app.py) is provided to assess governance and risk mitigation strategies for the BioVolt project. The form uses a block-based rating scale where more filled blocks mean more effective:

Project File Structure

BioVolt_week_01_hw_principles_and_practices/
├── _index.md                      # This file — project documentation (Hugo page)
├── app.py                         # Interactive governance assessment application
├── requirements.txt               # Python dependencies
├── Biohacker_Lab.jpeg             # Lab overview photo
├── in_da_lab.jpeg                 # Working in the lab photo
├── Volt_Test.jpeg                 # High-voltage testing with insulation tester
├── rodeo-colorimeter.png          # IO Rodeo open colorimeter
├── BioVolt_govern_UI.png          # Screenshot of the application UI
└── Biovolt_Govern_Report.png      # Screenshot of the PDF report output

Prerequisites

• Python 3.x installed on your system
• tkinter (usually included with Python; on Linux you may need python3-tk)

Installation

1. Navigate to the project directory:

   cd BioVolt_week_01_hw_principles_and_practices

2. Install required dependencies:

   pip install -r requirements.txt

Running the Application

python app.py

How to Use the Form

1. Launch — The application opens a dark-themed window with the assessment matrix.

2. Read the instructions — System instructions are displayed at the top of the form explaining the block-based rating system.

3. Review each concern category — Three categories are presented, each with context questions:

   • Biosecurity Concerns — preventing GMO release, high-voltage mishandling, pathogen engineering
   • Equity Concerns — access, regulation, educational barriers, global equity
   • Environmental Concerns — microbial activity, non-human organisms, public concerns

4. Rate each action — For every action under each stakeholder (Researchers, Manufacturers, Industry, Organizations), click one of three block-rating buttons:

   • ●○○ — Minimally Effective (button highlights red)
   • ●●○ — Moderately Effective (button highlights amber)
   • ●●● — Most Effective (button highlights green)

5. Visual feedback — When you click a rating:

   • The selected button stays highlighted with its rating colour
   • A status indicator appears to the right showing your selection
   • Other buttons in the same row reset to their default state

6. Export to PDF — Click the “EXPORT TO PDF” button to generate a report containing:

   • Cover page with assessment date and completion count
   • Rating scale legend with colour-coded descriptions
   • Full assessment tables for each concern category
   • Colour-coded rows: green tint for Most Effective, amber for Moderate, red for Minimal
   • Block indicators (●●● / ●●○ / ●○○) printed in every row
   • Summary page with counts and percentages for each rating level

7. Reset — Click “RESET MATRIX” to clear all selections and start over.

Application Features

• Block-based rating scale — intuitive system where more blocks = more effective (no ambiguity)
• Dark theme UI — dark background with neon accent colours for readability
• Persistent button state — selected buttons remain highlighted with their rating colour
• Status indicators — each row shows the current selection in text beside the buttons
• Scrollable interface — mouse wheel support for navigating the full assessment matrix
• Neon accent bars — left-side accent bars on each concern card for visual hierarchy
• Colour-coded PDF output — rating cells are tinted to match their effectiveness level
• Summary statistics — PDF includes a final page with counts and percentages
• Empty export protection — warns you if no ratings are selected before exporting
• Form reset — one-click reset with confirmation dialog

Screenshots

Application UI — Dark-themed interface with block-based rating buttons and colour-coded status indicators:

PDF Report Output — Exported assessment with colour-coded rows, block indicators, and stakeholder ratings:

Governance / Policy Goals (Preventing Harm)

Focus on non-tool-function risks: Prevent environmental release of unintended GMOs, biosafety incidents from mishandling high-voltage + microbes, escalation to unsafe self-experimentation/human applications, or biosecurity concerns (e.g., pathogen engineering).
Core aims: Minimize biosafety/biosecurity harms, promote responsible use, avoid stifling innovation with heavy regulation, encourage informed DIYbio practices, and address public/environmental concerns.

Three Potential Governance/Policy Actions

Action 1: Community-Led Self-Governance with Voluntary Guidelines and Reporting

Goal: Foster peer accountability and safe practices through DIYbio networks, reducing risks via shared norms without external mandates.

Design:

• Opt-in: DIYbio communities, forums (e.g., Discord, Reddit, The ODIN users), and makerspaces.
• Fund: Crowdfunding, donations, or volunteer time.
• Approve: Community-elected moderators or biosafety working groups.
• Implement: Publish voluntary guidelines (e.g., “BioVolt Safety Protocol” on protocols.io or GitHub), require protocol sharing for builds, anonymous incident reporting (expand “Ask a Biosafety Expert” services).

Risks / What could go wrong (incorrect assumptions, uncertainties):
Assumes broad ethical participation - rogue actors may ignore; self-reporting misses hidden issues; low adoption if seen as “extra work.”

Assumptions, “Success” and “Failure” rubric:

• Success (best - 1): High adoption -> fewer accidents, strong norms against risky uses (e.g., no human trials), community self-corrects.
• Mid (2): Partial uptake -> safety improvements in visible projects, but gaps remain.
• Failure (worst - 3): Guidelines ignored -> no risk reduction, or “forbidden fruit” effect increases experimentation.
• Unintended consequences: Overly cautious norms suppress legitimate educational uses.

Action 2: Targeted Product Restrictions (e.g., Safety Warnings / Age Limits on Kits & Components)

Goal: Reduce impulsive or uninformed misuse by requiring clear hazard labels on high-voltage components (e.g., piezoelectric lighters, capacitors) or full kits, without banning access.

Design:

• Opt-in/compliance: Online sellers (Amazon, AliExpress), hardware stores, kit makers.
• Fund: Seller-borne costs.
• Approve: Consumer safety agencies or state-level consumer protection (e.g., modeled on CRISPR kit labeling laws).
• Implement: Mandatory labels (“Not for human use; biological hazard when combined with genetic material; 18+ recommended”).

Risks / What could go wrong:
Warnings may not deter determined users (parts sourced separately); patchy enforcement online/global; could increase black-market activity.

Assumptions, “Success” and “Failure” rubric:

• Success (best - 1): Warnings raise awareness, reduce naive accidents while preserving access.
• Mid (2): Labels added but often ignored by experienced users.
• Failure (worst - 3): Little impact on bad actors; adds cost/delays for legitimate builders.
• Unintended consequences: Drives activity underground, reducing community visibility/oversight.

Action 3: Treat as if it has a Regulatory Classification as Restricted Biotech Equipment (e.g., Licensing for High-Voltage Builds) Pledge reporting and Safe use.

Goal: Treat advanced DIY electroporators like controlled lab tools - require permits/training for >1,000 V devices to prevent proliferation to high-risk genetic work.

Design:

• Opt-in: Individual builders/users via registration.
• Fund: User fees.
• Approve: Government agencies (e.g., expanding CDC/NIH biosafety rules or local health depts).
• Implement: Permits, training requirements, inspections for community labs/shared spaces.
• Hazard Vulnerability Assessment (HVA) and Peer Review: Conduct a comprehensive HVA and require peer review through a pseudo-IRB-like entity - a multidisciplinary and independent review board focusing on environmental and human safety. This entity would evaluate proposed uses, assess risks, and provide guidance on safe protocols before high-voltage builds are deployed.

Risks / What could go wrong:
Hard to define safe thresholds; bureaucracy kills accessibility; overreach chills innovation globally.

Assumptions, “Success” and “Failure” rubric:

• Success (best - 1): Blocks worst misuse (e.g., pathogen work), funnels activity to supervised settings.
• Mid (2): Some compliance, but many unlicensed builds continue.
• Failure (worst - 3): Broad restrictions eliminate DIY benefits, push activity to unregulated regions.
• Unintended consequences: Harms global equity/education; favors institutional labs only.

Overall Tradeoffs & Prioritization

Prioritize Action 1 (community self-governance) as primary: Lowest overregulation risk, aligns with DIY ethos, adaptable to low current misuse evidence, leverages community goodwill.

Combine with Action 2 (targeted warnings) as secondary: Adds minimal external safeguard for public health, deters casual risks without bans.

Avoid/minimize Action 3 unless clear evidence of high-risk proliferation: Highest chance of killing accessibility and innovation, poor fit for low-harm tool like BioVolt.

Key uncertainties (misuse rates, community response, enforcement feasibility) favor lighter interventions. Monitor via voluntary reporting; escalate only if serious incidents arise. This balances empowerment with responsible governance for biosafety and preventing broader DIY genetic risks.

Made with love and the AI Slop is from Cursor-GLM 4.7

Week 1: Professor Questions

Answers organized by instructor, please click the question to reveal the answer!

Instructions: Click the triangle (▶) or question text to expand and view the full answer.

[SECTION 1] Questions from Professor Jacobson

Source: Lecture 2 slides

[SECTION 2] Questions from Dr. LeProust

Source: Lecture 2 slides

[SECTION 3] Question from Professor George Church

Source: Lecture 2 slides

[REFERENCES]

Primary Literature and Resources

DNA Replication Fidelity (Q1):

• Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell. 6th edition. Garland Science, 2014. Chapter 5: DNA Replication, Repair, and Recombination.
• Kunkel TA, Bebenek K. DNA replication fidelity. Annu Rev Biochem. 2000;69:497-529. doi:10.1146/annurev.biochem.69.1.497
• Iyer RR, Pluciennik A, Burdett V, Modrich PL. DNA mismatch repair: functions and mechanisms. Chem Rev. 2006;106(2):302-323. doi:10.1021/cr0404794

Genetic Code and Translation (Q2):

• Plotkin JB, Kudla G. Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet. 2011;12(1):32-42. doi:10.1038/nrg2899
• Gustafsson C, Govindarajan S, Minshull J. Codon bias and heterologous protein expression. Trends Biotechnol. 2004;22(7):346-353. doi:10.1016/j.tibtech.2004.04.006
• Tuller T, Carmi A, Vestsigian K, et al. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell. 2010;141(2):344-354. doi:10.1016/j.cell.2010.03.031

Oligonucleotide Synthesis (Q3-Q5):

• Caruthers MH. Gene synthesis machines: DNA chemistry and its uses. Science. 1985;230(4723):281-285. doi:10.1126/science.3863253
• Kosuri S, Church GM. Large-scale de novo DNA synthesis: technologies and applications. Nat Methods. 2014;11(5):499-507. doi:10.1038/nmeth.2918
• Hughes RA, Ellington AD. Synthetic DNA synthesis and assembly: putting the synthetic in synthetic biology. Cold Spring Harb Perspect Biol. 2017;9(1):a023812. doi:10.1101/cshperspect.a023812

Amino Acid Nutrition and Biosafety (Q6):

• Reeds PJ. Dispensable and indispensable amino acids for humans. J Nutr. 2000;130(7):1835S-1840S. doi:10.1093/jn/130.7.1835S
• WHO/FAO/UNU Expert Consultation. Protein and amino acid requirements in human nutrition. WHO Technical Report Series 935. Geneva: World Health Organization; 2007.
• USDA National Nutrient Database for Standard Reference (Release 28). Agricultural Research Service, U.S. Department of Agriculture. 2015.
• Crichton M. Jurassic Park. New York: Alfred A. Knopf; 1990.
• Mandell DJ, Lajoie MJ, Mee MT, et al. Biocontainment of genetically modified organisms by synthetic protein design. Nature. 2015;518(7537):55-60. doi:10.1038/nature14121 [Modern unnatural amino acid containment systems]

Document created: February 10, 2026
Author: James Utley, PhD
Affiliation: Syndicate Laboratories, Panama City, Panama
Course: HTGAA 2026 Spring — Week 1 Homework

Part 1: Benchling & In-silico Gel Art

Part 1: Benchling & In-silico Gel Art

Simulated restriction enzyme digestion with the seven enzymes specified in this week’s lab protocol: SalI, SacI, EcoRV, KpnI, BamHI, HindIII, and EcoRI. Used both the DNA Gel Art Interface (λ DNA) and Benchling (lambda phage genome NC_001416) to visualize digest patterns and verify cut-site predictions.

Lab protocol: Gel Art: Restriction Digests and Gel Electrophoresis

Benchling Digest — NC_001416 (Lambda Phage Genome)

Sequence: NC_001416 — Escherichia phage lambda, 48,502 bp (linear).

Benchling digest link: NC_001416 Digest — Benchling

Proof of Work — Screenshots

1. DNA Gel Art Interface — λ DNA Restriction Digests

Simulated gel electrophoresis using the DNA Gel Art tool. λ DNA was digested with various enzyme combinations (EcoRV + SacI, HindIII + PvuII, NdeI + SalI, etc.) across lanes 2–10. The table documents water, CutSmart buffer, λ DNA, and enzyme volumes per lane.

2. Benchling — NC_001416 Sequence Map with Restriction Sites

Linear map of NC_001416 in Benchling showing the raw sequence, annotated genetic features (e.g., xis, nul, lambdap genes), and restriction enzyme cut sites (PciI, AscI, PmeI, BsaI, KpnI, SacI, SalI, and others) along the 48.5 kb genome.

3. Virtual Digest Gel — NC_001416 with All Seven Required Enzymes

Simulated gel (Life 1 kb Plus ladder) showing digest results for NC_001416 with each of the seven required enzymes:

Enzymes Simulated

Part 3: DNA Design Challenge

Part 3: DNA Design Challenge

3.1 Choose Your Protein

Protein chosen: Superfolder Green Fluorescent Protein (sfGFP)

Why: sfGFP is a robust, rapidly maturing fluorescent protein derived from Aequorea victoria (Pédelacq et al., 2005). It is widely used in synthetic biology as a reporter—when expressed in cells, it fluoresces bright green under blue/UV light, enabling real-time visualization of gene expression, protein localization, and cell tracking. Its “superfolder” mutations improve folding efficiency in diverse hosts (including E. coli), making it ideal for expression experiments. It also connects directly to Part 4, where we build an expression cassette to make E. coli glow green.

Source: FPbase — Superfolder GFP | UniProt | GenBank: ASL68970

Protein sequence (amino acids):

MSKGEELFTGVVPILVELDGDVNGHKFSVRGEGEGDATNGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKRHDFFKSAMPEGYVQERTISFKDDGTYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNFNSHNVYITADKQKNGIKANFKIRHNVEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSVLSKDPNEKRDHMVLLEFVTAAGITHGMDELYK

(238 amino acids, ~26.8 kDa)

3.2 Reverse Translate: Protein → DNA

Using the Central Dogma in reverse: given a protein sequence, we infer a possible DNA sequence that could encode it. Because the genetic code is degenerate (multiple codons encode the same amino acid), many DNA sequences can produce the same protein. A simple reverse translation uses one valid codon per amino acid—here, E. coli preferred codons (most frequently used in highly expressed genes).

Tool used: Reverse translation with E. coli codon preferences (e.g., ExPASy Translate or similar tools; can also be done manually with a codon usage table).

Reverse-translated DNA sequence (one possible encoding):

ATGTCAAAAGGTGAAGAACTGTTTACCGGTGTGGTGCCGATTCTGGTGGAACTGGATGGTGATGTGAACGGTCACAAATTTTCAGTGCGTGGTGAAGGTGAAGGTGATGCTACCAACGGTAAACTGACCCTGAAATTTATTTGCACCACCGGTAAACTGCCGGTGCCGTGGCCGACCCTGGTGACCACCCTGACCTACGGTGTGCAGTGCTTTTCACGTTACCCGGATCACATGAAACGTCACGATTTTTTTAAATCAGCTATGCCGGAAGGTTACGTGCAGGAACGTACCATTTCATTTAAAGATGATGGTACCTACAAAACCCGTGCTGAAGTGAAATTTGAAGGTGATACCCTGGTGAACCGTATTGAACTGAAAGGTATTGATTTTAAAGAAGATGGTAACATTCTGGGTCACAAACTGGAATACAACTTTAACTCACACAACGTGTACATTACCGCTGATAAACAGAAAAACGGTATTAAAGCTAACTTTAAAATTCGTCACAACGTGGAAGATGGTTCAGTGCAGCTGGCTGATCACTACCAGCAGAACACCCCGATTGGTGATGGTCCGGTGCTGCTGCCGGATAACCACTACCTGTCAACCCAGTCAGTGCTGTCAAAAGATCCGAACGAAAAACGTGATCACATGGTGCTGCTGGAATTTGTGACCGCTGCTGGTATTACCCACGGTATGGATGAACTGTACAAA

(714 bp)

3.3 Codon Optimization

Why optimize codon usage? Different organisms prefer different codons for the same amino acid, based on tRNA abundance and other factors. Using rare codons can slow translation, cause ribosome stalling, and reduce protein yield. Codon optimization replaces codons with those most frequently used in the target organism, improving expression levels and folding. It also allows us to avoid restriction enzyme recognition sites (e.g., BsaI, BsmBI, BbsI) that would interfere with Golden Gate or other assembly methods.

Organism chosen: Escherichia coli (K-12)

Why E. coli? It is the standard workhorse for recombinant protein expression: well-characterized genetics, fast growth, simple culture, and widely available vectors and protocols. The HTGAA Part 4 exercise uses E. coli for the sfGFP expression cassette, so optimizing for E. coli keeps the workflow consistent.

Tool used: Twist Bioscience Codon Optimization Tool (avoiding Type IIs sites BsaI, BsmBI, BbsI as recommended).

Codon-optimized DNA sequence (for E. coli):

Using Twist Codon Optimization Tool, avoiding Type IIs sites BsaI, BsmBI, BbsI:

ATGAGCAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCCGTGGAGAGGGTGAAGGTGATGCTACAAACGGAAAACTCACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCGTGGCCAACACTTGTCACTACTCTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCACATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGACCTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAGGGTATTGATTTTAAAGAAGATGGAAACATTCTTGGACACAAACTCGAGTACAACTTTAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACGTTGAAGATGGTTCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGTCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAA

(717 bp; optimized for E. coli expression, restriction-site free — same sequence used in Part 4 expression cassette)

3.4 You Have a Sequence! Now What?

Technologies to produce sfGFP from this DNA:

1. Cell-dependent (recombinant expression in E. coli):

   • Clone the codon-optimized gene into an expression vector (e.g., pTwist Amp High Copy) with a constitutive or inducible promoter (e.g., BBa_J23106), RBS (e.g., BBa_B0034), and terminator (e.g., BBa_B0015).
   • Transform the plasmid into E. coli (e.g., DH5α, BL21).
   • Grow cells; the host RNA polymerase transcribes the DNA into mRNA, and ribosomes translate the mRNA into sfGFP.
   • The protein folds and forms its chromophore; cells fluoresce green under blue light (~488 nm excitation, ~510 nm emission).

2. Cell-free (in vitro transcription–translation):

   • Use a cell-free system (e.g., E. coli lysate, PURE system) with the DNA template.
   • Add NTPs, amino acids, and energy sources; the system transcribes and translates the gene without living cells.
   • Useful for rapid prototyping, toxic proteins, or when cell growth is impractical.

3. DNA synthesis (Twist, IDT, etc.):

   • Order the gene as a clonal or linear fragment from a synthesis provider.
   • Use it directly for cloning or cell-free expression, avoiding PCR or cloning from natural sources.

Flow: DNA → (RNA polymerase) → mRNA → (ribosomes + tRNAs + amino acids) → polypeptide → (folding + chromophore formation) → fluorescent sfGFP.

3.5 [Optional] How Does It Work in Nature?

Alignment of DNA, RNA, and protein: In the Central Dogma, DNA is transcribed to RNA (T→U), and RNA is translated to protein (3 nt → 1 aa). Tools like Benchling or Ronan’s gel art site can visualize this alignment.

Single gene → multiple proteins: Alternative splicing (eukaryotes) or alternative start codons/ribosomal frameshifting can produce multiple proteins from one gene. sfGFP is a single open reading frame, but in general, one gene can yield multiple isoforms through these mechanisms.

Part 4: Prepare a Twist DNA Synthesis Order

Part 4: Prepare a Twist DNA Synthesis Order

Practice exercise — building an sfGFP expression cassette in Benchling, preparing a mock Twist order, and annotating the plasmid.

4.1–4.2 Accounts & Build Your DNA Insert Sequence

Created Twist and Benchling accounts. Built the sfGFP expression cassette in Benchling with annotated parts:

• Promoter (BBa_J23106)
• RBS (BBa_B0034)
• Start codon (ATG)
• Coding sequence (codon-optimized sfGFP from Part 3)
• 7× His tag
• Stop codon (TAA)
• Terminator (BBa_B0015)

Proof of Annotation in Benchling

Benchling sequence link: sfGFP_expression_cassette · Benchling

Screenshot: Annotated Sequence Map in Benchling

The sequence map shows the sfGFP expression cassette (924 bp) with promoter, RBS, and sfGFP CDS annotated, plus restriction enzyme cut sites.

Screenshot: Circular Plasmid Map (sfGFP in pTwist Amp High Copy)

The full construct (3145 bp) in pTwist Amp High Copy, with insert, source, AmpR promoter, and vector backbone annotated.

Note: The color choices for the plasmid annotations are a reflection of my cringe-worthy color skills — consider yourself warned.

4.3–4.6 Twist Order Flow

• Selected Genes → Clonal Genes on Twist
• Uploaded FASTA (sfGFP expression cassette)
• Chose vector: pTwist Amp High Copy from Twist Vector Catalog
• Downloaded GenBank construct and imported into Benchling

Screenshot: Sequence Upload to Twist

Design Notes: Manual vs. Programmatic

Efficiency: Designing expression cassettes and plasmids can be far more efficient with Python and/or R — tools like DNA Chisel, PyDNA, or SynBioHub enable scripted design, validation, and export. Batch operations, automated codon optimization, and constraint checking become straightforward.

Learning value: Building the construct manually in Benchling — clicking through each part, copying sequences, and annotating by hand — offers a different kind of learning. You develop intuition for how promoters, RBSs, and CDSs fit together, where restriction sites fall, and what the plasmid “looks like” at each step. That tactile understanding is harder to get from a script. For a first expression cassette, the manual approach is worth the extra time.

    MANUAL (Benchling)              PROGRAMMATIC (Python/R)
    ─────────────────               ─────────────────────
    Click, paste, annotate           Script → design → export
    Slow, one construct at a time    Fast, many constructs
    Deep, tactile understanding     Scalable, reproducible
    "I built this"                   "I designed 50 of these"
    
    Both have their place. Start manual; scale with code.

Documented Deliverables

Part 5: DNA Read, Write, & Edit

Part 5: DNA Read, Write, & Edit

Answers framed around the BioVolt DIY electroporation pipeline: plasmid amplification → transformation → PCR verification → gel electrophoresis. What DNA would we read, write, and edit to make this frugal pipeline sing?

     ╔═══════════════════════════════════════════════════════════════╗
     ║  🧬 THE CENTRAL DOGMA MEETS BIOVOLT 🧬                         ║
     ║                                                               ║
     ║     READ          WRITE         EDIT                          ║
     ║       │              │             │                          ║
     ║       ▼              ▼             ▼                          ║
     ║   [Sequence]   [Synthesize]   [CRISPR]                        ║
     ║       │              │             │                          ║
     ║       └──────────────┼─────────────┘                          ║
     ║                      │                                        ║
     ║                      ▼                                        ║
     ║            ⚡ BIOVOLT ZAPS IT IN ⚡                             ║
     ║                 (E. coli glows green)                         ║
     ╚═══════════════════════════════════════════════════════════════╝

5.1 DNA Read

(i) What DNA would you want to sequence and why?

In the BioVolt pipeline: After electroporation, we transform E. coli with plasmids (e.g., sfGFP expression cassette). We run post-transformation PCR and gel electrophoresis to infer success—but we don’t know the exact sequence. Sequencing the plasmid (or PCR amplicon) confirms that:

• The insert is correct (no truncations, no wrong gene)
• Electroporation didn’t introduce mutations (high voltage can stress DNA)
• The expression cassette is intact for downstream experiments

Broader applications (aligned with BioVolt’s democratization goals):

• Environmental monitoring — e.g., sewage/wastewater DNA for microbiome analysis in Panama; biodiversity surveys
• Human health — disease-associated genes, pharmacogenomics
• DNA data storage — archival sequences in synthetic DNA
• Biobank validation — verifying stored samples

    ┌─────────────────────────────────────────────────────────────┐
    │  BIOVOLT PIPELINE: WHERE SEQUENCING FITS                    │
    │                                                             │
    │   Plasmid ──► PCR amp ──► BioVolt zap ──► Plate ──► Colonies│
    │      │                         │                    │       │
    │      │                         │                    │       │
    │      └─────────────┬────────────┴────────────────────┘      │
    │                    │                                        │
    │                    ▼                                        │
    │              "Did it work?"  ──►  SEQUENCE IT! 🔬           │
    │              (gel = maybe)       (sequence = certainty)     │
    └─────────────────────────────────────────────────────────────┘

(ii) What technology would you use and why?

Technology chosen: Oxford Nanopore (MinION) — third-generation sequencing

Why Nanopore for BioVolt / frugal labs:

• Portable — USB-sized device; runs on laptop; fits in a backpack. Ideal for Panama, field sites, or home labs.
• Real-time — base calling as reads stream; no batch wait.
• Long reads — can span full plasmids; fewer assembly gaps.
• Low capital — compared to Illumina, much cheaper to get started.
• No PCR required for some workflows — direct DNA sequencing possible (native DNA).

         NANOPORE SEQUENCING (simplified)
         
              ╭───-╮
    DNA ────► │ ▓▓ │  ← pore in membrane
              │ ▓▓ │     (ionic current changes per base)
              ╰───-╯
                 │
                 ▼
           ╔═══════════╗
           ║  A T G C  ║  ← base caller (Guppy, etc.)
           ║  ▓ ▓ ▓ ▓  ║     converts squiggle → sequence
           ╚═══════════╝

5.2 DNA Write

(i) What DNA would you want to synthesize and why?

For BioVolt: The expression cassettes we electroporate! Specifically:

• sfGFP plasmid — promoter + RBS + sfGFP CDS + terminator (e.g., BBa_J23106, BBa_B0034, sfGFP, BBa_B0015). This is the “make E. coli glow green” construct we build in Part 4.
• Custom reporters — e.g., biosensors that fluoresce in response to environmental cues (pH, metals, toxins) for citizen-science monitoring.
• Validation controls — known sequences for PCR/gel positive controls in the frugal pipeline.

Broader: Therapeutics (mRNA vaccines), genetic circuits, DNA origami, gene clusters for metabolic engineering.

    WHAT WE SYNTHESIZE FOR BIOVOLT:
    
    ┌────────────────────────────────────────────────────────────┐
    │  [Promoter]─[RBS]─[ATG]─[sfGFP]─[His]─[TAA]─[Terminator]   │
    │       │                    │                               │
    │       └── always on        └── glows green under UV        │
    │                                                            │
    │  Twist / IDT makes this. BioVolt zaps it in. Done. 🟢      │
    └────────────────────────────────────────────────────────────┘

(ii) What technology would you use and why?

Technology: Column-based phosphoramidite synthesis (e.g., Twist Bioscience, IDT) — the industry standard for gene synthesis.

Why: High fidelity, scalable, cost-effective for genes and gene fragments. Twist can deliver clonal genes (circular) ready for transformation—perfect for BioVolt.

    PHOSPHORAMIDITE SYNTHESIS (cartoon)
    
    Base + Base + Base + ...  →  oligo  →  assemble  →  gene
    
        A   T   G   C   A   T   ...
        │   │   │   │   │   │
        ▼   ▼   ▼   ▼   ▼   ▼
    ┌───┴───┴───┴───┴───┴───┴───----┐
    │  ████ ████ ████ ████ ████     │  ← solid support (column)
    │  add → couple → oxidize → cap │  (repeat ~hundreds of times)
    └─────────────────────────────- ┘

5.3 DNA Edit

(i) What DNA would you want to edit and why?

For BioVolt:

• Improve electroporation efficiency — edit E. coli to knock out or modify genes that affect membrane composition, cell wall, or DNA repair (e.g., recA, mutS) to get more transformants per zap.
• Biosensor chassis — edit strains to express reporter circuits (e.g., GFP under metal-responsive promoter) for environmental sensing in the DIY pipeline.
• Safety — auxotrophic markers, kill switches, or containment edits for responsible DIYbio.

Broader: Human therapeutics (e.g., sickle cell), agriculture (nitrogen fixation, disease resistance), conservation (genetic rescue), longevity research.

    EDIT E. coli FOR BETTER BIOVOLT TRANSFORMATION?
    
         Wild-type E. coli              Edited E. coli
              │                              │
              │  "Membrane too tough"        │  "Softer membrane?"
              │  "DNA repair too good?"      │  "Fewer repair enzymes?"
              │                              │
              ▼                              ▼
         ⚡ BioVolt ⚡                  ⚡ BioVolt ⚡
              │                              │
              ▼                              ▼
         10³ CFU/µg                    10⁵ CFU/µg?  🎯
              │                              │
            "Meh"                      "Now we're talking!"

(ii) What technology would you use and why?

Technology: CRISPR/Cas9 (with HDR for precise edits) — or base editors for single-nucleotide changes without double-strand breaks.

Why: Programmable, precise, widely adopted. gRNA design is straightforward; many tools (Benchling, etc.) support it.

    CRISPR/Cas9 IN ACTION (simplified)
    
    gRNA:  "Find this sequence"  ── ┐
                                    ├──►  Cas9  ──►  CUT! ✂️
    DNA:   ...TARGET...PAM...     ──┘
    
    Before:  ────[TARGET]────
    After:   ────╲     ╱────   (cell repairs: NHEJ or HDR)
                  ╲   ╱
                   gap
    
    BioVolt could deliver Cas9 RNP + donor via electroporation! ⚡

Summary: Read, Write, Edit → BioVolt

    ╔════════════════════════════════════════════════════════════════╗
    ║                     BIOVOLT + DNA TOOLKIT                      ║
    ║                                                                ║
    ║   WRITE (Twist)     ──►  plasmid with sfGFP                    ║
    ║         │                                                      ║
    ║         ▼                                                      ║
    ║   EDIT (optional)   ──►  tune E. coli for better zapping       ║
    ║         │                                                      ║
    ║         ▼                                                      ║
    ║   ⚡ BIOVOLT ⚡     ──►  transform cells                         ║
    ║         │                                                      ║
    ║         ▼                                                      ║
    ║   READ (Nanopore)   ──►  confirm plasmid sequence              ║
    ║                                                                ║
    ║   Result: Frugal, validated, democratized synthetic biology.   ║
    ╚════════════════════════════════════════════════════════════════╝

Part 1: Protocol Script

Part 1: Opentrons Protocol — T-Rex QR Code

Python protocol for the Opentrons OT-2 that deposits fluorescent bacteria to reproduce the T-Rex QR code on a black agar plate. Black pixels in the 33×33 grid are mapped to plate coordinates; one color (Green) is used for the monochrome design.

Protocol Code Block 1 — Main Protocol

Copy this into the first code block of the HTGAA26 Opentrons Colab notebook (after the prerequisite setup):

from opentrons import types

metadata = {
    'author': 'James Utley',
    'protocolName': 'T-Rex QR Code Bio-Art',
    'description': 'Deposits fluorescent E. coli to create a scannable QR code with T-Rex on black agar',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

well_colors = {
    'A1': 'Red',
    'B1': 'Green',
    'C1': 'Orange'
}

# 33x33 QR code pixel grid (1 = black/deposit, 0 = white/skip)
# Extracted from T-Rex QR code image
QR_PIXELS = [
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0],
    [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0],
    [0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0],
    [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0],
    [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]


def run(protocol):
  ##############################################################################
  ###   Load labware, modules and pipettes
  ##############################################################################

  tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')
  pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])
  temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)
  temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul', 'Cold Plate')
  color_plate = temperature_plate
  agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')
  center_location = agar_plate['A1'].top()
  pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

  ##############################################################################
  ###   Helper functions
  ##############################################################################

  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}")

  def dispense_and_detach(pipette, volume, location):
    above_location = location.move(types.Point(z=location.point.z + 5))
    pipette.move_to(above_location)
    pipette.dispense(volume, location)
    pipette.move_to(above_location)

  ##############################################################################
  ###   T-Rex QR Code patterning
  ##############################################################################

  PIXEL_SIZE_MM = 2.0   # spacing between pixels (mm)
  VOLUME_UL = 0.5       # 0.5 uL per drop (~117 uL total, fits in 200uL PCR strip well)
  COLOR = 'Green'

  # Collect (row, col) for all black pixels
  black_pixels = []
  for row in range(len(QR_PIXELS)):
    for col in range(len(QR_PIXELS[row])):
      if QR_PIXELS[row][col] == 1:
        black_pixels.append((row, col))

  # One tip, one color - aspirate in batches of 20uL (max pipette volume)
  pipette_20ul.pick_up_tip()
  source = location_of_color(COLOR)

  for i in range(0, len(black_pixels), 20):
    batch = black_pixels[i:i+20]
    vol = min(len(batch) * VOLUME_UL, 20)
    pipette_20ul.aspirate(vol, source)

    for row, col in batch:
      # Map pixel (row,col) to plate coords: center at (16,16), 2mm per pixel
      x_mm = (col - 16) * PIXEL_SIZE_MM
      y_mm = (row - 16) * PIXEL_SIZE_MM
      loc = center_location.move(types.Point(x=x_mm, y=y_mm))
      dispense_and_detach(pipette_20ul, VOLUME_UL, loc)

  pipette_20ul.drop_tip()

Protocol Code Block 2 — Simulation / Visualization

Copy this into the second code block (runs after the first):

# Execute Simulation / Visualization -- don't change this code block
protocol = OpentronsMock(well_colors)
run(protocol)
protocol.visualize()

Notes

• 233 black pixels — uses ~117 µL total (0.5 µL per drop); aspirated in batches of 20 µL; fits in 200 µL PCR strip well.
• Single color (Green) — monochrome design; change COLOR to 'Red' or 'Orange' if desired
• 2 mm pixel spacing — fits within ~64 mm on a 90 mm plate
• One tip per color — avoids cross-contamination; only one color used here

Standalone Protocol File

For Opentrons App validation or direct upload: trex_qr_protocol.py

Google Colab Notebook

HTGAA26 Opentrons Colab — make a copy and paste the protocol code blocks above into the notebook.