Week 1 HW: Principles + Practices

1.1 A Neighborly Bio-literacy Learning System for Non-Scientists, Living in a Disaster-Prone World

Full disclosure: My house burned down in the Palisades, California fire last year with 5,000 other homes and it inspired me to see neighborhood disaster as a rich opportunity for study.

Rather than treating bio-literacy as isolated content mastery, this project frames bio-literacy as ethical sense-making within one’s own community and around community-based problems. Bio-literacy is understood as the ability to know ourselves and our world by asking questions, interpreting uncertainty, engaging responsibly, and building trust with biological systems. These capacities become more meaningful—and more powerful—when grounded in local concerns and lived experience. There is no shortage of biology-based shared community challenges: food security, extreme weather and fire, infectious disease, and environmental instability.

The project draws on local, embodied, and experimental pedagogies—such as role play, physical modeling, dialogue, and narrative—to make biological systems felt rather than merely understood abstractly. Participants develop bio-literacy in their “own backyard,” investigating biological questions that matter to them, their families, and their neighbors. In this way, Neighbor Gap Bridge (neighborgapbridge.com) reframes bio-literacy as a situated, relational practice rather than a distant technical competence.

Why this matters We are living in a world of disaster uncertainty in which consequential biological decisions—about health, environment, food systems, and governance—are increasingly made by non-biologists. Bio-literacy’s closest historical parallel is computer literacy: a decades-long project that succeeded in widespread participation, but not widespread understanding. This project reimagines the starting point of bio-literacy as the learner’s own backyard, privileging local problems as invitations into biological understanding, community participation, and compassion. This project is situated in the overall transformative experience of a disaster victim, creating opportunity for high level sensemaking and ability to know ourselves and our world by asking questions, and building trust with multiple systems including biological systems.

Existing bio- and AI-literacy efforts often optimize for scale, rigor, or engagement in isolation. This project instead optimizes for connection and meaning—situating learning within relationships, shared stakes, and ethical reflection.

Visionary (Infrastructure Design) What is missing is a governance-aligned learning infrastructure that treats ethical sense-making, uncertainty, pluralism, and participation as core learning outcomes rather than peripheral concerns. This project explores what such an infrastructure might look like, and what forms of governance, partnership, and institutional support would be required to sustain it.

Near-Future (Programmatic Pilot) Local, problem-based, intergenerational synthetic biology learning—using scaffolded play and embodied curriculum in partnership with LAUSD—focused on community-relevant biological questions.

Close-In (Rapid Prototyping / Extreme Events) Mobile syn-bio workshops and learning labs responding to “extreme events,” such as wildfire. For example, intergenerational workshops with residents affected by the Palisades fire to explore the biology of fire-resistant mycelium-based materials, alongside the design and fabrication of protective artifacts for future resilience.

1.2 Governance/Policy Goals for a Neighborly Bio-Literate Future

Governance Goal 1: Equitable Access Without Dilution

• Open access (low or no cost)
• Universal Design for Learning
• Multi-generational participation
• Not restricted to credentialed elites
• Engage Creative athletics
• Engages local biological problems

Governance Goal 2: Epistemic Pluralism

• Interdisciplinary sources
• Diverse instructors and perspectives
• Recognition that different perspectives change what becomes knowable
• Embodies Learning
• Learning is felt

Governance Goal 3: Trustworthy Sense-Making

• Transparency of sources
• Open and updatable materials
• Clear articulation of uncertainty
• Avoidance of false certainty or hype
• Care and Compassion based Learning
• Feminine Technology of learning
• Treat Error as opportunity

Governance Goal 4: Ethics as Infrastructure (Not Add-On)

• Ethics embedded in delivery, not add-ons
• Democratic dialogue and controversy included
• Anticipation of ethical roadblocks
• Delayed closure where appropriate

1.3 Potential Governance Actors + Actions

NSF-funded experimental bio literacy learning labs

Purpose Bio-education funding prioritizes content mastery and workforce development. This action proposes NSF funding streams specifically for experimental, embodied bio literacy learning environments aimed at non-specialists.

Design

• Competitive grants for interdisciplinary teams (science + education + design0
• Competitive Grants for Neighorhood non scientists
• Emphasis on process documentation, not standardized outcomes
• Publicly available learning artifacts and reflections
• Ethics embedded throughout the learning experience
• Robust digital share community spaces

Assumptions

• Embodied and experimental pedagogy improves ethical sense-making
• Non-specialists can meaningfully engage without technical mastery
• NSF will value exploratory education research
• People actually want to work when they have been affected by trauma

Risks of Failure

• Failure: Projects become performative or symbolic rather than substantive
• Failure: Difficulty evaluating progress without traditional metrics
• Say to day survival becomes more important

Department of Education Guidance on Bio-literacy + Trust

Purpose Currently, bio education standards focus on factual knowledge. This action proposes non-binding federal guidance recognizing bio literacy as an ethical and civic competency.

Design

• Advisory frameworks (not mandates)
• Alignment with Universal Design for Learning
• Encouragement of dialogue-based and participatory approaches
• Recognition of uncertainty and ethical debate as learning outcomes

Assumptions

• Federal guidance can shape discourse without enforcement
• Educators want permission to teach uncertainty and ethics
• Bio literacy can be framed as civic preparation
• Bio Literacy can be framed astrauma informed

Risks of Failure

• Failure: Guidance is ignored or politicized
• Failure: Oversimplification for scale
• Risk of “success”: Bio literacy reduced to compliance checklists

MIT Life-long Kindergarten as Model

Purpose Traditional science education often prioritizes correctness, abstraction, and expert authority. This governance action supports play-based, experimental science literacy models that cultivate curiosity, agency, and ethical orientation before formal expertise. Rather than teaching biology directly, the approach develops habits of inquiry—iteration, questioning, and reflection—that are transferable to bio literacy contexts.

Design

• Learning environments structured around play, making, and experimentation
• Tools that lower barriers to participation (no prerequisite mastery)
• Emphasis on remixing, peer learning, and public sharing
• Ethics embedded implicitly through collaboration, attribution, and care
• These workshops are part of a holistic plan for discovery and recovery

Assumptions

• Play supports deeper engagement and long-term learning
• Ethical orientation can emerge through participation, not instruction alone
• Habits of inquiry transfer across domains (e.g., from computation to biology)
• Framing can be sensitive enough to support engagement during or after a crisis
• these Workshops are optional

Risks of Failure

• Failure: Play is dismissed as insufficiently rigorous
• Failure: Ethical dimensions remain implicit and unarticulated
• Play becomes instrumentalized or gamified, losing its exploratory power and sensitivity
• This becomes very Kumbaya and does not move our collective inderstanding forward

1.4 Scoring Table

–> (1= lowest)

Week 02 Homework: DNA Read, Write and Edit

Part 1: Benchling & In-silico Gel Art

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

No lab access

Part 3: DNA Design Challenge

3.1. Choose your protein.

I chose Green Fluorescent Protein (GFP) because it is widely used in biotechnology as a visual reporter protein. Its ability to fluoresce green when exposed to blue light makes it an elegant example of how DNA sequences can encode observable biological functions. This is a random choice, I love green and this is HTGAA!

The 2008 Nobel Prize in Chemistry was awarded to Osamu Shimomura, Martin Chalfie, and Roger Y. Tsien for the discovery and development of Green Fluorescent Protein (GFP)

Protein Sequence: P42212|GFP_AEQVI Green fluorescent protein OS=Aequorea victoria MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence. DNA Nucleotide Sequence 1 tacacacgaa taaaagataa caaagatgag taaaggagaa gaacttttca ctggagttgt 61 cccaattctt gttgaattag atggtgatgt taatgggcac aaattttctg tcagtggaga 121 gggtgaaggt gatgcaacat acggaaaact tacccttaaa tttatttgca ctactggaaa 181 actacctgtt ccatggccaa cacttgtcac tactttctct tatggtgttc aatgcttttc 241 aagataccca gatcatatga aacagcatga ctttttcaag agtgccatgc ccgaaggtta 301 tgtacaggaa agaactatat ttttcaaaga tgacgggaac tacaagacac gtgctgaagt 361 caagtttgaa ggtgataccc ttgttaatag aatcgagtta aaaggtattg attttaaaga 421 agatggaaac attcttggac acaaattgga atacaactat aactcacaca atgtatacat 481 catggcagac aaacaaaaga atggaatcaa agttaacttc aaaattagac acaacattga 541 agatggaagc gttcaactag cagaccatta tcaacaaaat actccaattg gcgatggccc 601 tgtcctttta ccagacaacc attacctgtc cacacaatct gccctttcga aagatcccaa 661 cgaaaagaga gaccacatgg tccttcttga gtttgtaaca gctgctggga ttacacatgg 721 catggatgaa ctatacaaat aaatgtccag acttccaatt gacactaaag tgtccgaaca 781 attactaaaa tctcagggtt cctggttaaa ttcaggctga gatattattt atatatttat 841 agattcatta aaattgtatg aataatttat tgatgttatt gatagaggtt attttcttat 901 taaacaggct acttggagtg tattcttaat tctatattaa ttacaatttg atttgacttg 961 ctcaaa

3.3. Codon optimization. ATGGTCTCAAAAGGTGAAGAATTGTTTACAGGTGTCGTACCTATACTTGTAGAACTCGATGGTGATGTTAATGGTCATAAATTTTCGGTCTCAGGAGAAGGTGAAGGAGACGCGACTTATGGTAAACTCACTTTAAAATTCATATGTACAACTGGTAAATTACCTGTTCCATGGCCGACTTTAGTGACAACGTTGACGTATGGTGTTCAATGTTTTAGTCGTTATCCTGATCATATGAAACAACATGATTTCTTTAAAAGTGCAATGCCTGAGGGTTATGTTCAAGAACGGACGATTTTCTTTAAAGATGATGGGAATTACAAAACTCGCGCAGAAGTCAAATTTGAAGGAGACACACTGGTAAATCGTATAGAACTTAAAGGTATTGACTTTAAAGAAGATGGAAATATTTTAGGTCATAAACTTGAATACAATTTGAACTCCCATAATGTCTACATAATGGCAGACAAACAGAAGAATGGAATAAAAGTTAATTTTAAAATACGCCATAATATTGAAGATGGTTCGGTCCAACTGGCAGATCATTATCAACAGAATACTCCAATTGGAGATGGTCCAGTCTTGTTACCAGATAATCATTATCTCAGTACTCAATCAGCGCTCTCTAAGGATCCAAATGAAAAGCGTGACCATATGGTGTTGCTCGAATTTGTTACAGCGGCAGGCATTACATTAGGAATGGATGAATTATATAAATAG

3.4. You have a sequence! Now what? Describe in your words the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both. I read about how fluorescent green protein is used in molecular biology to identify and track protein movement and gene expression. I am not yet ready to describe how a DNA sequence can be transcribed and translated into this protein. The best I can do here “in my own words” is to go back to the central dogma / flow of genetic information: DNA >RNA> Protein This is what I think happened in this week’s HW

Part 4: Prepare a Twist DNA Synthesis Order: Design the full machine (Expression Cassette?) that makes bacteria glow.

4.1. Create a Twist account and a Benchling account

4.2. Build Your DNA Insert Sequence Let’s make a sequence that will make E. coli glow fluorescent green under UV light by constitutively (always) expressing sfGFP (a green fluorescent protein):

Promoter TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC RBS CATTAAAGAGGAGAAAGGTACC Start Codon ATG Coding Sequence GTCTCAAAAGGTGAAGAATTGTTTACAGGTGTCGTACCTATACTTGTAGAACTCGATGGTGATGTTAATGGTCATAAATTTTCGGTCTCAGGAGAAGGTGAAGGAGACGCGACTTATGGTAAACTCACTTTAAAATTCATATGTACAACTGGTAAATTACCTGTTCCATGGCCGACTTTAGTGACAACGTTGACGTATGGTGTTCAATGTTTTAGTCGTTATCCTGATCATATGAAACAACATGATTTCTTTAAAAGTGCAATGCCTGAGGGTTATGTTCAAGAACGGACGATTTTCTTTAAAGATGATGGGAATTACAAAACTCGCGCAGAAGTCAAATTTGAAGGAGACACACTGGTAAATCGTATAGAACTTAAAGGTATTGACTTTAAAGAAGATGGAAATATTTTAGGTCATAAACTTGAATACAATTTGAACTCCCATAATGTCTACATAATGGCAGACAAACAGAAGAATGGAATAAAAGTTAATTTTAAAATACGCCATAATATTGAAGATGGTTCGGTCCAACTGGCAGATCATTATCAACAGAATACTCCAATTGGAGATGGTCCAGTCTTGTTACCAGATAATCATTATCTCAGTACTCAATCAGCGCTCTCTAAGGATCCAAATGAAAAGCGTGACCATATGGTGTTGCTCGAATTTGTTACAGCGGCAGGCATTACATTAGGAATGGATGAATTATATAAA 7x His Tag CATCACCATCACCATCATCAC Stop Codon TAA Terminator CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

4.3. On Twist, Select The “Genes” Option 4.4. Select “Clonal Genes” option 4.5. Import your sequence I had to stop here because Twist would not accept my .fasta file. When I loaded it kept adding.txt 4.6. Choose Your Vector No can do

Part 5: DNA Read/Write/Edit

5.1 DNA Read

What DNA would you want to sequence (e.g., read) and why?

The Smell of Renewal: mapping post-fire soil chemistry + culture as a recovery marker

I would want to sequence DNA from soil bacteria called Streptomyces that produce geosmin, a key molecule behind petrichor (the earthy smell after rain). I’m interested in this because after a major fire, soil ecosystems change dramatically, and I want to understand what microbial communities survive, return, or disappear during recovery.

Why sequence it? The big “why” is that my home burned down in the Palisades fire last year. As I recover from the trauma of the fire, I find myself deeply drawn to the repair and restoration of my neighborhood — both physically and emotionally. I am particularly interested in sequencing soil bacteria such as Streptomyces, which produce geosmin, a key molecule behind petrichor — the earthy smell after rain.

Sequencing would allow me to identify which Streptomyces species or strains are present in post-fire soil, compare them to soil from unaffected areas, and observe how the microbial “signature” of a burned landscape changes over time. This could support environmental monitoring by tracking soil recovery and ecosystem health after disaster.

Because petrichor is strongly tied to emotional memory and renewal, understanding the biology behind it could connect ecological recovery with human recovery after disaster.

(CHAT GPT: How would I do this?)To study these microbial communities, I would use 16S rRNA gene sequencing, a common method for identifying and comparing bacterial species in environmental samples. By extracting DNA from soil and sequencing this conserved bacterial marker gene, I could determine which Streptomyces strains are present and how their abundance changes over time following a fire.

What I learned this week

• Understood the Central Dogma in a functional way.
• Learned what promoters and RBS actually do.
• Codon-optimized a gene.
• Wrestled with file formats (real-world friction).
• Designed a sequencing project grounded in lived experience.
• Named 16S rRNA as a method.
• Connected six diverse interdisciplinary areas of inquiry

Fire Soil Microbes Memory Recovery Design Biology

Week 03 Homework: Lab Automation

1/Create a Python file to run on an Opentrons liquid handling robot.

This is what I want to do, but I am still working on it. Happy Late Valentines Day!

2/ Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

Bryant Jr. et al., 2023 — “AssemblyTron: Automated DNA Assembly Using the Opentrons OT-2.” Synthetic Biology (Oxford University Press).

This paper describes an automated workflow that connects DNA design software to the Opentrons OT-2 liquid-handling robot. Rather than manual pipetting, the robot executes highly standardized molecular biology workflows.The innovation is novel because it is integration of design software and robotic execution. This reduces human error and makes it easier to reproduce experiements. Although this is challening information for me, I can see how it might lower the bar for entry into syn bio experiements and and speed up design cycles. If HTGAA’s mission is to democratize access to cutting-edge bioengineering and synthetic biology education and foster global “biological literacy” by equipping diverse, distributed participants with the skills and laboratory knowledge to design, experiment, and create with living organisms, then this Opentron is a gamechanger.

3/ Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.

In my wildfire soil project, automation might add rigor to the process of detecting subtle microbial differences in post-fire environments. My samples might be: Burned soil, Unburned soil, Sunflower rhizosphere soil, Adjacent burned soil away from roots. For each sample I will need to: Create standardized slurry. Perform serial dilutions. Plate onto defined media, Record colony morphology and counts, Measure pH

4/ Three Final Project Ideas

What if post-fire soil holds a molecular archive of both hope + disturbance, and we could build biological instruments that translate that archive into visible signals — making ecological memory perceptible to communities rebuilding after disaster?