Week 1 HW: Principles and Practices

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Concept

Create new BioArt experiences for members of a community MakerSpace where our stated goal is to Make, Learn, and Share. The MakerSpace has recently opened a BioArt Studio, led by Karen Ingram, co-author of “BioBuilder - Synthetic Biology in the Lab” (ISBN 978-1-491-90429-9).

My applications are inspired by the innovative use of living systems to create art & design. Concepts incorporate digital imaging, interactive 3d and microprocessing to create algorithmic artwork, influenced and driven by the biological science found in the collection of experimental solutions described below: (Click to expand each item)


BioPhotoLab

Exploring 2D and 3D visual imaging techniques to discover new applications and experiences suitable for a community MakerSpace.

Concept #1: SlimeOgraphy
  • Imaging with light-following organisms.
  • Imaging with photoreactive synthetic proteins.
  • Experimenting with Slime Mold to determine if organisms can be guided and trained with light to create organic designs.
  • High Probability, Ease of Access, Generative Art
  • Aligns with Makerspace ethos, with derivative output via multiple media formats
Concept #2: BioTerrain
  • Terraforming with Image Maps.
  • Translate organic interactions into realtime interactive terrain maps that can be explored using immersive virtual reality
  • Experimenting with slime mold and fluorescent bacterial cultures
  • Slime mold “reader” can leverage imagery from previously 2D generated image sequences to create immersive virtual worlds.
  • Fluorescent bacterial cultures can be interpolated into displacement maps, and texture maps.
  • Both type of input methods will become part of a wider narrative that allows for creative virtual exploration using game engine mechanics.
  • The capture of image sequences leads to time-based controls to visualize change.
Concept #3: BioScanner
  • Event Based Triggers : Machine Vision Detection of Change
  • Similar to IOT “Internet of (Almost Any) Things”
  • Building on the previous experiments, the introduction of change results in a condition that will trigger an event, or automation.
  • A simplified gateway will send an encoded message that can be visualized over time.
  • The unique nature of a biofeedback loop allows for a bi-directional conversation between the experiment and participating scientist.
  • An entire API can be developed that leads to a notification platform that seeks to identify key triggers and events.
  • High level of governance, potential risk, and personal identity protection required as data is flowing from the source. May be encoded at rest.
Concept # 4: BioEmulsion Print
  • Paper based coating that is light-sensitive and photo reactive
  • Emulsion coating that is applied to paper and other materials that can be exposed via an enlarger and creates a bio-digital original
  • Advanced understanding of Protein Synthesis from samples that result in a range of photo emulsions and papers.
  • Leverages the darkroom lab to expose and print
  • Can be a digital file transmission or analog optical projection
  • Similar to sun prints or cyanotypes.

Governance Design & Purpose

This governance model outlines the actions of the BioPhotoLab within the MakerSpace “BioArt Studio.” By integrating biology with creative mediums—such as Slimeography, BioTerrain, and BioEmulsion—the initiative provides a public and member-driven workspace to foster experiences based on science, technology, engineering, art, and math (STEAM).

The model addresses critical dependencies on membership-driven funding and the need for standardized best practices in a shared environment. It prioritizes a transition from simple completion or attendance tracking metrics to an activity-based training model (using experience APIs) to monitor safe, scalable, and inclusive biotechnology exploration.

A leading purpose is to develop a Makerspace focus area, “BioPhotoLab,” that is deemed accessible and can be experienced by people with a wide range of abilities. We will demonstrate how Bioengineering is well suited to the concepts of Universal Design while encouraging technological creativity and community knowledge sharing.


Governance Policies

The following options evaluate proposed actions against core governance pillars: Safety, Privacy, Digital Rights/IP, and Accessibility.

Governance Policies Governance Policies

Evaluation of Risks and Assumptions

  • Assumptions: Success assumes that funding (dues, grants, donations) remains stable and that “Universal Design” (if accessible for a person with a disability, it is good for everyone) is adopted. It assumes learners will practice safe operation and intent to share knowledge.
  • Risks of Failure: Potential failure points include membership attrition, lack of succession planning for instructors, and the perception that class attendance equates to workcell competency.
  • Risks of “Success”: Unintended consequences of success may include challenges with proprietary IP/Patents from corporate R&D and the need for rigorous Digital Rights Management to combat “AI hallucinations” or attribution infringement.

Governance Model with Matrix Ranking

Governance Policies Governance Policies
  • Segment A: Selected Actor: MakerSpace Instructors, MakerSpace Members
  • Segment B: Selected Action: Activity Logging
  • Segment C: Selected Actor: Disability and Accessibility Organizations
  • Segment D: Selected Actions: ADA Legislation, Web Accessibility Guidelines

Governance Matrix Analysis

My governance matrix uses the rubric of Design, Purpose, Assumptions, and Risks of Failure/Success to align Actors (Personas) with Actions. The resulting table is color coded to show a relative heatmap of ratings, along with a total by row and column to highlight outliers.

Segment A: Makerspace Instructors and Members

This grouping represents the majority of best-scoring results, represented by MakerSpace Instructors and MakerSpace Members who may be considered the primary target audience for “BioPhotoLab” activities and experiments with governance.

Segment B: Activity Logging

“Activity Logging” is a high-rated Action, and has been prioritized as it will allow for measurable, realistic and verifiable data to be generated in support of the objectives of safely creating BioPhoto art, while teaching and learning with others, in a growing MakerSpace community. A well governed framework can address the need to maintain anonymity and privacy, as well as an opt-in approach to managed certified access. The assumption is that activity data will drive better participant engagement , higher rates of adherence to safety protocols, with increased knowledge retention and sharing.

Segment C: Disability and Accessibility Organizations

The governance actions related to ADA (Americans with Disabilities Act) legislation, as well as evolving WCAG (Web Content Accessibility Guidelines) represent the best scores when looking across the full range of Actors, which is an indicator that Universal Design may have a powerful impact across a wide range of people of all abilities. As I perform BioPhotoLab experiments, the lens of accessibility becomes a priority when seeking to solve human-centric challenges.

Segment D: ADA Legislation, Web Accessibility Guidelines

The target audience for governance activities is defined as any organization that supports Universal Design, Accessibility, disability awareness, legislation, advocacy, and of course, people with disabilities, including perceived, hidden, disclosed or non-disclosed. Privacy is a key consideration in this segment. The assumption is that we will safely, privately and publicly engage with this audience for maximized community engagement. This segment may also carry the most risks, in that it shows high rankings in nearly all governance Actions. A playbook is a likely solution to help drive adoption.

Reflection

The ethical concerns that arose for me this week were centered on data privacy and safety. The concept that (almost) anyone can grow (almost) anything means that extra care needs to be taken to protect and track the techniques used in synthetic bioengineering. The importance of safety training was emphasised, and there may be a pathway for online listeners as well as in-person participants. I imagined a virtual training simulator to enhance biosafety best practices, based on similar work I have done in the past.

Additionally, the intellectual property needs to be managed and shared much like the history of digital images that can now be combined and altered freely. Personal DNA that can be modified for therapeutic outcomes can also lead to unintended circumstances.

With Ai engines and algorithms being freely shared, the potential for Ai innovation is balanced with Ai disruption and contamination.

My proposed actions are to take a high level view and begin to track events and actions with full context to visualize the evolving landscape, using my project, the BioPhotoLab as a benchmark.

By “opting in” to a framework that shows participation, engagement and reflection in near realtime, we may begin to steer the behavioral data towards a desired state, and quickly identify outliers.

For participants who seek full transparency and verification, opting in with a unique identifier means that we can attribute works to an origin, and explore pathways that lead to greater discovery in an ethical and governed manner.

Risk or resistance occurs when personally identifiable data is leaked or unsecured, but the ability to discern verified sources from artificial or unethical sources may hold more weight.

In a lighter sense, tracking and visualizing behavioral change through engagement metrics and reflective feedback loops creates a culture of knowledge sharing in parallel, or adjacent to formally tracked and managed training completions. .

Highly engaged learners and practitioners demonstrate greater levels of ethical and well goverened best practice with opportunities for continual improvement.

Appendix

Mindmap:Initial Design

Instructions:

  • Use Middle-Mouse-Wheel to zoom in/out
  • Use Left Mouse Button to pan around map
  • use Reset Icon to reset view
graph TB
    subgraph "BioArt Projects"
        BP[BioPhotoLab]
        SL[Slimeography]
        BT[BioTerrain]
        BS[BioScanner]
        BE[BioEmulsion]
    end
    
    subgraph "Evaluate"
        ASS[Assumptions]
        TO[Trade-offs]
        UN[Uncertainties]
        SC[Scalability]
        ACT[Actions]
    end
    
    subgraph "Assumptions Details"
        ASS1["MakerSpace 'BioLab' dependency"]
        ASS2[Knowledge Sharing through Class activities]
    end
    
    subgraph "Trade-offs Details"
        TO1[Public workspace with emerging capabilities]
        TO2[Anonymous utilization]
    end
    
    subgraph "Uncertainties Details"
        UN1[Cloud Lab workstream availability]
        UN2[Standardized best practices]
    end
    
    subgraph "Actions Framework"
        PUR[Purpose: What is done now and what changes]
        DES[Design: What is needed to make it work]
        ASMP[Assumptions: What could you have wrong]
        RISK[Risks of Failure & Success]
    end
    
    subgraph "Governance"
        G1[Exist Safely]
        G2[Enforce Privacy]
        G3[Provide Digital Rights Management]
        G4[Monetization]
        G5[Integration with other frameworks]
        G6[AI Influences]
        G7[Enable Accessibility]
    end
    
    subgraph "Exist Safely Details"
        G1A[Ensuring no contamination risk]
        G1B[Providing certified lab and material safety training]
        G1C[Logging all activities]
    end
    
    subgraph "Enforce Privacy Details"
        G2A[Protecting personally identifiable information]
        G2B[Opting in/out of managed accounts]
    end
    
    subgraph "Digital Rights Management Details"
        G3A[Including metadata with attribution]
    end
    
    subgraph "AI Influences Details"
        G6A[infringement]
        G6B[hallucination/slop]
    end
    
    subgraph "Accessibility Details"
        G7A[Meeting ADA guidelines]
        G7B[Meeting WCAG3 guidelines for Web Accessibility]
        G7C[Benchmarking usability]
    end
    
    subgraph "MakerSpace Administrators"
        MSA1["Action 1: Encouraging technological creativity and community<br/>No Change"]
        MSA2["Action 2: Membership Acquisition/Retention<br/>Recommending marketing campaigns"]
        MSA3["Action 3: Funding dependent on membership dues,<br/>class revenue, donations, grants<br/>Recommending marketing campaigns and data support models"]
    end
    
    subgraph "MakerSpace Instructors"
        MSI1["Action 1: Instructor-defined curriculum<br/>Must be documented and standardized<br/>Observational assessments for certification"]
        MSI2["Action 2: Provide specialized training<br/>and certification to members and non-members"]
        MSI3["Action 3: Succession planning,<br/>enrollment planning, certification management"]
    end
    
    subgraph "MakerSpace Members"
        MSM1["Action 1: Evaluate level of knowledge,<br/>experience, interest"]
        MSM2["Action 2: Participation, Knowledge Retention,<br/>Observational Assessment<br/>Using experience APIs for activity-based training"]
        MSM3["Action 3: Intent to demonstrate<br/>and practice what was learned"]
        MSM4["Action 4: Safe operation"]
        MSM5["Action 5: Knowledge Sharing"]
    end
    
    subgraph "Disability & Accessibility Awareness Organizations"
        DA1["Action 1: Accessible activities"]
        DA2["Action 2: Universal Design<br/>If accessible for person with disability,<br/>good for everyone - Ron Mace"]
        DA3["Action 3: Develop BioPhoto Lab<br/>that is accessible and experiential<br/>Find breakthrough in Accessibility"]
    end
    
    subgraph "Public Visitors"
        PV1["Action 1: Safe, accessible lab"]
        PV2["Action 2: Increase awareness of Biotechnology"]
        PV3["Action 3: Increase opportunity for STEAM experiences<br/>Bio-ethical experience for public awareness"]
    end
    
    subgraph "Corporate R&D"
        CR1["Action 1: Corporate experiential learning"]
        CR2["Action 2: Corporate grant funding models"]
        CR3["Action 3: Proprietary IP, Patents, Commercialization"]
    end
    
    subgraph "Academic Institutions"
        AC1["Action 1: Knowledge Sharing with SMEs<br/>and Thought Leaders<br/>Standards of self-reported activities<br/>with data-driven dashboards"]
        AC2["Action 2: Access to Cloud services and solutions"]
        AC3["Action 3: Partnerships, outreach"]
    end
    
    BP --> ASS
    SL --> ASS
    BT --> ASS
    BS --> ASS
    BE --> ASS
    
    ASS --> ASS1
    ASS --> ASS2
    
    TO --> TO1
    TO --> TO2
    
    UN --> UN1
    UN --> UN2
    
    ACT --> PUR
    ACT --> DES
    ACT --> ASMP
    ACT --> RISK
    
    PUR --> G1
    DES --> G1
    ASMP --> G1
    RISK --> G1
    
    G1 --> G1A
    G1 --> G1B
    G1 --> G1C
    
    G2 --> G2A
    G2 --> G2B
    
    G3 --> G3A
    
    G6 --> G6A
    G6 --> G6B
    
    G7 --> G7A
    G7 --> G7B
    G7 --> G7C
    
    G1 --> MSA1
    G1 --> MSI1
    G1 --> MSM1
    G1 --> DA1
    G1 --> PV1
    G1 --> CR1
    G1 --> AC1
    
    MSA1 --> MSA2
    MSA2 --> MSA3
    
    MSI1 --> MSI2
    MSI2 --> MSI3
    
    MSM1 --> MSM2
    MSM2 --> MSM3
    MSM3 --> MSM4
    MSM4 --> MSM5
    
    DA1 --> DA2
    DA2 --> DA3
    
    PV1 --> PV2
    PV2 --> PV3
    
    CR1 --> CR2
    CR2 --> CR3
    
    AC1 --> AC2
    AC2 --> AC3
    
    style BP fill:#90EE90
    style SL fill:#90EE90
    style BT fill:#90EE90
    style BS fill:#90EE90
    style BE fill:#90EE90
    style G7 fill:#FFD700
    style DA3 fill:#FFD700
Actor Governance Analysis

MakerSpace Administrators

The administrative role centers on sustaining and scaling the Makerspace’s core mission of encouraging technological creativity, learning-by-making, and community knowledge sharing. While the foundational purpose remains unchanged, key risks and assumptions relate to long-term viability: membership acquisition and retention directly influence funding, which is currently dependent on a mix of dues, class revenue, donations, grants, and member self-funding. These revenue streams are inconsistent and time-bound, particularly with respect to rent and grants. The proposed response emphasizes data-informed marketing campaigns to support membership growth and to generate evidence that can unlock alternative or supplemental funding models, while recognizing that not all donations are monetary and that growth must be matched with training capacity and governance maturity.

MakerSpace Instructors

Instructors are positioned as self-governing designers of curriculum and learning objectives, with responsibility extending beyond instruction to documentation, standardization, and succession planning. To ensure continuity, growth, and safety, curricula must be formalized and paired with clear rubrics that support observational assessment, certification, and compliance. The instructional purpose includes delivering specialized training and certifications to both members and non-members, reinforcing the Makerspace’s educational value. However, risks emerge around instructor availability, enrollment planning, certification management, and long-term succession, requiring governance structures that prevent knowledge silos and instructor burnout while maintaining consistent evaluation standards.

MakerSpace Members

Member participation is highly variable in terms of prior knowledge, experience, interests, and learning styles, which introduces significant assumptions into training and access models. A key misconception addressed is that class attendance alone equates to workcell access or operational competence. Because the Makerspace is not an accredited institution and learning is voluntary and experiential, governance must prioritize measurable, repeatable engagement over simple completion metrics. The proposal emphasizes observational assessment, feedback loops, and the use of privacy-preserving experience APIs to assess program “health” at a cohort level. Certification is non-anonymous and may lead to expanded access, increasing the importance of intent to practice, safe operation, and knowledge sharing as ongoing responsibilities rather than one-time achievements.

Disability & Accessibility Awareness Organizations

Accessibility organizations contribute assumptions, design principles, and purpose grounded in Universal Design, particularly the idea that solutions accessible to people with disabilities ultimately benefit everyone. Their involvement centers on ensuring activities are meaningfully accessible and on co-designing experiences that address unmet needs within the disability and accessibility community. The proposed BioPhoto Lab workcell serves as a concrete demonstration of how bioengineering aligns naturally with Universal Design principles, offering an experiential, inclusive activity suitable for a wide range of abilities. Beyond compliance, the aspirational goal is to enable innovation that could lead to genuine breakthroughs in accessibility, positioning the Makerspace as a site of applied, inclusive experimentation.

Public Visitors

For public visitors, the primary assumptions are that the Makerspace environment must be demonstrably safe, accessible, and well-governed. The purpose of engagement is to increase awareness of biotechnology and related STEAM fields through carefully designed, bio-ethical experiences that are approachable without requiring prior expertise. By lowering barriers to entry and emphasizing safety and accessibility, these public-facing experiences can serve as both educational outreach and a pathway to deeper participation, including eventual membership. Effective governance is essential here, as public interactions directly shape reputation, trust, and the perceived legitimacy of biotechnology in a community context.

Corporate R&D

Corporate R&D engagement is framed around experiential learning opportunities and potential grant-based funding models, with assumptions that industry partners may support exploratory, pre-competitive activities. However, significant risks arise around proprietary information, intellectual property, patents, and commercialization pathways. Governance must therefore clearly delineate boundaries between open, educational activities and protected corporate interests. Without explicit controls, collaboration risks either chilling participation due to IP concerns or unintentionally exposing proprietary assets, making this actor group highly sensitive to policy clarity and contractual safeguards.

Academic Institutions

Collaboration with academic institutions is intended to elevate the Makerspace by integrating subject-matter expertise, thought leadership, and social learning into a broader lifelong learning framework. The purpose is not formal accreditation but the creation of a shared baseline for advancing the “Art of Biotechnology” as a multidisciplinary medium. Assumptions include access to cloud services and digital infrastructure that support self-reported activity tracking, attribution, and data-driven dashboards. These tools enable scientific reflection, reproducibility, and deeper collaboration while allowing activities to be traced back to their original context. Partnerships and outreach are therefore central design elements, positioning the Makerspace as a bridge between academic rigor and experiential, community-based learning.


Ai Prompt References

The Governance Policy section was distilled directly from my original “Mind Map” (using ChatGPT 5.2 with the following prompt:

  • You are a biotechnology research scientist creating a governance model around the introduction of a new activity within a Makerspace BioArt lab. Using the exact verbiage provided without changing the intent, summarize this mind-map with topics into a clear, concise summary starting with a high level overview, a bold statement of purpose, and a well-organized matrix of options that can be ranked.

The Matrix was created from the source MindMap using the following prompt:

  • Create a scoring matrix from 1-3 or n/a for the following ACTORS compared to the ACTIONS listed.
    Maintain strict hierarchy:
  • Actions
  • Purpose, Design, Assumptions, Risks of Failure & “Success”
  • Purpose: What is done now and what changes are you proposing?
  • Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)
  • Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?
  • Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

A series of refinement promps were required to format the results into a usable format for ranking purposes.

Matrix Refinement with ChatGPT 5.2
  1. Create a governance scoring matrix
  • You asked for a 1–3 / N-A scoring matrix comparing Actors against Actions, with strict hierarchy across Purpose, Design, Assumptions, Risks, and a detailed governance rubric (safety, privacy, DRM, monetization, AI, accessibility).
  1. Clarify that scores should be user-entered, not assigned
  • You corrected the approach to request a blank scoring table where you would enter values using the rubric (1 = best, 3 = worst).
  1. Group the matrix by numbered Actions in a single table
  • You requested consolidation into one unified table, grouped by Action numbers rather than separate dimensions.
  1. Create a ranking table with rubric columns
  • You specified the exact governance rubric to become table columns and asked that scoring cells be provided for each Actor × Action.
  1. Group content by Actor
  • You instructed that Actions be grouped under each Actor rather flattened globally.
  1. Pre-populate N/A where governance criteria did not apply
  • You asked for governance cells to be marked N/A when an Actor did not reference or control that rubric area.
  1. Revert to the unpopulated version
  • You reversed that decision and requested a clean matrix with no prefilled values.
  1. Recreate the matrix grouped under each Actor
  • You asked for a fresh rebuild, preserving grouping and rubric structure.
  1. Remove N/A and revert one step again
  • You clarified you wanted a fully blank, neutral baseline.
  1. Further normalize the Action column
  • You instructed that:
  • “Action 1 / 2 / 3” become a unique row ID
  • Parenthetical terms (Purpose, Design, Risk, Assumptions) become a new parsed field
  • Descriptions be separated cleanly for analysis
  1. Add narrative summaries under each Actor header
  • You asked that the explanatory text originally in parentheses be synthesized into plain-language summary paragraphs under each Actor.
  1. Convert the matrix into a Google Sheet
  • You requested export of the structured matrix into a Google-Sheets-compatible file.
  1. Confirm whether actor summaries were included in the export
  • You explicitly asked for verification and correction if the narrative summaries were missing.
  1. Resolve the omission if needed
  • You implicitly authorized modification of the file to include the summaries, which resulted in adding a second sheet.
  1. Summarize all prompts from this session
  • You requested this final meta-summary of your own prompts.

The Actors (Personas) were further refined using the following Prompt:

Under each Actor group, include a paragraph that summarizes the commentary provided under sections that contained ()

Response: (See Appendix for results)

Below are clear narrative summaries, one paragraph under each Actor group, synthesizing only the commentary you provided in the parenthetical sections. I’ve stayed faithful to your language and intent, tightening it into governance-ready prose without adding new assumptions.


Link to Week 2 Prework can be found under /honmework/Week2_Lecture_Prep/_index.md and is heavily dependent upon ChatGPT to formulate answers while cross-referencing content for better understanding.

Subsections of Week 1 HW: Principles and Practices

Week 1 HW: Week 2 Lecture Prep

Lecture Prep: My approach was to view the slides, then seek direct answers via ChatGPT, then review the slides to find corresponding answers. It is allowing me to begin to comprehend the depth of the subject matter. I look forward to the reinforcing live presentations.

  • Professor Jacobson

    • Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?
      • 1:106
      • In contrast, the human genome is 3:109 or many magnitudes higher.
    • How many different ways are there to code (DNA nucleotide code) for an average human protein?
      • Average human protein length ≈ 400 amino acids
    • In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?
      • Because DNA is not just a protein recipe. The sequence carries many layers of information beyond amino acids.
  • Dr. LeProust

    • What’s the most commonly used method for oligo synthesis currently?
      • Phosphoramidite solid-phase synthesis
    • Why is it difficult to make oligos longer than 200nt via direct synthesis?
      • small per-base imperfections compound exponentially, and the chemistry has no way to “fix” them once they happen.
    • Why can’t you make a 2000bp gene via direct oligo synthesis?
      • Because chemical oligo synthesis breaks down long before you reach that length, for fundamental probabilistic, chemical, and practical reasons. A 2000 bp gene is two orders of magnitude beyond what direct synthesis can support.
  • Professor Church

    • [Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?
    • The 10 essential amino acids

Histidine

Isoleucine

Leucine

Lysine

Methionine

Phenylalanine

Threonine

Tryptophan

Valine

Arginine

My view is now informed by the concept that “No lysine available → the organism stops functioning”.