Week 1 HW: Principles and Practices

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.

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.

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

• 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

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

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.

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.

Week 2 HW: DNA Read-Write-Edit

Checklist

• Part 0: Basics of Gel Electrophoresis
  • Attend Lecture (2 of 3)
  • Attend Recitation
  • Review 2025 recording (3 of 3)
• Part 1: Benchling & In-silico Gel Art
• Part 2: Gel Art - Restriction Digests and Gel Electrophoresis (Optional- for those with Lab access)
  • Design Simulation
• Part 3: DNA Design Challenge
  • 3.1 Choose your Protein
  • 3.2 Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.
  • 3.3 Codon optimization
  • 3.4. You have a sequence! Now what?
  • 3.5. [Optional] How does it work in nature/biological systems?
• Part 4: Prepare a Twist DNA Synthesis Order
  • 4.1. Create a Twist account and a Benchling account
  • 4.2. Build Your DNA Insert Sequence
  • 4.3. On Twist, Select The “Genes” Option
  • 4.4. Select “Clonal Genes” option
  • 4.5. Import your sequence
  • 4.6. Choose Your Vector
• Part 5: DNA Read/Write/Edit
  • 5.1 DNA Read
    • (i) What DNA would you want to sequence (e.g., read) and why?
    • (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?
  • 5.2 DNA Write
    • (i) What DNA would you want to synthesize (e.g., write) and why?
    • (ii) What technology or technologies would you use to perform this DNA synthesis and why?
  • 5.3 DNA Edit
    • (i) What DNA would you want to edit and why?
    • (ii) What technology or technologies would you use to perform these DNA edits and why?

Part 1: Benchling & In-silico Gel Art

In this section, I was able to successfully sign up for Benchling, request to join HTGAA (pending), and create a new project. I was able to find the Lambda DNA sequence in the FASTA database, which I copied and pasted. I then found the downloadable file in GenBank, which I imported into Benchling. It took me a few tries to get multiple Digests to appear, once I selected multiple restriction enzymes and ordered the tabs before Virtual Digest. I exported the resulting image as a .PNG as well as my NC_001416 Project “Linear Map” and “Sequence Map” as well as the Lambda Map from GenBank, as PDFs for future reference.

Part 2: Gel Art

Illustration by Eric Schneider: Simulated Gel Electrophoresis using node based image editing software, “Adobe Substance Designer”

Part 3: DNA Design Challenge

• 3.1 Choose your protien

I chose Candida antarctica Lipase B (CalB) since it has the ability to break down polylactic acid, or PLA, a commonly used filament in 3D printing.

My design intent is to reduce the amount of microplastics that reach our ecosystem. The best place to start remediation may be at the source; the waste stream of PLA that is generated in a MakerSpace. By introducing a biological process that depolymerizes PLA waste, we may contribute to a solution while providing governance and building awareness.

From a BioArt perspective, this is the first step in creating and containing the lactic acid and CO2 that may be generated, for downstream use in feeding and growing colorful algae. In turn, powdered algae pigment can be extracted, showing how PLA can help to create colorful pigments used in painting and other mediums.

• 3.2 Reverse Translate

  I was able to find a suitable Protein for this design challenge by using Ai Prompts and comparing results between ChatGPT and Claude. ChatGPT led me to Proteinase K https://www.ncbi.nlm.nih.gov/nuccore/X14689 which turned out to be very challenging due to complexity of the construct, and actually caused Twist to “freeze” when attempting to synthesize.

  I even conducted a rapid experiment where I asked Claude Ai to provide the translation, which it suprisingly did, very confidently. However, I ran into the same complexities when attempting to create a TWIST order.

  I went back to the NIH database and found C.antarctica (LF 058) gene for lipase B https://www.ncbi.nlm.nih.gov/nuccore/Z30645 which, according to Claude Ai, would lead to better results with less complexity. I exported a FASTA file for the protein’s genetic structure.

In TWIST, the Lipase B approach fell into the “standard” complexity level, so I am sticking with that translation. Which also validates that the follow-up Claude AI inquiry led to a good result. (see appendix for summary of prompt usage)

• 3.3 Codon Optimization

I used the Twist tool to optimize Codons. It showed me two regions that had repeating sequences that could be optimized.

Question: It seems that the Start and Stop codons were automatically added in, as ATG, and TAA but I want to better understand when and how to ensure they are present manually, with dependency on selected expression. (Note: Answer was found by properly annotating)

I chose e.coli as I learned that it is predictable and suitable for this sequence. Yeast may be used for a higher yield, but with possibly more optimization of repeating codons needed. I completed the Twist optimization, and downloaded the sequence to view in Benchling to learn more about the strucutre.

• 3.4 You have a sequence! Now what?

This protein can be created from DNA from either clonal or strand synthesis. The dna sequence I have identified can be inserted into a host plasmid which is cloned in an industrial-scale lab that can provide quality, speed and editing capabilities. The cell-based method provides more synthetic control and expected outcomes, acting like a factory. The cell-free method may introduce toxins and have lower yield. In advanced industrial production, both may work together for rapid prototyping and scalability.

• 3.5 [Optional] How does it work in nature/biological systems?

  The ability to transcribe from different start points in the sequence leads to diveristy in proteins created.

  

  Illustration by Eric Schneider: Lipase B 3D model from UniProt https://www.uniprot.org/uniprotkb/P41365/entry#structure and rendered with ChimeraX https://www.rbvi.ucsf.edu/chimerax

  ---

Part 4: Prepare a Twist DNA Synthesis Order

I realized that my prior attempt to create an order was incomplete, as I had not fully optimized or annotated my sequence. I started “from scratch” and optimized my sequence in TWIST, then exported back to Benchling, where I prepared a sequence with the proper annotations. I took this back into Twist and prepared an order. I exported the new Plasmid back to Benchling. This “answered” my initial question related to Annotating start and stop codons, which was a key learning for me.

Part 5: DNA Read/Write/Edit

• Part 5: DNA Read/Write/Edit
  • 5.1 DNA Read
    • (i) What DNA would you want to sequence (e.g., read) and why?
      • I would like to sequence the DNA of a Lipase as it appears to be well suited for the depolymerization of PLA. I would also like to sequence a Cutinase as it has similar properties, as well as Proteinase K which may be best for industrial-scale applications. I am intrigued by the potential for a hybrid solution . I am also interested in harnessing any CO2 emissions for downstream processing or pigmented algae growth.
    • (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 UniProt to locate Proteins with sequences.
      • I would use TWIST or other standalone optimization tools to minimize repeats in a sequence
      • I would use Benchling as the primary method of visualizing sequences to be able to annotate and construct sequences with better probability of success when ordering Clones or Strands.
      • I would use TWIST for the speed, quality, and configuration capabilities when building Plasmids.
      • I would again use Benchling to visualize Plasmids once constructed.
      • I also learned about ChimeraX to 3D visualize Nucleotides and molecular bonds
  •  5.2 DNA Write
    •  (i) What DNA would you want to synthesize (e.g., write) and why?
      • In support of my PLA depolymerization design, I would want to manage and control the throughput through synthetic means, in contrast of depending on natural biodegradation, which may happen only under the most optimal conditions such as heat, sunlight/UV and presence of enzyme producing organisms.
    •  (ii) What technology or technologies would you use to perform this DNA synthesis and why?
      • Using a technology like TWIST as well as a safe and operational synthetic biology lab, I feel that a repeatable solution can be designed that can scale to the global use case of 3D printed PLA filament sources of microplastic waste reduction
  •  5.3 DNA Edit
    •  (i) What DNA would you want to edit and why?
      • I would like to edit the DNA of enzymes that biodegrade PLA to create higher yield, lower temperature requirements, and safe industrial processing to ensure the production is accessible to the quickly growing market segment. This may lead to greater awareness of the growing problem of microplastics through educational Makerspace activities that demonstrate this concept.
    •  (ii) What technology or technologies would you use to perform these DNA edits and why?
      • I would start with well-tested and proven enzymes such as LipaseB to ensure a baseline for any future experimentation. I would follow well-defined procedures of synthesizing DNA. For example, eColi is deemed a good vector, and yeast is also compatible. Once I have validated that a sequence can be synthesized, I would like to order via Twist, and collaborate with a Node Lab to conduct a PLA experiment with a control group, and measure PH, Co2 emissions, and weight delta, as well as temperature monitoring.

Appendix

Ai Prompts

Chat GPT was used to explore the environmenal and ecological impact of microplastics, which led me to the idea of capturing waste at the source.

Here is a condensed list of prompt themes used:

• What biological system (enzyme) can depolymerize PLA into lactic acid?
• What environmental problem does PLA create, especially regarding microplastic persistence in oceans?
• How can PLA waste be prevented from entering mixed waste streams through source segregation?
• What experimental conditions are required to depolymerize PLA at small scale?
• How can successful depolymerization be quantitatively measured (mass balance and lactic acid detection)?
• How can the experiment avoid generating microplastic through mechanical fragmentation?
• What happens to PLA in marine environments or when ingested by sea life?
• How can lactic acid or derived CO₂ be reused in biological systems (plants, algae)?
• How can algae-derived pigment serve as a material outcome of the carbon loop?

Claude Ai seemed to better undertand the boiengineering context:

• What are some examples of polyester hydrolase
• what (enzymes) cuts down PLA the best
• Confirm which are considered synthetic and effective
• what is proteinase K derived from
• what enzyme will work best for DNA replication
• is eColi or yeast better
• clarify cell-dependent or cell-free methods, of synthetic biology

Week 3 HW: Lab Automation

Focus on Lab Automation research, with creative examples of OpenTrans instruction sets using Python. Final project slide to be included in Node deck.

Opentrons Art

This week started witn an exploration of the Opentrons Art web app found at https://opentrons-art.rcdonovan.com

I was able to quickly upload an image and randomize the colors, to generate a point paired data set. I really like the bitmap rasterization and creative expression found in the gallery.

My investigation is based on my background in high resolution digital imaging. I wanted to better understand the pixel to microliter (uL) relationship. I see that with a 200 uL maximum quantity and a 90-100 mm Petrie Dish, it would seem that there are some basic constraints.

I look at that as an opportunity and design challenge to maximize resolution for the purpose of future scientific discovery. Similar to Moore’s law of exponential growth, the imaging industry has experienced the same trends, given today’’s 8K resolution and greater camera sensors.

Another reference point is with Twist labs, who have discovered how to overcome scale and quality limitations through in-silica transformation of a defined lab scale.

My approach was to explore how vector based graphics, defined by a series of points and splines, could be leveraged to create what is considered “infinite resolution” or at the very least, scalable and adjustable to meet the target output.

SVG, or “Scalable Vector Graphics” are the source of my BioArt for this activity. The entire library of icons we use in this Markdown format is a good example of what’s possible!

I used Claude Ai to explore a web-friendly code base that would allow me to generate the key value pairs needed to script a Python function in the Opentrons protocol. The React/JS framework made it possible to design a User Interface (Ui) that allows for selection of any SVG, to render a resolution independent sample to the screen.

Dynamic features include assignment of a Color from an available list, increase in “Pitch” which is the number of points that are spaced along the computed line segments. Most importantly, is “Radius” which includes a value for uL, which relates to the size of a droplet in OpenTrons. The output is a PNG for a quick visual reference, and a JSON file or Text file for future parsing. I chose a simple Copy/Paste Text field to obtain the list of x,y point pairs, for use in Python for Opentrons.

Screenshot of SVG-to-Opentrons Converter web app by Eric Schneider

I processed several sample images and ran into a slight issue with how SVG segments are deemed continuous, so I refined the parser to handle each line segment individually. I also introduced GitHub to maintain a sense of version control as a web application can quickly grow, or become corrupt, by Ai agents.

I then focused on ensuring the web application could appear inside of our preferred Colab environment, using Python and iFrame libraries. However, that is “sandboxed” and can’t share data directly. (Which is why the copy-paste is important to expedite). I tried to replicate the solution in Colab, but most things broke.

I moved on to the Opentron Simulator in Colab, with my new Data Set. I have an intermediate understanding of coding, and with the help of Claude Ai, I was able to articulate my need for a recursive list that would not only plot the points needed for pipetting, but also manage aspiration in batches of 20, not exceeding 200 uL.

After some basic Python formatting errors, I was able to preview the results via the Simulation module, and it was a very close match to my design intent.

See Appendix - Python Code

Reflection: I noticed that I was able to control the results of Vector for a high quality line that uses the full range of X, Y to the 10th of a millimeter (1 decimal point). Of course there is still the limitation of 80 mm diameter and 200 uL saturation, but I am encouraged that this technique can be refined for the purpose of high resolution design intent. I’m thinking about:

• BioCircuits that follow continual line traces for current
• BioSensors with defined sizes and shapes that are scalable
• BioArt that mirrors iconography and symbols, with dot-pitch resolution controls.
• BioPhotos that strive for incremental bitmap resolution at the microscopic level.

Imaging App- Future enhancement ideas:

• Z depth may impact Radius.
• Multiple SVG Layers, for multi-color assignments.
• Save/load to a repository
• Data sharing with Colab workspaces.
• Integration of JSON for data sharing
• Replicate application in Python in Colab natively.
• Integrate color selection into color location.
• Branching existing Automation Art code and exploring how to contribute to codebase.

OpenTrons Lab:

I was able to coordinate a working session with an OpenTrons OT-2, with Karen Ingram at the Charlotte Makerspace “BioArt Studio” which is an emerging destination for bioscience and art.

We attempted to load my protocol with vectorized points, but we encountered errors partially due to some code bugs which were quickly resolved. However, my Labware profiles were not defined for this platform configuration.

We deferred additional debugging in favor of using a known working Protocol for this session, which led to the output shown here. This is a good test since it shows the current state of functionality.

I learned how to launch and calibrate the equipment for an automated production run. I also observed an opportunity to 3D print a calibration target that would make centering the gantry over a printable art medium like watercolor paper inside of a petrie dish. We discussed a custom hold-down to keep the paper flat for more control over quality.

Our BioArt Studio session concluded with a request for a copy of a working Protocol file, so I could “reverse engineer” and configure my Protocol with the correct Labware settings. I installed a local copy of OpenTrons controller app, and was able to edit the script to include available Labware, as well as suppress the Thermal plate as it is not used in this model, and required adjustments to handling of the Z axis. Our next working session will fine-tune and test the Automation & Design protocol.

Update: 4/25/26 - The Protocol file was updated with the reassigned Labware, and was able to run the following design at 0.5uL with success:

Research Paper

I am sharing a link to an essay written by Karen Ingram, that illustrates the influence of automation on BioArt, including OpenTrons Ot-2 renderings.

https://biogeneticblooms.substack.com/p/the-blue-rose-of-metaphor-and-mystery

Be sure to browse the essays and artwork in this collection!

To learn more about the intersection of Biotechnology & Art , I am citing a research topic published by Cambridge Press. https://www.cambridge.org/engage/coe/article-details/660b2e409138d23161e8ebdf?show=item

I am excited about the field of synthetic Bioscience and Art as a result of our recent collaboration. I am grateful for the knowledge sharing and access to the BioLab.

Final Project

My Final project has been positively influenced by this week’s automation activity, as it validates that I can strive to achieve some specific lab results using the automated OpenTrons OT-2 as a tool in the process.

The path I will take for my final project starts with the identification of a Protein that can be synthesized to ensure my work is based on biotechnology best practices. The use of TWIST as a provider of automated creation of a Plasmid is the 1st step in the automation workflow.

Once I have a product, I expect to use the OpenTrons automation platform to construct a series of experiments in a host medium that will Grow into Art.

I plan on 3D printing supporting assemblies that will allow me to grow a photographic “film negative” plate, which could be a modified petrie dish that acts as a film back on a customized camera body and lighting rig.

I plan on creating a unique “exposure calibration” plate that will assist in lab test cases.

My long-range goal is to achieve a sustainable, repeatable solution that leverages automation and can scale up based on future demand for a BioPhoto “Lab” experience. I believe we are at pivotal moment in science and automation similar to when George Eastman revolutionized the photography industry through film and camera development for mass consumption. Many other industrial design solutions surround this theme.

My Final Project will reflect (and develop) artifacts of biotechnology and photography.

Checklist:

• Review this week’s recitation and this week’s lab for details on the Opentrons and programming it.
• Generate an artistic design using the GUI at https://opentrons-art.rcdonovan.com
• Write your own Python script which draws your design using the Opentrons.
• If you use AI to help complete this homework or lab, document how you used AI and which models made contributions.
• Sign up for a robot time slot if you are at MIT/Harvard/Wellesley or at a Node offering Opentrons automation.(Alt:MakerspaceCharlotte)
• Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
• Write a description about what you intend to do with automation tools for your final project.
• Final Project Ideas - Submit one slide to Node

Appendix - Python Code

from opentrons import types

metadata = {
    'author': 'Eric Schneider',
    'protocolName': 'Rasterizr',
    'description': 'SVG to OT',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
    # 2.7
}

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################
#original HTGAA: 
#TIP_RACK_DECK_SLOT = 9 #HTGAA
#COLORS_DECK_SLOT = 6 #HTGAA
#AGAR_DECK_SLOT = 5 #HTGAA
#PIPETTE_STARTING_TIP_WELL = 'A1'

#Makerspace Charlotte: 
TIP_RACK_DECK_SLOT = 6 #MSC
COLORS_DECK_SLOT = 3 #MSC
AGAR_DECK_SLOT = 1 #MSC
PIPETTE_STARTING_TIP_WELL = 'A1'  # *****TO BE CONFIRMED****

# TO DO: update these colors and wells to match your actual color plate layout
well_colors = {
    'A1': 'Red',
    'B1': 'Green',
    'C1': 'Orange'
}


def run(protocol):

    ##############################################################################
    ###   Load labware, modules and pipettes
    ##############################################################################

    # Tips
    tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')

    # Pipettes
    pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])   #HTGAA same

    # Modules
    #   temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT) #HTGAA temp module only, (not MSC)

    # Temperature Module Plate
    #temperature_plate = temperature_module.load_labware(
       # 'opentrons_96_aluminumblock_generic_pcr_strip_200ul',    #HTGAA
       # 'opentrons_6_tuberack_nest_50ml_conical'
        #'Cold Plate'
   # )

    # Choose where to take the colors from
    #color_plate = temperature_plate


    #new no temperature module that adds Z height issue
    color_plate = protocol.load_labware(
    'opentrons_6_tuberack_nest_50ml_conical', COLORS_DECK_SLOT)

    # Agar Plate
    # agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate'). #HTGAA
    #Makerspace Charlotte CUSTOM AGAR PLATE 3D PRINTED WITH PETRIE DISH HOLDER
    agar_plate = protocol.load_labware('biorad_96_wellplate_200ul_pcr', AGAR_DECK_SLOT, 'Agar Plate')

    # Get the top-center of the plate, make sure the plate was calibrated before running this
    center_location = agar_plate['A1'].top()

    pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

    ##############################################################################
    ###   Patterning
    ##############################################################################

    ###
    ### Helper functions for this lab
    ###

    # pass this e.g. 'Red' and get back a Location which can be passed to aspirate()
    def location_of_color(color_string):
        for well, color in well_colors.items():
            if color.lower() == color_string.lower():
                return color_plate[well]
        raise ValueError(f"No well found with color {color_string}")

    # For this lab, instead of calling pipette.dispense(1, loc) use this: dispense_and_detach(pipette, 1, loc)
    def dispense_and_detach(pipette, volume, location):
        """
        Move laterally 5mm above the plate (to avoid smearing a drop); then drop down to the plate,
        dispense, move back up 5mm to detach drop, and stay high to be ready for next lateral move.
        """
        assert(isinstance(volume, (int, float)))
        #above_location = location.move(types.Point(z=location.point.z + 5)) #original HTGAA
        above_location = location.move(types.Point(z=5))
        pipette.move_to(above_location)
        pipette.dispense(volume, location)
        pipette.move_to(above_location)

    ###
    ### YOUR CODE HERE to create your design
    ###

    ## reminder set Z
    agar_plate.set_offset(x=0.00, y=0.00, z=0.00)

    # start by picking up tip
    pipette_20ul.pick_up_tip()

    # PASTE a list of Current Coordinates (will be dynamic load once integrated or automated)
    currentCoords = [
        [-6.1, 26.8], [-7.9, 25.7], [-8.6, 23.8], [-9.9, 22.6], [-11.3, 21.5],
        [-12.1, 19.7], [-14.2, 19.7], [-15.3, 21.4], [-17.2, 22.3], [-19.3, 22],
        [-20.8, 20.7], [-21.4, 18.7], [-20.8, 16.7], [-19.2, 15.4], [-17.2, 15.1],
        [-15.3, 16.1], [-14.2, 17.8], [-12.1, 17.8], [-11.3, 15.9], [-9.9, 14.8],
        [-8.6, 13.6], [-7.3, 12.3], [-5.9, 11.1], [-6.4, 9.6], [-8.5, 9.6],
        [-10.6, 9.5], [-12.1, 8.1], [-12.3, 6], [-13.3, 4.9], [-15.4, 4.4],
        [-17.3, 3.6], [-19.1, 2.5], [-20.6, 1], [-21.8, -0.7], [-22.7, -2.6],
        [-23.4, -4.6], [-23.8, -6.6], [-24.1, -8.7], [-24.6, -10.6], [-26, -12.1],
        [-26.8, -14.1], [-26.7, -16.2], [-25.9, -18.1], [-24.4, -19.5], [-23.4, -18.3],
        [-24.7, -16.6], [-25, -14.6], [-24.2, -12.7], [-22.5, -11.6], [-20.4, -11.5],
        [-18.7, -12.7], [-17.8, -14.6], [-18.1, -16.6], [-19.4, -18.2], [-18.5, -19.5],
        [-16.9, -18.1], [-16.1, -16.2], [-16.1, -14.1], [-16.8, -12.2], [-18.2, -10.6],
        [-18.6, -8.8], [-18.3, -6.7], [-17.7, -4.7], [-16.7, -2.9], [-15.1, -1.5],
        [-13.2, -0.6], [-12.3, -1.9], [-12.3, -4], [-12.3, -6.1], [-12.1, -8.2],
        [-10.6, -9.4], [-11.2, -11.3], [-13.1, -11.9], [-13.2, -14], [-13.2, -16.1],
        [-12.4, -17.7], [-10.5, -18.5], [-11.2, -20.5], [-13.1, -21.1], [-14.1, -22.9],
        [-14, -25], [-12.7, -26.6], [-10.7, -26.9], [-8.6, -26.9], [-6.5, -26.9],
        [-4.4, -26.9], [-2.3, -26.8], [-2.3, -24.7], [-2.3, -22.6], [-2.3, -20.5],
        [-2.3, -18.4], [-2.3, -16.3], [-2.3, -14.2], [-2.3, -12.1], [-2.3, -10],
        [-0.7, -9.5], [1.4, -9.5], [2.3, -11.3], [2.3, -13.4], [2.3, -15.5],
        [2.3, -17.6], [2.3, -19.7], [2.3, -21.8], [2.3, -23.9], [2.3, -26],
        [4, -26.9], [6.1, -26.9], [8.2, -26.9], [10.3, -26.9], [12.4, -26.7],
        [13.9, -25.3], [14.1, -23.2], [13.4, -21.3], [11.5, -20.5], [10.5, -19.3],
        [11, -17.7], [13, -17.4], [13.2, -15.4], [13.2, -13.3], [12.6, -11.4],
        [10.5, -11.3], [10.8, -9.4], [12.2, -7.9], [12.3, -5.8], [12.3, -3.7],
        [12.3, -1.6], [13.4, -0.7], [15.3, -1.6], [16.8, -3], [17.8, -4.9],
        [18.4, -6.9], [18.6, -9], [18, -10.8], [16.7, -12.4], [16, -14.3],
        [16.2, -16.4], [17.1, -18.3], [18.7, -19.6], [19.3, -18.1], [18, -16.4],
        [17.9, -14.3], [18.8, -12.5], [20.6, -11.5], [22.7, -11.6], [24.3, -12.9],
        [25.1, -14.8], [24.6, -16.8], [23.3, -18.5], [24.6, -19.4], [26.1, -17.9],
        [26.8, -15.9], [26.7, -13.8], [25.9, -11.9], [24.4, -10.5], [24.1, -8.5],
        [23.8, -6.4], [23.3, -4.3], [22.6, -2.4], [21.7, -0.5], [20.4, 1.2],
        [18.9, 2.6], [17.1, 3.7], [15.1, 4.5], [13.1, 4.9], [12.3, 6.8],
        [11.7, 8.8], [9.9, 9.5], [7.8, 9.6], [5.9, 10.3], [6, 12.3],
        [8.1, 12.3], [8.6, 13.9], [10.1, 14.9], [11.3, 16.1], [11.8, 17.8],
        [13.9, 17.8], [15.1, 16.3], [16.9, 15.2], [18.9, 15.2], [20.6, 16.5],
        [21.4, 18.4], [21, 20.4], [19.5, 21.9], [17.5, 22.3], [15.6, 21.6],
        [14.3, 19.9], [12.4, 19.7], [11.3, 20.7], [10.7, 22.3], [8.7, 23],
        [8.3, 25], [6.9, 26.5], [4.9, 26.9], [2.8, 26.9], [0.7, 26.9],
        [-1.4, 26.9], [-3.5, 26.9], [-5.6, 26.8], [-2.6, 23], [-3.1, 21.4],
        [-4, 22.8], [3.8, 23], [3.3, 21.5], [2.5, 22.8], [-8.6, 18.7],
        [-8.7, 16.7], [-9.6, 17.5], [-9.6, 19.5], [-8.9, 21], [-8.6, 19.1],
        [9.2, 20.9], [9.6, 19.1], [9.6, 17.1], [8.6, 17.1], [8.6, 19.1],
        [8.9, 21], [-2.5, 19.3], [-1.8, 17.5], [0.1, 16.9], [1.9, 17.6],
        [2.7, 19.4], [4.1, 18.7], [3.5, 16.8], [2, 15.5], [0, 15.1],
        [-1.9, 15.5], [-3.5, 16.7], [-4.1, 18.6], [-2.7, 19.5], [4.1, 10.9],
        [3.4, 9.6], [1.4, 9.6], [-0.6, 9.6], [-2.6, 9.6], [-4.1, 10.1],
        [-4.1, 12.1], [-2.2, 12.3], [-0.2, 12.3], [1.8, 12.3], [3.8, 12.3],
        [-4, 4.9], [-2.2, 4], [-0.9, 2.4], [-0.5, 0.5], [-0.9, -1.5],
        [-2.2, -3.1], [-4, -4], [-6, -4], [-7.8, -3.1], [-9.1, -1.5],
        [-9.6, 0.4], [-9.1, 2.4], [-7.9, 4], [-6.1, 4.9], [-4.1, 4.9],
        [9.3, 4.7], [8.8, 3.2], [6.8, 3.2], [4.8, 3.2], [2.8, 3.3],
        [2.8, 4.9], [4.8, 5], [6.8, 5], [8.8, 5], [9.1, 1.3],
        [9.1, -0.4], [7.1, -0.4], [5, -0.4], [3, -0.4], [1.4, 0.3],
        [2.7, 1.4], [4.7, 1.4], [6.7, 1.4], [8.7, 1.3], [9.3, -2.6],
        [8.7, -4], [6.7, -4.1], [4.7, -4.1], [2.7, -4.1], [0.8, -3.9],
        [1.2, -2.3], [3.2, -2.3], [5.2, -2.3], [7.2, -2.3], [9.2, -2.4],
        [-7.8, -14.6], [-8.4, -16], [-10.4, -16], [-11.3, -14.9], [-11, -13.2],
        [-9, -13.2], [-7.8, -14.1], [11.3, -14.6], [10.7, -16], [8.7, -16],
        [7.8, -14.9], [8.2, -13.2], [10.2, -13.2], [11.3, -14.1], [-6, 3],
        [-7.4, 1.7], [-7.6, -0.3], [-6.5, -1.8], [-4.5, -2.2], [-2.9, -1.2],
        [-2.3, 0.7], [-3.2, 2.4], [-5, 3.2]
    ]

    batch_size = 20
    total = 0

    for i in range(0, len(currentCoords), batch_size):
        batch = currentCoords[i: i + batch_size]
        coordCount = len(batch)

        print(f"\nBatch {i//batch_size + 1}: aspirating {coordCount} units")
        pipette_20ul.aspirate(coordCount, location_of_color('Green'))

        for x, y in batch:
            adjusted_location = center_location.move(types.Point(x, y))
            dispense_and_detach(pipette_20ul, 1, adjusted_location)
            total += 1
            print(f"  Dispensed at ({x}, {y}) — running total: {total}")

    print(f"\nTotal objects processed: {total}")

    pipette_20ul.drop_tip()

Week 4 HW: Protein Design Part I

This week focuses on how sequence, structure, and energetics can be modeled and manipulated to create or optimize proteins with specified functions.

Part A - Conceptual Questions

For my homework, I initated a conversation with Claude Ai using Sonnet v4.6. My prompts use a method I use to start with a question, allow me to provide my answer, and receive an evaluation of my response with reinforcing key learning concepts. (Expand to see detailed responses to my answers.). I find this approach to be more interactive and leads to better knowledge retention.

Question 1: How many amino acid molecules are in a 500g piece of meat?

Using the given parameters — 500g of meat, average amino acid weight of 100 Daltons.

Question 2:Why do humans eat beef but not become a cow? Eat fish but not become a fish?

We digest and metabolize proteins rather than absorb and mutate. Our proteins are protected and encoded.

Question 3: Why are there only 20 natural amino acids?

Because that represents a large number of variations — when combined with every possible sequence, the combinations are exponentially high in the billions.

Question 4: Can you make other non-natural amino acids? Design some new ones.

Your answer: Yes — evolution converged on 20, but others may exist outside evolutionary pressure, possibly arising from light spectrum properties or geological timescales.

Question 5: Where did amino acids come from before enzymes that make them, and before life started?

Your answer: Geological, light energy, electrical, and even gravitational forces were all at play resulting in amino acid formations before our understanding of life emerged.

Question 6: If you make an α-helix using D-amino acids, what handedness would you expect?

An alpha-helix creates a right-handed coil.

Question 7: Can you discover additional helices in proteins?

Yes, since a protein may have many evolutionary and disrupted or folded variations.

Question 8: Why are most molecular helices right-handed?

Due to molecular electrical charge initiating primary bonds resulting in a right-handed twist, with left-handed helices possible under favorable conditions.

Question 9: Why do β-sheets tend to aggregate? What is the driving force?

β-sheets aggregate because they are flat and linear in design with bonding properties, repeating in a pattern or weave.

Question 10: Why do amyloid diseases form β-sheets? Can you use amyloid β-sheets as materials?

Amyloid diseases form β-sheets due to misfolding pathology. Since they are β-folds they are sticky and thermodynamically strong, difficult to clear — the same properties that would make an excellent material such as a synthetic cement.

Question 11: Design a β-sheet motif that forms a well-ordered structure.

A motif that acts as a 3-dimensional weave on the x, y, and z axis — resulting in a textile stronger than a simple x,y weave, useful in environments requiring strong resistant materials like Kevlar or heat resistant tiles.

Part B: Protein Analysis and Visualization

Briefly describe the protein you selected and why you selected it.

Identify the amino acid sequence of your protein.

How long is it? What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.

How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.

Does your protein belong to any protein family?

Identify the structure page of your protein in RCSB

When was the structure solved? Is it a good quality structure? Good quality structure is the one with good resolution. Smaller the better (Resolution: 2.70 Å)

Are there any other molecules in the solved structure apart from protein?

Does your protein belong to any structure classification family?

Open the structure of your protein in any 3D molecule visualization software:

PyMol Tutorial Here (hint: ChatGPT is good at PyMol commands)

Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.

Color the protein by secondary structure. Does it have more helices or sheets?

Color the protein by secondary structure. Does it have more helices or sheets?

Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?

Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?

Checklist

• Briefly describe the protein you selected and why you selected it.
• Identify the amino acid sequence of your protein.
• How long is it? What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.
• How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.
• Does your protein belong to any protein family?
• Identify the structure page of your protein in RCSB
• When was the structure solved? Is it a good quality structure? Good quality structure is the one with good resolution. Smaller the better (Resolution: 2.70 Å)
• Are there any other molecules in the solved structure apart from protein?
• Does your protein belong to any structure classification family?
• Open the structure of your protein in any 3D molecule visualization software:
• PyMol Tutorial Here (hint: ChatGPT is good at PyMol commands)
• Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.
• Color the protein by secondary structure. Does it have more helices or sheets?
• Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?
• Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)?