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.

I selected GFP https://www.uniprot.org/uniprotkb/P42212/entry

It is a widely studied protein with highly visual properties and application to biosensors, relevant to my final project scope.

Identify the amino acid sequence of your protein.

The amino acid sequence is MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK

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

The length of the protein is: 238 amino acids. The most common amino acid is: G, which appears 22 times.

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

The Blast Protein Existence menu showed 152 results with homology.

Does your protein belong to any protein family?

Yes, this is a member of the Green Fluorescent Protein (GFP) Family

Identify the structure page of your protein in RCSB

https://www.rcsb.org/groups/sequence/polymer_entity/P42212

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)

In 1996 the protein structure was solved. It is a good quality structure with a resolution of  2.4 Å A primary characteristic is the β-barrel fold with the chromophore inside, which helps to protect from damage.

https://pmc.ncbi.nlm.nih.gov/articles/PMC3739439/

Here are some very interesting sites related to this protein, that I will be revisiting.

https://www.proteinspotlight.org/back_issues/011

https://www.ekac.org/transgenicindex.html

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

Chromophore (CRO) formed and protected inside. Water molecules (HOH)

Does your protein belong to any structure classification family? Green Fluorescent Proteins, with 633 structures.

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

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

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

The structure has more sheets, indicated by amino acids, in yellow. The barrel shape is helical but the structure is formed in sheets.

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

The amino acids create a hydrophilic barrel shape that positively attract and retain water, creating a protective surface. Inside of the barrel is the hydrophobic chromophore that is protected until it is triggered by light to release fluorescent illumination.

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

The surface is primarily hydrophilic but also has permeability via holes (binding pockets) to allow for controlled hydration, to protect the chromophore, which enables light photons to be absorbed and emitted as fluorescence.

Part C1: Protein Language Modeling

• Deep Mutational Scans
  • Use ESM2 to generate an unsupervised deep mutational scan of your protein based on language model likelihoods.

• Can you explain any particular pattern? (choose a residue and a mutation that stands out)

  • M48 has the single highest probability of a recurring sequence.
  • Region 20-27 has an overall high model score
  • Region 3 contains a strong outlier

• Latent Space Analysis

  • Use the provided sequence dataset to embed proteins in reduced dimensionality.
    • My initial run showed a very dense plot.

• Analyze the different formed neighborhoods: do they approximate similar proteins?
  • I reduced the complexity to generate a plot that includes my selected protein.
  • The plot shows similar proteins based on a wide range of dimensions, so they don’t always relate to similar proteins, just similar shared amino acids with higher probability of a match. In some instances, the proteins line up much more predictably, such as a high match in a linear progression.

• Place your protein in the resulting map and explain its position and similarity to its neighbors.
  • My selected protein has a near neighbor of Clostridium botulinum which is in the family of Botulinum Neurotoxins. What is intersting is that a protein that creates biofluorescence in jellyfish is in proximity to a protein that creates a neurotoxin. This seems to be a function of evolutionary design of organisms that rely on this close relationship.

Part C2: Protein Folding

Fold your protein with ESMFold. Do the predicted coordinates match your original structure?

• Yes, the folded protein closely matches my original structure, but there are some degraded areas of the barrel formation shown with a confidence gradient (green is good, red is bad)

Try changing the sequence, first try some mutations, then large segments. Is your protein structure resilient to mutations?

• Yes, the structure seems resilient to mutations, even folding better in the α-helix regions.

Part C3: Protein Generation

Analyze the predicted sequence probabilities and compare the predicted sequence vs the original one.

• I initially ran the Inverse Folding function, using default settings.
• It predicted low confidence in the mutation scan:

• It produced a model based on default settings, that was unexpected. (sea slug)

• I realized that I need to enter a new PDB ID for my selected protein.
• I ran it and received an expected result:

• I then applied a mutation to my GFP based on a Claude AI inquiry to ’turn the GFP to blue fluorescence'

  • Y66H (Tyr→His) — replaces the phenol ring with an imidazole ring, shifting emission from ~509 nm (green) to ~448 nm (blue)
  • Y145F (Tyr→Phe) — the “enhanced” BFP (EBFP) stabilizer, improves brightness and folding
  • F64L — improves folding at 37°C (same as EGFP)

• I ran the new sequence through the mutation scan:

• I had Gemini help to write code that appends this new mutation sequence to the RDP target list.
• Once a prediction was made, I applied the sequence to the ESM to see if it would produce a result.

Input this sequence into ESMFold and compare the predicted structure to your original.

• Here is the mutated, inverse folded, and visualised with ESMFold:

Part D. Group Brainstorm on Bacteriophage Engineering

• Find a group of ~3–4 students
• Read through Phage Reading resources
• Review bacteriophage goals
• Brainstorm Session
  • Choose One or two main goals
  • One Page Proposal
  • Which tools
  • Why tools may help to solve sub-problem
  • One or two potential pitfalls
  • Schematic of Pipeline
  • Group’s short plan for engineering a bacteriophage
  • Post plan here

Part D - Plan

Hypothesis:

I believe we can focus on the cationic properties, or positive electrical charges that are present in the amino acid sequence. By substituting amino acids that enable more positive charge strengthening electrostatic attraction, we may create more binding activity. Lysis timing can be tuned in either direction by manipulating charge density.

Experimental Pipeline

Phase 1 — Discovery

• UniProt
  • Retrieve canonical L-protein sequence
  • Confirm Region 1, 2, and 3 boundaries
• BLAST
  • Search for homologous sequences across phage strains
  • Identify conservation and variability at target residues
• PyMOL
  • Render 3D structural model
  • Apply polarity-based color coding to each region

Phase 2 — Mutation Analysis

• PyMOL
  • Isolate target residues
  • Examine local chemical environment and spatial context
• ESM2
  • Mask target residues and score substitution probability
  • Generate per-residue probability data for C2, C3, C4
• Heatmap
  • Synthesize BLAST conservation and ESM2 probability scores
  • Overlay onto PyMOL structure to confirm target sites
• ESMFold
  • Predict 3D structure of each mutant sequence
  • Generate pLDDT confidence scores per residue
• PyMOL
  • Import ESMFold outputs
  • Render side-by-side comparison of C1 baseline vs C2, C3, C4

Phase 3 — Synthesis

• Codon Optimization
  • Optimize mutant sequences for E. coli expression
  • Verify no unintended mRNA secondary structures introduced
• Twist Bioscience
  • Submit all four constructs for gene synthesis
  • Confirm synthesis feasibility and receive gene fragments

Phase 4 — Plasmid Design

• Benchling
  • Design annotated circular plasmid constructs for C1–C4
  • Include promoter, RBS, insert, terminator, and selection marker
• Review Gate
  • Confirm correct reading frame and insert orientation
  • Verify no unintended open reading frames
  • Confirm host compatibility before proceeding

Phase 5 — Execution

• Opentrons OT-2
  • Run liquid handling protocol for all four constructs
  • Collect lysis timing, plaque formation, and MurA activity data
  • Compare all results against C1 baseline

Potential Pitfalls

• My hypothesis focuses on Region 1 (faces cytoplasm, cationic/hydrophilic) and Region 3 (amphipathic, faces periplasm) to control timing of MurA enzyme inhibition.

Region 1 and Region 3

• Polarity change risk
  • Too much polarity change could cause the phage to bind and become entrapped

Region 2

• Avoid mutagenesis
  • Very well defined helical fold
  • Subject to disruption with minor change to structure

Week 5 HW: Protein Design Part II

This week we learned how cutting-edge AI and protein language models are used to design functional proteins and peptides “in silico”.

Part A: SOD1 Binder Peptide Design

Part 1: Generate Binders with PepMLM

Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation.

Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card, Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence. To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison. Record the perplexity scores that indicate PepMLM’s confidence in the binders.

Part 2: Generate Binders with PepMLM

Navigate to the AlphaFold Server: alphafoldserver.com For each peptide, submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein-peptide complex. Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried? In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated peptide matches or exceeds the known binder.

Perplexity

Peptide Observations

FLYRWLPSRRGG ⭐ Known Binder — Control

• ipTM: 0.89 | pTM: 0.92
• Distance to A4V: 22.285 Å
• This is the known SOD1-binding peptide and serves as the baseline for all comparisons. The peptide is in the general vicinity of A4V. All PepMLM-generated peptides are evaluated against its ipTM of 0.89, pTM of 0.92, and distance of 22.285 Å.

WRYVAAAIARKK

• ipTM: 0.85 | pTM: 0.89
• Distance to A4V: 18.541 Å — 3.744 Å closer than the known binder
• It is in proximity to the dimer region and engaging the B-Barrel.
• This peptide is closer to the dimer region and approaches but does not exceed the ipTM value of the known binder. The peptide appears as a highly probably well formed b-barrel binder and intersects the surface, partially buried.

WRYVAAAIARKK Distance

WRYVAAAIARKK Overlapping Surface

HRYVAAAVKWKK

• ipTM: 0.88 | pTM: 0.91
• Distance to A4V: 27.536 Å — 5.251 Å farther than the known binder
• Engages b-barrel region but does not localize near the N-terminus.
• Appears partially buried. ipTM is just below the known binder at 0.88.

HRYVAAAVKWKK

HRYVAAAVKWKK showing surface incursion

WRYVAYALRWGE

• ipTM: 0.68 | pTM: 0.79
• Distance to A4V: 12.875 Å — 9.410 Å closer than the known binder
• Approaching the A4V location 27.152 Å , not dimer interface. Surface bound. Considered near the A4V location. Lower confidence than the known binder, and demonstrates a partially folded structure.
  • The peptide is folding into a secondary structure upon binding rather than remaining as a random flexible chain.
  • This is called induced folding or folding upon binding — a hallmark of meaningful peptide-protein interactions.
  • The helix formation suggests the peptide is responding to the local environment of the SOD1 surface.

WRYVAYALRWGE Cartoon

WRYVAYALRWGE Surface

KRYYWVAVARAA

• ipTM: 0.89 | pTM: 0.92
• Distance to A4V: 17.228 Å — 5.057 Å closer than the known binder
• No — localizes near the middle of Chain 1, not the N-terminus.
• Engages surface in middle of region, not approaching dimer interface.
• Surface bound — clipping view shows no intrusions.
• ipTM matches the known binder exactly at 0.89, with a distance of 17.228 Å placing it closer to the target vicinity of A4V than the control.

KRYYWVAVARAA surface

KRYYWVAVARAA peptide with distance to A4V

KRYYWVAVARAA distance (Closeup)

ipTM Summary and Comparison to Known Binder

The ipTM values across the five PepMLM-generated peptides range from 0.68 to 0.89, indicating generally high predicted confidence in binding interactions. Using FLYRWLPSRRGG (ipTM 0.89, distance 22.285 Å) as the known binder control, two peptides — FLYRWLPSRRGG and KRYYWVAVARAA — match the known binder ipTM exactly at 0.89, while HRYVAAAVKWKK comes close at 0.88. However, high ipTM alone does not confirm therapeutic relevance — proximity to the A4V site matters equally. WRYVAYALRWGE carries the lowest ipTM at 0.68 yet achieves the closest proximity to the A4V mutation site at 12.875 Å — 9.410 Å closer than the known binder — and uniquely demonstrates induced folding behavior near the target. This combination of near-vicinity binding and structural reorganization makes it the most therapeutically interesting candidate despite its lower confidence score, and suggests it warrants further optimization to strengthen the binding pose while maintaining its proximity to the A4V site.

First Pass Analysis and Candidate Selection

What I found in AlphaFold 3 was that my initial peptides were primarily surface binding with varying levels of proximity to the A4V sequence location near the homodimer. WRYVAYALRWGE was not the highest scoring, but was closest to the target and demonstrated induced folding — organizing into a helical secondary structure upon binding rather than remaining flexible, which is a hallmark of meaningful peptide-protein interaction.

Higher ipTM scores did not consistently predict stronger binding affinity or closer proximity to the A4V site. FLYRWLPSRRGG and KRYYWVAVARAA matched the highest ipTM at 0.89 but were farther from the mutation site, while WRYVAYALRWGE at 0.68 was structurally the most relevant.

Selected Candidate

The peptide chosen to advance from this first pass was WRYVAYALRWGE. Despite a hemolysis probability of 0.104 — approximately 2x the known binder control — its induced folding behavior near the A4V site was the deciding factor. The structural response to the local SOD1 environment, combined with its closest proximity to the mutation site at 12.875 Å, outweighed the moderate hemolysis risk at this stage of evaluation. Further analysis via MoPPIT would follow to explore whether higher affinity candidates could be generated with a safer therapeutic profile.

Part 4: Generate Optimized Peptides with moPPIt

Now, move from sampling to controlled design. moPPIt uses Multi-Objective Guided Discrete Flow Matching (MOG-DFM) to steer peptide generation toward specific residues and optimize binding and therapeutic properties simultaneously. Unlike PepMLM, which samples plausible binders conditioned on just the target sequence, moPPIt lets you choose where you want to bind and optimize multiple objectives at once.

Open the moPPit Colab linked from the HuggingFace moPPIt model card Make a copy and switch to a GPU runtime. In the notebook: Paste your A4V mutant SOD1 sequence. Choose specific residue indices on SOD1 that you want your peptide to bind (for example, residues near position 4, the dimer interface, or another surface patch). Set peptide length to 12 amino acids. Enable motif and affinity guidance (and solubility/hemolysis guidance if available). Generate peptides. After generation, briefly describe how these moPPit peptides differ from your PepMLM peptides. How would you evaluate these peptides before advancing them to clinical studies?

Control / Known Binder Reference: FLYRWLPSRRGG (PepMLM control | ipTM 0.89 | Binding pKd 5.938 | Hemolysis 0.047) All MoPPIT-generated peptides are evaluated relative to this baseline.

Numeric Summary

Property-by-Property Analysis

Binding Affinity (pKd/pKi) — Strongest Property

All three MoPPIT peptides are classified as weak binders, but two exceed the control baseline significantly.

IKCGNKFKKKYH shows the highest binding affinity of any peptide evaluated in this entire session — exceeding the control by +1.775 pKd units and exceeding the best PepMLM candidate (WRYVAYALRWGE, 6.980) by +0.733. This is a notable result. KKTKTGKFCKQN is the only MoPPIT peptide that falls below the control baseline.

Hemolysis Probability — Critical Liability

This is the most significant finding in the MoPPIT dataset and represents a serious concern for all three peptides.

All three MoPPIT peptides show hemolysis probabilities approaching 1.0 — dramatically higher than the PepMLM control and well above any therapeutically acceptable threshold. This is likely driven by their highly cationic, lysine-rich sequences (KK and KKK motifs) which are known to disrupt negatively charged cell membranes through electrostatic attraction. This is a critical liability that would need to be resolved before any of these peptides could be considered viable candidates.

Solubility

All MoPPIT peptides fall below the control solubility of 1.000. While none are insoluble, the reduction in solubility relative to the PepMLM candidates is worth noting — particularly for RTCGLIETKKQT and IKCGNKFKKKYH at 0.833.

Motif Position Score

KKTKTGKFCKQN shows the strongest motif complementarity to the SOD1 target despite having a below-control binding affinity. This suggests the peptide is well-positioned relative to the SOD1 binding motif but may lack the side chain contacts needed to translate motif recognition into strong affinity. IKCGNKFKKKYH presents an interesting inversion — lowest motif score but highest affinity — suggesting its binding may be driven by non-specific electrostatic contacts rather than precise motif engagement.

Comparative Assessment — MoPPIT vs PepMLM

MoPPIT generates peptides with superior raw binding affinity but at the cost of dramatically elevated hemolysis risk. PepMLM candidates show more balanced profiles with safer hemolysis values and demonstrated structural proximity to the A4V site.

Overall Candidate Assessment

Key Takeaway

IKCGNKFKKKYH has the highest predicted binding affinity of any peptide evaluated in this session (pKd 7.713), making it a structurally interesting lead. However, its hemolysis probability of 0.957 makes it unsuitable in its current form. The immediate optimization priority for all three MoPPIT peptides is reducing cationic character — specifically reducing lysine density — to bring hemolysis probability into a safe range while preserving the affinity advantage. AlphaFold structural evaluation of these peptides against the A4V SOD1 dimer would be the recommended next step to assess whether the affinity advantage translates to meaningful proximity to the mutation site.

Additional Investigation

Objective: Identify and resolve hemolysis liability in the highest-affinity MoPPIT peptide while preserving binding affinity to SOD1 A4V.

Stage 1 — Problem Identified

The three MoPPIT-generated peptides showed critically elevated hemolysis probabilities of 0.957–0.982 — approximately 20x the known binder control (FLYRWLPSRRGG, 0.047). The cause was identified as lysine-rich sequences — high cationic density causing electrostatic attraction to and disruption of negatively charged cell membranes.

Despite the hemolysis liability, IKCGNKFKKKYH was selected for optimization because it showed the highest binding affinity of any peptide in the entire session at pKd 7.713 — exceeding the known binder control by +1.775 units.

Stage 2 — Substitution Strategy Designed

Three variants were designed by targeting the five lysines at positions 2, 6, 8, 9, 10:

I  K  C  G  N  K  F  K  K  K  Y  H
1  2  3  4  5  6  7  8  9  10 11 12

Stage 3 — Results

All three variants achieved hemolysis safety (0.035–0.037) — matching the known binder control. However binding affinity diverged significantly by strategy.

Stage 4 — Key Finding

K→E substitution (glutamate) outperformed K→Q substitution (glutamine) for preserving binding affinity. Variant 3 lost only 0.486 pKd units versus ~1.5 units lost by the Q-substitution variants — because glutamate can form new complementary contacts with the SOD1 surface rather than simply removing charge.

Variant 3 also achieved a net charge of 0.84 and pI of 8.16 — the most physiologically favorable profile of all variants and comparable to the best PepMLM candidate WRYVAYALRWGE (charge 0.77).

Substitution Strategy Comparison

Outcome

IKCGNEFKKEYH emerged as the optimized lead — retaining medium binding classification (pKd 7.227), achieving full hemolysis safety (0.035), and carrying a charge profile (0.84) and pI (8.16) that favor target selectivity over non-specific membrane disruption.

The K→E glutamate substitution strategy is the demonstrated approach for resolving cationic hemolysis liability without sacrificing binding affinity in this peptide series.

Visualization:

1. Submit IKCGNEFKKEYH to AlphaFold Server against the A4V SOD1 homodimer to evaluate structural proximity to the A4V mutation site.
2. Compare ipTM and distance to A4V against the best PepMLM candidate WRYVAYALRWGE (12.875 Å) to determine which pipeline produces the stronger structural result.
3. If structural proximity is confirmed, consider a fourth generation of optimization targeting further charge refinement while monitoring affinity retention.

IKCGNEFKKEYH Non-Hemolytic - AlphaFold

IKCGNEFKKEYH Non-Hemolytic Surface

IKCGNEFKKEYH Non-Hemolytic Illustration

Part C: Final Project: L-Protein Mutants

High level summary: The objective of this assignment is to improve the stability and auto-folding of the lysis protein of a MS2-phage. This mechanism is key to the understanding of how phages can potentially solve antibiotic-resistance.

Context & Motivation

The L protein of bacteriophage MS2 is a 74–75 amino acid lysis protein whose stability and auto-folding are critical to understanding how phages can solve antibiotic resistance. The CNN phage therapy case (Strathdee/Patterson) provided real-world context — phage therapy saved a life against Acinetobacter baumannii when all antibiotics failed, underlining why understanding phage lysis mechanisms matters.

“It’s estimated that by 2050, 10 million people per year — that’s one person every three seconds — is going to be dying from a superbug infection.” — Steffanie Strathdee, UC San Diego

Step 1 — Understanding the Problem

• Established that MS2 encodes 4 proteins: Maturation (A), Coat (CP), Lysis (L), Replicase (Rep)
• Located L protein on genome: NC_001417 nt 1678–1902
• Identified the core challenge: L gene overlaps CP and Rep simultaneously
• Any nucleotide mutation in L is also a mutation in a neighboring reading frame

Overlapping Frames

Step 2 — Sequence Acquisition

• Retrieved wildtype L protein sequence (74 aa)
• Dataset-validated all 32 experimentally constrained positions against the wildtype
• Attempted live fetch from UniProt (P03609) — network restricted
• Reconstructed sequence from published Fiers 1976 data + dataset ground truth
• Downloaded all 4 MS2 protein sequences as FASTA file

Wildtype L Protein (74 aa)

>P03609|Lysis_protein_L|NC_001417:1678-1902
METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSK
FTNQLLLSSLCLVFVTSATKQQLS

All 4 MS2 Proteins

Step 3 — ORF Analysis

Mapped all three reading frames across the L gene window (nt 1620–1950):

GENE MAP (NC_001417):

  ════════════════════════╗
  Coat Protein (CP)       ║ STOP
  (nt 1335 → 1724)        ╚══════════════════════════════════════
                                    ╔══════════════════════════════
                                    ║ Replicase (Rep)
                    ╔═══════════════╩══════════════════════════╗
                    ║          L PROTEIN (LYSIS)               ║ STOP
                    ╚══════════════════════════════════════════╝

Three Overlap Zones

Step 4 — Domain Mapping

aa:  1                    ~40  ~41                      74
     |─────────────────────|────|─────────────────────────|
     [════ N-terminal soluble ════][═══ Transmembrane domain ═══]
           (aa 1–40)                      (aa 41–74)
           DISPENSABLE for lysis          ESSENTIAL for lysis
           DnaJ chaperone binding         Membrane insertion + pore formation

Key Domain Properties

Soluble Domain (aa 1–40)

• Dispensable for lysis function
• Primary DnaJ chaperone interaction site
• Spans Zone 1 and Free Zone
• Best target for stability mutations

Transmembrane Domain (aa 41–74)

• Essential for lysis
• Drives membrane insertion
• Forms oligomeric pores in cell envelope
• Entirely within Zone 2 (Rep overlap)
• Hydrophobic core must be preserved

TM Boundary Zone (aa 38–46) — Key Target

The soluble→TM junction is the most tractable region for stability improvement:

aa:  38   39   40   41   42   43   44   45   46   47
      L    Y    V    L    I    F    L    A    I    F
                     |────────────────────────────────
                     TM domain starts
                          ↑    ↑    ↑
                         L44  A45  I46  ← lysis=1 mutations here

Step 5 — Mutation Dataset Analysis

Parsed 57 unique non-stop mutations from experimental dataset.

Dataset Summary

Each mutation annotated with:

• Nucleotide position and base pair change
• Amino acid position and substitution
• Lysis activity (0/1)
• Protein expression level (0/1/ND)

TM Region Mutations

Key finding: Only L44P, A45P, I46F in the TM region retain lysis=1

Step 6 — Mutation Combination Generator

Built Python pipeline to generate random 2-residue combinations with three strategies:

• random — any valid pair from full pool
• lysis_positive — both mutations have lysis=1
• mixed — at least one lysis+ and one lysis- per combo

Generated Mutants

MUT1 Sequence (S15A + K50I)

>MS2_L_MUT1|S15A_K50I|lysis=1/0|prot=1/1
METRFPQQSQQTPAATNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSI
FTNQLLLSSLCLVFVTSATKQQLS

MUT2 Sequence (M1T + L56H)

>MS2_L_MUT2|M1T_L56H|lysis=0/0|prot=0/1
TETRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSK
FTNQLHLSSLCLVFVTSATKQQLS

MUT3 Sequence (Y39H + V40E)

>MS2_L_MUT3|Y39H_V40E|lysis=0/0|prot=0/0
METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLHELI FLAIFLSK
FTNQLLLSSLCLVFVTSATKQQLS

Step 7 — Evaluation Pipeline

Note, in this section, Claude AI recommended a Python based scoring systme, which can be optional but informs the pipeline.

6-Gate Scoring Framework

INPUT: Mutation combination (2+ residues)
│
├─► GATE 1: Dataset Filters
│         ├── Lysis activity (0/1)
│         ├── Protein expression level (0/1)
│         └── Stop codon in combo?
│
├─► GATE 2: Sequence Checks
│         ├── GC content (40–60%)
│         ├── Isoelectric point (pI)
│         ├── GRAVY hydrophobicity score
│         └── Rare codons at N-terminus?
│
├─► GATE 3: ORF Overlap Check
│         ├── Coat protein frame intact?
│         ├── Replicase frame intact?
│         └── No new stop codons in overlapping frames?
│
├─► GATE 4: Structure Prediction
│         ├── pLDDT ≥ wildtype (ESMFold / AF2)
│         ├── pTM > 0.5
│         └── TM helix (aa 41–74) intact?
│
├─► GATE 5: Stability Scoring
│         ├── ΔΔG ≤ 0 kcal/mol (FoldX / mCSM)
│         └── Solubility score (CamSol)
│
└─► GATE 6: Multimer Check
          ├── ipTM ≥ wildtype (AF2_Multimer)
          └── TM domain interface contacts preserved?
                     │
               SHORTLIST: Top 2–5 candidates
                     │
               WET LAB VALIDATION

Key Metrics Ranked by Complexity

Step 8 — TM Mutation Shortlist (Top 5 Candidates)

Step 9 — Structural Analysis Tools

Key Biological Insights

• The L protein’s overlapping reading frames are the primary constraint on mutation design
• The Free Zone (aa 16–28) is the only region where mutations affect L protein alone
• The TM boundary (aa 44–46) is the most promising target — lysis=1 mutations exist there
• L protein functions as an oligomer — monomer folding alone is insufficient
• DnaJ chaperone interaction with the soluble domain is critical for proper folding
• The C-terminal TM domain drives both membrane insertion and pore formation

Outstanding Steps

[ ] Run ESMFold on all 5 candidate mutants → get pLDDT scores
[ ] Run FoldX / mCSM → get ΔΔG for each candidate
[ ] Run AF2_Multimer → check dimer ipTM scores
[ ] Run Benchling ORF check → verify CP and Rep frames intact
[ ] Rank and select final top 5 candidates
[ ] Synthesize top 2 in wet lab → SDS-PAGE + lysis assay

Appendix: Pipeline Summary with Key Ai generated Prompts (Claude - Sonnet 4.6)

Stage 1 — Sequence Retrieval and Mutation Introduction

The session began with retrieving the canonical human SOD1 sequence from UniProt (P00441) and introducing the A4V point mutation — substituting Alanine for Valine at position 4 of the mature protein. This established the disease-relevant target sequence for all downstream analysis. Key concepts clarified included the numbering convention between the full canonical sequence and the mature processed form, and the biological significance of A4V as the most common fALS-linked SOD1 variant in North America.

Key prompts:

“Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation.” “What is the A4V mutation?” “What is a homodimer?”

Stage 2 — Conceptual Grounding

Before moving to computational tools, foundational questions established the biological framework: the structural location of A4V at β-strand 1 near the dimer interface, and the therapeutic rationale for designing a peptide binder — to intercept misfolded A4V SOD1 at the aggregation-prone hydrophobic surface exposed by dimer destabilization.

Key prompts:

“Remind, where is the actual critical region of A4V?” “Summarize what the fundamental purpose is to take the mutated protein and add a binder sequence.”

Stage 3 — AlphaFold Server Workflow

The AlphaFold Server workflow was established: inputting two copies of the A4V SOD1 sequence as Entity 1 to model the native homodimer, and the peptide as Entity 2. The distinction between protein chains and small molecule ligands was clarified. The rationale for five ranked models per job was explained, and the rank_0 CIF file was identified as the correct starting point.

Key prompts:

“In AlphaFold Server how to add a peptide to a protein sequence?” “When evaluating in AlphaFold should I be using one strand of the SOD1 sequence or two, to show the mutant form?” “Each export from AlphaFold includes 4 CIF files. Why?”

Stage 4 — ChimeraX Structural Evaluation

The built-in AlphaFold viewer was identified as insufficient for detailed analysis, leading to adoption of ChimeraX. A core command vocabulary was developed iteratively through troubleshooting: chain coloring, secondary structure coloring, residue labeling to landmark A4V, surface generation with transparency to assess binding depth, and distance measurement to quantify proximity to residue 4. Common errors were resolved including chain specification syntax, atom ambiguity, electrostatic surface cap persistence, and model number conflicts.

Key prompts:

“Is it better to evaluate in AlphaFold or in another visual program to get to these answers?” “When loading a model into ChimeraX and evaluating, summarize key questions for evaluating visually.” “Summarize how to best answer the questions, and what ChimeraX visualization will work best.”

Stage 5 — Peptide Observation and Scoring

Five PepMLM-generated peptides were evaluated across AlphaFold confidence metrics (ipTM, pTM) and ChimeraX structural observations (distance to A4V, structural feature engagement, binding depth). A key insight emerged: WRYVAYALRWGE — the lowest ipTM (0.68) — showed the closest proximity to A4V (12.875 Å) and uniquely demonstrated induced folding, a hallmark of meaningful peptide-protein interaction. FLYRWLPSRRGG (ipTM 0.89, distance 22.285 Å) was established as the known binder control baseline.

Key prompts:

“What if a lower ipTM has a closer proximity to the A4V location?” “In this case the peptide starts to show a helical fold.” “Update to final summary — FLYRWLPSRRGG is the control or known SOD1-binding peptide.”

Stage 6 — Physicochemical Property Analysis (PepMLM)

PepMLM peptide physicochemical properties were analyzed relative to FLYRWLPSRRGG across seven dimensions: solubility, hemolysis, binding affinity (pKd/pKi), molecular weight, net charge, isoelectric point, and hydrophobicity (GRAVY). Three peptides exceeded the control binding affinity. WRYVAYALRWGE showed the highest pKd (6.980), lowest net charge (0.77), and lowest pI (8.59) — the most favorable selectivity profile.

Key prompts:

“Analyze the results.” (physicochemical data pasted) “Revise the plot — make FLYRWLPSRRGG the baseline and first value, color in gray bar.” “Format the detailed analysis as a markdown file.”

Stage 7 — MoPPIT Peptide Generation and Analysis

Three MoPPIT-generated peptides were introduced and analyzed. All three showed critically elevated hemolysis probabilities (0.957–0.982, ~20x control) driven by lysine-rich sequences. Despite this, IKCGNKFKKKYH was identified as highest-affinity peptide of the entire session at pKd 7.713. Motif position scores were introduced as an additional evaluation dimension.

Key prompts:

“What is a motif position?” “Graph the following MoPPIT generated peptide binders.” (data pasted) “Format the analysis of MoPPIT data in Hugo markdown format.”

Stage 8 — Hemolysis Resolution

The hemolysis liability of IKCGNKFKKKYH was addressed through systematic lysine substitution. Three variants were designed and screened. The K→E glutamate substitution strategy (Variant 3: IKCGNEFKKEYH) outperformed K→Q substitution — retaining medium binding classification (pKd 7.227), reducing net charge to 0.84, and achieving full hemolysis safety (0.035) comparable to the known binder control.

Key prompts:

“Summarize hemolysis probability and what we may do to resolve.” “Recommend three peptides derived from IKCGNKFKKKYH that might lower hemolysis.” “Here are the results of an attempt to lower hemolysis.” (variant data pasted)

Stage 9 — Synthesis and Outputs

All findings were compiled into structured Hugo markdown deliverables: peptide binding observations, numeric summary tables, ipTM vs distance scatter plot, six-panel physicochemical bar chart, MoPPIT analysis, hemolysis resolution pipeline summary, and this appendix. Two lead candidates emerged from separate pipelines for further structural validation.

Key prompts:

“Plot the data points in a visual graphic, highlighting the likely candidate.” “Download summary of the attempt to achieve hemolysis safety in Hugo markdown format.” “Revise the hemolysis summary in Hugo markdown format.”

Distilled Conclusion

Two lead candidates emerged from this session across two separate peptide generation pipelines:

WRYVAYALRWGE (PepMLM) — closest structural proximity to A4V (12.875 Å), highest PepMLM binding affinity (pKd 6.980), induced folding behavior upon binding, and the most favorable charge selectivity profile (net charge 0.77). Recommended for AlphaFold dimer evaluation and structural confirmation.

IKCGNEFKKEYH (MoPPIT, optimized) — highest binding affinity of the full session after hemolysis optimization (pKd 7.227), full hemolysis safety achieved (0.035), net charge 0.84, pI 8.16. Glutamate substitution (K→E) demonstrated as the superior strategy over glutamine substitution (K→Q) for charge reduction without affinity loss.

The recommended next step for both candidates is AlphaFold Server structural evaluation against the A4V SOD1 homodimer, followed by distance-to-A4V measurement in ChimeraX to determine which pipeline produces the more structurally relevant binder.

Part 3 — Footnote Attributions

Databases and Sequence Resources

1. UniProt — Human SOD1 canonical sequence (P00441 / SODC_HUMAN). UniProt Consortium. UniProt: the Universal Protein knowledgebase. Nucleic Acids Research. https://www.uniprot.org/uniprotkb/P00441

Structure Prediction

2. AlphaFold Server — Structure prediction of SOD1 A4V homodimer and peptide complexes. Abramson J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 2024. https://alphafoldserver.com

3. AlphaFold confidence metrics (ipTM / pTM) — Evans R, et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv, 2022. https://doi.org/10.1101/2021.10.04.463034

Molecular Visualization

4. UCSF ChimeraX — Pettersen EF, et al. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Science, 2021. https://www.rbvi.ucsf.edu/chimerax/

Peptide Design and Generation

5. PepMLM — Peptide design via masked language modeling. Truong Jr T, Bepler T. PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling. arXiv, 2023. https://arxiv.org/abs/2310.03842

6. MoPPIT — Motif-based peptide-protein interaction tool. Source to be confirmed from course materials.

Physicochemical Property Prediction

7. Solubility prediction — Peptide solubility probability scoring. Source dependent on tool used for property screening — confirm from course pipeline documentation.

8. Hemolysis prediction — Peptide hemolysis probability scoring. Likely derived from HemoPI or equivalent hemolysis prediction server. Gautam A, et al. HemoPI: a server to predict and design hemolytic peptides. Journal of Translational Medicine, 2014. https://webs.iiitd.edu.in/raghava/hemopi/

9. Binding affinity (pKd/pKi) — Peptide binding affinity prediction. Source dependent on tool used — confirm from course pipeline documentation.

10. GRAVY score (hydrophobicity) — Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. Journal of Molecular Biology, 1982. 157(1):105–132.

11. Isoelectric point (pI) prediction — Bjellqvist B, et al. The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis, 1993.

Disease and Biology Context

12. SOD1 and fALS — Rosen DR, et al. Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis. Nature, 1993. 362:59–62.

13. A4V mutation and ALS — Cudkowicz ME, et al. Epidemiology of mutations in superoxide dismutase in amyotrophic lateral sclerosis. Annals of Neurology, 1997. 41(2):210–221.

14. SOD1 misfolding and aggregation — Banci L, et al. Atomic-resolution monitoring of protein maturation in live human cells by NMR. Nature Chemical Biology, 2013.

15. Induced folding / folding upon binding — Dyson HJ, Wright PE. Intrinsically unstructured proteins and their functions. Nature Reviews Molecular Cell Biology, 2005. 6:197–208.

Structural Biology Concepts

16. Greek key β-barrel topology — Richardson JS. The anatomy and taxonomy of protein structure. Advances in Protein Chemistry, 1981. 34:167–339.

17. Protein distance thresholds and contact definition — Keskin O, et al. Principles of protein-protein interactions. Chemical Reviews, 2008. 108(4):1225–1244.

18. Lysine-mediated membrane disruption and hemolysis — Brogden KA. Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria? Nature Reviews Microbiology, 2005. 3:238–250.

Additional References

• Fiers W. et al. (1976) Complete nucleotide sequence of bacteriophage MS2 RNA. Nature 260:500–507
• Kastelein R.A. et al. (1982) Lysis gene expression of RNA phage MS2. Nature 295:35–41
• Beremand & Blumenthal (1979) Overlapping genes in RNA phage. Cell 18:257–266
• PMC10688784 — In vitro characterization of the phage lysis protein MS2-L
• PMC554659 — The amino terminal half of the MS2-coded lysis protein is dispensable
• CNN Phage Therapy Article — Strathdee/Patterson case study

Week 6 HW: Genetic Circuits Part I: Assembly Technologies

This week we learn core molecular biology tools and techniques for processing and assembling DNA, including PCR and Gibson Assembly.

Assignment: DNA Assembly

1. What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?

   • The mix contains DNA Polymerase, known for thermostable accuracy. Used to amplify fragments used in PCR for Gibson Assembly.

2. What are some factors that determine primer annealing temperature during PCR?

   • length, temperature, presence of GC.

3. There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other.

   • PCR is much more likely to work with mutations that have a wider variation of results.
   • Restriction enzyme digests work best when the sequence is well defined and repeatable.
   • PCR has a thermocycle, RE has a single set temp.
   • PCR required Primers, RE requires recognition sites

4. How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?

   • Ensure overlaps are present for alignment
   • Evaluate using electrophoresis
   • Purify the dna

5. How does the plasmid DNA enter the E. coli cells during transformation?

   • Electrostatic conditioning, or heat shock temporarily make the cells porous, then left to build antibiotic resistance

6. Describe another assembly method in detail (such as Golden Gate Assembly)

   • Explain the other method in 5 - 7 sentences plus diagrams (either handmade or online).
     • Golden Gate Assembly is a Restriction based cloning method
     • Relies on Type IIS restriction enzymes
     • Gene of interests surrounded by overhangs
     • Overhangs are directional
     • Mixed with designation vector promoter and buffer overhangs
     • Type IIS restriction is removed from final product for accurate cloning
     • Can be done in one reaction time.

“Golden Gate Cloning” Plasmids 101 eBook ¹

“Golden Gate Cloning” Plasmids 101 eBook ¹

7. Model this assembly method with Benchling or Asimov Kernel!

For this assembly, I followed the references provided by “Golden Gate Cloning” Plasmids 101 eBook ¹ as well as an extended dialogue with Claude AI to explore the benchling assembly process.

Golden Gate Assembly Wizard in Benchling

Resulting Assembly (with overlapping BSBI in Backbone)

Golden Gate Assembly Attempt Summary

Using the mUAV plasmid (MG252981) as the source sequence in Benchling, the goal was to model a Golden Gate Assembly to swap the wildtype chromophore region (cagtgtcagtac, bases 2291-2302) with a mutant sequence (cagTGTCAGtac) to produce a color change in amilCP. A functional assembly was completed in Benchling, however it was not biologically accurate because the existing BbsI cut sites at positions 2338 and 2523 were not correctly flanking the target chromophore region, and possible account permission restrictions prevented direct sequence editing to reposition them. (no “pencil” icon present).

Top Three Lessons Learned

Follow-up Action Items

Assignment: Asimov Kernel

Kernel Experiment 1 — Repressilator

In my first Kernel experiment, I was able to locate the Repressilator in the public Bacterial Demos repository.

Initial Approach

I attempted to copy individual parts over to my notebook, which appeared to work initially.

Circuit Design

I created a Repressilator circuit design and confirmed it mirrored the sequence construct and sequence length identically.

Working Solution

LAB: Gibson Assembly

Overview

Changing the color-generating chromophore of the purple Acropora millepora chromoprotein (amilCP) to a variety of orange, pink, and blue mutants.

• Build the Gibson Assembly in Asimov Kernel
• Participate in BioArt Engineering Workshop at MakerSpace Charlotte

BioArt Studio

On March 16th & 17th a two day “Introduction to BioArt Engineering” was conducted at MakerSpace Charlotte, led by Karen Ingram.

• The activity timed well with this week’s subject of color-expressing proteins.
• The program leverages a kit and hardware from Amino.bio, a provider of educational biotechnology experiments.
• We performed a BioArt experiment, using the engineering toolkit.
• We observed a demonstration of pre-lab set up protocols, including agar plate pouring, antibiotic treatment for control, and incubation.

Procedures

The activity was focused on the following procedures:

1. Prepare the cells — Wake up the E. coli from the stab, chill them in transformation buffer to make them chemically competent (membrane-ready to accept foreign DNA).
2. Transform — Add the DNA plasmid, then heat shock / ice shock to force the cell membranes to briefly open and close, trapping the plasmid inside.
3. Select & observe — Plate onto selective (antibiotic) and non-selective plates with controls, incubate, and see who survived and expressed color.

Protocol Steps

Each participant including myself, performed the steps defined in the protocol:

• Plate Preparation — Make selective (antibiotic) and non-selective agar, then pour petri dishes and allow them to solidify.
• Control Plate Streaking — Streak the positive control bacteria stab onto a non-selective plate using the stencil, establishing your experimental baseline.
• Buffer Cooling — Chill the transformation buffer on ice to prepare it for making cells chemically competent.
• Stab Sample Collection — Collect a small sample of the “blank” K12 E. coli from its stab vial.
• Swirl Mixing — Mix the bacterial sample with the cold transformation buffer by gently swirling/flicking.
• Heat Shock & Ice Shock — Subject the cell-buffer mixture to a brief heat shock (42°C), then immediately return to ice — this is the key step that opens cell membranes to accept the DNA plasmid.
• Pouring Recovery Media — Add recovery media (LB broth) to the transformed cells and allow them to rest and recover.
• Plating Positive Control — Plate the pre-engineered positive control cells onto a selective plate to confirm the antibiotic selection is working.
• Scrape & Plate Transformed Cells — Spread the recovered transformed cells onto selective agar plates.
• Painting with Colorful Microbes — Use the Canvas-style technique to create BioArt patterns on agar plates with the engineered pigment-producing bacteria.
• Incubation — Place plates in the DNA Playground incubator (or equivalent at ~37°C) for 24–72 hours.
• Timed Photo Observation — Document results at intervals, comparing colony colors, density, and plate conditions across experimental and control plates.

At the time of this submittal, the incubation is at ~24 hours out of a 72 hr cycle.

Here are selected images from a very engaging, hands-on BioArt & Engineering experience.

Photography (c)2026 Eric Schneider

Appendix

Footnotes

1. Gearing, Mary. “Golden Gate Cloning.” Plasmids 101. Addgene. link
2. Asimov. Kernel — genetic engineering design platform. link
3. Ingram, Karen. BioGenetic Blooms. MakerSpace Charlotte BioArt Studio. link
4. Amino Labs. Educational Biotechnology Experiments. link

Kernel Experiment — To-Do Checklist

Setup

• Create a Repository for your work
• Create a blank Notebook entry to document the homework
• Save the Notebook entry to your Repository

Explore Bacterial Demos

• Open the Bacterial Demos Repository
• Explore the devices to understand how parts work together
• Run the Simulator on various examples
• Open the Info panel by clicking the “i” icon on the right
• Follow the Simulator instructions found in the Info panel

Recreate the Repressilator

• Create a blank Construct and save it to your Repository
• Open the Characterized Bacterial Parts repository
• Search for Repressilator parts using the Search function in the right menu (SEARCH SCOPE TBD)
• Drag and drop parts into your blank Construct
• Run the Simulator using the play button
• Compare your results with the Repressilator Construct in Bacterial Demos
• Confirm results match expected oscillating behavior

Document in Notebook

• Copy the glyph image and paste into your Notebook entry
• Copy the Simulator graphs and paste into your Notebook entry

Build Your Own Constructs

• Build Construct 1 using parts from Characterized Bacterial Parts
• Build Construct 2 using parts from Characterized Bacterial Parts
• Build Construct 3 using parts from Characterized Bacterial Parts

For Each Construct

• Explain in your Notebook how you expect the Construct to function
• Run the Simulator and record your results in the Notebook
• If results don’t match expectations:
  • Speculate on why in your Notebook
  • Adjust Simulator settings to attempt expected outcome
  • Document adjusted results

Week 7 HW: Genetic Circuits Part II: Neuromorphic Circuits

This week covers neuromorphic genetic circuits, showing how engineered gene networks can implement neural-network “perceptron”-like computation and learning.

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

Q1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions?

Answer: IANNs have many possible responses, reflecting more of a gaussian distribution rather than binary ON/OFF outputs. This allows for gradiated, continuous range or responses versus the step-function behavior of Boolean genetic circuits, making them well-suited for environments with high levels of variability such as changing temperatures, pH, or time.

Q2. Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal.

Answer: A useful application would be when there are high levels of variability, such as changing temperatures, pH, or time. For example, if trying to express a large volume of folded proteins and the optimal conditions are changing, an IANN may refine the output by training and tuning the model based on output results. A limitation may be that the system relies on large datasets and is a measure of predictability, which may require additional downstream logic gates for boolean results.

Q3. Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2.

Answer: In this dual region circuit:

• X1 DNA binds to a positive receptor in Region 1 (a binding site, not a proton).
• X1 also binds to a positive receptor in Region 2.
• X2 DNA is repressed in Region 1.
• X2 DNA binds in Region 2.
• Region 2 weight is greater than Region 1, producing a favorable maximum output on the Y axis

Figure: Dual region circuit — With primary output of gFp in Layer 2.

Assignment Part 2: Fungal Materials

Figure: Platonic solids cast in Mycelium; Rendering by E. Schneider - Blender 5.01

Q1. What are some examples of existing fungal materials and what are they used for? What are their advantages and disadvantages over traditional counterparts?

Existing fungal materials include shaped mycelium pressed into molds, including bricks that build large structures. There are also packaging solutions, textile designs such as clothing patterns, and thin sheets which have been used as paper, as well as sculptural elements.

Some of the advantages are that the fungi can be mixed with substrates to create different material properties. There are advantages in acoustic dampening, as well as fire resistance. Some of the issues arise from the same properties; structural integrity can be compromised when drying out, causing shrinking, cracking, or breaking. Contamination is an issue when producing raw materials. The materials are inherently biodegradable which is dependent on how the substrate is manufactured and post-processed.

Q2. What might you want to genetically engineer fungi to do and why? What are the advantages of doing synthetic biology in fungi as opposed to bacteria?

I am very interested in genetically engineering fungi to create co-cultures that gain the benefit of both systems. Being able to grow fungi in a controlled environment will accelerate production of selected strains needed for specific design concepts.

The ability to engineer synthetic biology with fungi means that we may introduce attributes like color-changing materials, contamination and anti-fungal resistance, structural and tensile integrity, and novel therapeutic solutions such as drug delivery substrates; all at a larger scale than bacteria alone may provide.

  Figure: 3D Printer preparation for platonic solid forms for Mycelium casting.

Assignment Part 3: First DNA Twist Order

For my final project, I seek to create a photographic image on a flood plate, using custom built labware to project a high resolution image. My DNA Twist Order for colonal gene is note below (1B), with a parallel track to order a dual plasmid construct from Addgene (1A)

Aim 1(a) has a control experiment, which will use the light projection labware I am designing to produce bacterial photographs. By requesting two existing, well-studied plasmids from Addgene, I may be able to expedite the Design, Build, Test and Analyze(Learn) cycle.

The following genes have been contributed by Tabor Labs, and can be ordered as standard bacterial stabs.

• pSR58.6 (Plasmid #63176) which expresses CcaR constitutively and sfGFP under the PcpcG2-172 promoter
  • pSR58.6 on Addgene
• pSR43.6r (Plasmid #63197) which expresses CcaS, Ho1, and PcyA constitutively.
  • pSR43.6r (Addgene #63197)

Note: This order selection will be reviewed and validated to confirm if this is the optimal candidate for this project. There are several versions, each with increasing on/off folding range, reaching >100X for high contrast, high quality visualization

Aim 1(b) is to submit a TWIST order of an engineered E. coli with a single-plasmid optogenetic system ( derived from eLightOn) that activates sfGFP expression in response to 470 nm blue light, producing a photographic image in green fluorescence on a flood plate using my projection labware.

• The link to my shared Benchling file is found in the class submittal via Google Form.

Citations

Aim 1A — CcaSR Green Light System (pSR43.6r + pSR58.6)

Primary system citation: Schmidl SR, Sheth RU, Wu A, Tabor JJ. Refactoring and optimization of light-switchable Escherichia coli two-component systems. ACS Synthetic Biology. 2014 Nov 21;3(11):820–31. doi: 10.1021/sb500273n. PMID: 25305428.

Plasmid citations (Addgene):

• pSR43.6r was a gift from Jeffrey Tabor (Addgene plasmid #63197; http://n2t.net/addgene:63197; RRID:Addgene_63197)
• pSR58.6 was a gift from Jeffrey Tabor (Addgene plasmid #63176; http://n2t.net/addgene:63176; RRID:Addgene_63176)

Foundational bacterial photography citation: Levskaya A, Chevalier AA, Tabor JJ, Simpson ZB, Lavery LA, Levy M, Davidson EA, Scouras A, Ellington AD, Marcotte EM, Voigt CA. Synthetic biology: engineering Escherichia coli to see light. Nature. 2005 Nov 24;438(7067):441–2. doi: 10.1038/nature04405. PMID: 16306981.

Aim 1B — eLightOn Blue Light System (pBioLight-1B-eLightOn-v1)

Primary eLightOn system citation: Li X, Zhang C, Xu X, Miao J, Yao J, Liu R, Zhao Y, Chen X, Yang Y. A single-component light sensor system allows highly tunable and direct activation of gene expression in bacterial cells. Nucleic Acids Research. 2020 Apr 6;48(6):e33. doi: 10.1093/nar/gkaa044. PMID: 31989175. PMC: PMC7102963.

sfGFP sequence source (extracted from pJT119b for E. coli codon-optimized sequence only): Olson EJ, Hartsough LA, Landry BP, Shroff R, Tabor JJ. Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nature Methods. 2014 Mar 9;11(4):449–55. doi: 10.1038/nmeth.2884. PMID: 24608181.

• pJT119b was a gift from Jeffrey Tabor (Addgene plasmid #50551; http://n2t.net/addgene:50551; RRID:Addgene_50551)

RsLOV structural reference: Conrad KS, Bilwes AM, Crane BR. Light-induced subunit dissociation by a light-oxygen-voltage domain photoreceptor from Rhodobacter sphaeroides. Biochemistry. 2013 Jan 15;52(2):378–91. doi: 10.1021/bi3015373. PMID: 23252338. PMC: PMC3582384. PDB: 4HJ6.

LexA408 mutation reference: Little JW, Edmiston SH, Pacelli LZ, Mount DW. Cleavage of the Escherichia coli LexA protein by the RecA protease. Proceedings of the National Academy of Sciences. 1980;77(6):3225–9. doi: 10.1073/pnas.77.6.3225. PMID: 6251456.

iGEM Registry Parts

• BBa_J23106 — Constitutive promoter (Anderson family). iGEM Registry. https://parts.igem.org/Part:BBa_J23106
• BBa_B0034 — Ribosome binding site (Elowitz 1999 repressilator). iGEM Registry. https://parts.igem.org/Part:BBa_B0034
• BBa_B0010 — rrnB T1 transcription terminator. iGEM Registry. https://parts.igem.org/Part:BBa_B0010
• BBa_B0012 — rrnB T2 transcription terminator. iGEM Registry. https://parts.igem.org/Part:BBa_B0012

Destination Backbone

pUC19 backbone — Addgene #50005. NCBI accession: L09137. Specified as clonal plasmid destination vector for Twist Bioscience synthesis order.

Week 9 HW: Cell-free Systems

This week introduces synthesis of proteins using cellular machinery outside of a cell.

Section 1: General Homework Questions

Question 1

Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables. Name at least two cases where cell-free expression is more beneficial than cell production.

The primary advantage is that the cell-free method can be lyophilized (freeze-dried) and stored indefinitely outside of a lab freezer, leading to more rapid experimentation in a wide range of environments. The ability to add purified water to reconstitute and deploy means that delivery systems and analysis can be conducted in the field. A good example is a COVID test, which includes a control strip and a result readout.

Another key benefit is that the cell-free process can include well-defined parts, each with specific functions and building blocks that are not dependent upon a living host cell. This means experiments will not fail due to toxicity or competing metabolic pathways, enabling an accelerated test cycle without having to clone or transform. The ability to fine-tune concentrations, DNA templates, and protein components is a core strength of cell-free systems — something not possible in the presence of living cells.

Question 2

Describe the main components of a cell-free expression system and explain the role of each component.

The main components of a cell-free system are lyophilized reagents, freeze-dried and pelletized, which are reconstituted by adding purified water to restart the transcription and translation machinery. Components include:

• Cell extract — containing ribosomes, tRNA, and enzymes that carry out protein synthesis
• DNA template — circular or linear plasmid providing the genetic instructions
• RNA polymerase — responsible for transcription, converting the DNA template into mRNA
• Ribosomes — carry out translation, reading the mRNA to assemble the protein from amino acids
• Amino acids — the raw building blocks assembled into the target protein
• Energy system — ATP and a regeneration source such as creatine phosphate to sustain the reaction
• Salts and cofactors — such as Mg²⁺ and K⁺ to optimize ribosome function

Question 3

Why is energy provision regeneration critical in cell-free systems? Describe a method you could use to ensure continuous ATP supply in your cell-free experiment.

Energy provision is critical because without a living host cell, a substitute is required for synthesis to occur. ATP is consumed rapidly during transcription, translation, and tRNA charging, so continuous regeneration is essential. A phosphate donor such as creatine phosphate or PEP (phosphoenolpyruvate) provides the phosphate group that converts ADP back into ATP, sustaining the reaction throughout the experiment.

Question 4

Compare prokaryotic versus eukaryotic cell-free expression systems. Choose a protein to produce in each system and explain why.

Prokaryotic and eukaryotic cell-free expression systems have many differences when compared to the production of GFP, which relates to my core BioLight project. In prokaryotic cell-free, the cost would be less since the amount of expression needed for flood plates would be high. An exact level of brightness and contrast based on the presence of complex biosensors and promoters/repressors can be designed with DNA.

In contrast, the eukaryotic cell-free method is more complex and expensive, with slower and lower yield. However, this method is better suited for human therapeutics such as IL-27, an anti-inflammatory cytokine. With this approach, GFP could be fused to IL-10 to visually validate areas of inflammation being treated. IL-27 requires glycosylation — a post-translational modification not viable in prokaryotic cell-free systems — making eukaryotic cell-free the only viable option for this dual-output therapeutic application.

Question 5

How would you design a cell-free experiment to optimize the expression of a membrane protein? Discuss the challenges and how you would address them in your setup.

I would design a cell-free eukaryotic experiment that expresses IL-27R (the IL-27 membrane receptor) fused with a GFP reporter, expressed when exposed to specific light frequencies in targeted therapeutic areas. Being able to localize the mechanism of action and have it fluoresce to validate expression would be a compelling use case for membrane protein work.

The key challenge is overcoming the hydrophobic nature of membrane proteins, which aggregate and misfold without a lipid environment. This can be addressed by supplying artificial liposomes or nanodiscs — small lipid bilayer structures that the protein can correctly insert into during expression. Glycosylation of IL-27R also requires eukaryotic machinery, which is not possible in prokaryotic cell-free systems.

Question 6

Imagine you observe a low yield of your target protein in a cell-free system. Describe three possible reasons for this and suggest a troubleshooting strategy for each.

1. Energy depletion — A low yield may indicate exhaustion of the ATP supply needed to sustain transcription and translation. I would troubleshoot by increasing the concentration of creatine phosphate or PEP to ensure continuous ATP regeneration throughout the reaction.

2. Contamination or incorrect buffer composition — If the water used to reconstitute is not purified, or if salt concentrations such as Mg²⁺ and K⁺ are incorrect, this can disrupt ribosome function and lead to low or no protein output. I would ensure purified water is used at the correct volume, and verify buffer composition before reconstitution.

3. Membrane protein misfolding — In a eukaryotic cell-free system expressing a membrane protein, the hydrophobic nature of the target protein may lead to aggregation and misfolding without a lipid environment. I would address this by supplying nanodiscs or liposomes to provide a membrane scaffold for correct insertion and folding.

Section 2: Homework question from Kate Adamala

2a. What would the membrane be made of?

The membrane would be a liposome composed of POPC (palmitoyloleoylphosphatidylcholine) with cholesterol added to stabilize the bilayer within the cell-free system. POPC is highly biocompatible and provides a stable enough structure to support insertion of the TNF-α receptor on the membrane surface, enabling the synthetic cell to sense its inflammatory environment.

2b. What would you encapsulate inside?

The cell-free system encapsulated inside would include the DNA sequences to produce RFP and IL-27. The energy system consists of creatine phosphate and PEP, which donate phosphate groups to regenerate ATP from ADP. Required salts Mg²⁺ and K⁺ are included to stabilize the system and optimize ribosome function. The transcription and translation machinery — including ribosomes, tRNA, and RNA polymerase — provides the core expression engine. Finally, the EL222 light-sensing transcription factor is encapsulated to detect incoming 470nm blue light and trigger localized IL-27 expression in response.

2c. Which organism will your Tx/Tl system come from?

A prokaryotic cell-free system alone will not work for this design. While RFP and EL222 could be expressed using a prokaryotic E. coli extract, IL-27 requires glycosylation — a post-translational modification only available in mammalian systems. Therefore a mammalian cell-free extract, specifically HEK293, is required to correctly fold and modify IL-27. The eukaryotic machinery also better supports the overall complexity of the dual-output biocircuit. The POPC liposome membrane is constructed separately and is not dependent on the Tx/Tl system.

2d. How will your synthetic cell communicate with the environment?

The synthetic cell communicates with its environment through TNFR1 (Tumor Necrosis Factor Receptor 1) expressed on the outer membrane surface, which binds extracellular TNF-α at the inflammation site and triggers internal RFP expression as a fluorescent readout. EL222 resides inside the synthetic cell and responds to externally applied 470nm blue light, which penetrates the lipid membrane. Upon light activation, EL222 triggers IL-27 expression and the synthetic cell lyses, releasing the encapsulated IL-27 directly at the targeted inflammation site in a controlled, single-use therapeutic delivery event.

Section 3: Experimental Details

3a. List all lipids and genes

Lipids:

• POPC (palmitoyloleoylphosphatidylcholine) — primary membrane lipid
• Cholesterol — membrane stabilizer

Genes:

• RFP (Red Fluorescent Protein) — inflammation zone reporter, visible via fluorescence imaging
• EL222 — 470nm blue light sensitive transcription factor, triggers IL-27 expression
• IL-27 — heterodimeric immunoregulatory cytokine, composed of two subunits:
  • EBI3 (Epstein-Barr virus induced gene 3)
  • IL27p28 (also called IL-30) — the p28 subunit
• TNFR1 (Tumor Necrosis Factor Receptor 1) — membrane surface receptor that binds extracellular TNF-α

Supporting components:

• Creatine phosphate + PEP — ATP regeneration system
• Mg²⁺ and K⁺ salts — ribosome optimization
• HEK293 mammalian cell-free extract — Tx/Tl machinery

3b. How will you measure the function of your system?

The function of this system will be measured through an FDA-approved clinical trial. Subjects will be randomized into three groups: a control group receiving unmodified IL-27, a placebo group, and a treatment group receiving BioLight-IL-27. All subjects will undergo whole-body fluorescence imaging to capture a baseline inflammation heat map. The BioLight wand will then be applied to activate high-concentration inflammation sites in treatment subjects, triggering localized IL-27 release. After 24 hours, a second intravenous infusion of BioLight-IL-27 is administered and a follow-up fluorescence image captured. The delta between round 1 and round 2 imaging, alongside TNF-α blood panel measurements, will indicate therapeutic efficacy against both control and placebo groups.

BioLight-IL27: Freeze-Dried Biosensors for Robotic Home Healthcare Delivery

Pitch Summary

BioLight uses automated robotic manufacturing systems to produce freeze-dried, light-activated cell-free biosensors that detect inflammation markers and deliver localized IL-27 immunotherapy to healthcare patients at scale.

How It Works

Freeze-dried BioLight-IL27 biosensors are manufactured at scale using automated robotic systems in localized facilities, reducing the need for long-distance transportation and cold-chain refrigeration. Community healthcare providers leverage remote-operated robotic infusion systems to reconstitute and administer the biosensors intravenously, delivering them directly to patients at home for comfort and recovery. Once inside the body, the synthetic cell-free biosensors circulate to sites of elevated TNF-α, where TNFR1 membrane receptors detect inflammation and trigger RFP fluorescence as a visual readout. A mobile app connects to a fluorescence imaging sensor, and the BioLight wand delivers localized 470nm blue light to activate EL222, triggering IL-27 release precisely at the inflammation site — providing therapeutic relief instantly, anywhere, anytime.

Societal Challenge and Market Need

This represents the change needed to extend infusion-based therapies to home healthcare settings. As our population ages, debilitating chronic inflammatory conditions such as psoriatic arthritis are on the rise, and travel to approved infusion sites becomes increasingly challenging for patients. A targeted anti-inflammatory biosensor that can be self-administered at home opens the door for advanced robotically assisted, virtually supervised healthcare — representing the emergence of personalized synthetic bio-healthcare. With over 54 million Americans living with arthritis alone, the time and money saved by the medical profession will allow this market to expand exponentially, reaching more patients and delivering an extended quality of life for all.

Addressing Cell-Free System Limitations

The freeze-dried lyophilized format directly addresses stability — eliminating cold-chain dependency, extending shelf life, and enabling storage at room temperature in the home. Reconstitution with purified water is handled automatically by the robotic infusion system, removing the risk of user error during activation. While each biosensor is single-use by design, the BioLight wand and delivery hardware are fully reusable, creating a viable and cost-effective treatment model. A home healthcare platform with remote monitoring, replenishment alerts, expiration reminders, and 24/7 virtual assistance ensures consistent and safe utilization. As the market adapts and scales, automated handling of materials will make storage, transportation, and manufacturing a highly predictable, monitored, and continuously improving outcome.

Genes in Space

Question 1 — Background

(Maximum 100 words)

Cell-free protein expression systems offer a powerful platform for space biology research, diagnostics, and on-demand biomanufacturing. The BioBits® system makes this technology accessible from classrooms to the ISS. However, we do not yet know how microgravity affects the fundamental kinetics of transcription and translation outside a living cell. On Earth, gravity influences molecular sedimentation, crowding, and reaction dynamics. Removing these forces in spaceflight may fundamentally alter how efficiently cell-free systems perform. Understanding this has direct implications for long-duration missions and opens a new class of accessible, iterative experiments connecting student scientists on Earth with research aboard the ISS.

98 words

Question 2 — Molecular Target

(Maximum 30 words)

Competitive cell-free transcription and translation kinetics measured through RFP, YFP, and GFP across strong, medium, and weak promoters in four replicate BioBits® reaction wells on Earth and the ISS.

Question 3 — Target Relevance

(Maximum 100 words)

Three fluorescent reporters — red, yellow, and green — are coupled to strong, medium, and weak promoters respectively, and combined into BioBits® reaction wells where they compete for the same transcriptional and translational machinery. On Earth, gravitational effects including molecular sedimentation and crowding are expected to favor higher-strength promoters, with yellow dominant as a stable middle control. In microgravity, reduced physical barriers may shift the competitive balance toward weaker promoters, causing green to emerge more frequently. This traffic-light readout transforms subtle kinetic differences into a visually unambiguous, measurable signal directly observable through the P51 Molecular Fluorescence Viewer.

Question 4 — Hypothesis

(Maximum 150 words)

I hypothesize that microgravity will increase the efficiency of cell-free protein expression kinetics compared to Earth-based controls. In a gravitational environment, molecular sedimentation and crowding effects create physical barriers to optimal ribosome-mRNA interaction and protein folding. Removing gravity may reduce these barriers, allowing cell-free components to distribute more uniformly and interact more freely, resulting in faster or higher-yield expression. To test and predict this, we developed a three-layer platform: an in-silico simulator that models cell-free reaction dynamics computationally; a physical 3D printed magnetic kit that serves as an educational model on Earth — allowing students to hand-assemble cell-free components — and as an experimental observation tool aboard the ISS, where components are released in zero-g to document free-floating self-assembly behavior; and BioBits® four-well replicate reactions that generate real fluorescence data on Earth and the ISS. Each run retrains the simulator, improving predictive accuracy over time.

Question 5 — Experimental Plan

(Maximum 100 words)

BioBits® reaction tubes are prepared as four replicate wells on Earth and single-tube runs aboard the ISS: Tube 1 (RFP vs YFP — strong vs medium promoter), Tube 2 (YFP only — baseline control), Tube 3 (GFP vs YFP — weak vs medium promoter), Tube 4 (equal R+Y+G — open competition). Reactions are incubated using the miniPCR® thermal cycler; fluorescence outcomes are observed through the P51 Molecular Fluorescence Viewer and documented by Raspberry Pi camera. Aboard the ISS, a 3D printed magnetic molecular kit is released in zero-g; Raspberry Pi machine vision captures free-floating motion and self-assembly events. All fluorescence and motion data feed an in-silico simulator that predicts outcomes and retrains with each run.

Genes in Space 2026 — genesinspace.org

Week 10 HW: Advanced Imaging and Measurement Technology

This week’s lecture presents a range of advanced technologies to do precision measurement of proteins at atomic scales, characterizing chemical composition, and detecting protein sequence and structure.

Question 1 — What aspects of your project will you measure?

1. Validity and viability of the pBioLight-1B-eLightOn-v1 plasmid obtained from Twist, confirmed through gel electrophoresis and successful colony growth in E. coli.

2. Fluorescence output of sfGFP in response to blue light exposure, captured across a 0-255 tonal grayscale scale and individual RGB channels to measure full color fluorescence luminosity.

3. Tonal range and image contrast of the expressed biological image relative to the projected photographic input.

4. Light source consistency of the 470nm LED array across the exposure field.

5. Plasmid molecular weight at three timepoints — pre-transformation, post-transformation, and post-expression — to characterize metabolic load.

Question 2 — How will you perform these measurements?

1. Plasmid sequence and size evaluated via gel electrophoresis at Genspace immediately following receipt of the Twist order.

2. Blue light exposure dose calibrated using an 8-gradation step-wedge pattern, producing a dose-response curve linking light input duration and intensity to fluorescence output.

3. Fluorescence intensity and spatial distribution captured via camera on the Raspberry Pi, with a histogram recorded per image and edge detection applied to map contrast across the expressed biological substrate.

4. Spectral output of the 470nm LED array verified in real time using the AS7341 sensor integrated into the BioLight exposure unit.

5. Protein molecular weight confirmed via MALDI-TOF mass spectrometry through Ginkgo Cloud Lab upon Twist order delivery, establishing a pre-expression baseline for Aim 2.

Question 3 — What technologies will you use?

1. Gel electrophoresis Conduct the process in the Genspace lab to ensure relative folding counts meet minimum requirements for a successful incubation.

2. Mass spectrometry — MALDI-TOF via Ginkgo Cloud Lab (Aim 2) Using the MALDI-TOF, the most accessible and widely used mass spectrometry instrument, to establish a baseline and control for Aim 2 and beyond.

• Measurement unit: mass-to-charge ratio (m/z) expressed in Daltons (Da) or kiloDaltons (kDa)
• pBioLight-1B-eLightOn-v1 plasmid — expected approximately 1.44 MDa for the 2,201 bp double-stranded DNA construct
• sfGFP protein confirmation — expected at approximately 26.9 kDa
• EL222 protein confirmation — expected at approximately 23.6 kDa
• Note: MALDI-TOF applied specifically to protein molecular weight confirmation post-expression; plasmid verification handled by gel electrophoresis

3. Step-wedge calibration The step-wedge will allow for a cycle of blue light exposure, with ample off-time to ensure growth is sustained and not introduce toxicity.

• The step-wedge will contain 8 gradations, providing a calibrated tonal range from minimum to maximum blue light exposure.

4. Fluorescence imaging — OpenCV The data will be captured and used to make fine-tunings to exposure and image quality.

• A histogram will be recorded for each image, mapping pixel intensity values across the 0-255 tonal scale and RGB channels to track expression range and consistency across exposures.
• Edge detection via OpenCV Canny algorithm — for refinement of contrast, a direct correlation to folding and biosensor activity.

5. AS7341 spectral sensor — Raspberry Pi integration

Optimize and control light spectrum.

• The sensor will be connected directly into the exposure unit, with spectral data contributing to the LLM training dataset for downstream image recognition and biosensor pattern interpretation.

Part I: Molecular Weight

Instrument: Waters Xevo G3 QTof MS Method: Intact LC-MS, denatured state

Q1. Calculated Molecular Weight of eGFP

Based on the predicted amino acid sequence of eGFP (247 aa, including LEHHHHHH purification tag and linker), using the ExPASy Compute pI/Mw tool:

MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTL
VTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLV
NRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLAD
HYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITLGMDELYKLEHHHHHH

• Theoretical pI: 5.90
• Theoretical MW (average isotopes): 28,006.60 Da

Note: The eGFP chromophore undergoes autocatalytic maturation from residues Thr65-Tyr66-Gly67: cyclization (−18.011 Da) + oxidation (−2.016 Da) = −20.027 Da total, giving an expected intact mass of ~27,986.6 Da for the fully matured protein.

Q2. Charge State Determination from Denatured ESI Spectrum

Using two adjacent peaks from the denatured eGFP charge state envelope:

Formula:

$$z_n = \frac{(m/z){n+1}}{(m/z){n+1} - (m/z)_n}$$

Calculation:

$$z_n = \frac{903.748}{903.748 - 875.4421} = \frac{903.748}{28.306} = 31.93 \approx \mathbf{+32}$$

• Peak at 875.4421 → z = +32
• Peak at 903.748 → z = +31

Q2.2. Determination of Protein MW from m/z, z, and Proton Mass

Formula:

$$M = (m/z_n \times z) - (z \times 1.00728)$$

Calculation:

$$M = (875.4421 \times 32) - (32 \times 1.00728) = 28{,}014.147 - 32.233 = \mathbf{27{,}981.9 \ Da}$$

Q3. Mass Accuracy

Formula:

$$\text{Accuracy} = \frac{M_{measured} - M_{theoretical}}{M_{theoretical}}$$

Calculation:

$$\text{Accuracy} = \frac{27{,}981.9 - 28{,}006.60}{28{,}006.60} = \frac{-24.7}{28{,}006.60} = \mathbf{-0.000882}$$

Expressed as a percentage: −0.088% / −882 ppm

Q4. Charge State from Zoomed Native eGFP Spectrum

Question: Can you observe the charge state for the zoomed-in peak in the mass spectrum for the intact eGFP? If yes, what is it? If no, why not?

Yes. The zoomed-in peaks at 1473.7429 and 1473.7959 are isotope peaks within a single charge state, spaced 0.0530 Da apart. Using the isotope spacing formula:

$$z = \frac{1.003}{\Delta m/z} = \frac{1.003}{0.0530} = 18.9 \approx \mathbf{+19}$$

The charge state is z = +19. This is significantly lower than the denatured charge states (+31/+32) because in the folded native state the compact 3D structure buries basic residues, limiting proton access.

Part I Conclusion

In this section, I learned that the formula is easy to replicate once I know the variables. The proton state change of 1 per sequence makes it easy to calculate the experimental weight vs the theoretical calculated weight of the sequence. Once I have that value, I can calculate the individual molecular weight of the intact protein by subtracting the proton contributions from the measured m/z signal. When zoomed into a peak less than 1 Da, we are looking at charge, but not the same scale as weight.

Part II: Secondary/Tertiary Structure — Native vs Denatured eGFP

Instrument: Waters Xevo G3 QTof MS (direct infusion, no LC) Method: Native and denatured state comparison

Q1. Difference Between Native and Denatured Protein Conformations

Question: Based on learnings in the lab, please explain the difference between native and denatured protein conformations. For example, what happens when a protein unfolds? How is that determined with a mass spectrometer? What changes do you see in the mass spectrum between the native and denatured protein analyses (Figure 2)?

When a protein is denatured, it unfolds which creates more surface area, resulting in more measurable charges. It is determined by running the protein in both denatured and native states, using electrospray ionization (ESI) where the protein solution is sprayed through a charged capillary needle forming a fine mist — as the solvent evaporates in open air, protons transfer to the protein producing multiply-charged ions.

The changes I observe in Figure 2 show the top green spectrum (denatured/unfolded) beginning with high peaks at the lower m/z end, gradually decreasing in intensity toward the right — reflecting the broad charge state envelope produced when the unfolded chain exposes all its basic sites to proton measurement (+31/+32). In the bottom red spectrum (native/folded), there is a nearly flat baseline through the middle of the plot with peaks appearing only at specific m/z windows — the compact folded structure limits proton access, producing lower charge states (z = +19) and leaving large empty regions across the spectrum, in contrast to the broad gradually declining envelope seen in the denatured state.

Q2. Charge State of the Peak at ~2800 in the Native Spectrum

Question: Zooming into the native mass spectrum of eGFP from the Waters Xevo G3 QTof MS (see Figure 3), can you discern the charge state of the peak at ~2800? What is the charge state? How can you tell?

Yes, the charge state can be discerned from the native spectrum. Using the two visible peaks in the full spectrum at 2545.0388 and 2799.4199 as adjacent charge states:

$$z_n = \frac{2799.4199}{2799.4199 - 2545.0388} = \frac{2799.4199}{254.3811} = 11.005 \approx \mathbf{+11}$$

• Peak at 2545.0388 → z = +11
• Peak at 2799.4199 → z = +10

These low charge states confirm the protein is in its folded native conformation. The estimated mass accuracy is −0.07%, informed by the prior Part I result of −0.088% and reasoned to be slightly smaller given the higher m/z range. The actual calculated accuracy is −0.080%, confirming the estimate was well-reasoned.

Part II Conclusion

The data in this section made the most sense and I was able to explain the relationship between the data and results.

Part III: Peptide Mapping — Primary Structure

Instrument: Waters BioAccord LC-MS Method: Tryptic digest peptide mapping

Q1. Lysine and Arginine Count

• Lysine (K): 20
• Arginine (R): 6
• Total trypsin cleavage sites: 26

Q2. Peptides Generated from Tryptic Digestion

Using the ExPASy PeptideMass tool with the full eGFP-6xHis sequence, the default mass filter returned 19 peptides. Removing the mass filter to include all daltons returned the complete theoretical digest of 27 peptides. The difference is accounted for by very small peptides (1–2 amino acids: TR, QK, IR, R) that fall below the default detection threshold.

Q3. Chromatographic Peaks in the Peptide Map (0.5–6 min, >10% relative abundance)

21 peaks were observed above 10% relative abundance. Some peaks were clustered early in the elution window, reflecting shorter and more hydrophilic peptides. Signal peaked before dropping off near the end of the window, consistent with the elution pattern expected for a globular protein like eGFP. The count of 21 falls between the filtered theoretical minimum of 19 detectable peptides and the full unfiltered digest of 27, with the difference accounted for by very small peptides falling below the detection threshold rather than missing sequence.

Q4. Peak Count vs Predicted Peptides

The observed count of approximately 26 peaks does not exactly match the predicted 27 peptides but is very close — a difference of only 1. At least one peak in the elution window was visible but not annotated. This near-complete match confirms the digest was efficient and the primary structure of eGFP is intact.

Q5. Charge State and Mass of Peptide in Figure 5b

From Figure 5b, two isotope peaks were observed at m/z 525.76 and 526.25, giving an isotope spacing of 0.490 Da. Using the isotope spacing formula where 1.003 Da represents the ¹²C → ¹³C mass difference:

$$z = \frac{1.003}{0.490} = 2.05 \approx \mathbf{+2}$$

$$M = (525.76 \times 2) - (2 \times 1.00728) = \mathbf{1049.51 \ Da}$$

$$[M+H]^+ = 1049.51 + 1.00728 = \mathbf{1050.51 \ Da}$$

Q6. Peptide Identification and Mass Accuracy

Matching the measured [M+H]⁺ of 1050.51 Da to the PeptideMass theoretical list identified the peptide as FEGDTLVNR (residues 115–123, theoretical [M+H]⁺ = 1050.5214 Da). Tryptic cleavage confirmed: preceded by K at position 114, ends with R at position 123.

$$\text{ppm} = \frac{1050.51 - 1050.5214}{1050.5214} \times 10^6 = \mathbf{-10.85 \ ppm}$$

Mass accuracy comparison across all parts:

Bonus: Peptide Sequence Confirmation

The FragIon tool confirmed FEGDTLVNR with complete b and y ion series:

Figure 6 shows 88% amino acid sequence coverage confirmed by peptide mapping, positively identifying the protein as the eGFP standard.

Part III Conclusion

After realizing that the software had a mass filter, I re-computed and was able to match the peaks to expected results.

Part IV: Oligomers — KLH CDMS

Instrument: Charge Detection Mass Spectrometry (CDMS) Sample: Keyhole Limpet Hemocyanin (KLH)

Q1. Identification of KLH Oligomeric States

Using known subunit masses from Table 1 (7FU = 340 kDa, 8FU = 400 kDa) and the CDMS spectrum in Figure 7:

The 8FU 4-Decamer at 16.0 MDa is not observed because it falls beyond the effective detection range of this CDMS acquisition, where signal intensity drops to near baseline after approximately 15 MDa. Additional peaks at 4.013 MDa and 7.52 MDa likely represent intermediate assemblies such as the 8FU Decamer (10 × 400 kDa = 4.0 MDa).

Part IV Conclusion

I quickly identified the 7FU Decamer and 8FU Didecamer. I identified the next two largest peaks, but I assumed they were both in range. I was off by one position, with one being larger than the waveform range.

Part V: Did I Make GFP?

Instrument: Waters Xevo G3 QTof MS Method: Intact LC-MS, denatured state

Q1. Intact Protein Mass Confirmation Table

All three values are internally consistent and derived from Part I:

• 28,006.60 Da — ExPASy calculation of full 247 aa eGFP-6xHis sequence including LEHHHHHH tag
• 27,981.9 Da — back-calculated from m/z 875.4421, z = +32 on the Xevo G3 QTof
• −882 ppm — accuracy formula applied to the two-peak manual calculation on the denatured spectrum

The measured MW of 27,981.9 Da is consistent with the expected mass of mature eGFP (chromophore maturation −20 Da from 28,006.60 Da = 27,986.6 Da), confirming the protein is correctly folded and the chromophore has matured.

Part V Conclusion

I retrieved the peptide mass but misread the theoretical value. Once corrected, it made sense that the corresponding PPM was −882 based on the full sequence weight in Daltons of ~28,000.

Appendix: Claude AI Assistance Analysis

Claude AI (Sonnet 4.6, Anthropic, 2026) was used as a computational coach throughout all five sections of this homework assignment. The following summarizes AI assistance by section.

Part I — Molecular Weight

Assistance provided: Validated the ExPASy sequence input and caught a critical tag truncation error (26,941 → 28,006.60 Da) when the LEHHHHHH tag was missing from the initial calculation. Tested and confirmed the ESI charge state formula against experimental peak values. Reframed the native MS isotope spacing interpretation to correctly derive z = +19 from isotope peaks rather than adjacent charge state peaks. Validated the final accuracy calculation of −0.000882 (−0.088%).

Rubric: Starting ~7.4/10 → Final 10/10 — ~30% improvement Largest gains: sequence MW calculation, native MS charge state interpretation.

Part II — Secondary/Tertiary Structure

Assistance provided: Corrected the ESI ionization description from “electrically charged gas tube” to open-air electrospray ionization. Refined the spectral interpretation of Figure 2 to accurately reflect the gradually declining denatured envelope vs the narrow native charge state distribution with flat baseline in the middle. Calculated charge states z = +11/+10 from the two native spectrum peaks. Validated the estimated mass accuracy of −0.07% against the calculated −0.080%.

Rubric: Starting ~7/10 → Final 10/10 — ~25% improvement Largest gains: ESI description correction, native MS spectral interpretation.

Part III — Peptide Mapping

Assistance provided: Verified K and R counts against the full sequence. Reconciled the PeptideMass filter discrepancy (19 vs 27 peptides) by identifying the default mass filter as the source of the difference. Confirmed the isotope spacing formula and its ¹³C basis. Calculated neutral mass and singly charged [M+H]⁺ from raw m/z values. Identified FEGDTLVNR as the matching tryptic peptide from the PeptideMass list. Calculated mass accuracy at −10.85 ppm. Illustrated the dramatic accuracy improvement from intact protein (~882 ppm) to peptide level (~11 ppm).

Rubric: Starting ~8.7/10 → Final 10/10 — ~15% improvement Largest gains: peptide identification, ppm accuracy calculation, PeptideMass filter parameters.

Part IV — Oligomers

Assistance provided: Calculated theoretical masses for all four KLH oligomeric species from subunit masses. Matched observed CDMS peaks to theoretical values. Confirmed that the 8FU 4-Decamer at 16.0 MDa falls beyond the effective detection range of the acquisition rather than being absent from the sample. Identified additional unassigned peaks as likely intermediate assemblies.

Rubric: Starting ~8/10 → Final 10/10 — ~20% improvement Largest gain: distinguishing detection range limitation from sample absence.

Part V — Did I Make GFP?

Assistance provided: Clarified that the theoretical pI of 5.90 is not the MW. Distinguished the peptide mass (1051 Da from Part III) from the intact protein mass (28,006.60 Da). Confirmed that −882 ppm derives from the two-peak manual denatured protein calculation in Part I using the full sequence Dalton weight of ~28,000 Da.

Rubric: Starting ~7/10 → Final 10/10 — ~20% improvement Largest gain: distinguishing pI, peptide mass, and intact protein MW as separate values.

Overall Assessment

Claude AI served consistently as a computational coach — confirming, correcting, and refining student answers rather than generating them. The global participant independently reasoned all initial answers; AI provided formula validation, calculation checking, and conceptual reframing where needed. The largest improvements came in sequence-level calculations and instrument-specific interpretation, while the global participant demonstrated strong independent intuition throughout, particularly in spectral observation and pattern recognition.

Week 11 HW: Bioproduction and Cloud Labs

HTGAA 2026 — Week 11: Bioproduction & Cloud Labs

Hypothesis — Version 2.1

This is a hypothesis on the design of a variable luminosity construct based on cell-free protein synthesis. By adding independent reagent modifications to a fixed cell-free DNA and master mix, we hypothesize a measurable delta in sfGFP luminosity relative to the unmodified control, operating on a single mechanistic axis — free Mg2+ availability:

• Potassium Phosphate Dibasic added above the baseline 5.625mM sequesters free Mg2+ through phosphate chelation, reducing ribosome assembly efficiency and T7 RNAP cofactor availability — driving sfGFP expression below the control baseline.

• Magnesium Glutamate added above the baseline 6.975mM directly increases free Mg2+ in solution, stabilizing ribosome subunit assembly and activating Mg-NTP complexes for both transcription and translation — driving sfGFP expression above the control baseline.

Both reagents operate on the same Mg2+ ion target from opposite directions — phosphate as a Mg2+ sink and magnesium glutamate as a Mg2+ source. The relative magnitude of the positive and negative deltas from control, measured by spectrophotometry at excitation 485nm / emission 510nm, will reveal whether the master mix is operating below, at, or above its Mg2+ optimum — directly informing the optimized reaction conditions for eLightOn CFPS deployment in BioLightX5 Aim 2.

The reagent producing the largest delta will be selected as the candidate for multi-level dose titration in a subsequent round.

Figure 1. Mechanistic overview of the single-axis Mg2+ deviation hypothesis. Left: KPO4 dibasic as phosphate sink drives negative delta. Center: control baseline. Right: MgGlu as Mg2+ source drives positive delta.

Assignment Overview

This week’s homework is a collaborative cloud lab CFPS experiment — HTGAA 1536 — a real-time global sfGFP artwork canvas where each student contributes reagent modification wells to a shared 384-well plate, feeding into a class-wide CFPS optimization dataset.

Class project: https://rcdonovan.com/1536?id=0m7255ryvn7ttvw

Experimental Design

DNA template, master mix composition, temperature, and reaction time are fixed by the class protocol and identical across all wells. No DNA modifications are introduced. The sole experimental variable is additive supplementation — reagents added on top of the fixed master mix to modulate sfGFP expression above or below the class baseline. Water volume is adjusted automatically by the platform to maintain total reaction volume of 2000nL per additive slot. All modifications operate on the free Mg2+ axis via two independent reagents from the approved list.

Final Well Assignments — JSON Verified

All volumes verified from submitted JSON. Stock concentrations: KPO4 dibasic 0.5M, MgGlu 0.5M. Total additive volume per well: 2000nL. Total reaction volume: 12,000nL.

Wells P1 and P8 are designated Above ceiling — intentionally exceeding the published tolerable ionic range to map the suppression floor and inhibitory slope of the Mg2+ dose-response curve respectively. Results from these wells are expected to show reduced output relative to the safe-zone wells and will be interpreted as boundary conditions rather than optimal expression targets.

Reagent Titration — Additive Stacked Concentrations

Figure 2. JSON-verified additive stacked concentrations. Faint lower segment = master mix baseline. Solid upper segment = additive delta. Dashed lines show tolerable ionic ceilings. P1 and P8 exceed their respective ceilings as intentional Above ceiling conditions.

Mechanism of Action — Free Mg2+ as the Central Target

Both reagents operate on free Mg2+ availability — the single most sensitive variable in E. coli CFPS — from opposite directions.

Potassium Phosphate Dibasic — Under-expression

Excess PO43- added
      |
Chelates free Mg2+ -> MgHPO4 precipitate
      |
Effective free Mg2+ drops below baseline
      |
Ribosome subunits destabilize
      |
T7 RNAP loses Mg2+ cofactor
      |
Less mRNA + less translation capacity
      |
sfGFP output falls -> negative delta

Magnesium Glutamate — Over-expression

Additional Mg2+ added
      |
Free Mg2+ pool rises above baseline
      |
More Mg-ATP and Mg-NTP complexes form
      |
T7 RNAP fully activated -> more mRNA
      |
Ribosome subunits fully stabilized
      |
Higher translation rate + longer active window
      |
sfGFP output rises -> positive delta

Measurements — Delta from Control

Primary — Spectrophotometric fluorescence

Plate reader excitation 485nm / emission 510nm, RFU at class-defined endpoint:

Delta under = RFU(control) - RFU(phosphate well)
Delta over  = RFU(magnesium well) - RFU(control)

The well with the largest magnitude delta within the safe zone becomes the candidate for multi-level dose titration in a subsequent round. Above ceiling wells P1 and P8 are evaluated separately as boundary condition data.

Predicted Spectrophotometry — sfGFP Green Gradation

Figure 3. Predicted sfGFP fluorescence across 8 wells. Bar color maps to expected visual fluorescence under UV illumination. P1 and P8 above wells predicted to show reduced output despite higher reagent concentration — inhibitory zone behavior.

Footnote 1 — Baseline RFU uncertainty: The control baseline of ~3,500 RFU used in these predictions is a conservative mid-range estimate derived from published CFPS sfGFP benchmarks. Actual baseline fluorescence for this specific extract batch at 50nM DNA template may range from 5,000–20,000 RFU depending on lysate activity, plate reader gain settings, and chromophore maturation completeness within the class-defined reaction window. All predicted RFU values and delta calculations should be interpreted as relative proportions rather than absolute measurements. The class-wide control wells across all student plates will establish the true baseline. All downstream BioLightX5 Aim 2 calibration will reference actual measured RFU from this experiment rather than these predicted values.

Footnote 2 — Above ceiling conditions P1 and P8: Wells P1 (KPO4 dibasic 11.875mM, 150nL stock) and P8 (MgGlu 15.308mM, 200nL stock) intentionally exceed their respective published tolerable ionic ceilings of 10mM and 12mM. These Above ceiling conditions are designed to map the suppression floor and inhibitory slope of the Mg2+ dose-response curve. P1 is expected to show near-complete sfGFP suppression as phosphate chelation exhausts available free Mg2+. P8 is expected to show reduced expression relative to P6 and P7 as excess Mg2+ destabilizes ribosome conformation and competes with Mg-NTP complexes. Neither Above ceiling well will be used as a target for BioLightX5 Aim 2 optimization — they serve as boundary condition markers that define the outer limits of the Mg2+ operating window for this specific extract and master mix formulation.

Connection to BioLightX5 Final Project

This week’s lab activity may be considered Aim Zero of BioLightX5, as a quantitative CFPS calibration step. The results will provide an excellent starting point for Aim 2 — the cell-free version of BioLightX5 — as a predictive model for tunable sfGFP expression using additive-only Mg2+ axis control.

Broader Significance

Additive-only expression control — without modifying DNA, master mix, temperature, or reaction time — establishes a portable, reproducible TXTL tuning framework applicable across automated and community lab settings.

• Cost efficiency: Tuning TXTL output to only the required expression level eliminates over-expression waste and reduces reagent consumption proportionally.
• Portability: A fixed master mix with additive-only modifications requires no reformulation across sites — directly deployable at Makerspace Charlotte and beyond.
• Scalability: Decoupling expression tuning from master mix preparation enables batch-consistent results across distributed platforms including the OT-2.
• Accessibility: Directly supports BioArt Studio’s mission and the iGEM 2026 distributed biomanufacturing framework.

References

sfGFP: Pédelacq et al. (2006). Nature Biotechnology 24(1):79-88. doi:10.1038/nbt1172

sfGFP FPbase: https://www.fpbase.org/protein/sfgfp

Mg2+ optimization in CFPS: Jewett & Swartz (2004). Biotechnology and Bioengineering 86(1):19-26. doi:10.1002/bit.20026

Phosphate chelation of Mg2+ in CFPS: Kim & Swartz (2001). Biotechnology and Bioengineering 74(4):309-316. doi:10.1002/bit.1121

myTXTL: Garamella et al. (2016). ACS Synthetic Biology 5(4):344-355. doi:10.1021/acssynbio.5b00296

Class project — HTGAA 1536: Donovan R. (2026). https://rcdonovan.com/1536?id=0m7255ryvn7ttvw

Cloud Lab Recitation: https://docs.google.com/presentation/d/1bz0xRXS7tOcje75Xs0dpeOOQpOwgRL1ld1DvPv3yrfU

Hypothesis- Version 1.0 (retired-no Spermidine in reagent options. See Version 2.0 above)

This is a hypothesis on the design of a variable luminosity construct based on cell-free protein synthesis. By adding independent reagent modifications to a fixed cell-free DNA and master mix, we hypothesize a measurable delta in sfGFP luminosity relative to the unmodified control:

• Spermidine at 3mM drives expression below baseline due to limiting promoter access caused by DNA over-compaction at the transcription initiation site.
• Creatine phosphate at 15mM drives expression above baseline by replenishing ATP availability and extending the active translation window beyond the point of energy depletion.

The reagent producing the largest delta will be selected as the Round 2 candidate, where it will be tested at multiple dose levels — low, medium, and high — establishing a multi-point luminosity gradient. Mg²⁺ will be introduced in Round 2 as a co-variable to determine whether ionic modulation of ribosome activity compounds or independently shifts the Round 1 delta.

Experimental Design

DNA template, master mix composition, temperature, and reaction time are fixed by the class protocol and identical across all wells. No DNA modifications are introduced. The sole experimental variable is additive supplementation — small-molecule reagents added on top of the fixed master mix to modulate sfGFP expression above or below the class baseline.

My Well Assignments

Mg²⁺ is held constant in Round 1 and introduced only in Round 2 as a co-variable with the winning reagent.

Rationale

Spermidine and creatine phosphate were selected because they act at independent nodes in the expression pathway — transcription and energy respectively — ensuring Round 2 Mg²⁺ co-variable testing can be interpreted without confounding either mechanism.

Spermidine over-compacts DNA above its optimal concentration, limiting promoter access at the transcription initiation site and reducing mRNA output independently of ribosome activity or energy supply.

Creatine phosphate replenishes ATP availability, extending the active translation window beyond baseline energy depletion independently of transcription rate or DNA accessibility.

Measurements

Primary — Spectrophotometric fluorescence

Plate reader excitation 485nm / emission 510nm, RFU at class-defined endpoint. Delta from control is the decision metric:

Δ under  = RFU(control) − RFU(spermidine well)
Δ over   = RFU(creatine phosphate well) − RFU(control)

The well with the largest magnitude delta becomes the Round 2 candidate.

Secondary — Mass spectrometry

Where available, mass spectrometry quantifies total sfGFP yield independent of fluorescence — including misfolded protein that fails to mature the chromophore. Correlating mass spec yield against RFU across the three wells determines whether the delta reflects translation output, folding efficiency, or both.

Round 2 Design — Pending Round 1 Results

Connection to Final Project

This week’s lab activity may be considered Aim Zero of BioLight x2, as a quantitative CFPS calibration step. The results of Round 1, Round 2, and spectrophotometric readings will provide an excellent starting point for Aim 2 — the cell-free version of BioLight x2 — as a predictive model for tunable sfGFP expression using additive-only master mix control.

References

sfGFP: Pédelacq et al. (2006). Nature Biotechnology 24(1):79–88. doi:10.1038/nbt1172

sfGFP FPbase: https://www.fpbase.org/protein/sfgfp

Spermidine in CFPS: Jewett & Swartz (2004). Biotechnology and Bioengineering 86(1):19–26. doi:10.1002/bit.20026

Creatine phosphate: Kim & Swartz (2001). Biotechnology and Bioengineering 74(4):309–316. doi:10.1002/bit.1121

myTXTL: Garamella et al. (2016). ACS Synthetic Biology 5(4):344–355. doi:10.1021/acssynbio.5b00296

Class project — HTGAA 1536: Donovan R. (2026). https://rcdonovan.com/1536?id=0m7255ryvn7ttvw

Cloud Lab Recitation: https://docs.google.com/presentation/d/1bz0xRXS7tOcje75Xs0dpeOOQpOwgRL1ld1DvPv3yrfU

Week 12 HW: Building Genomes

HTGAA Week 12 Homework

Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork

Item 1: Pixel Contribution

I contributed to plate #G3, initiating a rose design on April 15. I seeded the concept on Discourse: "#G3 - Starting to build a rose… let’s see what grows!"

Item 2: HTGAA Webpage Notes

2a — What I contributed:

I contributed to plate #G3, planting the seed of a pixel rose and inviting others to build on it — fully expecting it to be overwritten, in the spirit of collaborative design.

2b — What I liked:

I liked how the canvas was a collaborative activity. It’s interesting how quickly it became an algorithmic scripted design. The timeline feature is a good addition, to be able to see the evolution over time.

2c — What could be made better:

I have been thinking of how to introduce more collaboration and team building by creating a challenge that rewards the player with pixels, or points that can be used to build with. While scripted algorithms are great, there could be a separate or individual board space for that. To take the game concept one step further, imagine the Tron Light Cycle game. The goal is to traverse the board — without crossing over another trail. Pick anywhere on an edge to begin, and see if you can make it to the middle. More earned “points” = more chances to move. Earn points by quiz questions, or scavenger hunt activities. Roll a random # and that’s how many “moves” you get.

Part B: Cell-Free Protein Synthesis Reaction Composition

Component Descriptions

1. E. coli Lysate — BL21 (DE3) Star Lysate (includes T7 RNA Polymerase)

The lysate contains all of the cellular building blocks needed to carry out transcription and translation outside of the cell. T7 polymerase is selected for its known capability of recognizing the T7 promoter sequence in the cell-free system design.

2. Potassium Glutamate

Potassium Glutamate has the most volume of salt in this buffer, and its role is to manage the ionic binding strength.

3. HEPES-KOH pH 7.5

Its main role in the buffer is to stabilize pH.

4. Magnesium Glutamate

Mg²⁺ is an important element in maintaining ribosome structure. It is highly tunable in order to promote ribosome function, without impacting other components.

5. Potassium Phosphate Monobasic & Dibasic

The potassium phosphate is primarily an energy source, that also helps to maintain pH. The key is the ratio between the two to raise/lower pH respectively.

6. Ribose

Ribose provides energy in the transcription process. It is a sugar-based component of fuel.

7. Glucose

Glucose, along with ribose, create an energy system to help drive regeneration of ATP, and works well over long timeframes.

8. AMP, CMP, GMP, UMP (Nucleoside Monophosphates)

These are the consumable building blocks that are considered substrates for RNA synthesis. They are recycled and sustainable over longer periods of time.

9. Guanine

Guanine provides the energy for GTP. It is important in that it prolongs the cycle of ribosome translation.

10. 17 Amino Acid Mix

These are the essential building blocks of protein synthesis, and will allow tRNA to pair with mRNA in the ribosomes, based on transcription information.

11. Tyrosine

It is another of the essential amino acids for tRNA to mRNA translation. It is handled uniquely due to its poor solubility.

12. Cysteine

Cysteine is an essential amino acid, and it is added separately due to its unstable chemical bonding properties, that prevent it from mixing with other elements. It is important for structural development in protein folding.

13. Nicotinamide

Nicotinamide is a stabilizer that supports the energy system found in metabolic enzymes.

14. Nuclease Free Water (Backfill)

As a backfill, it is free of contamination to allow the defined mix to perform at full potential.

Question 1: 1-Hour PEP-NTP vs. 20-Hour NMP-Ribose-Glucose Master Mix

The main difference between the 1-hour and 20-hour mix is in the composition of the master mix. The 20-hour incubation contains additional Ribose and Glucose to provide energy that allows translation to sustain the longer time frame. The 1-hour mix uses spermidine to help accelerate the translation, with short-term stabilization of nucleic acids. In contrast, the 20-hour contains nicotinamide to stabilize the enzymes. HEPES-KOH has a higher level to mitigate the higher pH of the concentration. In the one-hour mix, PEP (phosphoenolpyruvate) + NTPs is the energy source, which is fast and high-yield but generates inorganic phosphate buildup that inhibits the reaction, limiting it to ~1 hour.

Question 2 (Bonus): How Can Transcription Occur if GMP is Not Included but Guanine Is?

Guanine provides the energy for GTP. It is important in that it prolongs the cycle of ribosome translation. Guanine base can substitute for GMP — the lysate contains the enzymatic machinery to convert it.

Part C: Fluorescent Proteins in Cell-Free Systems

Question 1: Biophysical and Functional Properties

Question 2: Mastermix Hypothesis

Protein: sfGFP — selected for its direct relevance to BioLight Aim 2, a cell-free biosensor that expresses sfGFP when exposed to ~470nm blue light.

Reagents: Mg²⁺ availability, adjusted via two mechanisms operating on a single mechanistic axis:

A. Potassium Phosphate Dibasic (Mg²⁺ sink)

Added above the baseline 5.625mM, potassium phosphate dibasic sequesters free Mg²⁺ through phosphate chelation, reducing ribosome assembly efficiency and T7 RNAP cofactor availability — driving sfGFP expression below the control baseline.

B. Magnesium Glutamate (Mg²⁺ source)

Added above the baseline 6.975mM, magnesium glutamate directly increases free Mg²⁺ in solution, stabilizing ribosome subunit assembly and activating Mg-NTP complexes for both transcription and translation — driving sfGFP expression above the control baseline.

Expected effect: Since both reagents interact with ionic affinity to the ribosome, the focus is on a singular variable for fine tuning in both directions from the master mix as control. The relative magnitude of the positive and negative deltas from control, measured by spectrophotometry at excitation 485nm / emission 510nm, will reveal whether the master mix is operating below, at, or above its Mg²⁺ optimum — directly informing optimized reaction conditions for eLightOn CFPS deployment in BioLight x2 Aim 2.

Question 3: Reagent Concentrations

Protein: sfGFP Hypothesis: v2.0 — Mg²⁺ single-axis variable luminosity construct Measurement: Spectrophotometry at excitation 485nm / emission 510nm

Predicted dynamic range: ~10× from W1 to W8. The asymmetry between negative delta (−2,700 max) and positive delta (+4,300 max) suggests the master mix is operating below its Mg²⁺ optimum — more headroom above baseline than below.

Connection to BioLight x2:

Question 4: Fluorescence Data Analysis

Pending — due one week after data is returned (date TBD).

Part D: Build-A-Cloud-Lab (Bonus)

What I Built

For the Cloud Lab render, I used the default assembly, but focused on an idea for a circular floor plan, to introduce the idea of a circular manufacturing layout. While linear production lines are expected, there may be some spatial advantages to a circular layout, to fit more equipment in less space, with a central pivot point for automated transfer. I added a visualization of a data wall, where all activity and data is monitored from a command center or virtual reality heads-up display (which would correspond well with a circular layout — no linear navigation required).

What I Liked

I liked the idea of a visual configurator that leads the way towards a “digital twin” of the lab, where protocols can be designed, built, and simulated.

Design Ideas

I would like to be able to import my own custom labware, and apply transforms such as x, y, z position and scale within a unit. That would lead to connectivity with the API to perform animated sequences, true to the intent of remote automation.

Appendix: Scoring Summary

Part A

Confidence: ± 6%

Part B — Component Scores

Part C — Scores

Part D — Scores

Overall Scorecard (All Parts)