Week X HW: Test

• Hello ¹

Week 1 HW: Principles and Practices

Week 1 Biological Engineering Application Governance Exercise

• I’m interested in developing phage chassis capable of targeting bacteria that commonly cause infections during spaceflight. I’m interested in developing these phage chassis because:

  • They would give astronauts and future space travelers more autonomy in countering ad hoc novel infections that may occur during long-duration missions
  • They would help contribute to greater personalization of medical care for broader ranges/more diverse spacefaring populations, who will likely travel, work, and live in space for extended periods of time/longer than traditional space missions to date
  • This development may help counter novel terrestrial infections for medically underserved populations

• Governance Goal 1 (G1): Preventing/Mitigating Malicious Dual-Use (i.e., ensuring)

  • Appropriate biosecurity (including cyberbiosecurity) controls
  • Safe deployment conditions

• Governance Goal 2 (G2): Empowering Autonmous and Equitable Use

  • ‘Plug-and-play’ functionality (i.e., any mission or crew should be able to intuitively use solution – advanced technical acumen not required)
  • Solution costs and distribution should develop with access across wide range(s) of demographics and socio-economic strata in mind

• Governance Action 1: Phage Safety Refusal (PSR)

  Purpose:

  • Current State: There are no proactive mechansisms preventing malicious dual-use phage development (i.e., developing a phage speicifcally designed to degrade host mRNA in healthy cells).
  • Proposed Changes: Easily implementable phage production host ‘kill switches’ that can be deployed if n^th concentrations of healthy cells accidentally or deliberately targeted by phage therapy

  Design:

  • Existing global space health biotechnology base (consisting of private firms, academia, and government-affiliated research centers) need to opt-in and actively invest in space-based phage therapies at adequate enough levels to ensure this notion of a phage defensive ‘kill switch’ can be validated and deployed, or invalidated and no longer pursued
  • Diverse space health regulators (consisting of a variety of agencies across nation-state governments) would need to approve said ‘kill switches’ (ex. Food and Drug Administration in the United States)
  • Patients participating in clinical trials of the ‘kill switch’ would need to feel comfortable enough with the solution to participate
  • Investors and members of private industry must see enough potential in the ‘kill switch that they see it as worthy of their time and investment
  • Astronauts and future spacefarers must be comfortable enough with the ‘kill switches’ to consent to their use

  Assumptions

  • The underlying notion of malicious dual-use phage applications, particularly in a space biotechnology context. Assumptions underneath this assumption include:
    • Viable malicious dual-use phage applications within a space health context and
    • Malicious actors would see and act on the benefits of phage interventions for targeted action against healthy cells in adversarial/antagonistic persons
  • That defensive anti-phage ‘kill switches’ even make sense to pursue technically

  Risks of Failure & “Success”

  • No viable malicious dual-use phage application concern (terrestrially or in a space context)
  • No interest in developing anti-phage ‘kill switches’
  • Developing anti-phage ‘kill switches’ is too technically intensive/not scalable to address the diverse needs of potential future users
  • Not enough funding to develop necessary research body of knowledge to make anti-phage ‘kill switches’ viable and safe to use in a space health context
  • Anti-phage ‘kill switches’ successfully developed and deployed and become another chapter in the never-ending story of phages v. bacteria (i.e., phages adapt to the ‘kill switches’ over time, rendering them ineffective)

• Governance Action 2: Space Medicine Access Consortia (SMAC)

  Purpose

  • Current State: The field of space medicine is still in its relative nascency. While space health consortia exist, there are no consortia explicitly aimed at making space medicine advancements as broadly accessible as possible to both spacefaring and terrestrial populations.
  • Proposed Changes: Charter and develop a consortia explicitly aimed at making space medicine advancements as broadly accessible as possible to both spacefaring and terrestrial populations. Ideally this consortia should comprise members from private industry, academia, government-affiliated research centers, and allied partners

  Design:

  • Potential and future committed consortia partners from across private industry, academia, government-affiliated research centers, and allied partners need to see the value of the consortia’s mission (i.e., they need to see and align with how the consortia’s explicit charter to make space medicine advancements as broadly accessible as possible to both spacefaring and terrestrial populations would benefit their organization)
  • Consortia partners need to agree on:
    • Rules of the road (this will likely need to be contractual)
    • Research scope(s) for their organizations
  • Concrete milestones indicating consortia success, stagnation, or failure in achieving its goals
  • Adequate funding to see consortia from inception through the point where most of its charter has been fulfilled (a somewhat analogous example might be the pool of institutions the Information Processing Technology Office (IPTO) at the Defense Advanced Research Projects Agency (DARPA) pooled together when creating the ARPANET, which later evolved to become the modern Internet, and the shifts in IPTO’s mission over time as a result of these developments)

  Assumptions

  • Critical mass of stakeholders:
    • cares about making space medicine advancements accessible to spacefaring and terrestrial populations
    • can see the value of a dedicated consortia to make space medicine advancements more accessible for spacefaring and terrestrial populations
    • can agree to the rules of the road necessary to make consortia viable
    • see the value of making phage chassis capable of targeting bacteria that commonly cause infections during spaceflight ‘plug-and-play’ and cost-effective for end-users (i.e., they wouldn’t prioritize some other space medicine intervention)
  • Consortia can achieve its charter, with greater phage chassis accessiblity as a step on its path to success

  Risks of Failure & “Success”

  • No or not enough interest in:
    • making space medicine advancements more accessible for spacefaring and terrestrial populations
    • developing consortia to make space medicine advancements more accessible for spacefaring and terrestrial populations
  • SMAC not viable due to:
    • Too much burden/not enough return on investment for potential consortia members (i.e., they don’t see the value)
    • Disagreeements over rules of engagement
    • Disagreement over research scope for participating members (i.e. who does what)
    • Inadequate implementation pathways (i.e., most of SMAC’s work languishes in the ‘valley of death’)
  • SMAC prioritizes other space medicine interventions over making phage chassis capable of targeting bacteria that commonly cause infections during spaceflight ‘plug-and-play’ and cost-effective for end-users
  • SMAC might ‘succeed’ too much, and as a result, the loudest or wealthiest SMAC members might eventually see value in exercising disproportionate control over the entity, diluting its charter

• Governance Action 3: Space Applied Biomedicine Repository (SABR)

  Purpose

  • Current State: Numerous space medicine guides have been developed by NASA, including the NASA Astronaut Medical Operations Handbook, Advanced Diagnostic Ultrasound in Microgravity (ADUM) Protocols, and OCHMO-STD-100.1A, otherwise known as the NASA Medical Standard. Commercial space missions largely lean on these guides for their missions
  • Proposed Changes: While NASA’s precedent is useful and organizations like the Translational Research Institue for Space Health (TRISH) have proposed tailored guidance for commercial space travelers, there is no existing repository explicitly focused on lessons learned and best practices for applied biomedicine in a space context. As opposed to a static guide, this work be more like a git-based repo, that could be updated with lessons learned, remaining customaizable for large numbers of future spacefarers and their medicial needs

  Design

  • User base:
    • willing to contribute to and derive value from SABR
    • with enough technical know-how to contribute to and derive value from SABR
  • Stakeholder(s) willing to pay subscription costs to run (likely git-based) SABR technical back-end
  • Easy to understand guidance and lessons learned that can be easily ingested in text, video, or audio form on (likely) simple, non-bandwidth intensive network connected devices

  Assumptions:

  • A (likely git-based) repository is an optimal vehicle for distributing recursively improving or community-based applied biomedicine lessons-learned for the space medicine community and future missions
  • This repository would contain accumulated, useful insights regarding how phage chassis capable of targeting bacteria that commonly cause infections during spaceflight can be deployed or administered:
    • safely (i.e. without malicious dual-use) across a wide variety of space missions
    • in a ‘plug-and-play’ or affordable manner
  • Enough of a user base:
    • willing to contribute to and derive value from SABR
    • with enough technical know-how to contribute to and derive value from SABR
  • Stakeholder(s) willing to pay subscription costs to run (likely git-based) SABR technical back-end
  • Easy to understand guidance and lessons learned that can be easily ingested in text, video, or audio form on (likely) simple, non-bandwidth intensive network connected devices (i.e., SABR can adequately work even in very bandwidth constrained conditions)

  Risks of Failure & “Success”

  • Not enough interest in SABR (potential users or contributors don’t see its value)
  • SABR is:
    • too:
      • early/the timing’s not right
      • abstract and remote in its guidance (i.e., the actual users or contributors who might derive value from its content see its content as too over their heads, technical, jargon-filled, or not applicable for their specific use cases)
    • a useful application, just not for the safe, easy-to-use, or cost-efective deployment of phage chassis capable of targeting bacteria that commonly cause infections during spaceflight
    • bandwidth-constrained to such a degree that it’s untenable for all intensive purposes
  • Can’t find stakeholder(s) willing to pay subscription costs to run (likely git-based) SABR technical back-end
  • SABR works so well and grows to such an extent that discerning actual mission-relevant content of value from the repository becomes a challenge for uninitiated users (i.e., filtering is a challenge – hard to separate filler from useful content)

• Biological Engineering Application Governance Scoring Rubric

• Biological Engineering Application: Phage chassis capable of targeting bacteria that commonly cause infections during spaceflight

• The rubric below works as follows: Policy goals and sub-goals are listed vertically, while each of the governance actions are listed next to the respective column header titled ‘Option’. Governance actions are scored from 1-3 based on how well they fulfil each policy goal and sub-goal. A score of 1 indicates a governance action does a poor job at fulfilling a policy goal, a 2 indicated a governance action does an OK job at fulfilling a policy goal, and a 3 indicates a governance action is the best at fulfilling a policy goal.

• Based on the scoring above, I’d probably prioritize SABR. Given the difficulties around space-based governance or governance in remote conditions, I think an organization like a TRISH or the Organization for Space Medicine, Engineering, and Design (OSMED) might be a good starting point, promoter, or convener to begin making something like SABR a reality. The more I think about it, the varied jurisdictions and varied, unclear regualtory regimes at the nation-state level make the idea of a more open-source/tribal knowledge-based self-governing solution appealing (or at bare minimum it shows a gap that could potentially be filled). I also think that if the timing was right (i.e., enough useful information could be added by a community of contributors) this could actually help fulfill some of the policy goals associated with the phage chassis project, not by a lot of sanctioned formal policy-making perhaps, but by community contribution, input, and/or agreed-upon best practices. That said, I can see and understand the trade-offs between formal policy-making at the nation-state level and more grassroots normative development of best practices as a result of doing this exercise. The glaring uncertainties that remain are whether or not the repository’s timing is right and/or if a dedicated user and contributor base could coalesce around it

• Ethical Considerations: Given the mechanics of phages, specifically their ability to hijack host cell tRNAs, ribosomes, and amino acids, I was somewhat surprised that there wasn’t more mention of potential deliberate malicious dual-use of phages. Maybe it’s my relative nascent understanding of the life sciences, the limitations of my research on this topic, or maybe the topic itself is either not researched or considered extensively. If this is true and the notion of potential deliberate malicious dual-use of phages might be a little bit left field, not well understood, or not well defined, perhaps convening working groups might be a sensible governance action, as these groups can often help map areas of concern for emerging dual-use technologies. Maybe distributing outputs from these working groups (i.e., white papers) to relevant academic journals, technical standards bodies, or policymakers might also be a worthwhile governance action.

All supporting prompts for the governance exercise above listed below:

Week 1 Homework Questions

Professor Jacobson Questions

• Two widely used polymerases are thermus aquaticus (Taq) and pyrococcus furiosus (Pfu) ¹. Taq has error rates ranging between 1 x 10^-5 to 2 x 10^-4 errors per base pair per doubling while Pfu has error rates of 1.3 x 10^{6 2 3}. Compared to the length of the human genome, 3 x 10⁹ base pairs, this comes out to apprxoimately 3.3 x 10^-13%, 6.6 x 10^-12%, and 3.3 x 10^-2% ⁴. Biology deals with this discrepancy during DNA replication through proofreading when it detects inaccurate nucleotides. When the polymerase detects that an inccorect base has been added, the polmyerase enzyme makes a cut in the chemical bond, releasing the incorrect nucleotide ⁵. If errors are made after replication, a mismatch repair is initiated. This is where enzymes recognize incorrectly added nucleotides and dispose of them. Nucelotide excision repair is another way nature corrects these errors. This occurs when ezymes remove and replace incorrect bases via cuts at the 3 and 5 prime ends of the incorrect base ⁶.
• An average human protein is approximately 375 amino acids long ⁷. As each codon consists of 3 letters, rough math indicates there are approximately 3³⁷⁵ number of potential coding sequences, an extremely large number of combinations ⁸. Some of the reasons all these different codons don’t code for the protein of interest are:
  • Codon Bias: Some codons are represented during transcription at a far greater level than others, traditionally due to more abundant transcription RNA (tRNA), ensuring higher levels of expression ^{9 10}.
  • mRNA Structure: Certain mRNA structures can be impacted by certain codon expression (i.e., become less stable), and therefore become more susceptible to degradation ¹¹.
  • Translation Accuracy Issues: Non-optimal codons decrease protein translation efficiency, due to a form of crowding in the ribosome, the area in the cell where protein production takes place ¹².

Dr. LeProust Questions

• Solid-phase phosphoramidite synthesis is the most common currently used oligo synthesis method ¹³.
• It’s difficult to make oligos longer than 200nt via direct synthesis because coupling errors/inefficiencies compound to the point where one ends up with lots of short, incomplete fragments ¹⁴.
• A 2000bp gene has 4000 nucleotides ¹⁵. Based on the answer to the previous question, creating a 2000bp is not currently feasible due to the accumulation of coupling errors/inefficiencies, even when stitching together smaller oligos or using novel enzymatic methods are taken into account ^{16 17}.

George Church Question

• Question 3 Response: ARPA-H Biostablization Sytem (BoSS) Grant Response

  All supporting prompts listed below answer:

Concept Summary

The team behind Pyrococcus furiosus-Inspired Molecular Staples (PFIMS) seeks to develop small organic molecules capable of binding to the ‘grooves’ of DNA and proteins across a variety of temperatures. If successfully developed, PFIMS would allow heat-proofing a biologic across a variety of temperatures without the need for dehydration. By locking protein folds through ionic pull, we can stablize biologics for longer periods of time (TA1). Our work will scale this system to scalable cell processing across an array of temperatures and use cases (TA2).

Innovation and Impact

PFIMS is inspired by extremophile biology. Pyrococcus furiosus can survive at temperatures of 100 degrees Celsius in ocean vents. By mimicking pyrococcus furiosus’ molecular heat shield, we can keep cells alive and functioning for exteneded periods of time at refrigerator or room temperature. Unlike modern cryopreservation methods that employ various forms of freezing that can harm cells during thawing, our stabilization solution stabilizes proteins using byproducts proteins naturally produce. Not only are cold temperatures avoided entirely, but once scaled this solution will slash costs for biologics shipping. Most importantly, PFIMS can work for any cell type, as it builds off fundamental biological features, such as protein folding and membrane strength.

Proposed Work

We plan to develop a novel, functioning bench-top bioprocessing system inspired by pyrococcus furiosus. We will create a polyamine-based stabilization medium that will power this bioprocessing system for a standard biologic (ex. cell therapy or antibodies). PFMIS’ approach is grounded in existing literature on stablization for high temperature DNA/protein stabilization via polyamines, small organic molecules with muliple amino groups (Bae, 2018) (Despotović, 2020) (Oshima, 2007). These polymaines act as a form of ‘molecular staples’ in preliminary modeling efforts (Vieille, C.,2001).

Key Milestones and Deliverables

Phase 1: Synthesize the branched polyamine formulation. Deliverable: Optimized medium prototype based on a standard biologic.

Phase 2: Integrate the stabilization medium into bioprocessing hardware, likely a singlee-use bioreactor to for initial prototype followed by a larger testing and deplouyment within a media/buffer prep mixer. Deliverable: Protoyped biostabilization device.

Phase 3: Validate stability metrics for a model biologic using PFIMS at room temperature over time. Deliverable: Validation report and delivery of biostabilization system capable of scaled biostablization across nth biologics

Technical Risks and Mitigations

Risk: Polyamines may exhibit toxicity at necessary concentratioons. Mitigation: Screening polyamines for reduced toxicity levels; introducing wash and resuspension steps into bioprocessing.

Risk: Stabilization mechanism may exhibit difficulty transferring from archaic single-celled microbes like pyrococcus furiosus to eukaryotic cells (cells containing nuclei and organelles). Mitigation: Tests across multiple cell types

Use Case

PFIMS enables rapid delivery of life-saving biologics and therapeutics in low-resourced or contested conditions. This allows for dramatically cheaper shipment of biologics to locations such as rural communities without robust public health infrastructure, remote or relatively isolated geographies, or active conflict zones.

Sources

• Bae DH, Lane DJR, Jansson PJ, Richardson DR. The old and new biochemistry of polyamines. Biochim Biophys Acta Gen Subj. 2018 Sep;1862(9):2053-2068. doi: 10.1016/j.bbagen.2018.06.004. Epub 2018 Jun 8. PMID: 29890242.
• Despotović Dragana, Longo Liam, et. al. Polyamines mediate folding of primordial hyperacidic helical peptides into stable amyloid-like fibrils. Biochemistry, 60(4), 257–267.
• Oshima T. Unique polyamines produced by an extreme thermophile, Thermus thermophilus. Amino Acids. 2007 Aug;33(2):367-72. doi: 10.1007/s00726-007-0526-z. Epub 2007 Apr 12. PMID: 17429571.
• Vieille C, Zeikus GJ. Hyperthermophilic enzymes: sources, uses, and molecular mechanisms for thermostability. Microbiol Mol Biol Rev. 2001 Mar;65(1):1-43. doi: 10.1128/MMBR.65.1.1-43.2001. PMID: 11238984; PMCID: PMC99017.

All supporting prompts listed below:

Week 2 HW: DNA Read, Write, and Edit

Part 0: Basics of Gel Electrophoresis

• Per instructions for this part, I attended the 02/10 lecture and 02/11. Additionally I attended all 3 Bootcamp sessions.

Part 1: Benchling & In-silico Gel Art

• Make a free account at benchling.com

Benchling Account Creation Confirmation

• Import the Lambda DNA

Benchling Phage Lambda DNA Import Confirmation_02.12.26

• Simulate Restriction Enzyme Digestion with the following Enzymes:

  • EcoRI

  Benchling EcoRI Enzyme Digest Confirmation

  • HindIII

  Benchling HindIII Enzyme Digest Confirmation

  • BamHI

  Benchling BamHI Enzyme Digest Confirmation

  • KpnI

  Benchling KpnI Enzyme Digest Confirmation

  • EcoRV

  Benchling EcoRV Enzyme Digest Confirmation

  • SacI

  Benchling SacI Enzyme Digest Confirmation

  • SalI

  Benchling SalI Enzyme Digest Confirmation

• Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artowrks

  

  Mind the Gap (Or a Most Wondrous Cave) ➕

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

Part 3: DNA Design Challenge

3.1 Choose your protein

• I chose the Mantis Fibroin 1 protein because for some reason when I received this assignment, my mind flipped to an insect protein, and then from there, a praying mantis. Upon further research, I was pleased with where my intuition lead me. The Mantis Fibroin 1 protein helps comprise the mantis’ ootheca, otherwise known as its egg casing. What’s fascinating about these proteins is that they create this coiled yet flexible foam-like structure around the mantis’ eggs. This protein piqued my interest, as it might have biomimetic potential. The Mantis Fibroin 1 protein is listed below ¹²:

tr|I3PM87|I3PM87_9NEOP Mantis fibroin 1 OS=Pseudomantis albofimbriata OX=627833 GN=MF1 PE=2 SV=1 MDSKMLCVSLLLAVFCLWYTEASPLEEKYGEKYGDMEEYQRGTEDSRAVINDHTAKVASQ SARGMVNKAKTTEAAARSNEQLSKDRQYYYREYLKKADYHKKKALEYEQLSAAENAKIAY HESKQKDWETKARESDVQCRDAEAKYEQSYTRSRELKRESIIAYVQAAMHHAEASGDHMK ADRAKDIARDMMRKAESLRGDASNHYQRSEEDKNKARSEKVKAHQNADNSQRHHTACRAY DQEGLKTRLSSKANMMRQIHSSLLAERSHSLAREDGLAADLSHKLAEELARMSEESGAIS KINSGEERGYSNKVRQDEVKAHELAVSKRMMGAEVADNSEMISLAQAKDGSLDEGENYKL STFYADDSTKNMLPDSRGQMSYGDE

3.2 Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

• A translated Mantis Fibroin 1 protein nucleotide sequence of most likely codons is below, as well as evidence showing how I inserted the Mantis Fibroin 1 protein UniProt information into the reverse translation tool

atggatagcaaaatgctgtgcgtgagcctgctgctggcggtgttttgcctgtggtatacc gaagcgagcccgctggaagaaaaatatggcgaaaaatatggcgatatggaagaatatcag cgcggcaccgaagatagccgcgcggtgattaacgatcataccgcgaaagtggcgagccag agcgcgcgcggcatggtgaacaaagcgaaaaccaccgaagcggcggcgcgcagcaacgaa cagctgagcaaagatcgccagtattattatcgcgaatatctgaaaaaagcggattatcat aaaaaaaaagcgctggaatatgaacagctgagcgcggcggaaaacgcgaaaattgcgtat catgaaagcaaacagaaagattgggaaaccaaagcgcgcgaaagcgatgtgcagtgccgc gatgcggaagcgaaatatgaacagagctatacccgcagccgcgaactgaaacgcgaaagc attattgcgtatgtgcaggcggcgatgcatcatgcggaagcgagcggcgatcatatgaaa gcggatcgcgcgaaagatattgcgcgcgatatgatgcgcaaagcggaaagcctgcgcggc gatgcgagcaaccattatcagcgcagcgaagaagataaaaacaaagcgcgcagcgaaaaa gtgaaagcgcatcagaacgcggataacagccagcgccatcataccgcgtgccgcgcgtat gatcaggaaggcctgaaaacccgcctgagcagcaaagcgaacatgatgcgccagattcat agcagcctgctggcggaacgcagccatagcctggcgcgcgaagatggcctggcggcggat ctgagccataaactggcggaagaactggcgcgcatgagcgaagaaagcggcgcgattagc aaaattaacagcggcgaagaacgcggctatagcaacaaagtgcgccaggatgaagtgaaa gcgcatgaactggcggtgagcaaacgcatgatgggcgcggaagtggcggataacagcgaa atgattagcctggcgcaggcgaaagatggcagcctggatgaaggcgaaaactataaactg agcaccttttatgcggatgatagcaccaaaaacatgctgccggatagccgcggccagatg agctatggcgatgaa

Mantis Fibroin 1 UniProt Information Inserted into Reverse Translation Tool

3.3 Codon Optimization

• We need to optimize codon usage so a particular sequence can be expressed with greater fidelity, reliability, and efficiency in a host organism. I’ve chosen to optimze the codon sequence for Saccharomyces cerevisiae (baker’s yeast), because it:
• is commonly used as a host in biotechnology applications
• folds in a manner closer to insect protein folding
• is apparently easier to work with than mammalian cells

A codon optimized Mantis Fibroin 1 nucleotide sequence is shown below, as well as evidence showing how I showing how I inserted the Mantis Fibroin 1 protein nucleotide sequence information into the codon optimization tool

ATGGACAGTAAGATGTTATGTGTCTCCTTATTGTTGGCTGTTTTTTGTTTATGGTATACTGAAGCTTCCCCATTAGAAGAAAAGTATGGTGAAAAGTACGGTGACATGGAAGAGTACCAAAGAGGTACTGAAGATTCAAGAGCAGTTATTAACGATCATACTGCTAAAGTTGCTTCCCAATCCGCCAGAGGTATGGTTAATAAGGCTAAGACTACAGAAGCTGCTGCTAGAAGTAATGAACAATTATCTAAAGATAGACAATACTATTACAGAGAATATTTGAAAAAGGCTGATTATCATAAGAAGAAAGCTTTGGAATATGAACAGCTTTCAGCTGCTGAAAATGCAAAAATTGCTTATCATGAATCTAAACAAAAAGACTGGGAAACGAAAGCCAGAGAATCCGATGTTCAATGTCGTGATGCTGAAGCAAAATATGAACAATCTTACACAAGGTCCAGAGAACTGAAAAGGGAATCTATTATTGCTTATGTTCAAGCTGCTATGCATCATGCTGAAGCTAGCGGTGATCACATGAAAGCTGATAGAGCTAAAGATATCGCTAGAGATATGATGAGAAAGGCAGAATCCTTAAGGGGTGACGCTAGCAACCATTATCAGAGATCCGAAGAAGATAAGAATAAGGCCAGATCTGAAAAGGTTAAAGCTCATCAAAACGCTGATAATTCTCAAAGACATCATACTGCATGCAGAGCGTATGACCAAGAAGGTTTAAAGACGAGATTGAGCTCAAAAGCCAACATGATGAGACAAATTCACTCCTCACTACTGGCTGAAAGATCTCATTCATTAGCAAGAGAAGACGGTCTTGCGGCCGATTTATCACATAAGTTGGCTGAAGAATTAGCTAGAATGTCCGAAGAATCAGGTGCTATATCTAAAATAAACTCAGGTGAAGAAAGAGGCTATTCGAATAAAGTGAGACAAGATGAGGTTAAAGCACATGAATTGGCTGTTAGCAAAAGAATGATGGGTGCTGAAGTTGCTGATAATTCGGAGATGATTAGTTTGGCACAAGCTAAAGACGGTTCTTTAGATGAAGGTGAGAACTATAAATTATCCACTTTTTATGCAGACGATTCTACAAAAAATATGCTACCAGATTCTAGGGGTCAAATGTCTTACGGTGATGAA

Mantis Fibroin 1 nucleotide sequence codon optimized for Saccharomyces cerevisiae (baker’s yeast)

3.4: You have a sequence! Now what?

• Several cell-dependent technologies could be used for producing the codon-optimized Mantis Fibroin 1 protein. One such technology, a yeast system, has already been pursued in the previous steps of this section, as Saccharomyces cerevisiae (baker’s yeast) is a form of yeast. Bacterial systems, such as E. coli could also be used for producing the protein in host cell culture, although this would require different codon optimization. Other cell-dependent technologies could have included insect or mammalian-based systems, although I’m not sure of the value of expressing an insect-associated protein in another insect-based host (although this may be a failure of imagination on my part). As mentioned previously, mammalian systems could also be used, but apparently mammalian cells are more difficult to work with than bacterial or yeast-based hosts. Cell-free methods for producing the codon-optimized Mantis Fibroin 1 protein would involve breaking open a cell, extracting relevant ribosomes, enzymes, tRNAs, etc., and then taking these contents and combinining them with a DNA template (in this case our Mantis Fibroin 1 protein nucleotide sequence), energy sourcesm relevant amino acids, and a reaction buffer. There’s a time advantage of cell-free methods over cell-dependent methods of protein expression.

3.5 Optional: How does it work in nature/biological systems?

• From my research in answering this question, I think the answer is that post-transcriptional process called alternative splicing occurs, where non-coding mRNA (introns) are cut and removed, while coding regions (exons) remain ³. It’s pretty fascinating because this splicing can create several different types of mRNA molecules, and therefore different proteins. This increases the efficiency with which different proteins can be expressed within a particular organism.
• See below

Attempted Mantis Fibroin 1 Alignment

All Section 3 Prompts Listed Below

Part 4: Prepare a Twist DNA Synthesis Order

4.1: Create a Twist account, and Benchling account

Twist Account Creation

Benchling Account Creation Confirmation

4.2: Build Your DNA Insert Sequence

Original Sequence Insertion NOTE: Think I may’ve started off inserting the wrong sequence. This may have been potentially fixed when I inserted a sfGFP sequence from NCBI*

Codon Optimization NOTE: Think I may’ve started off inserting the wrong sequence. This may have been potentially fixed when I inserted a sfGFP sequence from NCBI*

Corrected NCBI Sequence Insertion

Corrected NCBI Sequence Codon Optimization

Start Codon Annotation

Stop Codon Annotation

Promoter BBa_J23106 Insertion

RBS Insertion

Coding Sequence Insertion

7x His Tag Insertion

Terminator BBa_B0015 Insertion

Sequence Linear View

• Downloaded Sequence FASTA file (via Mac OS TextEdit). See below:

  HQ873313 (codon optimized) TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGCCATTAAAGAGGAGAAAGGTACCatgAGCAAAGGAGAAGAACTTT TCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCCGTGGAGAGGGTGA AGGTGATGCTACAAACGGAAAACTCACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCGTGGCCAACACTT GTCACTACTCTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCACATGAAACGGCATGACTTTTTCAAGAGTG CCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGACCTACAAGACGCGTGCTGAAGTCAA GTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAGGGTATTGATTTTAAAGAAGATGGAAACATTCTTGGACAC AAACTCGAGTACAACTTTAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCA AAATTCGCCACAACGTTGAAGATGGTTCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCC TGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGTCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATG GTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAAcgtaaaggcgaggagctgt tcactggtgtcgtccctattctggtggaactggatggtgatgtcaacggtcataagttttccgtgcgtggcgagggtga aggtgacgcaactaatggtaaactgacgctgaagttcatctgtactactggtaaactgccggtaccttggccgactctg gtaacgacgctgacttatggtgttcagtgctttgctcgttatccggaccatatgaagcagcatgacttcttcaagtccg ccatgccggaaggctatgtgcaggaacgcacgatttcctttaaggatgacggcacgtacaaaacgcgtgcggaagtgaa atttgaaggcgataccctggtaaaccgcattgagctgaaaggcattgactttaaagaagacggcaatatcctgggccat aagctggaatacaattttaacagccacaatgtttacatcaccgccgataaacaaaaaaatggcattaaagcgaatttta aaattcgccacaacgtggaggatggcagcgtgcagctggctgatcactaccagcaaaacactccaatcggtgatggtcc tgttctgctgccagacaatcactatctgagcacgcaaagcgttctgtctaaagatccgaacgagaaacgcgatcatatg gttctgctggagttcgtaaccgcagcgggcatcacgcatggtatggatgaactgtacaaatgaCATCACCATCACCATC ATCACtaaCCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTG AACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

4.3: On Twist, Select the Genes Option

Selected Genes

4.4: Select the ‘Clonal Genes’ Option

Clonal Genes Selected see results in subsections 4.5 and 4.6 below

4.5: Import your sequence

Imported Sequence (Step 1)

Imported Sequence (Step 2)

4.6: Choose Your Vector

Chose Vector

Downloaded Construct

Imported sequence into Benchling and viewed resulting plasmid

All Section 4 Prompts Listed Below

Part 5: DNA Read/Write/Edit

5.1 DNA Read

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

• I want to sequence the DNA of the bdelloid rotifer Adineta genus, a microscopic-sized invertebrate that kind of looks like a worm. I’m fascinated by its ability to sustain cryptobiosis for thousands (in the case of a bdelloid rotifer thawed out in Russia in 2015, more than 24,000!!) of years. Transgenesis of the bdelloid rotifer’s cryptobiotic abilities in mammalian organisms could have profound impacts on the future of the species, specifically the ability for groups of homo sapiens or other future sapiens forms to engage in interstellar travel over large durations of time and space. More information on the bdelloid rotifer Adineta genus and its cryptobiotic abilities can be found in the footnote at the end of this sentence ⁴.

• (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’d use Next-Generation Sequencing (NGS) on my DNA because it’s well-suited for transgenesis. It’s fast, high-resolution, and allows for massively parallel sequencing (if desired). More importantly, it’s highly precise, meaning it can pinpoint transgene locations within a host genome.

• Is your method first-, second- or third-generation or other? How so?

  • It’s a second-generation sequencing method, as it emerged in the 2000’s after the advent of Sanger sequencing in the 1970’s and before the advent of single-molecule sequencing in the 2010’s.

• What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.

  • If the gene exists in an organism, the initial input is extracted genomic DNA from that particular organism. Otherwise, the initial input can be a plasmid (if the DNA’s already cloned) or complementary DNA (cDNA) if only mRNA is available. Essential preparation steps listed below (assuming gene exists in an organism):
    • Isolate or extract the gene; lyse cells or tissues from donor, remove proteins and contaminants
    • Fragment the isolated or extracted gene into fragments of apprxo. ~200-600bp
    • Convert ragged ends into blunt or sticky ends
    • Attach adapter sequences to ends of each fragment
    • Enrich fragments of the intended size (i.e., the size you want), removing any remaining small artifacts and excess adapters
    • Review fragement size distribution
    • Convert double-stranded library into single strands if necessary
    • Load into sequencing instrument

• What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?

  • The essential NGS steps are listed below:
    • Preparation (see above)
    • Amplification: Many copies of each DNA fragment are created on flow cells. One end of each fragment sticks to a primer, gets copied to a complementary strand, and bends over to stick to a primer. This bridging repeats n^th times, forming amplified (hence the name) clusters of these identical grouped fragments
    • Sequencing: A polymerase enzymes adds 1 colored flourescent nucleotide to each strand in each cluster of grouped fragments. A camera takes a picture of recording the color for the given nucleotide (revealing its A, T, G, or C base) then chemicals are used to wash away any free-floating nucleotides that aren’t part of a given cluster.
    • Base Decoding/Base Calling: A computer analyzes these colored image clusters, assigning each cluster a sequence of bases based on its colors. This analysis then becomes a text string of DNA code, with confidence ratings per base sequence based on the resolution of the read ⁵

• What is the output of your chosen sequencing technology?

  • See above. NGS outputs a text string of DNA code, with confidence ratings per base sequence

5.2 DNA Write

• (i) What DNA would you want to synthesize (e.g., write) and why?)

  • See answer to 5.1 (i). I’m fascinated by the potential of transgenesis of the bdelloid rotifer Adineta’s cryptobiotic abilities for interstellar travel, and therefore, am very interested in reading and editing its sequence.

  NOTE: I found a bdelloid rotifer Adineta sequence in the link in the footnote. However, the raw FASTA information is so long that upon insertion into this webpage, it seemingly broke the webpage, or caused it to freeze up (pardon the unintentional cryptobiosis pun) ⁶

• (ii) What technology or technologies would you use to perform this DNA synthesis and why?

  • I’d use PCR amplification, chemical synthesis, and restriction enzymes and ligation to perform this DNA synthesis. I’d use PCR so I can make many copies of the original DNA, I’d use chemical synthesis so I can clone the DNA into a given plasmid, encapsulating the DNA for desired level expression in an appropiate vehicle, and I’d use restriction enzymes and ligation for precise synthesis.
  • What are the essential steps of your chosen sequencing methods?
    • PCR amplification:
      • Denaturation: Heat DNA so it breaks into stingle strands
      • Annealing: Cool the DNA, allowing primers to bind to sites in target gene
      • Extension: DNA polymerase adds nucleotides from the primer starting point, allowing each strand to fully copy
    • Chemical synthesis:
      • Deprotection: Remove protecting DMT group
      • Base Coupling: Add protected phosphoramidite nucleotide for phosphate linkage
      • Capping: Cap the chain to prevent errors
      • Oxidation: Stabilize phosphate triester bonds
    • Restriction enzymes and ligation:
      • Stitch oligos from prior chemical synthesis step into a complete gene for insertion into plasmid
      • Add flanking restriction enzymes at ends of oligos as needed
      • Clean the isolated DNA via gel extract
      • Use matching restriction enzymes to incubate the gene insert and its plasmid vector. This incubation process recognizes specific sequences in the gene insert and cut, creating blunt and sticky ends
      • Use DNA ligase enzyme to mix compatible ends
  • What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?
    • A scalability challenge is that it’s difficult to synthesize a sequence of more than 200nt via direct synthesis methods because at a certain point, too many errors accumulate in the synthesized sequence

5.2 DNA Edit

• (i) What DNA would you want to edit and why?
  • See answer to 5.1 (i). I’m fascinated by the potential of transgenesis of the bdelloid rotifer Adineta’s cryptobiotic abilities for interstellar travel, and therefore, am very interested in reading and editing its sequence.
• (ii) What technology or technologies would you use to perform these DNA edits and why?
  • I’d use CRISPR-Cas9 to perform these DNA edits because when performing transgenesis from an invertebrate to a mammalian vertebrate, it allows for non-random, precise insertion of large amounts of genetic data with reduced risk of unintended or off-target effects.
  • How does your technology of choice edit DNA? What are the essential steps?
    • CRISPR-Cas9 edits DNA through a multi-stage mechanism. This mechanism is broken down below:
      • Recognition: A single guide RNA (sgRNA) pairs with a Cas9 protein
      • This pair scans the genome for a 20bp DNA sequence if the sequence is next to a protospacer adjacent motif (PAM)
      • If there is a PAM next to the desired 20bp DNA sequence, the Cas9 makes a dobule-stranded break (DSB)
      • The DSB then triggers repair mechanisms (either non-homolgous end joining [NHEJ] or homology-directed repair [HDR]). These repair mechanisms allow the desired edited DNA to be incorporated into the sequence
  • What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?
    • Preparation:
      • Select target site(s)
      • Design and synthesize gRNA
      • Build DNA donor templates
      • Create mixture of recombinant Cas9 protein and purified gRNA
      • Preapre cells/embryos
    • Inputs to this process are gRNDA,Cas9 protein, donor DNA (usually linear templates), and a delivery vehicle (usually an injection buffer)
  • What are the limitations of your editing methods (if any) in terms of efficiency or precision?
    • HDR can have low efficiency in a transgenesis context
    • Off-target or unintended consequences can still occur
    • Need PAMs near target sequences for precise DSBs

All Section 5 Prompts Listed Below

Week 3 HW: Lab Automation

The Power of Lab Automation

Assignment: Python Script for Opentrons Artwork

• 0: Attended this week’s recitation and reviewed the lab information on programming Opentrons
• 1: Generated an artistic design using Ronan’s Opentrons GUI ¹
• 2: Artistic Design Python Script: See script in URL below:

  • https://colab.research.google.com/drive/1-pgSJt_aF9MydtG0szxz2YKoogNRLRhH#scrollTo=PsOgJ2DndZzt

• 3: Listing my sfgfp point coordinates from Ronan’s Opentrons GUI below (the shape is a rightward-facing green arrow):

  • [(6.6,11), (8.8,11), (11,11), (8.8,8.8), (11,8.8), (13.2,8.8), (11,6.6), (13.2,6.6), (15.4,6.6), (13.2,4.4), (15.4,4.4), (17.6,4.4), (15.4,2.2), (17.6,2.2), (19.8,2.2), (17.6,0), (19.8,0), (22,0), (-22,-2.2), (-19.8,-2.2), (-17.6,-2.2), (-15.4,-2.2), (-13.2,-2.2), (-11,-2.2), (-8.8,-2.2), (-6.6,-2.2), (-4.4,-2.2), (-2.2,-2.2), (0,-2.2), (2.2,-2.2), (4.4,-2.2), (6.6,-2.2), (8.8,-2.2), (11,-2.2), (13.2,-2.2), (15.4,-2.2), (17.6,-2.2), (19.8,-2.2), (22,-2.2), (24.2,-2.2), (-22,-4.4), (-19.8,-4.4), (-17.6,-4.4), (-15.4,-4.4), (-13.2,-4.4), (-11,-4.4), (-8.8,-4.4), (-6.6,-4.4), (-4.4,-4.4), (-2.2,-4.4), (0,-4.4), (2.2,-4.4), (4.4,-4.4), (6.6,-4.4), (8.8,-4.4), (11,-4.4), (13.2,-4.4), (15.4,-4.4), (17.6,-4.4), (19.8,-4.4), (22,-4.4), (24.2,-4.4), (26.4,-4.4), (-22,-6.6), (-19.8,-6.6), (-17.6,-6.6), (-15.4,-6.6), (-13.2,-6.6), (-11,-6.6), (-8.8,-6.6), (-6.6,-6.6), (-4.4,-6.6), (-2.2,-6.6), (0,-6.6), (2.2,-6.6), (4.4,-6.6), (6.6,-6.6), (8.8,-6.6), (11,-6.6), (13.2,-6.6), (15.4,-6.6), (17.6,-6.6), (19.8,-6.6), (22,-6.6), (24.2,-6.6), (17.6,-8.8), (19.8,-8.8), (22,-8.8), (15.4,-11), (17.6,-11), (19.8,-11), (13.2,-13.2), (15.4,-13.2), (17.6,-13.2), (11,-15.4), (13.2,-15.4), (15.4,-15.4), (8.8,-17.6), (11,-17.6), (13.2,-17.6), (6.6,-19.8), (8.8,-19.8), (11,-19.8)]

• 4: Used Gemini 2.5 Flash (built into Google Colab) to assist with completing the coding portion of the homework. I have some rough Python knowledge via a Codecademy course, which helped get things started (i.e., I did do some of the coding for this assignment).

All Gemini 2.5 Flash prompts are listed below:

• 5: Coordinating robot time slot with William & Mary node
• 6: Submitted Python file via assignment form (see screenshot below):

Python Form Submission Confirmation Screenshot

Post-Lab Questions

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

  • The paper An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings was published in the journal Micromachines in 2024 and describes using simple Commercial Off-the-Shelf (COTS) reagents and lab equipment, along with an Opentrons robot, to create an automated workflow for detecting diseases in low-resource settings ². What’s interesting about the paper is that the workflow the researchers designed reduced the time for detecting a pathogen (in this case meningitis) by approx. 18% (total timne of 118 min.) with an almost 5.8x reduction in cost for sample processing ³. The total cost of to run 8 samples for meningitis detection was approx. $126 USD, a cost savings that matters in a low-resourced environments. The findings and extension of this paper’s workflow shows promise for decentralized disease detection in low-resource settings during future health security incidents. Except for opening and closing of tube lids, the following four steps of the autonated workflow were completed by the Opentrons robot:
    • DNA isolation with Dynabeads
      • Sample incubation
      • Washing step
      • DNA resuspension
    • DNA amplification
      • Recombinase Polymerase Amplification (RPA) mix preparatoon
      • RPA amplification
    • DNA digestion
      • Exonuclease digestion
    • DNA dtection
      • Preparation of Vertical Flow Microarray (VFM) solutions
      • Addition of samples to VFM
      • Signal enhancement

  Associated AI prompts to address this question included below:

• Write a description about what you intend to do with automation tools for your final project.
  • Phage isolation experiments generally require enriching bacterial strains, filtering out bacterial particles, pouring the phage-containing mixture in with fresh bacteria on agar, plating and re-plating phage plaques (areas of phage propagation and bacetrial destruction on agar), and characterizing the resulting phage plaques on the agar. My working thoughts are that I might actually be able to create a somewhat automated workflow a-la the paper referenced in the previous question to help with the filteration through characterization steps of a phage isolation experiment. This might involve using an Opentrons robot and COTS equipment to:
    • Handle bacterial liquids
    • (Maybe) use an Opentrons module to shake bacterial liquids and reagents
    • Operate centrifuge for spinning bacteria
    • Pour agar with phage plaques
    • Operate micropipettes for phage testing
    • Operate Thermocycler module for amplifying specific lysed areas
    • Rapidly run software to characterize novel phage DNA

Note: The maeterial above is not set in stone. It’s an outline of potential automation options for a potential final project (Space (LEO and beyond) or Suborbital Phage Isolation)

Associated AI prompts to address this question included below:

Final Project Ideas

• Submitted 3 Final Project ideas in my node’s section of the slide deck (see screenshot below):

All supporting prompts for Final Project Ideas Slide listed below

Week 4 HW: Protein Design Part 1

South American Rattlesnakes (Crotalus durissus terrificus) with Crotamine protein

Part A: Conceptual Questions

1. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)

• I intake approx. 5 * 10²³ Daltons of amino acids when ingesting 500 grams of meat. This is based off results indicating I ingest approx. 10²¹ Daltons of amino acids when ingesting 1 gram of meat

2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?

• Humans eat beef but don’t become cattle, and eat fish but don’t become fish because genetic information from the lifeform being ingested isn’t transferred wholesale. Much of the genetic material being eaten is broken down during digestion and more importantly a human beings’ cells follow instructions derived from their DNA. Human beings’ cells utilize amino acids from the lifeform being ingestion, but perform this utilization according to specific genetic instructions. The lifeform being ingested and its amino acids are the raw materials the cell uses for various means.

3. Why are there only 20 natural amino acids?

• There are several broad reasons why there are only 20 standard natural amino acids. The first reason is that early on in the history of evolution, this group of amino acids became more or less ’locked in’, meaning that once the basic relationship between three letter codons and these 20 standard natural amino acids became widely distributed across the kingdom of life, it becamde too risky/dangerous from an evolutionary standpoint to alter this core set. Another reason is that the group of 20 gives enough range in structure and chemistry to build a large chunk of what evolution or directed evolution might desire. The other reasons seem to amount to various types of evolutionary trade-offs. Adding more than 20 amino acids to this standard set would add additional, potentially unwanted complexity, while decreasing the number of amino acids in the set might lead to issues with a lack of uniqueness with amino acids side chain sharing, which would in turn limit the functional flexibility of amino acids to do things like fold precisely.

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

• Yes you can. My attempts to design some new amino acids usng SwissSideChain and the Cryo-EM structure of Receptor Tyrosine Kinase ROS1 PDB file in PyMol (open-source) are shown below:

Attempt at creating a non-natural amino acid residue mutation of Tyrosine Kinase ROS1 using cyclohexanecarboxylic acid

Attempt at creating a non-natural amino acid residue mutation of Tyrosine Kinase ROS1 using cyclopropanecarboxylic acid

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

• Amino acids come from metabolic molecules within the cell. These metabolic molecules consist of carbon atom chemical backbone, inorganic nitrogen, and enzyme-facilitated chemical reactions. Before life as we know it started, amino acids originated from abiotic (not from living organisms) chemical reactions on Earth before the emergence of life as we know it. The chemical reactions occurred in the atmosphere, hydrothermal and oceanic vents, and via meteorite and comet (i.e., extraterrestrial) delivery

6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

• If I made an a-helix using D-amino acids, I’d expect left handedness because the majority of alpha helices are built from L-amino acids, which exhibit right handedness.

7. Can you discover additional helices in proteins?

• Yes additional helices can be discovered in proteins. These can include non-alpha helices and even π-helices. These can be discovered via x-ray crystallography, structure databases, or with the aid of AI prediction tools.

8. Why are most molecular helices right-handed?

• Most molecular helices are right-handed because L-amino acides force the helix backbones to conform to right-handedness. Selection pressures also favors this form of handedness in the helix for hydrogen atom bonding and overall functionality

9. Why do β-sheets tend to aggregate?

• β-sheets tend to aggregate because the edges of β-sheets have exposed nitrogen and hydrogen (donor) atomic pairs and carbon-oxygen pairs (acceptor) atoms. What happens is that when sheets get in close proximity these atomic pairs snap together, almost like a zipper.

  • What is the driving force for β-sheet aggregation?
    • The driving force for β-sheet aggregation is the attraction of the donor and acceptor atomic pairs

  Supporting prompts for this section listed below:

Part B: Protein Analysis and Visualization

• I selected the crotamine protein found in the South American rattlesnake, the protein’s abilities to penetrate cells as a toxin allows it to serve as a template for antivenom and potentially targeted therapies.

• The amino acid sequence of the crotamine protein protein is below ¹

  • AAF34911.1 crotamine [Crotalus durissus terrificus] MKILYLLFAFLFLAFLSEPGNAYKQCHKKGGHCFPKEKICLPPSSDFGKMDCRWRWKCCKKGSGK

  • This protein is 65 amino acids long. Its most frequent amino acid in this protein is lysine, which appears 11 times
  • There are 250 protein sequence homologs for the crotamine protein after running it in UniProt BLAST (ID: Q9PWF3)
  • Yes, this protein belongs to the crotamine-myotoxin family.

• Structural answers to this question are listed below:

  • The protein structure was discovered in 2005 ². It appears to be a mixed bag from a structural quality standpoint based on the NMR Structure Validation Report below: Crotamine protein RCSB NMR Structure Validation Report
  • No, there are no other molecules in the solved structure apart from the protein
  • It’s a defensin-like protein of the myotoxin family

• 3D visualization software (NGL Outputs)

  • “Cartoon”, “Ribbon”, and “Ball and Stick” combo
  • Protein coloring by secondary structure. I think it might have more sheets than helices, although I’m not sure
  • Protein by residue type. It seems like it has a good mix of hydrophobic vs hydrophilic residues
  • Protein surface visualization. It doesn’t seem like it has a lot of holes, although I’m not sure

All supporting prompts listed below:

Part C: Using ML-Based Protein Design Tools

C1. Protein Language Modeling

1. Deep Mutational Scans

   • See picture below: Crotamine Protein Deep Mutational Scan

   • There seem to be these really interesting columns that occur as one moves rightward past position 20 in the heatmap. There is a single almost uniform top-down color for these columns, which according to Gemini, indicate a special sensitivity. These are apparently disulfide bridges, which are quite important for holding the protein together (these are referred to as ‘anchors’)

2. Latent Space Analysis

   • See picture below Non-Crotamine tagged point cloud showing embedded proteins

   • Yes they do–there are approximations of similar proteins found throughout the cloud. Crotamine tagged point cloud showing embedded proteins and Crotamine’s location among embeddings

   • There are other proteins at the edge of the cloud that belong to either other viruses, or other organisms that might be found in similar locations/habitats to the South American rattlesnake (mice, chickens, humans), or other types of toxins or species carrying toxins. See picture below: Crotamine tagged point cloud showing embedded proteins and Crotamine’s location relative to its neighborhood

C2. Protein Folding

• Approach 1: It doesn’t look like the predicted coordinates match the original structure in the PDB much at all (see pictures below). Think this might be due to the fact that the original inputted structures in the PDB for this protein were apparently a bit of a mixed bag according to its NMR Structure Validation Report results (see previous section). Crotamine Protein PDB < > ESM Side-by-Side Structural Comparison (1)

  Crotamine Protein PDB < > ESM Side-by-Side Structural Comparison (2)

  Approach 2: Asked Gemini about this discrepancy and was recommended to input the mature sequence containing the last 42 residues. The result still visually seems to have some discrepancies compared with the original PDB visual upon first glance, but far less than the outputs under Approach 1 (see photo below) Crotamine Protein PDB < > ESM Side-by-Side Structural Comparison (3) – Post-Mature Sequence Input

• I tried the following mutations:

  • Cytesine Break: Per Gemini suggestion, replaced all the C residues to A (alanine). This notably decreased the predicted Local Difference Test (pLDDT) score of the outputed mutated sequence and the visual also seemed to indicate less structural integrity to the protein, implying less resilience to this mutation (see photo below) Crotamine Cytesine Break Mutation Results

  • Charge Swap: Per Gemini suggestion, replaced all the K (Lysine) and R (Arginine) residues to D (Aspartic Acid). This decreased the pLDDT score of the outputed mutated sequence a bit, but less than the Cytesine Break, indicating the protein was more comparably resilient to this mutation (see photo below) Crotamine Charge Swap Mutation Results

C3. Protein Generation

• Based on the heatmap below, there seems to be a difference between the predicted sequence probabilities here and the original heatmap generated in the ‘Deep Mutational Scans’ subsection. 48 of the 65 positions were changed and the sequence recovery rate was around 0.26, which didn’t seem all that promising Crotamine ProteinMPNN Results (1)
• Visually, this re-inserted ESMFold output also looks structurally different than the original protein structure (see photo below) Crotamine ProteinMPNN Results (2)

All supporting promopts for this section listed below:

Part D: Group Brainstorm on Bacteriophage Engineering

• My William & Mary Node Bacteriophage Engineering Group Brainstorm Google Doc. can be found here ³.

Week 5 HW: Protein Design Part 2

Using AlphaFold for Protein Optimization

Part A: SOD1 Binder Peptide Design

Part 1: Generate Binders with PepMLM

1. Retrieved human SOD1 sequence via UniProt (see photo below). Introduced A4V mutation via Gemini prompt (see sequence below). Human SOD1 sequence (A4V mutation not added)

Human SOD1 sequence (A4V mutation added)

MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

2. See results in later questions (answers and photos below)

3. See photos below

   Generated four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence

4. See photo below Added known SOD1-binding peptide FLYRWLPSRRGG for comparison

5. Perplexity scores listed below

NOTE: PepMLM Colab used to generate results above can be found here ¹

Supporting prompts for this section listed below:

Part 2: Evaluate Binders with AlphaFold3

1. Navigated to AlphaFold Server (see below):
2. See peptide results (ipTM scores and binding information) below in 3.
3. See ipTM and binding information results below:

• WRYGAAALAHKE Peptide:

  • ipTM: 0.31; peptide appears to bind near the dimer interface, and appears surface-bound, although it should be noted that the level of confidence indicated by the ipTM score is notably low, which can color the perception of these results. See photo below: WRYGAAALAHKE peptide AlphaFold Visualization Results

• WRYYAAAVELGE Peptide:

  • ipTM: 0.24; again peptide appears to bind near the dimer interface, and appears surface-bound, although it should be noted again that the level of confidence indicated by the ipTM score is again notably low, which can color the perception of these results. See photo below: WRYYAAAVELGE peptide AlphaFold Visualization Results

• WRYGPAVLALGK Peptide:

  • ipTM: 0.33; again peptide appears to bind near the dimer interface, and appears surface-bound, although once again the confidence of this assessment is not high based on the ipTM. See photo below: WRYGPAVLALGK peptide AlphaFold Visualization Results

• WLYYAVALALGE Peptide:

  • ipTM: 0.40; again peptide appears to bind near the dimer interface, and appears surface-bound, although once again the confidence of this assessment is not high based on the ipTM. See photo below: WLYYAVALALGE peptide AlphaFold Visualization Results

• FLYRWLPSRRGG Peptide:

  • ipTM: 0.29; peptide appears to engage with the β-barrel region somewhat and appears surface-bound–again the confidence of this assessment is not high based on the ipTM. See photo below: FLYRWLPSRRGG peptide AlphaFold Visualization Results

4. All of the ipTM values were low, meaning AlphaFold expressed notable uncertainty regarding peptide placement. It’s interesting to note that almost all of the PepMLM-generated peptides exceeded the FLYRWLPSRRGG 0.29 ipTM. Not sure what that means about techniques used to ascertain the relationship between the FLYRWLPSRRGG and the sequence, although it does seem to indicate PepMLM’s power

Supporting prompts for this section listed below:

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

1. See pasted peptide sequences, A4V mutant SOD1 sequences in target fields, and checked boxes results below
2. See pasted peptide sequences, A4V mutant SOD1 sequences in target fields, and checked boxes results below
3. See results below:

WRYGAAALAHKE Peptide:

• This peptide has weak binding affinity, is soluble, non-hemolytic, with a slightly positive net charge and a molecular weight of 1372.5. See results below: WRYGAAALAHKE Peptide PeptiVerse Results

WRYYAAAVELGE Peptide:

• This peptide has weak binding affinity, is soluble, non-hemolytic, with a slightly negative net charge and a molecular weight of 1427.6. See results below: WRYYAAAVELGE Peptide PeptiVerse Results

WRYGPAVLALGK Peptide:

• This peptide has weak binding affinity, is soluble, non-hemolytic, with a positive net charge and a molecular weight of 1330.6. See results below: WRYGPAVLALGK Peptide PeptiVerse Results

WLYYAVALALGE Peptide:

• This peptide has weak binding affinity, is soluble, hemolytic, with a negative net charge and a molecular weight of 1368.6. See results below: WLYYAVALALGE Peptide PeptiVerse Results

FLYRWLPSRRGG Peptide:

• This peptide has weak binding affinity, is soluble, non-hemolytic, with a positive net charge and a molecular weight of 1507.7. See results below: FLYRWLPSRRGG Peptide PeptiVerse Results

• There seems to be some relationship between higher ipTM scores and stronger predicted affinity, although it’s definitely not the type of relationship across the PepMLM-generated peptides that’s 1-to-1 or strong enough to indicate any direct form of causality. In fact the WRYYAAAVELGE peptide had the lowest ipTM score of 0.24, and yet it has the 2nd highest predicted affinity of the group (6.07). So again, we can’t say it’s a clean 1-to-1 relationship. While none of the PepMLM-generated peptides appear to have strong bindings in the general sense, the two strongest of the group, WLYYAVALALGE and WRYGPAVLALGK, are predicted to be soluble and hemolytic and soluble and non-hemolytic respectively.

Based on its Predicted Binding Affinity (5.77 pKd/pKi), Solubility (1.00), Hemolysis (Non-Hemolytic; 0.036), Net charge (ph 7) (1.76), and its Molecular Weight (1330.6 Da), it appears the WRYGPAVLALGK peptide best balances predicted binding and therapeutic properites, and therefore should be advanced based on this balance relative to the other PepMLM-generated peptides

Supporting prompts for this section listed below:

Part 4: Generate Optimized Peptides with moPPIt

1. Opened moPPit Colab

2. Made a copy and switched to a GPU runtime (see below) Switched to GPU runtime

3. Notebook results:

   • Pasted A4V mutat SOD1 sequence (see below) Pasting A4V mutant SOD1 sequence

   • Chose specific residue indices on SOD1 for peptides to bind (see below) Set specific residues indices on SOD1 for peptide binding

   • Set peptide length to 12 amino acids. Generated peptide (see below) Generated peptides

4. First off, these peptides have stronger and more specific binding than the previous PepMLM peptides. They also appear to achieve this stronger and more specific binding while simultaenously remaining non-hemolytic and soluble. It does appear that there was a slight dip in non-fouling, however. I would evaluate these peptides against the previous set, and also against the intended safety standards for anticipated means of therapeutic transmission (oral, intravenous, etc.)

NOTE: moPPit Colab used to generate results above can be found here ²

Supporting prompts for this section listed below:

Part B: BRD4 Drug Discovery Platform Tutorial (Optional)

• Did not complete Part B

Part C: Final Project: L-Protein Mutants

• Part C Homework and supporting prompts can be found at the hyperlink below ³
• Part C Colab Notebooks can be found at the hyperlinks located below ^{4 5}

Week 6 HW: Genetic Circuits Part 1

• Robot Crafting Genetic Circuit (Stylized)

DNA Assembly

1. What are some components in the Phusion High-Fidelity (HF) PCR Master Mix and what is their purpose?
   • HF DNA Polymerase: This is the enzyme responsible for copying DNA as it moves from the 5’ to the 3’ position across the DNA
   • Deoxynucleotide triphosphates (dNTPs): These are the DNA molecular building blocks, consisting of Adenine (A), Thymine (T), Cytosine (C), and Guanine (G) variants
   • HF Buffer: This consists of magnesium chloride, which is salt added to the reaction. It matters because it dissolves into Mg²⁺, which helps nucleotides bond during the reaction
2. What are some factors that determine primer annealing temperature during PCR?
   • Some factors that determine primer annealing tempeature during PCR include:
     • Primer lengths
     • Primer melting tempratures
     • GC content/sequence content
     • Buffer components
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: PCR creates new linear DNA fragments by via enzymatic amplification of a given region nth number of times. The PCR protocol essentially consists of setting up reaction mixes, denaturating the DNA into single strends, annealing so primers can anneal to specific complementary sequences, extension so the polymerase can syntehsize a new strand, and then repeating this as many times as neccessary. This method might be more useful when there is a specific fragment of DNA one wants to amplify for further use.
   • Restriction Enzyme Digests: Restriction Enzyme Digests create new linear DNA fragments by cutting DNA at specific points/recognition sites. The Restriction Enzyme Digest protocol consists of setting up a reaction mix, incubation, and then stopping the reaction. This method might be more useful when there is a specific fragment of DNA one wants to isolate for further analysis.
4. How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?
   • You can ensure the DNA sequences have appropriate 5’ –> 3’ orientation with corresponding overlaps. Fragments salso need to cover the relevant region for cloning, and also need to be inserted at the appropriate molar ratio relative to the plasmid backbone (vector). This is usually a 2:1 ratio.
5. How does the plasmid DNA enter the E. coli cells during transformation?
   • The plasmid DNA enters the E. coli either via heat shock (temperature change) or electroporation (high electrical voltage). Both methods shock the E. coli cell, causing its cell membrane to open for the plasmid DNA to enter.
6. Describe another assembly method in detail (such as Golden Gate Assembly)
   • DNA topoisomerase I (TOPO) Cloning: TOPO cloning’s traditionally used, as it’s a fast, reliable method for cloning products from PCR for later sequencing, etc. The first step in TOPO cloning is generating an insert with Taq polymerase via PCR. This creates inserts with an A-overhang, which can then help address the second step. The second step is to combine this PCR product with the TOPO vector. This is usually done for a couple of minutes. The insert’s 5’ OH/hydroxyl interacts with the TOPO DNA at its end, and as part of this process A and T base pairing occurs between the respective insert and the vector . Then the TOPO religates the strangs and dissociates, creating a closed circular plasmid with the given insert. See diagrams below:

• See results below:

  

Supporting prompts for this section listed below:

Asimov Kernel

1. See Repository below

   • 
     • Created Asimov Repository

2. See blank Notebook entry below

   • 
     • Created Asimov Notebook

3. See results below

   • 
   • 
   • 
   • 

   Explored Bacterial Repos Devices and ran Simulator on various examples (see above)

4. See Construct creation results below

   • Question 1-3 Results: Recreated Repressilator in empty Construct using Characterized Bacterial Parts repository parts, searched and selected parts using the Search function, and dragged and dropped parts into Construct (see photos below)
     • 
     • 
     • 
   • Question 4 Results: The Repressilator wasn’t running as expected so I re-made it and ensured I directlty copied and pasted everything. Then I re-ran the Simulation and the Repressilator operated as expected (see photos below)
     • 
     • 
   • Question 5 Results: Documented results in Notebook (see below)
     • 

5. See 3 Construct creation results below

   • Created 3 Constructs, explaining in each Notebook entry how I thought the Constructs should operate and why (see photos below)
     • 
     • 
       • Construct 1 Results
     • 
     • 
       • Construct 2 Results
     • 
     • 
       • Construct 3 Results

Supporting prompts for this section listed below:

Week 7 HW: Genetic Circuits Part 2

Genetic Circuits Part 2

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

1. Unlike traditional genetic circuits, IANNs are analog, and as such correspond more closely to the nature of biological systems (i.e, we’re not always looking for strict 0/1 binary logic, sometimes we’re looking to establish control across a range of values or space/time). This analog nature means they are more responsive, efficient, and biocompatible.

2. Zoonotic Reservor IANNs (ZRIANNs)

   • This is applying IANNs to proactively identify and eliminate zoonotic pathogens in host organisms (ex. cattle and other forms of livestock, bats). By establishing prior understanding of organism homeostatis, ZRIANNs can co-evolve their understanding of nominal organism cellular activity. Across ZRIANNs, this might act as a form of input. When there are deviations from said baselines due to the emergence of novel pathogens trigering events like elevated inflammation levels or unexpected apoptosis, this would act as another form of input, triggering the ZRIANNs to begin understanding novel pathogen behavior for elimination, which would be a form of output. Limitations might include the novel of an IANNs across diverse non-human hosts, difficulties in IANNs achieving homeostatic understanding and insights, and difficulties in IANNs detecting novel, essentially zero-day equivalent pathogens

3. See my attempt at a diagram below:

Intracellular multilayer perceptron attempt

Supporting prompts for this section listed below:

Assignment Part 2: Fungal Materials

1. Existing fungal materials include mycellium-based composites (MBCs), flexible fungal materials (FFMs), and pure mycellium materials. They’re used for use cases such as packaging, fashion, construction, and health and beauty products. Compared to their traditional counterparts, their advantages include greater environmental sustainability (including less use of petroleum and less harm to animals in their construction and use) with high levels of customization and low density Their disadvantage seem to be their load-bearing strength in some applications, and more variability as it’s biologically grown.
2. I’d personally be interested to see if fungi could be genetically engineered to improve air or water quality and filtration in remote environments where air or water filters may not always be available/readily attainable. My why is rather simple – it would seem rather convenient and environmentally sustainable if air and water filters could be biologically grown as opposed to manufactured and shipped through traditional processes. The advantages of doing synthetic biology in fungi as opposed to bacteria include greater ability to fold complex proteins and a greater ability to form larger, more complex macroscopic structures

Assignment Part 3: First DNA Twist Order

0. Reviewed Final Project documentation guidelines
1. Submitted Google Form
2. Created Benchling for insert sequence input (https://benchling.com/bio_star_39/f_/MMV1lxUm3Y-htgaa-final-project-working-folder/)

Week 9 HW: Cell-Free Systems

Homework Part A: General and Lecturer-Specific Questions

Cell-Free Systems

General Homework Questions

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.
   • Cell-free systems allow for a broader range of potential chemistries than those given to us from natural biology, expanding flexibility. Cell-free protein synthesis also allows for greater control over experimental variables because the entire protein expression construct is designed from scratch (i.e., we have the opportunity to bypass a lot of the compleity of natural cells). Cell-free expression is more beneficial than cell production if you want to rapidly protoype gene pathways and if you want an expression mechanism that’s more amenable to consistent, predictable modeling and analysis.
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 expression system are (based on elements described in this hyperlink ¹):
     • DNA template: Genetic code to begin Tx/Tl process
     • Ribosomes: Assembling amino acids into polypeptides
     • Enzymes: Catalyzing certain important chemical reactions necessary for the appropriate functioning of that cell-free expression system (ex. transcription and translation, energy generation)
     • Amino Acids: The core chemical building blocks of the proteins the cell-free expression system will express
     • Polymerases: Synthesizing DNA and RNA
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 regeneration is critical in cell-free systems because cell-free systems don’t consume enzymes to produce energy. They also need external energy sources to remove waste products. A workaround might be to have analogous enzymatic reactions (possibly based off shared common charges) within the cell-free system to produce energy
4. Compare prokaryotic versus eukaryotic cell-free expression systems. Choose a protein to produce in each system and explain why.
   • Prokaryotic cell-free expression systems allow for the colocation of transcription and translation. This might work well for proteins that need to be produced at high volume, like an industrial protease prtoein. Eukaryotic cell-free expression systems allow for more complex proteins to be built due to their nuclei. This might work well for the production of more advanced/technically complex proteins, like rabbit serum albumin.
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.
   • In a manner similar to Shuguang Zhang ‘molecular glove’ experiment, I’d try to essentially coat and/or surround the the membrane protein with hydrophilic proteins to attract and/or absorb water in the cell-free environment, so the membrane protein can incorporate into the liposome ². Challenges might include appropriate hydrophilic concentrations (which might be discerned via calculations or trial and error) or bonding between the hydrophilic proteins and the membrane proteins. This might be mitigated and/or the amount of error reduced through the use of computaitonal modeling and simulation tools like AlphaFold
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.
   • Suboptimal Ribosome Function: Examine ribosome mRNA transcription processes and modify as necessary
   • Suboptimal Transcription: Examine tRNAs for coding errors/misreads or inappropriate expression levels and modify as necessary
   • Suboptimal External Communication (i.e., yields cannot properly exit system at desired levels): Examine and modify membrane channel functionality as necessary

Supporting prompts for this section listed below:

Homework question from Kate Adamala

Based on Iulianna, T., Kuldeep, N. & Eric, F. The Achilles’ heel of cancer: targeting tumors via lysosome-induced immunogenic cell death. Cell Death Dis 13, 509 (2022). ³

Design an example of a useful synthetic minimal cell as follows:

1. Pick a function and describe it

• What would your synthetic cell do? What is the input and what is the output?
  • Increase apoptosis in mammalian cells with defective lysosomes. Input: Protein kinase R-like endoplasmic reticulum kinase (PERK). Output: Phosphorylated eukaryotic initiation factor 2 α-subunit (elF2a)
• Could this function be realized by cell-free Tx/Tl alone, without encapsulation
  • No, it appears that communication with the external environment as well as some form of an encapsulating membrane are necessary for these immunogenic cell death (ICD) reactions to properly work
• Could this function be realized by genetically modified natural cell?
  • Believe this function could be realized by a genetically modified natural cell. If PERK expression levels could be increased, this could increase elF2a phosphorylation
• Describe the desired outcome of your synthetic cell operation.
  • Increased PERK expression levels lead to increased elF2a phosphorylation

2. Design all components that would need to be part of your synthetic cell

• What would be the membrane made of?
  • Mostly phospholipids and some (a relative minority percentage) of cholesterol
• What would you encapsulate inside? Enzymes, small molecules.
  • PERK, elF2a, Adeonsine Triphosphate (ATP), GTP, Creatine Phosphate, Reporter (likely GFP)
• Which organism your Tx/Tl system will come from? Is bacterial OK, or do you need a mammalian system for some reason? (hint: for example, if you want to use small molecule modulated promotors, like Tet-ON, you need mammalian)
  • Believe a mammalian system would be needed as this is meant to mimic a homo sapiens-based eukaryotic system
• How will your synthetic cell communicate with the environment? (hint: are substrates permeable? or do you need to express the membrane channel?)
  • It should have permeable substrates, as my understanding of the PERK pathway seems to indicate that external communication with the environment via a permeable membrane is necessary for the PERK pathway to appropriately function (i.e., for the increase in the PERK expression levels to induce greater elF2a expression)

3. Experimental details
   • List all lipids and genes. (bonus: find the specific genes; for example, instead of just saying “small molecule membrane channel” pick the actual gene.)
     • Lipids: POPC, Cholesterol
     • Enzymes: Binding immunoglobulin protein (BiP), PERK, ATP, elF2B, Growth arrest and DNA damage-inducible 34 (GADD34)
     • Genes: HSPA5 gene/GRP78 (for BiP expression), ELF2AK3 (encodes PERK protein), EIF2S1 (encodes elF2a), ATF4, DDIT3, and PPP1R15A (latter 3 genes necessary for apoptotic response)
   • How will you measure the function of your system?
     • Measure presence of GFP reporter to show that ribosomes are shutting down and apoptosis is beginning

Supporting prompts for this section listed below:

Homework question from Peter Nguyen

• Write a one-sentence summary pitch sentence describing your concept.
  • Robotics Use Case: Thinking about using cell-free systems delivered/facilitated by drones to collect metagenomic samples from remote environments, for the pupose of expanding biosurveillance beyond the traditional wastewater sampling
• How will the idea work, in more detail? Write 3-4 sentences or more.
  • Drone (or potentially a larger drone ship like an evTOL) would deliver robots and kits with cell-free reactions. These robots might be similar to Mars rovers or bomb-defusing robots. The kit would ideally auto-unload once the drone has reached its given destination, the robot would have to complete a set (i.e., limited and discrete) number of specific steps to collect and store the metagenomic sample. If analysis of the metagenomic sample could be done in real-time or a short time duration, that would be beneficial. If this could not be done, there would essentially be a ‘packing’ step before the robot and the now-utilized kits return to their origin site for sample processing/analysis
• What societal challenge or market need will this address?
  • The need to expand biosurveillance beyond the purely human environment into more remote locales and animal populations
• How do you envision addressing the limitation of cell-free reactions (e.g., activation with water, stability, one-time use)?
  • Would need secure storage to maintain stability. Activation with water challenges would require an appropriate water disposal mechanism(s) either within/near the sample kit or facilitated by the robot without harming the robot (or the robot might have some form(s) of waterproof protection). One-time use isn’t an issue for this use case because there are ample examples from the world of biosurveillance where one-time sample collection is the aim

Homework question from Ally Huang

1. Provide background information that describes the space biology question or challenge you propose to address. Explain why this topic is significant for humanity, relevant for space exploration, and scientifically interesting.
   • As humans travel farther out into space, particularly on remote, potentially skeleton-crew missions, they may not be able to bring blood supplies for adverse events like necessary transfusions. Moreover, blood banks play an important terrestrial role that might need replication. The basic idea is to engineer liver cells to create blood proteins or blood-like fluid on demand a-la the high-level idea initially proposed in the hyperlinked Engineering Biology Research Consortium (EBRC) Roadmap document ⁴
2. Name the molecular or genetic target that you propose to study. Examples of molecular targets include individual genes and proteins, DNA and RNA sequences, or broader -omics approaches.
   • I’d like to study the albumin plasma protein
3. Describe how your molecular or genetic target relates to the space biology question or challenge your proposal addresses.
   • Albumin has a vital role in the liver’s production of blood-related proteins. Therefore, it would seem rather improbable to have engineered liver cells to create blood proteins without some sort of working albumin configuration or some analog
4. Clearly state your hypothesis or research goal and explain the reasoning behind it.
   • I’d like to study how to modulate or fine-tune albumin expression levels in microgravity, as it appears microgravity exposure can cause albumin levels to increase ⁵.
5. Outline your experimental plan - identify the sample(s) you will test in your experiment, including any necessary controls, the type of data or measurements that will be collected, etc.
   • I’d include purified albumin plasma protein as a sample in my experiment, as well as some form of small interfering RNA (siRNA) for lowering albumin expression, and GFP for measuring expression level change. I’d use Biobits and the P51 Molecular Fluorescence Viewer to measure the impact of siRNA modulation on albumin expression. I’d collect information on oncoctic pressure modulations across a microgravitty and terrestrial experimental configurations. The terrestrial configuration would serve as a control to indicate comparative rates of siRNA modulation efficacy. GFP expression would indicate certain levels of albumin expression post-siRNA modulation

Homework Part B: Individual Final Project

1. Put your chosen final project slide in the appropriate slide deck following the instructions on slide 1

   • Inserted slide in Committed Listener Deck

2. Submit this Final Project selection form if you have not already.

   • Submitted FInal Project selection form (see screenshots below)

3. Begin planning how you will write your final project documentation based on these guidelines

   • Began writing final project documentation based on hyperlinked guidelines

4. Prepare your first DNA order and put it in the “Twist (MIT)” or “Twist (Nodes)” tab of the 2026 HTGAA Ordering: DNA, Reagents, Consumables spreadsheet, as appropriate.

Week 10 HW: Advanced Imaging & Measurement Technology

Waters Corporation Mass Spectrometer

Homework: Final Project

For your final project:

• Please identify at least one (ideally many) aspect(s) of your project that you will measure.
  • Lysis Rate
  • Efficiency of Plating
• Please describe all of the elements you would like to measure, and furthermore describe how you will perform these measurements.
  • Lysis Rate: This measures the rate at which the mutated m. smegma mycobacteriophage lyses or destroys bacteria. This would be measured in a wet lab setting by comparing percentages of bacteria across a control and another plate that has been exposed to a mutated form of m. smegma mycobacteriophage
  • Efficiency of Plating: This measures the rate at which the mutated m. smegma mycobacteriophage can begin initiating a host infection. Believe this would also be measured in a wet lab setting by comparing percentages of bacteria across a control and another plate that has been exposed to a mutated form of m. smegma mycobacteriophage
• What are the technologies you will use (e.g., gel electrophoresis, DNA sequencing, mass spectrometry, etc.)? Describe in detail.
  • Lysis Rate: I’d likely use a microplate reader as part of a wet lab extension of the final project
  • Efficiency of Plating: I’d use a plauqe assay as part of a wet lab extension of the final project

Supporting prompts for this section listed below:

Homework: Waters Part I — Molecular Weight

1. Based on the predicted amino acid sequence of eGFP (see below) and any known modifications, what is the calculated molecular weight?

• The calculated molecular weight is 28006.60 Mw

2. Calculate the molecular weight of the eGFP using the adjacent charge state approach described in the recitation. Select two charge states from the intact LC-MS data and:

3. Determine z for each adjacent pair of peaks (n, n+1) using:

   • Chose 933.7349 and 965.9684 from the Figure 1 chart. Based on the formula z = ~28.96

4. Determine the MW of the protein using the relationship between m/z, MW, and z

   • MW = 27,983.85 Daltons

5. Calculate the accuracy of the measurement using the deconvoluted MW from 2.2 and the predicted weight of the protein from 2.1

   • The result of the measurement I got was -0.0008 ppm

6. 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?

• Believe the answer’s no because the zoomed-in peak from my understanding is not uniform. Instead it constitutes a variety of different charged states that cannot be discerned as a singular discrete value. Absolutely open to being wrong on this

Supporting prompts for this section listed below:

Homework: Waters Part II — Secondary/Tertiary Structure

1. 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
   • Believe native proteins are not manipulated in any way (i.e., their properties are not altered via heat or other impacts) while denatured proteins are proteins where these properties are destroyed via direct alteration, usually by applying something like heat or acidity. This is determined in a mass spectrometer via distribution of charges across respective proteins. In the denatured protein in Figure 2, there appears to be a somewhat more Gaussian charge distribution, where the native protein below has a more spread out charge distribution
2. 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?
   • Observing a +10 charge state. This because the two peaks near ~2800 (2799.4199 and 2799.6365) have a 0.2166 difference between them, which equates to ~+10 when the 1/z is calculated

Supporting prompts for this section listed below:

Homework: Waters Part III - Peptide Mapping - Primary Structure

1. How many Lysines (K) and Arginines (R) are in eGFP? Please circle or highlight them in the eGFP sequence given in Waters Part I question 1 above.

• There are 18 Lysines (K) and 6 Arginines (R) in eGFP. (See screenshot below)

2. How many peptides will be generated from tryptic digestion of eGFP? Believe 25 peptides will be generated

• Navigate to https://web.expasy.org/peptide_mass/
• Copy/paste the sequence above into the input box in the PeptideMass tool to generate expected list of peptides
• Use Figure 4 below as a guide for the relevant parameters to predict peptides from eGFP.
• Click “Perform the Cleavage” button in the PeptideMass tool and report the number of peptides generated when using trypsin to perform the digest.
  • Confirmed 25 generated peptides (see screenshot below)

3. Based on the LC-MS data for the Peptide Map data generated in lab (please use Figure 5a as a reference) how many chromatographic peaks do you see in the eGFP peptide map between 0.5 and 6 minutes? You may count all peaks that are >10% relative abundance.

   • I count 25 peaks

4. Assuming all the peaks are peptides, does the number of peaks match the number of peptides predicted from question 2 above? Are there more peaks in the chromatogram or fewer?

   • I see a chromatographic peak match

5. Identify the mass-to-charge (m/z) of the peptide shown in Figure 5b. What is the charge (z) of the most abundant charge state of the peptide (use the separation of the isotopes to determine the charge state). Calculate the mass of the singly charged form of the peptide based on its (m/z) and z.

   • The charge (z) of the most abundant charge state of the peptide equals ~2.0323. The mass of the singly charged form of the peptide is 1,067.4760

6. Identify the peptide based on comparison to expected masses in the PeptideMass tool. What is mass accuracy of measurement? Please calculate the error in ppm.

   • Think the peptide’s in position 115-123 (peptide sequence FEGDTLVNR). The mass accuracy of measurement’s ~0.0161 ppm

7. What is the percentage of the sequence that is confirmed by peptide mapping?

   • According to the PeptideMass tool, 90.7% of the sequence is confirmed by peptide mapping

Supporting prompts for this section listed below:

Homework: Waters Part IV - Oligomers

• 7FU Decamer
  • Sits directly to the left of the 4.013 peak between 0 and 5 MDa axis in Figure 7
• 8FU Didecamer
  • Very tall 8.33 MDa peak sitting between 5 and 10 MDa on the MDa axis in Figure 7
• 8FU 3-Decamer
  • The 12.67 MDa peak sitting between 10 and 15 MDa on the MDa axis in Figure 7
• 8FU 4-Decamer
  • The tiny peak sitting between 15 and 20 MDa on the MDa axis in Figure 7

Homework: Waters Part V - Did I Make GFP?

• See screenshots below:

	Theoretical	Observed/measured on the Intact LC-MS	PPM
Molecular weight (kDa)		27,983.85 Daltons	-0.0008 ppm

Week 11 HW: Bioproduction and Cloud Labs

Part 1: Global Pixel Artwork Cloud Lab Contribution

Made the following contributions to the Global Pixel Artwork Cloud Lab (see screenshots below)

Global Pixel Artwork Contributions (see above). Edited 4 pixels in the upper right hand corner of the image (changed them to sfGFP)

Week 12 HW: Bioproduction & Cloud Labs Part 2

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

1. Contribute at least one pixel to this global artowrk experiment before the editing ends on Sunday 4/19 at 11:59 PM EST.

   • Contributed 4 pixels to the global artwork experiment on Saturday 4/18

2. Make a note on your HTGAA webpages including:

   • what you contributed to the community bioart project

     • I contributed 4 pixels to the community bioart project. I changed 4 pixels in the upper right plate to sfGGP (see screenshots below)

       

       Community bioart project contributions – changed 4 upper right plate pixels to sfGFP (see above)

   • what you liked about the project

     • The project was a nice opportunity to contribute to a larger HTGAA effort. It was nice to see the creativity of the community at play! I also appreciated Ronan’s page for contributing pixels – very intuitive and easy to understand

   • what about this collaborative art experiment could be made better for next year

     • I’d probably say a bit more advance notice might have been useful. Perhaps a bit more clarity on ground rules. These are relatively minor nitpicks in the grand scheme of things

Part B: Cell-Free Protein Synthesis | Cell-Free Reagents

1. Referencing the cell-free protein synthesis reaction composition (the middle box outlined in yellow on the image above, also listed below), provide a 1-2 sentence description of what each component’s role is in the cell-free reaction.
   • E. coli Lysate
     • BL21 (DE3) Star Lysate (includes T7 RNA Polymerase)
       • I think BL21 (DE3) Star Lysate’s role is to provide the E. coli bacteria necessary to be synthesized into fluorescent proteins. Basically it seems to serve as a starting ingredient/necessary component, for lack of a better word

• Salts/Buffer
  • Potassium Glutamate
    • Helps aid in the elongation portion of the RNA –> protein translation process. In this context, I think elongation refers to the reaction timeframe
  • HEPES-KOH pH 7.5
    • Important cell buffer. It allows for extra buffering capacity if cell culture manipulation occurs for prolonged/longer than normal time period
  • Magnesium Glutamate
    • Helps stablizie ribosome construction. Ir also helps neutralize mRNA and DNA backbone negative charge
  • Potassium phosphate monobasic
    • Potassium source and buffer for the reaction, helping stabilize pH levels/keep them nominal during the reaction. Contains 1 replaceable atom
  • Potassium phosphate dibasic
    • Another potassium source and buffer for the reaction, helping stabilize pH levels/keep them nominal during the reaction. Contains 2 replaceable atoms
• Energy / Nucleotide System
  • Ribose
    • Key cell energy source. It’s crucial for creating adenosine triphosphate (ATP) the primary form of energy within cells
  • Glucose
    • Another essential energy source for cell processes. Also helps produce ATP
  • AMP
    • Adenosine Monophosphate (AMP) is a metabolite helping regulate energy levels. It acts as a form of an ATP sensor/response mechanism
  • CMP
    • Cytidine monophosphate (CMP) assists with RNA synthesis. It helps decompose RNA into ribonuclease (RNase)
  • GMP
    • Guanosine monophosphate (GMP) is key for RNA synthesis and regulates cellular signaling. Helps polymerize RNA
  • UMP
    • Uridine monophosate (UMP) is a pyrimidine compound. It also helps polymerize RNA.
  • Guanine
    • Nucleic acid base that pairs with cystosine in double-stranded DNA. It’s used to build RNA during the transcription process.
• Translation Mix (Amino Acids)
  • 17 Amino Acid Mix
    • The mix provides a group of compatible amino acids for translation by ribosomes. These are the materials ribosomes work with for translation into proteins
  • Tyrosine
    • Assists with protein synthesis. Also asists with phosphate group post-translational modification (PTM).
  • Cysteine
    • An amino acid used by ribosomes to build a protein chains during the translation process. Helps with protein folding and stability.
• Additives
  • Nicotinamide
    • Helps manage the process of cellular nutrients converting themselves to ATP and vice versa. Think this means it also might help with reaction energetic stability
• Backfill
  • Nuclease Free Water
    • Ensures appropriate reaction concentration. It also ensures no extraneous enzymes destroy reaction byproducts

2. Describe the main differences between the 1-hour optimized PEP-NTP master mix and the 20-hour NMP-Ribose-Glucose master mix shown in the Google Slide above.

• Believe the main difference between the 1-hour optimized PEP-NTP master mix and the 20-hour NMP-Ribose-Glucose master mix is that the 1-hour optimized PEP-NTP master mix optimizes for the fast production of flourescent proteins, while the 20-hour NMP-Ribose-Glucose master mix optimizes for flourescent proteins across a longer timespan. While this might seem obvious based on the slide content, my understanding is that the PEP-NTP master mix is more energy intensive (i.e., it essentially consumes more energy faster to create flourescent protein output) while the NMP-Ribose-Glucose master mix is comparatively less energy intensive (i.e., it essentially consumes either less energy slower across its 20 hour-reaction timespan to create flourescent protein output or it consumes the same amount of energy across its 20 hour-reaction timespan in a less energy intensive fashion). So, in essence, I think the main difference between these reactions comes down to their respective energy constumption levels

Supporting prompts for this section listed below:

Part C: Planning the Global Experiment | Cell-Free Master Mix Design

1. Given the 6 fluorescent proteins we used for our collaborative painting, identify and explain at least one biophysical or functional property of each protein that affects expression or readout in cell-free systems. (Hint: options include maturation time, acid sensitivity, folding, oxygen dependence, etc) (1-2 sentences each)

   • sfGFP
     • It has a relatively quick maturation time (13.6 min.). This means researchers can find out whether or not the cell-free reaction occurred successfully rather quickly if they were solely measuring this protein’s flourescence
   • mRFP1
     • It has a comparably longer maturation time (60 min.). This means researchers might need to wait a bit to determine whether or not the cell-free reaction occurred successfully if they were solely measuring this protein’s flourescence
   • mKO2
     • Acid sensitivitiy levels are 5.5 pKa. This means that if pH drops from the typical 7.5 pH of a common cell-free reaction, this will cause the flourescence to not show
   • mTurquoise2
     • It’s acid sensitivitiy levels are 5.5 pKa and its maturation time is also relatively quick (33.5 min.). This means its realtively resistant to drops in pH (i.e., a flourescence readout will still occur) and a researcher can discern whether or not a successful reaction occurred relatively quickly if they were solely measuring this protein’s flourescence
   • mScarlet_1
     • It’s acid sensitivitiy levels are 5.3 pKa and its maturation time is comparatively long (174 min.). This means the protein’s relatively senstive to pH drops from the common mean, and it will also take several hours for a researcher to discern whether or not a successful reaction occurred if they were solely measuring this protein’s flourescence
   • Electra2
     • It’s the second brightest of all the proteins in this list. It has a 61.48 brightness readout (the brightest protein is mScarlet_1 with a 70.0 brightness readout)

2. Create a hypothesis for how adjusting one or more reagents in the cell-free mastermix could improve a specific biophysical or functional property you identified above, in order to maximize fluorescence over a 36-hour incubation. Clearly state the protein, the reagent(s), and the expected effect.

   • I hypothesize that if I increase ribose and/or glucose reagent concentrations in the cell-free mastermix, it will increase sfGFP brightness over a 36-hour incubation period relative to its nominal brightness rate (54.15)

3. The second phase of this lab will be to define the precise reagent concentrations for your cell-free experiment. You will be assigned artwork wells with specific fluorescent proteins and receive an email with instructions this week (by April 24). You can begin composing master mix compositions here ¹

   • Test reagent master mix compositions based on the hypothesis above shown in the screenshots below

   Test master mix composition (increase ribose)

   Test master mix composition (increase glucose)

4. The final phase of this lab will be analyzing the fluorescence data we collect to determine whether we can draw any conclusions about favorable reagent compositions for our fluorescent proteins. This will be due a week after the data is returned (date TBD!). The reaction composition for each well will be as follows:

   • 6 μL of Lysate
   • 10 μL of 2X Optimized Master Mix from above
   • 2 μL of assigned fluorescent protein DNA template
   • 2 μL of your custom reagent supplements

Supporting prompts for this section listed below:

Week 13 HW: Scaling Health Innovation

Master Mix Concentrations

• See test Master Mix Concentrations below:

Week 1 Lab: Introduction to Pipetting and Dilutions

Overview

• • Date(s): 03/02/26 – 03/03/26
  • Notes: Reviewed lab materials outlined in ‘Overview’ protocol section (pipette types and tips, tubes, tube holders, and stock reagents) and concentration basics with Kate Carline (William & Mary Node TA). Discussed lab material functions and reviewed the basics of dilution math and pipetting technique.
  • Supporting Picture(s):
    • 

Part 1: Mixing Color

• Prepared tubes with red, yellow, and blue food coloring solution
• Marked 6 tubes with red, yellow, blue, red/yellow, blue/yellow, and red/blue combinations
• Added 500 uL to each each red, yellow, blue, red/yellow, blue/yellow, and red/blue combination solution tube
• See above – made combinations by mixing colors
• See above
• See above
• Dispersed concentrations onto wax paper to make design in lieu of petri plate

  • Supporting Part 1 and Part 2 photos below

    

    Practiced basic pipetting, mixing colors, and performing serial dilution

Part 2: Performing Serial Dilution

• Performed serial dilutions on MS/food coloring
• Made a final serial dilution reaction based on the information in the pre-lab
  • See pictures above

Week 2: DNA Gel Art

DNA Gel Art

Protocol Part 0: Designing My Gel Art / Expected Results and Walkthrough

Created a virtual digest in Benchling as a basis for DNA Gel Art (see below)

Benchling Virtual Digest (A Hidden Hello)

Protocol Part 1a: Preparing a 1% Agarose Electrophoresis Gel

Preared a 1% Agarose Electrophoresis Gel (see below)

Protocol Part 1a: Restriction Digest

Ran Restriction Digest (see images below)

Protocol Part 2: Gel Run

Performed Gel Run (see mp4s below)

Protocol Part 3: Imaging My Results With a Transilluminator

Took gel and prepared to image results (see below)

Final Results

Final result (see below)

//

Benchling Protocol Notes (sourced from Wiliam & Mary Node TA, Kate Carline)

NotI-HF: rCutSmart, incubates at 37C, 20,000 U/ml = 10 U/ul Kpn1 (Promega): Buffer J, incubates at 37C, 12,000 U/ml = 12 U/ul Sal1 (Promega): Buffer D, incubates at 37C. 10,000 U/ml = 20 U/ul

1.5 ug DNA 324 ng/ul of Kampy B 4.62 ul DNA for N and K 141.4 ng/ul Kampy C (Nanodrop after running out of Kampy B) 10.61 ul for S

15 units of enzyme 1.5 uL Not1-HF 1.25 ul Kpn1 0.75 ul Sal1

2 ul of each 10X Buffer

Remaining to 20 ul NFW 11.88 ul Not1 12.13 ul Kpn1 6.64 ul Sal1

Spin down briefly in picofuge

Incubated for 30 min at 37C

1% agarose gel 2 ul dye with 10 ul reaction 40 min 185 mA 150V //

Week 3 Lab: Opentrons Art

Opentrons Art Lab

Part 1: Flourescent Bacteria & Black Agar Script

See Flourescent Bacteria & Black Agar Script Colab Notebook Script here ^{1 2}

Part 2: Submission and Running Your Protocol

Traveled to William & Mary Node to complete this lab, as well as the Pipetting and DNA Gel Art Labs. During my time working this lab, I:

• Selected plates for the Opentrons robot
• Operated the Opentrons robot with the help of William & Mary students
• Ran my Opentrons code
• Dispensed Opentrons tips

Protocol photos and mp4 video loops shown below:

Part 3: Final Result

Here’s the final result, showing my Opentrons Art!

Week 4 Lab: Protein Design Part I

Lab Information

• Lab work can be found within the Week 4 HW Assignment in the hyperlink below ¹

Week 5 Lab: Protein Design Part II

Lab Information

• Lab work can be found within the Week 5 HW Assignment in the hyperlink below ¹

Week 6 Lab: Gibson Assembly

Gibson Assembly Lab

Pre-Lab: Primer and PCR (Part 1 of 3)

• Read this section and scanned the NuPack software hyperlink

Pre-Lab: Gibson Assembly (Part 2 of 3)

• Read this section

Pre-Lab: DpnI

• Read this section

Pre-Lab: Plasmid Transformation

• Read this section

Part 1: Polymerase Chain Reaction (PCR)

Prepared PCR (see photos below)

Part 1a: DpnI Digest

Completed DpnI Digest (see photos below)

Part 1b: DNA Purification and Quantification

Purified and quantified DNA. It seems at this point that I did something wrong in one of the proceeding protocol stages with my non-Blue chosen color, so instead of proceeding with both colors, I only proceeded with Blue, as the other color did not have an adequate concentration. See photos below for more documentation of this protocol step

Part 2a: Gibson Assembly

Completed Gibson Assembly. Incubated reaction per protocol (see photo below)

Part 2b: Transformation

Completed Transformation protocol step

Final Results

LOREM

//

Supporting prompts for analyzing the lab protocol listed below for reference

Week 7 Lab: Neuromorphic Circuits

LOREM IPSUM

Week 9 Lab: Cell-Free Systems

LOREM IPSUM

Individual Final Project

Group Final Project