Assignments: Class 1 Assignment Question 1
I propose a high-throughput microscopy tool to estimate intracellular PHA accumulation from granule count and size. Current standart quantification methods are slow, labor-intensive, and often require hazardous solvent-based extraction. By pairing PHA staining (e.g., Sudan Black B or Nile Red A) with automated imaging and machine-learning (ML) image segmentation, this approach could rapidly screen large libraries of environmental isolates and recombinant strains for high PHA producers.
Python Script for Opentrons Artwork Here’s my HTGAA 2026 Opentrons Art Python Script Submission.
The artistic design I created using the GUI is available here.
I heavily used the “Example 7 Microbial Earth” by Dominika Wawrzyniak, using pixels loaded from an external resource (a CSV file hosted on my GitHub page).
I used Dominika’s well documented Notion page from HTGAA21 to understand the code and replicate it for my case. I used Gemini assistance only to debug minor typos and syntax errors, and to identify which packages to import to execute the code.
Subsections of Homework
Week 1 HW: Principles and Practices
Assignments: Class 1 Assignment
Question 1
I propose a high-throughput microscopy tool to estimate intracellular PHA accumulation from granule count and size.
Current standart quantification methods are slow, labor-intensive, and often require hazardous solvent-based extraction. By pairing PHA staining (e.g., Sudan Black B or Nile Red A) with automated imaging and machine-learning (ML) image segmentation, this approach could rapidly screen large libraries of environmental isolates and recombinant strains for high PHA producers.
Future upgrades, offered as a premium beta for testing, could add a “material profile” output by predicting PHA chain-length class (SCL, MCL, or LCL) from staining/fluorescence response patterns using the lipophilic dyes. This would enable not only faster strain selection but also early-stage differentiation of polymer type, which is critical for downstream biotechnology applications.
A further upgrade could generate image-driven optimization suggestions from microscopy images. For example, if it detects a high level of extracellular debris consistent with cell lysis, or a high abundance of product granules outside the cells, it could recommend exploring strain-engineering strategies that alter cell membrane composition to increase tolerance to mechanical stress and support higher intracellular polymer accumulation as cytoplasmic granules.
Question 2
Gov / Policy Goal 1: Prevent harmful misuse
• Sub-goal 1.1 - Limit repurposability: Reduce the extent to which the tool can be used as a general-purpose and high-throughput optimization engine outside its intended PHA scope, for example by restricting supported dyes and limiting microscopy calibration parameters to validated settings.
• Sub-goal 1.2 - Increase accountability: ensure high-impact uses are traceable and that institutions have a mechanism to intervene if misuse is suspected.
Gov / Policy Goal 2: Promote safe, responsible operation and research integrity
• Sub-goal 2.1 - Standardize safe use: Require adherence to Standard Operating Procedures (SOPs) for staining, imaging, and waste handling.
• Sub-goal 2.2 - Ensure competent users: Require completion of a short training module, including lab safety + tool-specific quality control (QC) before users can access advanced features or export “final” reports.
• Sub-goal 2.3 - Maintain data quality: Require basic QC checks (controls, calibration, and logging of model version and imaging settings) to reduce false positives/negatives and prevent misinterpretations.
Gov / Policy Goal 3: Maintain access for constructive uses (equity and scientific progress)
• Sub-goal 3.1 - Preserve legitimate research utility: avoid governance mechanisms that unnecessarily slow routine PHA research and screening.
• Sub-goal 3.2 - Proportional governance: apply stricter controls only to higher-impact capabilities (e.g., advanced optimization suggestions), rather than restricting all use.
Question 3
Option 1:
General action: Norms combined with oversight mechanisms (social/regulatory governance)
Purpose: Currently, PHA quantification is typically validated through chemical extraction and analytical methods rather than standardized image-based measurement. A robust image-analysis tool like this would significantly increase throughput and expand where and how screening can be performed. If an image-analysis approach is positioned as a scalable screening tool, it should include safeguards to prevent use outside validated conditions. A responsible-use policy with “red flag” triggers would provide a proportional oversight mechanism.
Design:
• Actors: principal investigators (PIs) and laboratory personnel (primary users), microscopy core facility staff, the university biosafety office (or equivalent), and an institutional ethics/biosafety committee.
• Mechanism: implement a short pre-use declaration form and a responsible-use policy that defines “red flag” contexts (e.g., high-throughput work on unverified environmental isolates without provenance, use outside standard biosafety environments, or attempts to generalize the tool beyond PHA workflows).
• Trigger response: if a red flag is triggered, require review by the biosafety/ethics committee (or the biosafety office) and compliance with institutional requirements before experiments or tool access continue.
Assumptions:
• Users will accurately disclose the intended use and experimental context (or there will be sufficient deterrence to reduce misreporting).
• Red-flag criteria can be defined clearly enough to be actionable and consistent across labs.
• The institution has capacity to perform timely reviews without creating major delays for legitimate projects.
• Some level of auditing is feasible (e.g., metadata logs or usage reporting), which may require limited access to usage data.
Risks of failure and “success”:
• The policy becomes symbolic and is not followed; criteria are too vague to enforce; or users misreport their purpose to avoid review.
• Overly broad triggers could make oversight routine, slowing research and disproportionately burdening smaller or under-resourced labs (equity and access concerns).
Option 2:
Restrict advanced features: High-impact features require auditable access (accountability governance)
Purpose: Adding accountability for higher-impact features while keeping basic screening broadly accessible.
Design:
• Actors: tool developers (academic or company), institutions adopting the tool.
• Baseline access: basic PHA screening module available for standard use.
• Advanced access (premium/beta): requires institutional opt-in (verified affiliation, training completion, and standard operating procedures adherence).
• Logging: maintain run logs with technical metadata only (model version, stain, imaging settings, quality control pass/fail, solvent/waste metadata etc).
• Incident response: provide an incident-reporting channel so access can be suspended if misuse is suspected.
Assumptions:
• Logging and gating deter misuse without driving users to ungoverned copies.
• Metadata-only logs are sufficient for accountability without compromising privacy.
• Institutions are willing to administer opt-in and training requirements.
Risks of failure and “success”:
• Users bypass controls by using modified versions or alternative tools; logging becomes incomplete.
• Reduced accessibility and higher admin burden, potentially concentrating access in well-resourced labs.
• Analogy: similar to “KYC tiers” in financial systems: more powerful capabilities require stronger verification and auditability.
Option 3:
Just for PHA: Scope capabilities through validated workflows (technical strategy / design constraint).
Purpose: General-purpose screening tools are easier to repurpose. One way to limit their repurposability is by restricting the tool to validated PHA workflows.
Design:
• Actors: tool developers and maintainers; optionally journals or core facilities that require validated workflows for reporting.
• Technical constraint: restrict supported dyes and workflows to PHA-relevant staining and analysis; lock calibration parameters to validated microscopy settings; exclude generic “optimize any phenotype” modules.
• Reporting constraint: outputs are labeled as screening support, with clear limits on claims and recommended confirmatory methods for final quantification.
• The validated workflow remains useful across common lab setups and organisms.
• Users accept constraints rather than abandoning the tool.
Risks of failure and “success”:
• Restrictions are easily removed in forks / hacks etc; scope limits become ineffective.
• Reduced scientific and commercial usefulness, including for ethically beneficial non-PHA applications; may slow innovation.
• This is analogous to 3D printers that restrict materials and firmware settings: the core function remains available, but out-of-scope production becomes harder without intentional modification.
Question 4
Does the option:
Option 1
Option 2
Option 3
Enhance Biosecurity
• By preventing incidents
2
1
3
• By helping respond
2
1
3
Foster Lab Safety
• By preventing incident
2
2
1
• By helping respond
3
1
3
Protect the environment
• By preventing incidents
2
1
2
• By helping respond
3
2
3
Other considerations
• Minimizing costs and burdens to stakeholders
2
3
1
• Feasibility?
2
2
1
• Not impede research
2
3
3
• Promote constructive applications
2
1
3
Question 5
I would prioritize Option 3 as the primary governance approach, aimed at tool developers and maintainers. Although Option 3 has the weakest overall score, I assign higher weight to practical implementability and consistent adoption, since governance mechanisms that require sustained oversight or significant administrative capacity are often applied inconsistently in real research settings. Option 3 can be implemented directly in software and routine workflows by restricting the tool to validated PHA use cases (supported dyes, locked calibration ranges, and scoped outputs). This reduces repurposability by design rather than relying on user compliance, making the default use safer and more predictable while preserving the core constructive application: scalable PHA screening.
The key trade-off is that Option 3 scores poorly on “helping respond” (biosecurity and lab safety), because it provides limited traceability and fewer mechanisms for intervention after deployment. It also narrows beneficial extensions beyond PHA, potentially limiting constructive applications in adjacent domains.
This recommendation also rests on several assumptions and uncertainties: that capability scoping meaningfully reduces repurposability in practice; that users will not widely circumvent constraints via modified versions or alternative tools; and that the validated workflow generalizes across common microscopes, organisms, and staining conditions.
Final Reflection
The main new ethical concern for me was how quickly a tool designed for a narrow, constructive purpose (PHA screening) can become a general “scale-up enabler” once it is automated and paired with machine-learning image analysis. To address this, I would recommend capability scoping by restricting the tool to validated PHA workflows (supported dyes, locked calibration ranges, and scoped outputs)
Week 2 Lecture Prep
Homework Questions from Professor Jacobson:
Question 1
High-fidelity, proofreading-proficient replicative DNA polymerases have an error rate of ≈ 10⁻⁶ during synthesis under standard conditions. The human nuclear genome is about 3.2 × 10⁹ base pairs per haploid set. If errors happened at 10⁻⁶ per base, you’d expect roughly 3.2 × 10⁹ × 10⁻⁶ ≈ 3.2 × 10³ (≈ 3,200) errors per haploid genome copy. However, in living cells, the effective replication error rate is far lower once proofreading (3′→5′ exonuclease) and post-replication repair (such as mismatch repair, MMR) are included: a commonly cited order of magnitude is ≈ 10⁻⁹ to 10⁻¹⁰ errors per base pair per replication.
Question 2
Because of codon degeneracy, the same amino-acid sequence can be encoded by many DNA coding sequences. A rough average multiplicity per amino acid is about 3.05 synonymous codons. Given an average human protein of 1036 bp and that coding DNA uses 3 bp per amino acid, 1036 bp / 3 ≈ 345 codons. So the number of different DNA coding sequences that produce the exact same protein is on the order of ≈ 10¹⁶⁷.
In practice, though, synonymous variants are not always functionally equivalent. Some synonymous changes produce transcripts with different stability and structure. For example, synonymous substitutions can lead to hairpins or repetitive motifs that increase recombination and reduce construct stability. They can also change ribosome speed patterns (which can alter co-translational folding and lead to misfolding, aggregation, or altered activity). In addition, synonymous changes can inadvertently create or disrupt regulatory sequence motifs (e.g., polyadenylation signals or splicing enhancer/silencer elements in eukaryotes).
Homework Questions from Dr. LeProust:
The golden standard for synthesis of oligonucleotides is the solid-phase oligonucleotide synthesis (SPOS) based on phosphoramidite chemistry (Walther et al. 2020). However, this method struggles beyond ~200nt because every nucleotide is added in a multi-step cycle and small inefficiencies and side reactions compound with length.
Homework Question from George Church:
Question: What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?
Answer: The 10 essential amino acids in all animals are Arginine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, and Valine. Considering this, Jurassic Park’s biocontainment method is a joke, since it doesn’t create a unique dependency in animals: animals already can’t synthesize lysine. Also, as containment-by-dependency, it’s ecologically leaky because they did not consider the possibility that lysine was readily available in the environment. Lysine is available via plants and prey, so escape doesn’t remove access.
OBS: I answered this by consulting a Jurassic Park subreddit discussion.
Clicked “Digest” (the scissors icon in the right menu), selected “All enzymes,” found all seven using the search tool, and clicked “Run Digest.”
Part 3: DNA Design Challenge
3.1. Choose your protein: Poly(3-hydroxyalkanoate) polymerase subunit PhaC
I chose Polyhydroxyalkanoate synthase (PhaC) because it is involved in the catalysis of the reaction that polymerizes (R)-3-hydroxybutyryl-CoA to produce polyhydroxybutyrate (PHB), which is an important bioproduct of interest due to its plastic/polyethylene-like properties.
Biologically, PHB serves as an intracellular energy reserve material when cells grow under conditions of nutrient limitation.
Sequence of Polyhydroxyalkanoate Synthase (PhaC):
MATGKGAAASTQEGKSQPFKVTPGPFDPATWLEWSRQWQGTEGNGHAAASGIPGLDALAGVKIAPAQLGDIQQRYMKDFSALWQAMAEGKAEATGPLHDRRFAGDAWRTNLPYRFAAAFYLLNARALTELADAVEADAKTRQRIRFAISQWVDAMSPANFLATNPEAQRLLIESGGESLRAGVRNMMEDLTRGKISQTDESAFEVGRNVAVTEGAVVFENEYFQLLQYKPLTDKVHARPLLMVPPCINKYYILDLQPESSLVRHVVEQGHTVFLVSWRNPDASMAGSTWDDYIEHAAIRAIEVARDISGQDKINVLGFCVGGTIVSTALAVLAARGEHPAASVTLLTTLLDFADTGILDVFVDEGHVQLREATLGGGAGAPCALLRGLELANTFSFLRPNDLVWNYVVDNYLKGNTPVPFDLLFWNGDATNLPGPWYCWYLRHTYLQNELKVPGKLTVCGVPVDLASIDVPTYIYGSREDHIVPWTAAYASTALLANKLRFVLGASGHIAGVINPPAKNKRSHWTNDALPESPQQWLAGAIEHHGSWWPDWTAWLAGQAGAKRAAPANYGNARYRAIEPAPGRYVKAKA
Source: UniProt at https://www.uniprot.org/uniprotkb/P23608/entry#sequences
3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.
reh:H16_A1437 K03821 poly(R)-3-hydroxyalkanoate polymerase subunit PhaC EC:2.3.1.304 | (GenBank) phaC1; Poly(3-hydroxybutyrate) polymerase (N)
atggcgaccggcaaaggcgcggcagcttccacgcaggaaggcaagtcccaaccattcaaggtcacgccggggccattcgatccagccacatggctggaatggtcccgccagtggcagggcactgaaggcaacggccacgcggccgcgtccggcattccgggcctggatgcgctggcaggcgtcaagatcgcgccggcgcagctgggtgatatccagcagcgctacatgaaggacttctcagcgctgtggcaggccatggccgagggcaaggccgaggccaccggtccgctgcacgaccggcgcttcgccggcgacgcatggcgcaccaacctcccatatcgcttcgctgccgcgttctacctgctcaatgcgcgcgccttgaccgagctggccgatgccgtcgaggccgatgccaagacccgccagcgcatccgcttcgcgatctcgcaatgggtcgatgcgatgtcgcccgccaacttccttgccaccaatcccgaggcgcagcgcctgctgatcgagtcgggcggcgaatcgctgcgtgccggcgtgcgcaacatgatggaagacctgacacgcggcaagatctcgcagaccgacgagagcgcgtttgaggtcggccgcaatgtcgcggtgaccgaaggcgccgtggtcttcgagaacgagtacttccagctgttgcagtacaagccgctgaccgacaaggtgcacgcgcgcccgctgctgatggtgccgccgtgcatcaacaagtactacatcctggacctgcagccggagagctcgctggtgcgccatgtggtggagcagggacatacggtgtttctggtgtcgtggcgcaatccggacgccagcatggccggcagcacctgggacgactacatcgagcacgcggccatccgcgccatcgaagtcgcgcgcgacatcagcggccaggacaagatcaacgtgctcggcttctgcgtgggcggcaccattgtctcgaccgcgctggcggtgctggccgcgcgcggcgagcacccggccgccagcgtcacgctgctgaccacgctgctggactttgccgacacgggcatcctcgacgtctttgtcgacgagggccatgtgcagttgcgcgaggccacgctgggcggcggcgccggcgcgccgtgcgcgctgctgcgcggccttgagctggccaataccttctcgttcttgcgcccgaacgacctggtgtggaactacgtggtcgacaactacctgaagggcaacacgccggtgccgttcgacctgctgttctggaacggcgacgccaccaacctgccggggccgtggtactgctggtacctgcgccacacctacctgcagaacgagctcaaggtaccgggcaagctgaccgtgtgcggcgtgccggtggacctggccagcatcgacgtgccgacctatatctacggctcgcgcgaagaccatatcgtgccgtggaccgcggcctatgcctcgaccgcgctgctggcgaacaagctgcgcttcgtgctgggtgcgtcgggccatatcgccggtgtgatcaacccgccggccaagaacaagcgcagccactggactaacgatgcgctgccggagtcgccgcagcaatggctggccggcgccatcgagcatcacggcagctggtggccggactggaccgcatggctggccgggcaggccggcgcgaaacgcgccgcgcccgccaactatggcaatgcgcgctatcgcgcaatcgaacccgcgcctgggcgatacgtcaaagccaaggcatga
Source: KEGG at https://www.genome.jp/dbget-bin/www_bget?reh:H16_A1437
3.3. Codon optimization.
I optimized the phaC coding sequence for E. coli because it is a widely used chassis for recombinant protein expression and for rapid prototyping of metabolic engineering constructs.
I did this using the Benchling tool. I’ve selected the region of the AA sequence I wish to back translate and right clicked on the highlighted region. From the the codon optimization tab:
Host: E. coli K-12
Method: Match codon usage
GC content: Medium (0.33 to 0.66) cause the extremes may be inconvenient. High GC can create strong secondary structures and low GC can cause instability/repeats and can make synthesis harder.
Uridine depletion: off (not relevant for bacterial expression)
Hairpin parameters: Stem size: 8 and Window 50
Restriction sites: avoid BsaI, BsmBI, BbsI (Type IIS enzymes for Golden Gate compatibility since I would have to clone phaA and phaB also, not phaC single gene in one vector)
Patterns to reduce: AAAAAA and ATATATATA
I clicked on “Optimization preview” and got this result:
3.4. You have a sequence! Now what?
PhaC alone will not produce PHB. A minimal PHB pathway typically includes PhaA (β-ketothiolase) and PhaB (acetoacetyl-CoA reductase) in addition to PhaC (PHA synthase). PhaA and PhaB convert central metabolites (via acetyl-CoA) into (R)-3-hydroxybutyryl-CoA, which is the direct substrate that PhaC polymerizes into PHB. You will also need a host capable of supplying sufficient acetyl-CoA and NADPH.
Therefore, for PHB production in E. coli, phaA, phaB, and phaC are commonly co-expressed on the same plasmid (as a single operon with one promoter and RBSs for each gene) and grown under appropriate culture conditions (e.g., carbon excess and nutrient limitation) that favor polymer accumulation.
Part 4: Prepare a Twist DNA Synthesis Order
Project: pBBR1-MSC5::phaCAB
Cell-dependent recombinant expression approach: cloning the codon-optimized phaA, phaB and phaC coding sequences into E. coli K12
I would sequence DNA used for DNA-based digital data storage, because I’ve never did this before and would feel amazing to be able to instantly interpret the info like reading a book or something like this.
Maybe I’d use Illumina (second-generation, massively parallel short reads) sequencing for high-accuracy base calls and reliable decoding of short oligos and Nanopore (third-generation, single-molecule long reads)to validate longer constructs and integrity.
My input for using the Illumina method would be a DNA pool. This would have to go for a fragmentation stage, adapter ligation (indexes), and PCR amplicication). Throgh Illumina bases are decoded sequencing-by-synthesis with fluorescently labeled reversible terminators and the output is millions to billions of short reads (FASTQ) plus per-base quality scores. To decode that data it is required alignment/consensus and error correction.
I would synthesize a PHA production cassette for E. coli K12 (codon-optimized phaA + phaB + phaC) to enable rapid testing/studing of PHB production. I would use commercial gene synthesis (e.g., Twist) because it is practical, accurate. Essential steps would include oligo synthesis, oligo pooling, assembly into full-length gene/insert, cloning into plasmid. Among the limitations I’d face with this method is error compound since the probability increases with length. So long constructs often require assembly and clonal verification, adding time.
Aiming for increased expression of phaCAB and production of PHA I would edit E. coli metabolic and stress-tolerance genes to increase PHB yield, for example by improving acetyl-CoA/NADPH supply, reducing competing pathways, and increasing tolerance to intracellular polymer accumulation (reducing lysis under high load).
I would use CRISPR-based editing for targeted point mutations without double-strand breaks. RNA is guided direct Cas9 to a locus, DNA is cut and repaired via HDR using a donor template containing the desired edit. In the end I would confirm edits by sequencing. Among the limitations I’d say imprecision (off-target edits) and the fact that multiplex edits increase complexity and screening effort.
The artistic design I created using the GUI is available here.
I heavily used the “Example 7 Microbial Earth” by Dominika Wawrzyniak, using pixels loaded from an external resource (a CSV file hosted on my GitHub page).
I used Dominika’s well documented Notion page from HTGAA21 to understand the code and replicate it for my case. I used Gemini assistance only to debug minor typos and syntax errors, and to identify which packages to import to execute the code.
Like Dominika Wawrzyniak, I planned to introduce more colors, like in the image I generated in the Automation Art Interface. However, implementing this design into code turned out to be more difficult and tedious than anticipated, so I left it as one color (red).
Post-Lab Questions
Question 1
The paper “High-throughput experimentation for discovery of biodegradable polyesters” (Fransen et al., 2023) uses an Opentrons 1st-generation robot to automate a high-throughput biodegradation assay based on the clear-zone technique.
The researchers synthesized 642 polyesters and polycarbonates and tested their biodegradability using a clear-zone assay with Pseudomonas lemoignei. The Opentrons robot was repurposed as an automated imaging platform to capture time-lapse images of polymer degradation in 12-well plates, enabling consistent, large-scale monitoring over 13 days.
This automation allowed rapid generation of a large biodegradation dataset and supported machine learning models to predict polymer degradability from chemical structure.
Question 2
For my final project, I plan to use an Opentrons liquid-handling robot to automate a high-throughput microbial screening workflow for PHA producers. Isolates will be spotted in triplicate on 60-sector plates, maintaining the same indexed positions across all plates for direct comparison. I will first plate isolates on LB agar as a viability control, then inoculate mineral medium (MM; Ramsay et al., 1990) agar plates supplemented with single carbon sources (added as 10% v/v to reach typical final concentrations used in screening). PHA production and bacterial growth will be assessed qualitatively using a two-step staining workflow: (1) Sudan Black B staining (0.02% in 96% ethanol, followed by ethanol washes) to identify colonies that develop blue coloration, and (2) Nile Red A incorporated into MM (0.5 μg/mL) to rank selected isolates by UV fluorescence (312/365 nm). This setup allows rapid testing of many isolate × carbon source combinations, supporting selection of strains compatible with low-cost feedstocks and efficient bioprocessing.
Here’s my draft script for this exercise. Each “color” would correspond to a different bacterial isolate. I did not implement this in the script yet. The coordinate set is a starting layout and could be refined to achieve a more uniform, regular distribution across the plate.
Final Project Ideas
Added 3 slides with 3 ideas for an Individual Final Project in the appropriate slide deck for Commited Listeners here.