Week 1 HW: Principles and Practices

Class Assignment

Figure 1: The medicine ward in a typical government Indian hospital. The one shown is the medicine ward at my medical college.

1. First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

   Although I started off with imparting neuron-like properties in mycelial networks I decided to pivot to solving nosocomial infections with synthetic biology as the immediate benefits outweigh that of the former. In the wards , especially in under-resourced settings where I am from, maintenance of sterility and aseptic conditions in the post surgery wards and medicine wards is an under addressed issue. Under staffed , under resourced settings like mine, do not prioritise it leading to hospital acquired infections in the patients, often resulting in severe complications than which they came to the hospital for. With living colonisers that produce bactericidal and bacteriostatic chemicals (including fungicidal and antiviral) that generally cause secondary infections and HAIs(hospital acquired infections),synthetic biology has the scope to solve this problem saving over 100,000 lives in India alone, where people die of these HAIs. Low maintenance systems like these could change the way a lot of people are recovering during treatment in the wards. I would like to explore and build something tangible with Bacillus subtilis as the chassis organism , with quorum sensing to produce antimicrobial peptides against the priority pathogens causing hospital acquired infections

Figure 2: A diagram showing an example of the seek and kill mechanism in the engineered living surfaces. Here Bacillus subtilis acts as the chasis organism carrying the gene for Lysostaphin which is activated only on concentration of AIP produced by S. aureus crossing a certain threshold. This comprises of the sensor (Agr-C transmembrane protein in the genetically engineered B. subtilis), the logic gate(quorum) and the effector module (Lysostaphin produced by mRNA for the same activated by Agr-P protein)

2. Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

   Goal 1: Biosafety and Containment

   • Prevent uncontrolled spread of engineered colonizers beyond designated hospital surfaces.
   • Prevent horizontal gene transfer of engineered antimicrobial genes to environmental organisms.
   • Prevent environmental contamination by these engineered organisms.

   Goal 2: Patient Safety

   • Ensure the colonizer produces no toxins or allergens harmful to immunocompromised patients.
   • Ensure colonizers cannot cause opportunistic infections in vulnerable populations.

   Goal 3: Equitable Access

   • Keep unit cost below existing chemical disinfectant protocols.
   • Ensure technology is deployable without specialized infrastructure (cold chain, trained personnel).
   • Open-source genetic constructs and protocols to prevent IP monopolization.

   Goal 4: Environmental Safety

   • Prevent the colonizer from establishing in non-target environments (soil, water systems, community surfaces outside hospitals).
   • Ensure engineered antimicrobial peptide genes do not contribute to resistance development in wild microbial populations.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”

   Governance Actions

   1. Action 1 - Regulatory: Establishing a Regulatory Authority for Engineered Living Surfaces in Healthcare

      1. Purpose: Currently, India’s Genetic Engineering Appraisal Committee (GEAC) regulates environmental release of GMOs under the Environment Protection Act 1986, but no framework specifically addresses engineered microorganisms deployed on surfaces within healthcare infrastructure. Hospital surfaces occupy a grey zone-they are neither open environmental release nor contained laboratory use-and no existing regulatory body in India is equipped to evaluate the safety, efficacy, or deployment protocols of living antimicrobial surface systems in clinical settings.
      2. Design: I propose that ICMR (Indian Council of Medical Research) and CDSCO (Central Drugs Standard Control Organisation), along with GEAC, establish a joint Advisory Board specifically to evaluate proposals for engineered microorganisms intended for deployment within healthcare infrastructure. This board would scrutinize applications for biosafety, patient safety, and environmental containment, and approve relevant proposals. Funding for development and deployment could be channeled through existing government mechanisms such as BIRAC (Biotechnology Industry Research Assistance Council). International precedent exists: the EU’s Contained Use Directive for GMOs could serve as a template adaptable to India’s regulatory landscape.
      3. Assumptions: This assumes ICMR, CDSCO, and GEAC have the institutional capacity, funding, and political will to implement a new advisory framework. It also assumes that a centralized national framework can accommodate the enormous diversity of hospital settings across India-from tertiary urban centers to rural primary health centers-without becoming either too rigid or too permissive.
      4. Risks of Failure & “Success”:
         1. Failure: The approval process takes 5–10 years, during which no legal deployment pathway exists. This pushes hospitals toward unregulated, informal use of engineered organisms with no safety oversight-paradoxically increasing risk. Meanwhile, over 1,000,000 people continue to die each year from HAIs in India alone.
         2. Success: The regulatory pathway, once established, becomes a barrier to entry that only large, well-resourced biotech companies can navigate. These companies gain outsized influence over the advisory committee’s decisions, creating a commercial monopoly over engineered living surfaces and undermining equitable access.

   2. Action 2 - Technical: Engineering Auxotrophic Dependencies for Biocontainment

      1. Purpose: Currently, commercially available probiotic cleaning products use wild-type organisms with no engineered containment mechanisms. If these organisms spread beyond their intended surfaces, there are no built-in safeguards. I propose building auxotrophic dependencies into the B. subtilis chassis-engineering the organism to require a synthetic amino acid or nutrient not found in nature-so the colonizer cannot survive outside the hospital environment where that nutrient is actively supplied.
      2. Design: Academic synthetic biologists would develop and characterize the auxotrophic strains, validated by Institutional Biosafety Committees (IBCs). Standardized, well-characterized constructs would be deposited in repositories like Addgene or the iGEM Registry of Standard Biological Parts. Hospitals deploying the system would need to maintain a supply of the synthetic nutrient, which could be incorporated into routine surface treatment protocols. Redundant auxotrophies (requiring multiple synthetic nutrients simultaneously) would reduce the probability of reversion to environmental viability.
      3. Assumptions: This assumes we can effectively engineer genetic circuits with auxotrophic dependencies that reliably prevent survival outside the defined environment. It assumes reversion rates through mutation remain low enough to be safe-though published data on single auxotrophies in B. subtilis suggest reversion is non-trivial, making redundant safeguards essential. It also assumes that the hospital environment can be consistently maintained to support the colonizer’s engineered nutritional requirements.
      4. Risks of Failure & “Success”:
         1. Failure: Mutations in the engineered organisms bypass the installed auxotrophic dependencies, allowing survival outside the intended environment. A containment breach at scale could result in mass environmental contamination by antimicrobial-peptide-producing organisms, which becomes extremely difficult to remediate.
         2. Success: The auxotrophic dependency creates a new commercial bottleneck. Companies that hold patents on the specific synthetic nutrient required by the organism could monopolize the supply chain, establishing a cycle of dependence-you get the open-source organism for free, but you cannot use it without buying the proprietary nutrient. This undermines democratized access despite open-source genetic designs.

   3. Action 3 - Incentive: Open-Source Consortium for Engineered Living Surfaces

      1. Purpose: Currently, engineered antimicrobial surface organisms are being developed in isolated academic labs (ETH Zurich, MIT Media Lab) and private companies, with no standardized sharing of constructs, safety data, or deployment protocols. Patents and IP held exclusively by these institutions greatly hinder the development and distribution of this technology in under-resourced settings where it is needed most. Akin to how the Linux operating system emerged from the collective efforts of thousands of developers and disrupted proprietary software, I propose an international open-access repository modeled on iGEM’s Registry of Standard Biological Parts-specifically for engineered surface colonizers-where anyone can contribute designs, genetic circuits, safety profiles, and deployment protocols.
      2. Design: Requires seed funding from organizations such as the WHO, Wellcome Trust, or Gates Foundation. Participating labs deposit genetic constructs, characterized safety profiles, and validated deployment protocols into the repository. In exchange, they receive access to the full library and structured co-authorship frameworks that incentivize contribution. WHO prequalifies validated strains for deployment in low-resource countries, creating a fast-track regulatory pathway analogous to its Essential Medicines prequalification programme. This decentralized model would accelerate development and acceptance of engineered living surfaces across diverse healthcare settings globally.
      3. Assumptions: This assumes academic labs and companies will share constructs and protocols openly, which conflicts with current publication-priority and profit-driven incentive structures in science and industry. It assumes WHO has the institutional capacity to manage a prequalification programme for engineered organisms, which it currently does not. It also assumes that sufficient contributors will participate to reach a critical mass of useful, well-characterized parts.
      4. Risks of Failure & “Success”:
         1. Failure: No one contributes, and the repository remains empty-a common fate of open-source initiatives without strong network effects or institutional mandates.
         2. Success: Open access enables actors with malicious intent to access engineered organisms and their protocols. However, the constructs in question are surface colonizers, not pathogens, which limits their weaponization potential compared to other synthetic biology tools. A more realistic success risk is that unvetted, poorly characterized constructs get deployed in hospitals by groups without adequate biosafety expertise, causing harm that discredits the entire approach.

4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. Scoring Matrix *Scored 1–3 (1 = best/strongest contribution, 3 = weakest/least contribution, n/a = not applicable)

5. Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties.

   Audience: Indian Council of Medical Research (ICMR)

   I recommend a combination of all three governance actions, implemented in a phased sequence, because no single action adequately addresses all four policy goals.

   Phase 1 : Technical safeguards first. The auxotrophic dependencies (Action 2) should be engineered and validated before any deployment is considered. This is non-negotiable. Without built-in biocontainment, no regulatory framework or open-source initiative can compensate for an organism that escapes its intended environment. Redundant auxotrophies should be the minimum standard.

   Phase 2 : Open-source consortium in parallel. The open-source repository (Action 3) should be established concurrently with technical development, ensuring that safety data, construct characterization, and deployment protocols are shared from the beginning rather than locked behind institutional IP. This directly addresses the equitable access goals and prevents the commercial monopoly risks identified in Actions 1 and 2.

   Phase 3 : Regulatory framework informed by real data. The regulatory authority (Action 1) should be established after sufficient technical and safety data exists from the first two phases. This prevents the common failure mode of regulating a technology before it is understood, which leads to either overly restrictive or dangerously permissive frameworks.

   Key trade-offs: Speed versus safety remains the central tension. Moving too fast with deployment risks a containment failure that could set back the entire field of engineered living surfaces by a decade—eroding public trust in synthetic biology. Moving too slowly means people continue to die from preventable infections in hospitals that lack the resources for conventional sterility maintenance. The phased approach attempts to balance this by building safety into the organism first, sharing knowledge openly second, and formalizing regulation third.

   Uncertainties: It remains unclear whether auxotrophic containment can be made robust enough at scale, whether open-source incentives can overcome academic publishing pressures, and whether Indian regulatory bodies can develop new frameworks within a reasonable timeframe. These uncertainties should be revisited as the technology matures.

6. Reflecting on what you learned and did in class this week, outline any ethical concerns that arose, especially any that were new to you. Then propose any governance actions you think might be appropriate to address those issues. This should be included on your class page for this week. This week’s exploration of governance frameworks surfaced several ethical concerns that were new to me.

   The first is the tension between speed and safety in resource-constrained settings. In well-funded Western hospitals, the cost of waiting for perfect regulation is inconvenience. In under-resourced Indian hospitals where I am training, the cost of waiting is measured in lives. This creates an ethical pressure to deploy before full safety validation ,a pressure I now recognize must be resisted, because a single high-profile failure would destroy trust in this entire approach and harm far more people long-term.

   The second is the dual-use nature of open-source biology. I initially assumed open-sourcing was unambiguously good democratizing access, preventing monopoly. But the governance analysis forced me to consider that open access also means open access for actors without adequate biosafety training or with malicious intent. Akin to the quacks without formal medical education who practice in remote and under resourced areas causing the patients more damage and sometimes death. The realistic risk is not bioweapons (surface colonizers are poor candidates), but rather poorly characterized organisms deployed without adequate containment by well-meaning but under-equipped groups.

   The third and most personally challenging is the realization that the success of this technology could create new forms of dependency even while solving the original problem. If the auxotrophic nutrient becomes a patented commercial product, under-resourced hospitals could end up dependent on supply chains they cannot control or afford. This mirrors the broader pattern I have seen in Indian healthcare, where solutions designed for equity are captured by commercial interests. Governance must anticipate this from the beginning, not as an afterthought.

Week 2 Lecture Prep

Homework Questions from Professor Jacobson

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

   1. Error rate: The raw “bit-flip” rate of a standard DNA Polymerase is roughly $10^{-5}$ (1 in 100,000). With intrinsic proofreading (3’ → 5’ exonuclease activity), it drops to ≈ $10^{-7}$.
   2. The comparison to the human genome : The human genome is ≈ $3.2 \times 10^9$ base pairs (3.2 Gigabytes of code).
   3. The way biology deals with this discrepancy: A post processing algorithm called MMR is run to fix these errors. This brings the final error rate to ≈ $10^{-9}$ , which is around 3 errors per cell division which is low enough to preserve genetic code but high enough to trigger evolution- an optimal signal to noise ratio.

2. How many different ways are there to code for an average human protein? Why don’t they all work?

• The Coding Space: Due to codon degeneracy (61 codons for 20 amino acids), there are roughly $3^{N$ combinations for a protein of length $N$. For an average 400-amino acid protein, this is $3}{400}$ (approx $10^{190}$)
• Why most “codes” fail (Optimization constraints):
  1. Codon Bias: The cell has a limited “RAM” of specific tRNAs. Rare codons cause ribosomal stalling because the hardware isn’t available.
  2. mRNA Structure: Bad sequences can fold into tight hairpins, jamming the ribosome like a paper shredder.
  3. GC Content: If G/C is too high (>70%) or low, the DNA becomes structurally unstable or difficult to replicate

Homework Questions from Dr. LeProust

1. What’s the most commonly used method for oligo synthesis currently?

• Phosphoramidite Chemistry: A solid-phase synthesis method that builds DNA strands nucleotide by nucleotide on a support matrix (like a silicon chip or column).

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?

• Yield Decay: Synthesis is an exponential decay function. With 99.5% efficiency per step, a 200nt strand has a yield of $0.995^{200} \approx 36%$. Beyond this, the “noise” (truncated sequences and side reactions) drowns out the signal, making purification inefficient.

3. Why can’t you make a 2000bp gene via direct oligo synthesis?

• The Yield Cliff: The math makes it impossible: $0.995^{2000} \approx 0.004%$. You cannot “print” a gene this long atom-by-atom because the yield is effectively zero.
• The Solution: We switch from “printing” to “assembly.” We synthesize short tiles (e.g., 200bp) and stitch them together using methods like Gibson Assembly or Golden Gate Assembly.

Homework Question from George Church

1. What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

• The 10 Essentials: Phenylalanine, Valine, Threonine, Tryptophan, Isoleucine, Methionine, Histidine, Arginine, Leucine, Lysine.
• The Flaw: The Jurassic Park logic was security theater. Since all animals (including humans) are Lysine auxotrophs (we cannot produce it), a strictly Lysine-depeddent dinosaur would simply survive by eating chickens, soy, or humans—sources rich in Lysine.
• True containment requires engineering dependence on a Non-Canonical Amino Acid (ncAA)—a synthetic building block not found in nature. This ensures “Zero Leakage” because the organism cannot find the nutrient in the wild.

Citation: I used Gemini and Claude to stress test my ideas and refine them for the sections on auxotrophy All governance analysis, clinical observations, and project framing are my own work..

week-02-hw-dna-read-write-and-edit

Part 0: Basics of Gel Electrophoresis

I watched the lectures by George Church, Joe Jacobson, and Emily Leproust, as well as the recitation by Ice Kiattisewee on DNA Gel, restriction enzymes, Benchling, and Twist.

Key takeaways from lectures:

Gel electrophoresis works by taking advantage of the negative charge of the DNA molecule due to the phosphate group. DNA fragments being placed in the agarose gel (which is heated and then cooled) move according to their size under the effect of the electric field applied, separating based on their size. The pore size of the agarose gel is determined by the concentration of agarose - (with higher concentrations –> decreased pore size –> smaller fragments/finer fragments being separated easily. The ladder containing the weight of the DNA in kDa helps in estimating the size of the DNA based on its position in the gel. Ethidium bromide is used for staining resulting in the DNA to glow under the UV light.

I found the concept of using gel electrophoresis to create art interesting . The artwork done by the previous students was visually stunning. I felt that the benchling software is a really elegant tool to edit , add DNA sequences and simulate digestion of the genomes using restriction enzymes. I believe that I have not fully explored the potential it holds.

Part 1: Benchling & In-Silico Gel Art

Restriction Enzyme Digestion of Lambda DNA (48,502 bp)

I created a Benchling account and imported the Lambda phage DNA sequence (48,502 bp). I then simulated restriction enzyme digestion with each of the seven enzymes specified:

Screenshot 1: The imported Lambda DNA sequence in Benchling showing the circular/linear map. Screenshot 2: The EcoRI digest result showing the gel simulation + fragment table.

Screenshot 3: A multi-enzyme digest showing all 7 enzymes side by side on the simulated gel

Gel Art Design

On benchling , I virtually digested the DNA in 7 lanes with the following enzymes - EcoRI, SalI, SacI, HindIII, BamHI, KpnI, and EcoRV. I used Ronan’s website to iterate a few, but couldn’t effectively find a way to create the “HAI” pattern that I had envisioned.

Note: As a committed listener without wet-lab access, I completed this as an in-silico exercise.

Part 2: Gel Art - Wet Lab

As a committed listener at the London HTGAA node without direct wet-lab access for this week’s protocol, Part 2 is optional. The in-silico design in Part 1 serves as my documentation.

Part 3: DNA Design Challenge

3.1. Protein Choice: Lysostaphin

Protein: Lysostaphin (EC 3.4.24.75)
Source organism: Staphylococcus simulans biovar staphylolyticus
UniProt ID: P10547
Gene name: lss

Lysostaphin is a zinc metalloenzyme derived from S. simulans acting against S. aureus. The gene for production of lysostaphin can be inserted via a plasmid vector into B. subtilis which is a commonly used chassis organism by biotechnologists. (https://www.mdpi.com/1424-8247/3/4/1139)

The other proteins I had in mind were PlyKp104 and PlyCD(1-174). PlyKp104 is an endolysin derived from Klebsiella pneumoniae phage which can kill Klebsiella pneumoniae and Pseudomonas aeuroginosa. (https://pubmed.ncbi.nlm.nih.gov/37098944). Similarly PlyCD (1-174) has endolytic activity on Clostridium difficile species. It’s derived from a prophage targeting the same. (https://pmc.ncbi.nlm.nih.gov/articles/PMC4649177.)

Why I chose this protein: For my project that I had in mind - engineering living surfaces or biofilms to kill the common causative organisms causing nosocomial/HAIs (Hospital acquired infections) – the expression of these proteins I mentioned above (Lysostaphin, PlyKp104 and PlyCD) by Bacillus subtilis (chassis organism) are needed for the bactericidal action the bacteria causing HAIs.

S. aureus, including methicillin-resistant strains (MRSA), is among the most dangerous nosocomial pathogens. In the medicine wards of my medical college in India, I have seen patients admitted for routine conditions develop life-threatening staphylococcal secondary infections due to inadequate surface sterility. Lysostaphin is a potent weapon against precisely this pathogen.

Also, lysostaphin has already been successfully expressed in B. subtilis WB600 (the expression host relevant to my project) (https://www.aimspress.com/article/doi/10.3934/microbiol.2021017)

Mature lysostaphin protein sequence (246 amino acids, from UniProt P10547, residues 248–493):

>sp|P10547|LSTP_STASI Lysostaphin (mature form) OS=Staphylococcus simulans OX=1286 GN=lss
AATHEHSAQWLNNYKKGYGYGPYPLGINGGMHYGVDFFMNIGTPVKAISSGKIVEAGWS
NYGGGNQIGLIENDGVHRQWYMHLSKYNVKVGDYVKAGQIIGWSGSTGYSTAPHLHFQRM
VNSFSNSTAQDPMPFLKSAGYGKAGGTVTPTPNTGWKTNKYGTLYKSESASFTPNTDIIT
RTTGPFRSMPQSGVLKAGQTIHYDEVMKQDGHVWVGYTGNSGQRIYLPVRTWNKSTNTL
GVLWGTIK

The mature enzyme has two functional domains: an N-terminal catalytic domain (residues 1–148, M23 family zinc metalloendopeptidase) responsible for pentaglycine cleavage, and a C-terminal cell wall targeting domain (CWT, residues 149–246, SH3b family) that directs the enzyme to the staphylococcal surface by specifically recognizing the pentaglycine crosslinks.

3.2. Reverse Translation: Protein to DNA

Using the Central Dogma, I worked backwards from the protein sequence to determine the corresponding DNA sequence. I retrieved the native coding sequence for mature lysostaphin from the lss gene on plasmid pACK1 of S. simulans (GenBank Accession: M15686).

Native DNA sequence encoding mature lysostaphin (738 bp):

GCTGCAACACATGAACATTCAGCACAATGGTTAAATAATTATAAAAAAGGATACGGATAC
GGACCATATCCATTAGGAATTAACGGTGGAATGCACTATGGTGTTGATTTTTTTATGAAT
ATTGGAACACCTGTCAAAGCAATTTCATCAGGAAAAATTGTTGAAGCAGGTTGGTCAAAC
TATGGTGGCGGTAATCAAATAGGATTAATTGAAAATGATGGTGTACATCGCCAATGGTAT
ATGCATTTATCAAAATATAACGTAAAAGTCGGTGATTATGTTAAAGCAGGCCAAATAATT
GGTTGGAGTGGGTCTACCGGATATTCTACAGCTCCACATTTACATTTTCAAAGAATGGTA
AACTCATTTTCAAATTCAACAGCTCAAGATCCGATGCCATTTTTAAAAAGCGCAGGCTAT
GGAAAAGCAGGTGGTACAGTTACTCCAACTCCAAATACAGGTTGGAAAACAAATAAATAC
GGTACGCTCTACAAATCAGAATCTGCATCTTTTACACCTAATACTGACATAATAACAAGA
ACAACTGGACCATTTAGATCAATGCCACAATCTGGTGTATTAAAAGCCGGTCAAACTATT
CACTATGATGAAGTTATGAAACAAGATGGCCACGTCTGGGTAGGATATACGGGAAATTCA
GGTCAAAGAATATATTTACCTGTTAGAACTTGGAATAAATCAACAAATACTCTTGGTGTT
CTTGGGGAACTATAAA

Note: The final three nucleotides (TAA) represent the stop codon.

3.3. Codon Optimization

Why codon optimization is necessary:

Different organisms have different levels of tRNA for a specefic codon. If the tRNA for a specific codon isn’t abudant enough, the translation process can get stalled.

Target organism for optimization: Escherichia coli (K12 strain)

I chose E. coli because it is the standard workhorse for recombinant protein production and the most well-characterized expression system. The initial cloning, expression validation, and protein characterization will be performed in E. coli BL21(DE3) before eventually transferring the validated construct into B. subtilis for the final project application. E. coli also has the highest density of codon usage tables and optimization tools available.

Codon-optimized lysostaphin DNA sequence for E. coli expression (738 bp):

Using the Twist Bioscience Codon Optimization Tool, avoiding Type IIs enzyme recognition sites (BsaI, BsmBI, BbsI):

GCTGCGACCCATGAACATAGCGCGCAGTGGCTGAACAACTACAAGAAAGGTTATGGCTA
TGGTCCGTACCCGCTGGGCATTAATGGTGGTATGCACTATGGCGTGGACTTCTTCATGA
ACATCGGCACCCCGGTGAAAGCGATCAGCTCTGGCAAAATTGTGGAAGCCGGTTGGTCC
AACTATGGCGGTGGTAACCAGATCGGTCTGATCGAGAACGATGGTGTGCACCGTCAGTG
GTACATGCATCTGTCTAAATACAACGTGAAAGTGGGTGATTACGTCAAAGCGGGTCAGA
TCATTGGCTGGAGCGGTTCCACTGGTTATTCCACCGCGCCGCATCTGCACTTTCAGCGTA
TGGTGAACTCTTTCAGCAACTCCACCGCTCAGGATCCGATGCCGTTTCTGAAATCTGCG
GGCTATGGTAAAGCGGGTGGTACCGTGACTCCGACTCCGAACACCGGTTGGAAAACCAA
CAAATATGGTACGCTGTACAAAAGCGAAAGCGCCTCTTTTACCCCGAACACCGATATCAT
CACTCGTACCACCGGTCCGTTCCGTTCTATGCCGCAGAGCGGCGTGCTGAAAGCGGGCC
AGACCATTCACTATGATGAAGTGATGAAACAGGATGGTCACGTGTGGGTCGGTTACACC
GGCAACAGCGGTCAGCGTATCTACCTGCCGGTGCGTACCTGGAACAAAAGTACCAACAC
TCTGGGTGTGCTGTGGGGTACCATCAAATAA

Retrospective note: Since the deployment chassis for my final project is B. subtilis, direct codon optimization for B. subtilis would have been more strategically aligned. For the final project construct, a B. subtilis-optimized variant will be generated using the same Twist optimization pipeline.

3.4. From DNA Sequence to Protein: Production Methods

With the codon-optimized lysostaphin sequence in hand, there are two main routes to produce the functional protein:

Method 1: Cell-dependent expression in E. coli (Primary approach)

The codon-optimized DNA sequence is cloned into an expression vector (e.g., pET21 or pTwist Amp High Copy) downstream of a strong inducible promoter such as T7. The construct is transformed into E. coli BL21(DE3) competent cells. The process follows the Central Dogma:

1. Transcription: Upon induction with IPTG, T7 RNA polymerase binds the T7 promoter and synthesizes mRNA from the lysostaphin DNA template. The RNA polymerase reads the template strand 3’→5’ and produces mRNA 5’→3’, with each T in the DNA transcribed as U in the mRNA.

2. Translation: Ribosomes bind the mRNA at the ribosome binding site (Shine-Dalgarno sequence), recognize the AUG start codon, and begin translating the mRNA into protein. Each three-nucleotide codon in the mRNA specifies one amino acid via tRNA adaptor molecules. Translation proceeds until the ribosome encounters the UAA stop codon.

3. Folding and purification: The translated polypeptide folds into its native conformation. With a His-tag appended at the C-terminus, the protein can be purified using nickel affinity chromatography (IMAC), followed by size-exclusion chromatography to obtain pure, active lysostaphin.

Method 2: Cell-free expression (Alternative approach)

Cell-free transcription-translation systems (e.g., PURExpress or E. coli S30 extract) can produce lysostaphin without living cells. The DNA template (linear or circular) is added directly to the cell-free reaction mix containing ribosomes, tRNAs, amino acids, RNA polymerase, and energy sources (ATP, GTP). Transcription and translation occur simultaneously in a single tube within 2–4 hours.

Cell-free is advantageous for rapid prototyping - testing whether the construct produces active protein before committing to cell-based production. It is particularly relevant to my project because cell-free expression could eventually enable on-demand antimicrobial peptide production in resource-limited settings without cold chain requirements.

3.5. How It Works in Nature

How a single gene codes for multiple proteins:

At the transcriptional level, a single gene can produce multiple protein variants through several mechanisms:

1. Alternative splicing (in eukaryotes): Different exons are included or excluded from the mature mRNA, generating distinct protein isoforms from one gene. The DSCAM gene in Drosophila can produce over 38,000 splice variants.

2. Alternative promoters: Different promoters upstream of the same gene can initiate transcription at different points, producing mRNAs with different 5’ exons and therefore different N-terminal protein sequences.

3. Polycistronic mRNAs (in prokaryotes): A single operon produces one mRNA encoding multiple proteins, each translated from its own RBS. The lysostaphin operon itself contains both lss and the immunity factor lif gene.

Central Dogma alignment for the first 30 nucleotides of lysostaphin:

DNA (coding):  5'- GCT GCG ACC CAT GAA CAT AGC GCG CAG TGG -3'
                    ||| ||| ||| ||| ||| ||| ||| ||| ||| |||
DNA (template):3'- CGA CGC TGG GTA CTT GTA TCG CGC GTC ACC -5'
                          ↓ Transcription (RNA Pol reads 3'→5')
mRNA:          5'- GCU GCG ACC CAU GAA CAU AGC GCG CAG UGG -3'
                          ↓ Translation (Ribosome reads 5'→3')
Protein:            Ala Ala Thr His  Glu His  Ser Ala Gln Trp
                     A   A   T   H    E   H    S   A   Q   W

Note: All thymine (T) bases in DNA are transcribed as uracil (U) in mRNA. Each 3-nucleotide codon in the mRNA corresponds to exactly one amino acid in the protein.

Part 4: Prepare a Twist DNA Synthesis Order

4.1. Account Setup

I created accounts on both Benchling (benchling.com) and Twist Bioscience (twistbioscience.com).

4.2. Building the DNA Insert Sequence (Expression Cassette)

In Benchling, I created a new linear DNA sequence named “Lysostaphin_Expression_Cassette” and assembled the following expression cassette by concatenating the components in order:

Components:

Each component was annotated in Benchling by right-clicking and creating annotations with appropriate labels (Promoter, RBS, Start Codon, CDS: Lysostaphin, His-tag, Stop Codon, Terminator).

Screenshot 4: The annotated Linear Map showing all components labeled and color-coded.

I verified the complete assembly using Benchling’s Linear Map view to confirm all annotations are correctly positioned and non-overlapping. The link for review: https://benchling.com/s/seq-QorL0JNRMSvmklWTiDkE?m=slm-138RcqLoOew0hHCOHJtK The FASTA file was downloaded for upload to Twist.

4.3–4.5. Twist Order Setup

Screenshot 6: The Twist Genes product page.

On Twist Bioscience, I selected Genes → Clonal Genes and uploaded the FASTA file using the Nucleotide Sequence → Upload Sequence File option. Screenshot 7: The uploaded sequence in Twist’s interface

I chose clonal genes over gene fragments because clonal genes arrive as circular plasmid DNA that can be directly transformed into E. coli without an additional assembly step - saving 1–2 weeks of experimental time, as noted in the course materials. Screenshot 8: The vector selection showing pTwist Amp High Copy

4.6. Vector Selection

I selected pTwist Amp High Copy as the backbone vector from Twist’s vector catalog. Rationale:

• Ampicillin resistance - compatible with standard lab antibiotic stocks
• High copy number - maximizes plasmid yield per miniprep, important for downstream cloning and sequence verification
• ColE1 origin of replication - well-characterized, stable in E. coli

I downloaded the complete construct (GenBank format) from Twist and imported it into Benchling, confirming the circular plasmid map shows the lysostaphin expression cassette correctly inserted into the pTwist backbone.

Screenshot 9: Showing the plasmid uploaded to benchling.

For the final project, my construct will include:

• Fully annotated Benchling insert fragment (lysostaphin expression cassette)
• pTwist Amp High Copy as the Twist cloning vector
• Flanking restriction sites for future subcloning into B. subtilis shuttle vectors

Part 5: DNA Read/Write/Edit

5.1. DNA Read (Sequencing)

(i) What DNA would I want to sequence and why?

I would want to sequence the resistomes of hospital surfaces in Indian government hospitals - specifically, performing metagenomic sequencing of microbial communities on high-touch surfaces (bedrails, IV poles, doorknobs, ventilator tubing) in the medicine and post-surgical wards where I have worked.

Why this matters: The WHO priority pathogen list includes organisms like MRSA, vancomycin-resistant Enterococcus (VRE), carbapenem-resistant Acinetobacter baumannii, and extended-spectrum beta-lactamase (ESBL)-producing Klebsiella pneumoniae - all of which I have encountered clinically in my wards. Sequencing hospital surface microbiomes would reveal: which resistance genes (AMR genes) are circulating on surfaces, the taxonomic composition of the surface biofilm community, whether these surface communities match the organisms causing actual patient infections (proving the transmission chain), and how the resistome changes over time and in response to cleaning protocols.

This data would directly inform the design of my engineered B. subtilis colonizer by identifying which antimicrobial peptides the system needs to produce. Without knowing precisely what organisms inhabit these surfaces, the engineering is blind.

(ii) Sequencing Technology: Oxford Nanopore MinION

Generation: Third-generation sequencing technology. Unlike first-generation (Sanger, chain termination) and second-generation (Illumina, sequencing by synthesis with short reads), nanopore sequencing reads single DNA molecules in real-time without requiring amplification or fluorescent labeling.

Input preparation - essential steps:

1. DNA extraction: Use a bead-beating + column-based kit (e.g., Qiagen DNeasy PowerSoil) to lyse both Gram-positive and Gram-negative organisms from surface swabs.
2. Size selection: Optional - use SPRI beads or gel extraction to enrich for fragments >1 kb if long reads are desired.
3. Library preparation: Using the Rapid Sequencing Kit (SQK-RAD004): a transposase fragments the DNA and simultaneously attaches sequencing adapters in a single 10-minute step. Alternatively, the Ligation Sequencing Kit (SQK-LSK114) provides higher throughput with more steps: end repair/dA-tailing → adapter ligation → SPRI bead cleanup.
4. No PCR amplification required - native DNA is sequenced directly, preserving epigenetic modifications (methylation).

How it decodes bases (base calling):

A single strand of DNA is ratcheted through a biological nanopore (CsgG protein pore) embedded in a synthetic membrane. As each nucleotide passes through the constriction of the pore, it causes a characteristic disruption of the ionic current flowing through the pore. A neural network-based base caller (e.g., Dorado or Guppy) decodes the raw electrical signal (squiggle) into nucleotide sequences. The current signal is influenced by ~5–6 nucleotides simultaneously (the k-mer in the pore), so the base caller uses sequence context to make predictions.

Output:

• FASTQ files containing long reads (N50 typically 5–20 kb, with individual reads >100 kb possible)
• Real-time streaming output - reads are available within minutes of starting the run
• Per-read quality scores (Q-scores), with current chemistry (R10.4.1) achieving median Q20 (~99% accuracy)
• Methylation calls from the same raw signal (no bisulfite conversion needed)

Why MinION specifically: It costs ~$1,000 for the device (or free through Oxford Nanopore’s starter program), runs on a laptop via USB, requires no large infrastructure, and can be used at point-of-care - making it deployable in Indian hospitals where I work. For metagenomic surveillance, long reads resolve species-level taxonomy and link AMR genes to their host organisms on single reads, which short-read sequencing cannot do.

5.2. DNA Write (Synthesis)

(i) What DNA would I want to synthesize and why?

I would want to synthesize a modular antimicrobial peptide expression cassette library for Bacillus subtilis - specifically, a set of genetic constructs each encoding a different antimicrobial effector under the control of a quorum-sensing-responsive promoter. The library would include:

1. Lysostaphin (lss) - targets S. aureus including MRSA (the protein designed in Parts 3–4 above)
2. Dispersin B (dspB) - a glycoside hydrolase from Aggregatibacter actinomycetemcomitans that degrades poly-N-acetylglucosamine (PNAG) biofilm matrix, disrupting biofilms formed by S. aureus and S. epidermidis
3. Art-175 - an engineered endolysin-antimicrobial peptide fusion (artilysin) effective against Gram-negative pathogens including Acinetobacter baumannii and Pseudomonas aeruginosa

Each construct would use the same standardized architecture: [Quorum-sensing promoter] → [RBS] → [Signal peptide for secretion] → [Antimicrobial CDS] → [Terminator]. This modularity allows swapping effectors in and out depending on the pathogen landscape of a specific hospital - a “pharmacological palette” for engineered living surfaces.

The specific genetic sequences are: lysostaphin as described in Part 3.3, and I would design and order the others through Twist.

(ii) Synthesis Technology: Twist Bioscience Silicon-Based Phosphoramidite Synthesis + Enzymatic Assembly

Essential steps of Twist’s synthesis platform:

1. Oligo synthesis on silicon chips: Short oligonucleotides (up to ~200 nt) are synthesized in parallel on miniaturized wells etched into silicon wafers using phosphoramidite chemistry. Each cycle: (a) detritylation (remove 5’-DMT protecting group), (b) coupling (add next phosphoramidite monomer), (c) capping (block failed couplings), (d) oxidation (stabilize the new phosphodiester bond).

2. Oligo pool harvesting and error removal: Completed oligos are cleaved from the chip, and error-containing sequences are removed using enzymatic mismatch cleavage or hybridization-based selection.

3. Hierarchical assembly: Overlapping oligos are assembled into longer gene fragments through PCR-based assembly (e.g., polymerase cycling assembly or Gibson Assembly). Multiple fragments are then joined into the complete gene.

4. Clonal isolation: Assembled genes are cloned into the selected vector, transformed into E. coli, and individual clones are Sanger-sequenced to confirm 100% sequence accuracy. Only sequence-verified clones are shipped.

Limitations:

• Speed: Typical turnaround is 2–3 weeks for clonal genes, which limits rapid iteration cycles
• Accuracy: While final clonal products are sequence-perfect, the per-base error rate during oligo synthesis (~1 in 200–500 bases) necessitates the error-correction and clonal selection steps, adding time
• Scalability: Individual gene synthesis scales well, but whole-genome synthesis (>100 kb) remains prohibitively expensive and slow - the “gene writing gap” described by Hoose et al. (2023)
• Sequence constraints: Certain sequences are difficult to synthesize: extreme GC content (<25% or >75%), long homopolymer runs, extensive secondary structure, and repetitive sequences can cause synthesis failures
• Cost: While cost per base has dropped dramatically (~$0.07/bp for gene fragments), synthesizing entire pathways or genomes remains expensive

5.3. DNA Edit (Genome Editing)

(i) What DNA would I want to edit and why?

I would want to edit the genome of Bacillus subtilis 168 to create an optimized chassis for my engineered living surface colonizer. Specifically, three categories of edits:

Edit 1: Knockout of sporulation genes (spo0A, sigF).
B. subtilis forms endospores that are extremely resistant to disinfection and could spread the engineered organism beyond hospital surfaces. Deleting key sporulation regulators prevents spore formation, ensuring the organism remains in vegetative form and is susceptible to standard decontamination - a critical biocontainment feature.

Edit 2: Introduction of synthetic auxotrophies.
As I described in my Week 1 governance analysis, I would engineer dependence on a non-canonical amino acid (ncAA) not found in nature. Specifically, I would knock out genes in the biosynthetic pathway for an essential amino acid and replace them with an engineered aminoacyl-tRNA synthetase that charges tRNA with a synthetic analog. The organism can only survive when the synthetic nutrient is supplied - true containment beyond the “Jurassic Park lysine contingency.”

Edit 3: Integration of the quorum-sensing antimicrobial peptide circuits into the chromosome.
Rather than maintaining the constructs on plasmids (which can be lost and impose a fitness burden), I would integrate the engineered circuits directly into the B. subtilis chromosome at neutral loci (e.g., amyE, lacA). Chromosomal integration provides stable, single-copy expression without antibiotic selection - essential for real-world deployment where you cannot maintain antibiotic pressure.

(ii) Editing Technology: CRISPR-Cas9 combined with MAGE (Multiplex Automated Genome Engineering)

For targeted knockouts and insertions, I would use CRISPR-Cas9 as the primary editing tool, supplemented by recombineering for multiplex edits.

How CRISPR-Cas9 edits DNA - essential steps:

1. Guide RNA design: Design a 20-nt spacer sequence complementary to the target site in the B. subtilis genome, adjacent to a 5’-NGG-3’ PAM (protospacer adjacent motif) recognized by SpCas9. I would use Benchling’s CRISPR guide designer to score guides for on-target efficiency and minimize off-target sites.

2. Construct assembly: Clone the sgRNA (spacer + scaffold) into an expression vector under a constitutive promoter. Co-deliver or separately provide: (a) Cas9 protein or expression cassette, (b) the sgRNA, and (c) a repair template - a DNA fragment with 500–1000 bp homology arms flanking the desired edit (deletion, insertion, or substitution).

3. Delivery into B. subtilis: B. subtilis is naturally competent - it can take up exogenous DNA from the environment. Transform competent cells with the CRISPR plasmid and repair template simultaneously.

4. Double-strand break and repair: The Cas9-sgRNA complex binds the target DNA, verifies PAM recognition, and creates a blunt double-strand break (DSB) 3 bp upstream of the PAM. The cell’s homology-directed repair (HDR) machinery uses the supplied repair template to fix the break, incorporating the desired edit.

5. Selection and verification: Plate transformants on selective media. Screen colonies by PCR across the edited locus and confirm by Sanger sequencing.

Preparation required:

• Input materials: Cas9 expression plasmid (or purified Cas9 protein), sgRNA expression construct, repair template DNA (synthesized by Twist or generated by PCR), competent B. subtilis cells, selective antibiotics
• Design steps: Target site selection → off-target analysis → guide scoring → repair template design with homology arms → cloning and sequence verification

Limitations:

• Efficiency: In B. subtilis, CRISPR-Cas9 editing efficiency varies (50–95%) depending on the locus, guide quality, and repair template design. Unlike E. coli, B. subtilis has efficient HDR, but Cas9 toxicity can reduce viable transformants.
• Off-target effects: SpCas9 can tolerate mismatches at positions distal from the PAM, potentially causing unintended edits. This can be mitigated by using high-fidelity Cas9 variants (e.g., eSpCas9) and whole-genome sequencing of final strains.
• Multiplexing complexity: Editing multiple loci simultaneously (sporulation knockouts + auxotrophy + circuit integration) requires sequential rounds of editing with plasmid curing between rounds, which is time-consuming. MAGE-like approaches (Wannier et al., 2021) using ssDNA oligonucleotides can accelerate multiplex editing but are less established in B. subtilis than in E. coli.
• PAM restriction: The requirement for an NGG PAM limits targetable sites to ~1 in every 8 bp on average. This can be overcome by using Cas9 orthologs with different PAM preferences (e.g., SaCas9 recognizes NNGRRT, CjCas9 recognizes NNNNRYAC) or engineered PAM-relaxed variants (e.g., SpCas9-NG, SpRY).
• Large insertions: Integrating multi-kb constructs (full antimicrobial circuits) via HDR is less efficient than small edits. For large insertions, combining CRISPR-mediated counterselection with traditional B. subtilis integrative vectors at established loci (amyE, thrC) may be more reliable.

References

1. Sabala, I. et al. “Crystal structure of the antimicrobial peptidase lysostaphin from Staphylococcus simulans.” FEBS J. 281, 4112–4122 (2014).
2. Nazari, R. et al. “Cloning and expression of Staphylococcus simulans lysostaphin enzyme gene in Bacillus subtilis WB600.” Mol. Biotechnol. 63, 1043–1052 (2021).
3. Shendure, J. et al. “DNA sequencing at 40: past, present, and future.” Nature 550, 345–353 (2017).
4. Hoose, A. et al. “DNA synthesis technologies to close the gene writing gap.” Nat. Rev. Chem. 7, 144–161 (2023).
5. Wannier, T. et al. “Recombineering and MAGE.” Nat. Rev. Methods Primers 1, 7 (2021).
6. Wang, J.Y. & Doudna, J.A. “CRISPR technology: A decade of genome editing is only the beginning.” Science 379, eadd8643 (2023).
7. Bastos, M.C.F. et al. “Lysostaphin: A Staphylococcal Bacteriolysin with Potential Clinical Applications.” Pharmaceuticals 3, 1139–1161 (2010).
8. Recsei, P.A. et al. “Cloning, sequence, and expression of the lysostaphin gene from Staphylococcus simulans.” Proc. Natl. Acad. Sci. USA 84, 1127–1131 (1987).

Citation: I used Claude to help refine the codon-optimized sequence generation and to verify technical details of sequencing/editing technologies. All project framing, protein choice rationale, clinical observations, and design decisions connecting to my final project are my own work.

Week 3: Lab Automation & Opentrons Art

Part 1: Opentrons Agar Art - Biohazard Symbol

Design Concept

I conceptualised the biohazard symbol, which I thought of creating using parametric algorithms. I used the assistance of Claude (Anthropic) to generate the python code which I ran on Google Colab, in the copy provided by HTGAA. The results are shown below.

The artwork I conceptualised is a biohazard symbol rendered in fluorescent bacteria on black agar. I specifically chose this design because my final project focuses on engineering Bacillus subtilis to combat hospital-acquired infections (HAIs), which kill over 200,000 people annually in India alone.

Python Code

The complete Python script is embedded below and was also submitted via the course form. I used Claude (Anthropic) to help write the code, debug coordinate math and validate the Opentrons API calls, while I designed the concept, chose the geometry, and structured the protocol logic.

from opentrons import types
import math

metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
    'author': 'Anirudh Gangadharan',
    'protocolName': 'Biohazard Biosensor Symbol',
    'description': 'Parametric biohazard symbol representing engineered B. subtilis biosensor: purple crescents (threat detection), blue hub (sensing logic), pink safety ring (biocontainment)',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

well_colors = {
    'A1' : 'Red',
    'B1' : 'Blue',
    'C1' : 'Green'
}


def run(protocol):
  ##############################################################################
  ###   Load labware, modules and pipettes
  ##############################################################################

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

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

  # Modules
  temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)

  # Temperature Module Plate
  temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul',
                                                      'Cold Plate')
  # Choose where to take the colors from
  color_plate = temperature_plate

  # Agar Plate
  agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')  ## TA MUST CALIBRATE EACH PLATE!
  # Get the top-center of the plate, make sure the plate was calibrated before running this
  center_location = agar_plate['A1'].top()

  pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

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

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

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

  # For this lab, instead of calling pipette.dispense(1, loc) use this: dispense_and_detach(pipette, 1, loc)
  def dispense_and_detach(pipette, volume, location):
      """
      Move laterally 5mm above the plate (to avoid smearing a drop); then drop down to the plate,
      dispense, move back up 5mm to detach drop, and stay high to be ready for next lateral move.
      5mm because a 4uL drop is 2mm diameter; and a 2deg tilt in the agar pour is >3mm difference across a plate.
      """
      assert(isinstance(volume, (int, float)))
      above_location = location.move(types.Point(z=location.point.z + 5))  # 5mm above
      pipette.move_to(above_location)       # Go to 5mm above the dispensing location
      pipette.dispense(volume, location)    # Go straight downwards and dispense
      pipette.move_to(above_location)       # Go straight up to detach drop and stay high

  ###
  ### BIOHAZARD BIOSENSOR SYMBOL
  ###

  # --- Geometry Parameters ---
  LOBE_DIST = 8.5       # distance from center to each lobe center
  LOBE_R_OUTER = 10.0   # outer radius of each crescent lobe
  LOBE_R_INNER = 5.5    # inner cutout radius
  GAP_ANGLE = 25        # degrees of gap between lobes
  DOT_SPACING = 2.0     # mm between dots (>= 2mm to avoid merging)
  PLATE_R = 33.0        # max radius from center (within 40mm safety limit)

  lobe_degs = [90, 210, 330]  # three lobes at 120 degree intervals

  # --- Generate Red points: Three crescent lobes (threat/pathogen detection) ---
  red_pts = []
  for bd in lobe_degs:
      br = math.radians(bd)
      cx, cy = LOBE_DIST * math.cos(br), LOBE_DIST * math.sin(br)
      for gx in range(-15, 16):
          for gy in range(-15, 16):
              x, y = gx * DOT_SPACING / 2, gy * DOT_SPACING / 2
              px, py = cx + x, cy + y
              dl = math.sqrt(x**2 + y**2)
              do = math.sqrt(px**2 + py**2)
              if dl > LOBE_R_OUTER or do <= LOBE_R_INNER or do > PLATE_R:
                  continue
              ang = math.degrees(math.atan2(py, px)) % 360
              gap = False
              for gc in [150, 270, 30]:
                  ad = abs(ang - gc)
                  if ad > 180: ad = 360 - ad
                  if ad < GAP_ANGLE / 2: gap = True; break
              if gap: continue
              if not any(math.sqrt((px-ex)**2 + (py-ey)**2) < DOT_SPACING * 0.85 for ex, ey in red_pts):
                  red_pts.append((round(px, 1), round(py, 1)))

  # --- Generate Blue points: Central hub (sensing/logic core) ---
  blue_pts = [(0.0, 0.0)]
  for i in range(8):
      a = 2 * math.pi * i / 8
      blue_pts.append((round(2.5 * math.cos(a), 1), round(2.5 * math.sin(a), 1)))

  # --- Generate Green points: Outer safety ring (biocontainment) ---
  green_pts = []
  for i in range(44):
      a = 2 * math.pi * i / 44
      green_pts.append((round(22 * math.cos(a), 1), round(22 * math.sin(a), 1)))
  for i in range(44):
      a = 2 * math.pi * (i + 0.5) / 44
      green_pts.append((round(24.5 * math.cos(a), 1), round(24.5 * math.sin(a), 1)))

  # --- Helper: pick up tip, aspirate color, dispense all points, drop tip ---
  def pick_up_and_draw(color_name, points):
      pipette_20ul.pick_up_tip()
      batch_size = 19  # aspirate up to 19uL at a time (19 dots at 1uL each)
      for i in range(0, len(points), batch_size):
          batch = points[i:i + batch_size]
          pipette_20ul.aspirate(len(batch), location_of_color(color_name))
          for (px, py) in batch:
              loc = center_location.move(types.Point(x=px, y=py))
              dispense_and_detach(pipette_20ul, 1, loc)
      pipette_20ul.drop_tip()

  # --- Execute patterning ---
  pick_up_and_draw('Red', red_pts)      # Crescents: threat detection
  pick_up_and_draw('Blue', blue_pts)    # Hub: sensing logic gate
  pick_up_and_draw('Green', green_pts)  # Ring: biocontainment boundary

  # Done! All tips dropped.

After running the above code, I ran the code shown below to visualise the image and it is provided below.

# Execute Simulation / Visualization -- don't change this code block
protocol = OpentronsMock(well_colors)
run(protocol)
protocol.visualize()

=== VOLUME TOTALS BY COLOR ===
	Blue:		 aspirated 9	 dispensed 9
	Red:		 aspirated 168	 dispensed 168
	Green:		 aspirated 88	 dispensed 88
	[all colors]:	[aspirated 265]	[dispensed 265]

=== TIP COUNT ===
	 Used 3 tip(s)  (ideally exactly one per unique color)

AI Usage Disclosure

I used Claude (Anthropic) to write the code for the biohazard geometry.

Part 2: Post-Lab Questions

Published Paper Utilizing Laboratory Automation for Novel Biological Applications

Paper: Semiautomated Production of Cell-Free Biosensors

Citation: Brown, D.M., Phillips, D.A., Garcia, D.C., et al. (2025). Semiautomated Production of Cell-Free Biosensors. ACS Synthetic Biology, 14(3), 979–986. DOI: 10.1021/acssynbio.4c00703

Affiliations: Northwestern University (Department of Chemical & Biological Engineering, Center for Synthetic Biology); U.S. Army DEVCOM Chemical Biological Center.

Summary

Brown et al. (2025) present the first systematic study comparing manual versus robot-assembled cell-free biosensor reactions, using the Opentrons OT-2 liquid handling robot to automate the assembly of cell-free gene expression (CFE) reactions across full 384-well plates.

Cell-free biosensors represent a powerful class of synthetic biology diagnostics: DNA-encoded genetic circuits are executed in cell-free transcription-translation (TX-TL) systems, freeze-dried for ambient storage and field distribution, then rehydrated at the point of use to detect target analytes via colorimetric or fluorescent output. Previous work - notably Pardee et al. (2016) demonstrating Zika virus detection using toehold switches in cell-free systems - established the diagnostic potential of this platform. However, manufacturing has remained a manual, low-throughput bottleneck that limits translation from proof-of-concept to deployable diagnostics.

Automation Platform and Workflow

The OT-2 performed semiautomated assembly of CFE reactions, handling precise nanoliter-to-microliter transfers of cell extract, energy buffer, DNA template, and water into each well of a 384-well plate. The team benchmarked two cell-free reporter systems:

• A constitutive LacZ colorimetric reporter (absorbance-based readout)
• A GFP fluorescent reporter (fluorescence-based readout)

For each system, reactions were assembled both manually and via OT-2, enabling direct comparison of precision, reproducibility, and biosensor performance. The culminating experiment deployed a complete 384-well fluoride riboswitch biosensor array - a genetically encoded sensor element that activates gene expression in the presence of fluoride ions - with every reaction assembled by the OT-2.

Key Findings

1. Semiautomated OT-2 assembly produced biosensor reactions with comparable or improved consistency relative to manual assembly, reducing the well-to-well coefficient of variation that plagues hand-pipetted plates and directly degrades diagnostic reliability.

2. A full 384-well fluoride riboswitch biosensor array was successfully built and functionally validated in a single automated run, demonstrating the feasibility of medium-throughput biosensor production on affordable hardware.

3. The study provides the field’s first rigorous benchmarking data for automated versus manual CFE biosensor manufacturing, systematically quantifying where robotic and manual assembly diverge in performance characteristics.

Why Automation Was Essential

Three factors render automation indispensable for this application. First, cell-free reactions are exquisitely sensitive to pipetting precision - small volumetric errors in extract or DNA template concentration produce disproportionately large signal variability, which is unacceptable for a diagnostic biosensor that must deliver a reliable binary answer. The OT-2’s reproducible liquid handling directly addresses this failure mode. Second, scaling from proof-of-concept to deployment requires manufacturing hundreds to thousands of biosensor reactions per batch; manual assembly of 384-well plates is tedious, error-prone, and fundamentally unscalable. Third, the low cost of the OT-2 (~$5,000–$10,000) means that academic laboratories, field-deployed diagnostic operations, and resource-limited clinical settings can adopt semiautomated manufacturing without the capital expenditure of biofoundry-grade liquid handlers such as the Hamilton STAR or Beckman Biomek platforms.

Relevance to My Final Project

This paper is directly relevant to my proposed work on autonomous antimicrobial surfaces for hospital-acquired infection prevention. My project design incorporates both the OT-2 (for lysostaphin zone-of-inhibition assays and serial dilutions) and the Ginkgo Nebula cloud laboratory (for high-throughput toehold switch screening using the Echo → Bravo → PHERAstar workflow). Brown et al. demonstrate precisely the principle that underpins my automation strategy: affordable liquid handling robots can achieve the precision and throughput required to screen synthetic biology constructs at a scale that manual methods cannot support, particularly for cell-free biosensor applications where reaction assembly variability is a primary performance bottleneck. Their validation of OT-2-assembled cell-free biosensor arrays provides direct precedent for my planned Week 11 toehold switch screening experiments on Nebula’s cell-free platform.

Reference: Brown, D.M., Phillips, D.A., Garcia, D.C., et al. (2025). Semiautomated Production of Cell-Free Biosensors. ACS Synthetic Biology, 14(3), 979–986. https://doi.org/10.1021/acssynbio.4c00703

Question 2 : Automation Plan for Final Project

My final project - an engineered Bacillus subtilis surface colonizer for hospital-acquired infection prevention - has two primary components that benefit from automation: antimicrobial peptide optimization and biosensor screening.

Automation Scale 1: OpenTrons OT-2 (Bench-Scale, London Node)

Goal: Serial dilution and zone-of-inhibition assays for lysostaphin antimicrobial activity.

Pseudocode:

PROTOCOL: Lysostaphin Activity Screening
LABWARE:
  - Slot 1: 96-well deep-well plate (lysostaphin variants in columns 1-6)
  - Slot 2: 12-well reservoir (LB broth, S. aureus overnight culture)
  - Slot 4: Tiprack 200 uL
  - Slot 7: Tiprack 20 uL

PROCEDURE:
  # Step 1: Serial dilution of lysostaphin across rows
  FOR each column c in [1..6]:  # 6 construct variants
    pick_up_tip(200uL)
    aspirate(100uL, reservoir['LB'])
    FOR each row r in [B..H]:  # 7-point serial dilution
      dispense(50uL, plate[r][c])
      mix(3, 50uL)              # Mix by pipetting up/down
    drop_tip()

  # Step 2: Add S. aureus to all test wells
  FOR each well in plate[A1:H6]:
    pick_up_tip(20uL)
    aspirate(10uL, reservoir['S_aureus'])
    dispense(10uL, well)
    drop_tip()

  # Step 3: Incubate 16h at 37°C (manual)
  # Step 4: Read OD600 on plate reader (manual)

This protocol generates a dose-response matrix: 6 lysostaphin variants × 7 concentrations × triplicate, with automated serial dilution ensuring precise and reproducible concentration gradients.

Automation Scale 2: Ginkgo Nebula Cloud Lab (High-Throughput)

Goal: Screen toehold switch biosensor library for mecA mRNA detection (connects to Week 11 biosensor module).

Nebula Workflow (RAC Plate Flow):

Step 1: ECHO - Acoustic transfer of 384 toehold switch DNA constructs
        (NUPACK-optimized designs targeting mecA mRNA)
        Source: 384-well plate with switch DNA variants
        Destination: 384-well assay plate
        Volume: 100 nL per well

Step 2: BRAVO - Stamp CFPS reagent master mix into all 384 wells
        Source: Reservoir with PURExpress or custom CFPS lysate
        Volume: 5 uL per well

Step 3: MULTIFLO - Dispense mecA trigger RNA at 4 concentrations
        Quadrants of plate get 0, 10, 100, 1000 nM trigger
        Volume: 2 uL per well

Step 4: PLATELOC - Heat-seal the plate

Step 5: INHECO - Incubate at 37°C for 2 hours
        (Cell-free expression of toehold switch + GFP reporter)

Step 6: XPEEL - Remove seal

Step 7: PHERASTAR - Fluorescence readout (excitation 485nm, emission 520nm)
        Kinetic read: every 10 min for 6 hours
        Output: ON/OFF ratio matrix for all 384 designs

ANALYSIS:
  - Rank switches by ON/OFF ratio (trigger vs. no-trigger)
  - Select top 10 switches for integration into B. subtilis chassis
  - Feed results back to NUPACK for next design round (DBTL cycle)

This cloud lab workflow screens 384 biosensor designs in a single automated run - something that would take months manually. The top-performing switches will be integrated into my B. subtilis chassis to create the sense-and-respond circuit for MRSA detection.

Why Two Scales Matter

The OpenTrons protocol handles the effector optimization (how well does lysostaphin kill S. aureus?), while the Nebula workflow handles the sensor optimization (how sensitively can we detect MRSA?). Together, they close the loop on both halves of the sense-and-respond system - all through automation.

Preliminary Computational Results: Toehold Switch Design for mecA

To validate the feasibility of my biosensor approach, I wrote a Python pipeline to computationally design toehold switch candidates targeting the mecA mRNA of S. aureus N315 (GenBank: D86934). The mecA gene encodes PBP2a, the penicillin-binding protein that confers methicillin resistance - making it the definitive genetic marker for MRSA.

Method

I implemented the Series B toehold switch architecture from Green et al. (2014) Cell 159:925-939, screening 89 candidate positions across the first 303 nucleotides of the mecA coding sequence. Each candidate was evaluated on six criteria: trigger GC content (ideal 35-55%), toehold GC content (ideal 25-50%), absence of homopolymer runs >4 nt, absence of internal AUG codons, estimated stem thermodynamic stability, and predicted ON/OFF ratio.

Key Results

• 89 candidates screened, of which 30 scored ≥ 10/12 (high-confidence)
• Best candidate: Position 234, targeting the region encoding the transpeptidase catalytic domain
  • Trigger: 5'─AUAAAUACAGGAACUUCAGCAGCUGAUACAGGAGAU─3'
  • Trigger GC: 38.9% | Toehold GC: 41.7%
  • No internal AUG codons
  • Predicted ON/OFF ratio: 9.1×
  • Maximum homopolymer: 3 nt (synthesis-compatible)
• The position 150-260 region of mecA contains a cluster of high-scoring candidates, suggesting favorable mRNA secondary structure accessibility in this region

Significance

This computational screen identifies candidate toehold switches ready for experimental validation on Ginkgo Nebula during the Week 11 biosensor module. The top 10 designs can be synthesized by Twist Bioscience and screened in cell-free reactions using the cloud lab workflow described in my automation plan above.

Figure: Computational screening of toehold switch biosensor candidates targeting mecA mRNA. (A) Composite score distribution across the mecA 5’ coding region. (B) GC content profiles of top 10 candidates. (C) Predicted ON/OFF ratios. (D) Architecture of the top-ranked candidate at position 234.

Next Steps

1. Validate top 5 candidates with NUPACK (nupack.org) for full minimum free energy analysis
2. BLAST trigger sequences against the complete S. aureus transcriptome to confirm specificity
3. Order synthesis of top 10 switches from Twist Bioscience
4. Screen in cell-free system on Ginkgo Nebula during Week 11 biosensor module
5. Integrate the best-performing switch into the B. subtilis sense-and-respond chassis

The complete Python code and candidate data (JSON) are available in my documentation.

Part 3: Three Final Project Ideas

Idea 1 : Cell-Free Lysostaphin Optimization via AI-Guided DBTL on Nebula

What: Optimize recombinant lysostaphin expression in a cell-free protein synthesis system using automated Design-Build-Test-Learn cycles on Ginkgo Nebula’s cloud laboratory.

How: Codon-optimize lysostaphin variants for cell-free expression. Screen hundreds of reaction conditions (DNA concentration, Mg²⁺, temperature, incubation time) in parallel using Nebula’s RAC workflow: Echo acoustic transfer → Bravo master mix stamping → Inheco incubation → PHERAstar fluorescence readout. Validate top candidates via OpenTrons OT-2 serial dilution and zone-of-inhibition assays against S. aureus.

Expected output: Optimized cell-free lysostaphin expression protocol with quantified MIC data against clinical S. aureus isolates.

References: J. Hérisson, A. N. Hoang, A. El-Sawah, M. M. Khalil, J.-L. Faulon bioRxiv 2024.10.28.619828; doi: https://doi.org/10.1101/2024.10.28.619828

Idea 2 : Toehold Switch MRSA Detection Coupled to Antimicrobial Secretion in B. subtilis

What: Design RNA-based toehold switch biosensors targeting mecA mRNA (the methicillin resistance determinant) and integrate them with a T7 RNAP amplification cascade driving lysostaphin secretion in B. subtilis - creating an autonomous sense-and-kill genetic circuit.

How: Computationally screen toehold switch candidates across the mecA coding sequence using thermodynamic scoring (GC content, stem stability, predicted ON/OFF ratio). Validate top designs via cell-free screening on Nebula (Week 11 biosensor module). Integrate a two-stage amplification architecture: toehold switch → T7 RNA polymerase → T7 promoter → lysostaphin, converting weak linear signal into sharp sigmoidal response. Preliminary computational screen: 89 candidates evaluated, top candidate at position 234 with 9.1× predicted ON/OFF ratio.

Preliminary results (completed): I built a computational pipeline implementing the Series B toehold architecture and screened 89 candidate positions across the first 303 nt of mecA from S. aureus N315 (GenBank: D86934). 30 candidates scored ≥ 10/12 on a composite metric. Top candidate at position 234 (transpeptidase catalytic domain): 9.1× predicted ON/OFF ratio, 38.9% trigger GC, no internal AUG codons. These designs are ready for synthesis and experimental validation on Nebula.

Expected output: Validated toehold switch with >10-fold ON/OFF ratio + functional genetic circuit design for B. subtilis integration.

References: Valeri, J.A. et al. (2020). Sequence-to-function deep learning frameworks for engineered riboregulators. Nature Communications, 11, 5058. https://doi.org/10.1038/s41467-020-18676-2

Idea 3 : Deployable Living Antimicrobial Surface with Evolutionary Robustness for Resource-Limited Hospitals

What: Engineer a complete B. subtilis chassis that autonomously detects, reports, and kills antibiotic-resistant pathogens on hospital surfaces - designed for deployment across 25,000 Indian government hospitals where HAI rates exceed 30% in ICU settings.

How: Three integrated modules: (1) Sensing - multiplexed toehold switches detecting resistance markers for MRSA, VRE, and CRKP with T7 RNAP signal amplification; (2) Response - modular effector library (lysostaphin for MRSA, dispersin B for biofilms, Art-175 for Gram-negatives) with toxin-antitoxin addiction systems to enforce evolutionary stability; (3) Biocontainment - spo0A knockout (prevents sporulation) + synthetic amino acid auxotrophy; (4) Surveillance - MinION nanopore sequencing of the surface resistome to guide effector selection over time.

Expected output: Complete system design, computational modeling, and cell-free proof-of-concept for one sensor-effector pair. Deployable prototype targeting <$0.10/m² manufacturing cost.

References: Mehta, A., Rosenthal, V.D., Mehta, Y. et al. (2007). Device-associated nosocomial infection rates in intensive care units of seven Indian cities: Findings of the International Nosocomial Infection Control Consortium (INICC). Journal of Hospital Infection, 67(2), 168–174. https://doi.org/10.1016/j.jhin.2007.07.008