Pharm.D student exploring the scientific landscape—from mRNA sequencing to drug discovery and clinical applications. A complete beginner, but highly motivated and eager to learn, grow, and contribute to impactful biomedical research.
Biological Engineering Application / Tool Application:
I want to develop a bacterial biosensor for rapid detection of antibiotic-resistant pathogens in clinical samples. The biosensor uses engineered E. coli containing genetic circuits that activate fluorescent protein expression when they detect beta-lactamase activity or other resistance markers from nearby bacteria.
Part 1: Benchling & In-silico Gel Art Purpose This exercise demonstrates applied understanding of restriction enzyme digestion and gel electrophoresis through in-silico modeling. The workflow emphasizes correct experimental logic, lane interpretation, and band pattern analysis using professional bioinformatics tools.
Platform and Workflow All simulations were designed and executed using Benchling, a molecular biology platform widely used for DNA analysis, cloning design, and experimental planning. The use of Benchling enabled rapid iteration, accurate restriction mapping, and controlled visualization of gel electrophoresis outcomes.
Laboratory Automation: Opentrons Bio-Art Using the HTGAA26 Opentrons Colab as a framework, I developed a custom automation protocol to translate digital designs into biological patterns.
Implementation Documentation Technical Script: sami_tanveer_opentrons.py Protocol Logic: The script utilizes API Level 2.20 and a P20 Single-Channel Gen2 pipette. It features an optimized draw_points function that handles coordinate-based dispensing with batched aspiration to ensure mechanical efficiency and prevent cross-contamination between fluorescent strains. Design Interface: The design was mapped using the Opentrons Art GUI, ensuring precise coordinate placement for Red (mRFP1), Green (mClover3), Blue (Azurite), and Cyan (sfGFP) reporters. Visual Reference of Design Interface:
Biological Engineering Application / Tool Application:
I want to develop a bacterial biosensor for rapid detection of antibiotic-resistant pathogens in clinical samples. The biosensor uses engineered E. coli containing genetic circuits that activate fluorescent protein expression when they detect beta-lactamase activity or other resistance markers from nearby bacteria.
Part 1: Benchling & In-silico Gel Art Purpose This exercise demonstrates applied understanding of restriction enzyme digestion and gel electrophoresis through in-silico modeling. The workflow emphasizes correct experimental logic, lane interpretation, and band pattern analysis using professional bioinformatics tools.
Platform and Workflow All simulations were designed and executed using Benchling, a molecular biology platform widely used for DNA analysis, cloning design, and experimental planning. The use of Benchling enabled rapid iteration, accurate restriction mapping, and controlled visualization of gel electrophoresis outcomes.
Laboratory Automation: Opentrons Bio-Art Using the HTGAA26 Opentrons Colab as a framework, I developed a custom automation protocol to translate digital designs into biological patterns.
Implementation Documentation Technical Script: sami_tanveer_opentrons.py Protocol Logic: The script utilizes API Level 2.20 and a P20 Single-Channel Gen2 pipette. It features an optimized draw_points function that handles coordinate-based dispensing with batched aspiration to ensure mechanical efficiency and prevent cross-contamination between fluorescent strains. Design Interface: The design was mapped using the Opentrons Art GUI, ensuring precise coordinate placement for Red (mRFP1), Green (mClover3), Blue (Azurite), and Cyan (sfGFP) reporters. Visual Reference of Design Interface:
Application: I want to develop a bacterial biosensor for rapid detection of antibiotic-resistant pathogens in clinical samples. The biosensor uses engineered E. coli containing genetic circuits that activate fluorescent protein expression when they detect beta-lactamase activity or other resistance markers from nearby bacteria.
Why this application: As a Pharm.D student, I’ve witnessed how current resistance testing takes 24-48 hours using culture-based methods. This delay forces physicians to prescribe broad-spectrum antibiotics empirically, which often fails and accelerates resistance development. A rapid biosensor could provide results within 2-4 hours, enabling targeted antibiotic selection on the same day. The technology is HTGAA-feasible because it uses standard E. coli chassis, well-characterized promoters (like those responsive to beta-lactamase degradation products), and simple fluorescent reporters (GFP/RFP). This addresses a critical clinical gap—the time between infection diagnosis and appropriate treatment—using accessible synthetic biology techniques that I can learn and implement during the course.
2. Governance / Policy Goals
Primary Goal: Ensure the bacterial biosensor contributes to better patient outcomes and antimicrobial stewardship without creating environmental or biosecurity risks.
Sub-Goals
Goal 1: Enhance Biosecurity
Sub-goal 1a: Prevent the engineered biosensor strain from surviving outside laboratory/clinical settings
Sub-goal 1b: Ensure the technology cannot be easily modified to detect or enable harmful applications
Goal 2: Ensure Equitable Access
Sub-goal 2a: Make the biosensor affordable for resource-limited clinics where resistance is often highest
Sub-goal 2b: Share genetic circuit designs openly to enable local production and adaptation
Goal 3: Protect Environmental Health
Sub-goal 3a: Prevent accidental release of engineered bacteria into wastewater or soil
Sub-goal 3b: Ensure biosensor components are properly sterilized after use
Purpose: Currently, different labs use varying containment practices for engineered bacteria. I propose standardized protocols specifically for clinical biosensor applications that mandate genetic kill switches, auxotrophy (nutritional dependency), and proper waste sterilization.
Design:
All clinical biosensor strains must include auxotrophy for a non-natural amino acid
Genetic kill switches activated after 48 hours or upon temperature change
Users skip sterilization steps due to time pressure
Bacteria escape before kill switch activates
Risks of “Success”:
Over-engineering containment makes biosensor too expensive for routine use
Complexity of safety features reduces reliability of detection function
Option 2: Open-Source Design Registry with Safety Review
Purpose: Create a public database (similar to iGEM Registry) where biosensor genetic circuits are shared, peer-reviewed for safety, and rated for performance. This promotes equitable access while maintaining safety oversight.
Design:
Researchers submit biosensor designs to registry before publication
Tiering creates quality perception gap harming Tier 1 research funding
4. Scoring Governance Actions
Scale: 1 = best alignment with goal, 3 = weakest, n/a = not applicable
Policy Goal
Option 1: Containment
Option 2: Registry
Option 3: Validation
Enhance Biosecurity
• Prevent incidents
1
2
2
• Enable response
2
2
1
Ensure Equitable Access
• Affordable access
2
1
3
• Local adaptation
2
1
2
Protect Environment
• Prevent release
1
3
n/a
• Containment response
1
3
n/a
Other Considerations
• Minimize burden
2
1
3
• Feasibility
1
2
2
• Not impede research
1
2
3
• Promote applications
2
1
1
Scoring Rationale:
Option 1 provides strongest environmental protection through physical/genetic containment but doesn’t address equitable access
Option 2 excels at promoting access and knowledge sharing but has weaker environmental safeguards once designs are public
Option 3 ensures patient safety through validation but creates cost barriers and may slow beneficial research
5. Prioritized Recommendation
Recommended Strategy: Implement Option 1 (Containment Protocols) combined with Option 2 (Open Registry) for research phases, followed by Option 3 (Tiered Validation) for clinical translation.
Rationale:
For my biosensor project specifically, I would:
During HTGAA development: Use Option 1 containment (auxotrophy + kill switches) and share my circuit design via Option 2 registry for peer feedback
If pursuing clinical application: Progress through Option 3 tiers, starting with research validation (Tier 1), then clinical research (Tier 2) if results are promising
This layered approach allows me to innovate safely during the course while establishing pathways to clinical impact. The containment features protect against accidental release, open sharing promotes equitable access and scientific improvement, and tiered validation ensures patient safety without stopping early-stage research.
Target Audience:
HTGAA instructors and peers: For research-phase safety practices
MIT/Hospital IRBs: If transitioning to clinical testing
Clinical microbiology professional societies: For eventual diagnostic standards
Trade-offs & Uncertainties:
Kill switch reliability: Current technology has ~1-5% failure rate; need backup containment (auxotrophy)
Balancing openness vs. security: Sharing designs enables both beneficial adaptation and potential misuse; registry review helps but isn’t foolproof
Clinical validation costs: Tier 3 requirements may be prohibitive for academic proof-of-concept; might need industry partnership or grant funding for translation
6. Ethical Reflection
New Ethical Concern: This week’s discussions highlighted the “edgeless” quality of engineered organisms—once released, bacteria don’t respect geographical or temporal boundaries. Unlike chemical diagnostics that degrade predictably, live biosensors could theoretically persist and spread if containment fails. This made me realize that even diagnostic applications (which seem purely beneficial) carry environmental responsibilities that extend beyond the immediate user.
Proposed Governance Action: Require environmental impact assessments even for contained clinical applications. Specifically:
Before deploying biosensors in any clinical setting, model worst-case release scenarios (e.g., improper waste disposal, accidental spill)
Establish monitoring protocols for detecting engineered strains in local wastewater
Create rapid-response plans if biosensor bacteria are detected outside intended use areas
This shifts thinking from “it’s contained so it’s safe” to “what if containment fails, and how do we detect and respond?” As a future Pharm.D working at the interface of biology and medicine, I want to build the habit of anticipating unintended consequences, not just assuming good intentions equal good outcomes.
Subsections of Week 1 HW: Principles and Practices
Week 1: Professor Questions
Homework Questions from Professor Jacobson
Question 1: DNA Polymerase Error Rate
Question: 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?
Answer:
The error rate of DNA polymerase with proofreading is approximately 1 error per 10⁶ base pairs. The human genome is about 3.2 billion base pairs long. At this error rate, a single round of genome replication would introduce roughly 3,200 errors, which would be incompatible with stable life.
How Biology Resolves This Discrepancy
Biology relies on layered error correction mechanisms rather than polymerase accuracy alone:
Polymerase Proofreading Many DNA polymerases possess 3′→5′ exonuclease activity, which removes incorrectly incorporated nucleotides immediately during synthesis.
Mismatch Repair (MMR) Errors that escape proofreading are corrected post-replication by the mismatch repair system, involving proteins such as MutS, MutL, and MutH, which detect mismatches, excise the incorrect segment, and resynthesize it correctly.
Net Result: These combined systems reduce the final mutation rate to approximately 1 error per 10⁹–10¹⁰ base pairs, resulting in only a few errors per genome replication.
Question 2: Coding Diversity for Human Proteins
Question: How many different DNA sequences can encode an average human protein, and why do most of these sequences fail in practice?
Answer:
An average human protein is encoded by approximately 1,036 base pairs of DNA (about 345 amino acids). Because the genetic code is redundant, with 61 codons encoding 20 amino acids, there are an astronomical number of possible DNA sequences that can theoretically encode the same protein.
Why Most Synonymous Sequences Fail
Despite this theoretical diversity, most synonymous sequences do not function properly due to several biological constraints:
mRNA Secondary Structure Different sequences fold into different mRNA structures. Stable hairpins or loops can block ribosome binding, slow translation, or destabilize the transcript.
GC Content and Stability Extreme GC or AT content alters nucleic acid stability. Excessive GC content makes DNA and RNA difficult to unwind, while low GC content reduces structural stability.
RNA Cleavage Rules Certain sequences form structures recognized by RNases (e.g., RNase III), leading to premature mRNA degradation.
Codon Usage Bias Organisms prefer specific codons. Rare codons slow translation due to limited tRNA availability, reducing protein yield or causing misfolding.
Translation Kinetics and Folding Translation speed affects co-translational protein folding. Incorrect synonymous choices can produce misfolded, non-functional proteins.
Together, these constraints explain why only a small fraction of synonymous DNA sequences successfully produce functional proteins.
Homework Questions from Dr. LeProust
What’s the most common method for oligonucleotide synthesis?
The most common method is solid-phase phosphoramidite synthesis, a chemical process in which nucleotides are added stepwise to a growing DNA strand. Modern platforms perform this synthesis on silicon chips, enabling the parallel production of millions of oligonucleotides.
Why is it difficult to synthesize oligonucleotides longer than ~200 nucleotides?
As oligonucleotide length increases, small errors accumulate and coupling efficiency decreases, leading to truncated and incomplete products. This limits reliable direct synthesis to a few hundred nucleotides.
Why can’t a 2000 base-pair gene be made by direct oligonucleotide synthesis?
Chemical synthesis is limited to short DNA fragments. A 2000 base-pair gene must be constructed by synthesizing shorter oligonucleotides and assembling them into the full-length gene using enzymatic assembly and ligation methods.
Homework Questions from Professor Jacobson
Natural vs. Synthetic Biocontainment Strategies
Amino Acid Essentiality & Biocontainment
The ten essential amino acids for animals—those that must be obtained through diet—are phenylalanine (F), valine (V), threonine (T), tryptophan (W), isoleucine (I), methionine (M), histidine (H), arginine (R), leucine (L), and lysine (K).
The Lysine Contingency and Biological Reality
The “lysine contingency,” popularized by Jurassic Park, proposes limiting survival by making organisms dependent on lysine. In reality:
Natural dependency: All animals are already dependent on lysine and other essential amino acids obtained from the environment.
Poor containment: Lysine is abundant in nature, making this an ineffective biocontainment strategy.
Synthetic solutions: Research on genomically recoded organisms (GROs) replaces natural amino acid dependence with reliance on non-standard amino acids (NSAAs) that do not exist outside controlled environments.
This creates a synthetic contingency, ensuring engineered organisms cannot survive beyond the laboratory or production setting.
Citations and AI Prompt Disclosure
Key References:
Lajoie et al. (2013), Genomically Recoded Organisms Expand Biological Functions
Nyerges et al. (2022), Swapped genetic code blocks viral infections & gene transfer
AI Usage Disclosure: Standard biological facts were retrieved using internal knowledge. Google NotebookLM was used as a study aid. Lecture slides were uploaded to ChatGPT, and the following prompt was used:
“Teach me this lecture as a coherent essay. Explain all concepts from first principles, and clearly explain any new or technical terms when they appear.”
The connection to Prof. Church’s work and the lysine contingency was synthesized directly from the provided source materials.
Week 2 — DNA Read, Write, and Edit
Part 1: Benchling & In-silico Gel Art
Purpose
This exercise demonstrates applied understanding of restriction enzyme digestion and gel electrophoresis through in-silico modeling. The workflow emphasizes correct experimental logic, lane interpretation, and band pattern analysis using professional bioinformatics tools.
Platform and Workflow
All simulations were designed and executed using Benchling, a molecular biology platform widely used for DNA analysis, cloning design, and experimental planning. The use of Benchling enabled rapid iteration, accurate restriction mapping, and controlled visualization of gel electrophoresis outcomes.
DNA Sequence
Template: Lambda phage DNA
Length: 48,502 bp
Topology: Linear
The sequence was imported directly into Benchling’s molecular biology workspace for downstream analysis.
Restriction Digest Design
Single-enzyme restriction digests were simulated using the following enzymes:
EcoRI
HindIII
BamHI
KpnI
EcoRV
SacI
SalI
Each enzyme digest was treated as a discrete lane to preserve interpretability and allow controlled manipulation of band distributions.
Gel Electrophoresis Simulation
Virtual agarose gel electrophoresis was generated within Benchling. Lane order was intentionally adjusted across multiple iterations to explore:
Fragment size distribution
Band density contrast
Symmetry and spacing
Use of negative space
Iterative Design and Pattern Formation
Multiple gel configurations were evaluated. Early iterations did not produce coherent latent figures due to asymmetry or excessive band crowding. These attempts informed subsequent refinements in enzyme selection and lane arrangement.
Failed Iterations (Process Documentation)
Initial Draft/Notes:
Failed attempt 1:
Failed attempt 2:
Failed attempt 3:
Final Gel Art Outcome
The finalized configuration produced a smiley-face latent figure, emerging entirely from restriction fragment patterns. The visual effect was achieved through bilateral lane symmetry and controlled band separation.
Final Gel Image
Skills Demonstrated
Restriction enzyme selection and interpretation
Gel electrophoresis logic and band analysis
Proficient use of Benchling
Iterative experimental design in a computational environment
Part 2: DNA Design Challenge — GFP Biosensor
3.1. Choose Your Protein
I have chosen Green Fluorescent Protein (GFP) from Aequorea victoria for this assignment. GFP is directly relevant to my final project—a bacterial biosensor for rapid detection of antibiotic-resistant pathogens. In my biosensor design, GFP serves as the fluorescent reporter that signals the presence of beta-lactamase activity from resistant bacteria. Understanding GFP’s sequence and optimizing its expression in E. coli is critical for maximizing detection sensitivity within my target 2-4 hour response time. GFP is also a foundational tool in synthetic biology and understanding its design will help me build more effective genetic circuits. Its well-characterized nature, robust fluorescence, and established expression protocols make it both educationally valuable and practically essential for my biosensor application.
UniProt ID: P42212 (Wild-type GFP from Aequorea victoria)
sp|P42212|GFP_AEQVI Green fluorescent protein OS=Aequorea victoria OX=6100 GN=GFP PE=1 SV=1
MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK
3.2. Reverse Translation: Protein to DNA
Using Benchling, I reverse-translated the primary amino acid sequence of GFP into its corresponding nucleotide sequence. This allows us to work backward from the functional protein to the genetic “blueprint” required for synthesis.
3.3. Codon Optimization for E. coli
Target Organism:Escherichia coli (Strain OVST1/FEDL903).
Rationale: I optimized the sequence to align with the host’s tRNA availability (codon bias). This is critical in pharmaceutical R&D to prevent ribosome stalling and maximize protein yield.
Parameters: As shown in the optimization interface, I utilized the “Match codon usage” method while maintaining ideal GC content and avoiding hairpins.
3.4. From Sequence to Synthesis: Production Workflow
To produce the GFP protein from this designed DNA, I would utilize a cell-dependent expression system.
The full genetic construct, including the biosensor insert, is mapped below. This map includes critical regulatory elements such as the Promoter_J23106 and various reading frames.
Molecular Validation
Annotation: Detailed sequence annotations show restriction enzyme sites like BsaI and BseRI, which are essential for Goldengate assembly or standard cloning.
3.5. [Optional] How does it work in nature/biological systems?
This section demonstrates the Central Dogma in action. Each 3-nucleotide codon in the DNA is transcribed into an RNA codon, which is then translated by the bacterial ribosome into a specific amino acid.
Bioinformatics: Sequence retrieval, reverse translation, and 3D structure validation.
Synthetic Biology: Host-specific codon optimization and genetic circuit design.
Pharmaceutical R&D: Designing reporter systems for diagnostic biosensors.
Data Management: Systematic documentation and version control of genomic files.
Part 4: DNA Synthesis & Plasmid Construction
4.1. Account Setup & Design Environment
To transition from in-silico design to physical DNA synthesis, I established research accounts on Benchling (for sequence orchestration) and Twist Bioscience (for high-fidelity DNA synthesis). These platforms allow for a seamless transition between sequence design and manufacturing.
4.2. Building the Expression Cassette
I constructed a constitutive expression cassette for my biosensor. By substituting the generic sfGFP with my codon-optimized GFP sequence, I ensured the design is tailored for maximum expression in E. coli OVST1.
The cassette architecture includes:
Promoter (J23106): To ensure continuous transcription.
RBS (B0034): Optimized for bacterial translation initiation.
CDS: My optimized GFP sequence.
7x His Tag: Appended at the C-terminus for downstream protein purification.
Terminator (B0015): To define the transcription boundary.
4.3 – 4.5. Clonal Gene Selection & Import
I opted for Clonal Genes through Twist Bioscience. As a time-constrained researcher, this choice is strategic: clonal genes (circular DNA) arrive ready for direct transformation, typically reaching experimental results 1-2 weeks faster than linear gene fragments which require manual assembly.
I successfully converted my optimized amino acid sequence into a DNA FASTA file and imported it into the synthesis pipeline.
I integrated my linear expression cassette into the pTwist Amp High Copy circular backbone. This backbone provides Ampicillin resistance and a high-copy origin of replication, which is essential for achieving the high fluorescent signal density required for a diagnostic biosensor.
The final plasmid was exported as a GenBank file and re-imported into Benchling for final validation of the reading frames and restriction sites.
Proof of Work: Design Files
The following files represent the finalized outputs of the DNA “Write” process:
Rationale: While my biosensor identifies enzymatic activity, sequencing confirms the exact resistance mechanism. This guides precise antibiotic selection (personalized treatment) and allows for tracking the epidemiological spread of resistance genes within hospital environments.
(ii) Technology: Oxford Nanopore MinION (3rd Generation)
Why MinION: It offers a 6–12 hour turnaround, matching the rapid 2–4 hour window of my biosensor. Its portability and long-read capability (>10 kb) allow for sequencing entire resistance operons.
Input Preparation (~1 hour): 1. DNA extraction and quick lysis.
2. End-repair and A-tailing (30 min).
3. Adapter ligation of motor proteins and tethers (15 min).
4. Magnetic bead cleanup (10 min).
Mechanism & Base Calling: DNA is threaded through a synthetic nanopore by a motor protein. As each base disrupts the ionic current uniquely, neural networks decode the “squiggles” into ATGC sequences at ~450 bp/second.
Output: FASTQ files providing 500–5000× coverage of resistance genes with 95–99% accuracy.
5.2 DNA Write (Synthesis)
Synthesis allows for the physical construction of the digital designs created in Benchling.
(i) What DNA to synthesize and why?
I aim to synthesize a modular library of constructs (~5 kb total) to optimize biosensor performance:
Construct 1: GFP Biosensor (927 bp) - My primary design (Promoter J23106 - RBS - GFP_CDS - His tag - Terminator).
Construct 2: Beta-lactamase Inducible Promoter (~500 bp) - Ensures GFP is only expressed when resistance is detected, reducing false positives.
Constructs 3-4: Multi-color Reporters - Utilizing mCherry to detect aminoglycoside resistance simultaneously with beta-lactamase.
Constructs 5-6: Positive Controls - TEM-1 and CTX-M-15 sequences to validate biosensor sensitivity.
(ii) Technology: Twist Bioscience (Silicon-based Synthesis)
Essential Steps: 1. Oligo Synthesis: Millions of 40-80 nt oligonucleotides synthesized on a silicon chip via photolithography.
2. Gene Assembly: Overlapping oligos joined via Gibson Assembly.
3. Error Correction: Mismatch-specific nucleases remove sequences with synthesis errors.
4. Cloning & QC: Insertion into pTwist vectors followed by Sanger sequencing verification.
Limitations: Turnaround time is 10–15 business days; currently optimized for constructs between 300 bp and 5 kb. Accuracy remains high with a <1:3,000 bp error rate.
5.3 DNA Edit (Genome Engineering)
Editing moves the focus from diagnosis (Read/Write) to direct therapeutic intervention.
(i) What DNA to edit and why?
Target: Human CFTR gene to correct the ΔF508 mutation (a 3 bp deletion of CTT in exon 10).
Rationale: This mutation causes ~70% of Cystic Fibrosis cases. Correcting this deletion in lung epithelial cells via aerosol delivery represents a shift from managing symptoms to curative “molecular pharmacology.”
(ii) Technology: Prime Editing (PE3)
How it works: Uses a Cas9 nickase fused to a Reverse Transcriptase (RT) and a pegRNA (prime editing guide RNA) which encodes the target site and the 3 bp “CTT” correction.
Essential Steps: 1. Design pegRNA targeting the ΔF508 locus.
2. Deliver PE machinery + pegRNA via Inhaled AAV vectors.
3. The Prime Editor nicks the DNA; RT copies the corrected sequence from the pegRNA into the genome.
4. DNA repair completes the edit, precisely inserting the missing CTT.
Limitations & Advantages: Prime Editing is safer as it avoids double-strand breaks. While in-vivo efficiency is currently 5–25%, its high precision makes it ideal for non-dividing lung cells.
Skills Demonstrated
Clinical Bioinformatics: Metagenomic sequencing integration for diagnostic verification.
Synthetic Biology: Modular circuit design and high-fidelity synthesis strategy.
Molecular Pharmacology: Designing targeted gene-editing interventions for chronic genetic disease.
HTGAA 2026: Lab Automation & DNA Design
1. Laboratory Automation: Opentrons Bio-Art
Using the HTGAA26 Opentrons Colab as a framework, I developed a custom automation protocol to translate digital designs into biological patterns.
Protocol Logic: The script utilizes API Level 2.20 and a P20 Single-Channel Gen2 pipette. It features an optimized draw_points function that handles coordinate-based dispensing with batched aspiration to ensure mechanical efficiency and prevent cross-contamination between fluorescent strains.
Design Interface: The design was mapped using the Opentrons Art GUI, ensuring precise coordinate placement for Red (mRFP1), Green (mClover3), Blue (Azurite), and Cyan (sfGFP) reporters.
Visual Reference of Design Interface:
2. Post-Lab Questions & Research
Q1: Published Paper — SHERLOCK (Nature Protocols, 2019)
Paper:SHERLOCK: Specific High-sensitivity Enzymatic Reporter unLOCKing (PubMed: 31548639).
SHERLOCK is a CRISPR-based diagnostic tool that utilizes the collateral cleavage activity of Cas13. This paper is foundational to my final project because it demonstrates a detection method that is both highly sensitive and low-cost (~$0.60/reaction), making it ideal for the Pakistani clinical context.
Automation Connection: The paper highlights the transition from manual detection to high-throughput plates. Utilizing an Opentrons robot allows for the parallelization of hundreds of reactions, ensuring that carrier screening is consistent, rapid, and free from the human pipetting errors common in manual diagnostics.
Q2: Final Project — Automated Thalassemia Biosensor
Motivation
Thalassemia is a critical public health burden in Pakistan (5–8% carrier rate). Current testing (HPLC/Sequencing) is centralized and expensive ($50–$200). My project aims to develop a $3, 90-minute portable biosensor validated via automated workflows.
Technical Workflow
DNA Extraction: Rapid lysis from patient blood.
LAMP Amplification: Isothermal amplification at 65°C, removing the need for thermal cyclers.
Cas13 Detection: Programmed with gRNA targeting common HBB mutations.
Visual Readout: Result visualized via UV torch (glow = positive).
3. Project Presentation Assets
This section visualizes the engineering roadmap and clinical justification for the Automated Thalassemia Biosensor. These assets bridge the gap between initial molecular design and high-throughput diagnostic implementation.
Slide 1: The Clinical Burden (Pakistan Context)
Strategic Context: Identifying the socio-economic barriers to thalassemia screening in Pakistan. This slide establishes the “Why” behind the project—replacing expensive, centralized testing with a $3 portable alternative tailored for rural health clinics.
Slide 2: Molecular Diagnostic Logic (SHERLOCK)
Technical Mechanism: Leveraging the Cas13 enzyme for programmable DNA detection. This slide details the transition from a patient sample to a visible fluorescent readout using the SHERLOCK protocol.
Slide 3: High-Throughput Automation Roadmap
Systems Engineering: A comprehensive plan for scaling validation. By utilizing the Ginkgo Nebula cloud laboratory (including the Echo acoustic handler and PHERAstar plate reader), we move from manual testing to a parallelized 384-well screening environment.
Experimental Design Hub: [Copy of HTGAA26 Opentrons Colab](./Copy of HTGAA26 Opentrons Colab)
AI Methodology
I utilized Claude (Anthropic) and Google Gemini for script optimization—specifically for refactoring coordinate lists into the draw_points batching logic and structuring the Ginkgo Nebula high-throughput workflow. All clinical objectives and molecular strategies are my original work.
