Week 1 HW: Principles and Practices

Biological Engineering Application Proposal

1. First, describe a biological engineering application or tool you want to develop and why.

I propose the development of an optimized citrus-derived microbial consortium to accelerate composting of citrus-rich organic waste in urban systems. Citrus residues (such as orange and lemon peels) are often excluded from compost systems due to their high limonene content, acidity, and antimicrobial properties. These compounds inhibit early microbial succession, delay compost stabilization, and can reduce nutrient turnover efficiency. This project aims to:

• Isolate and characterize native microbial communities present in citrus peels.
• Identify strains tolerant to limonene and low pH conditions.
• Optimize microbial consortia that can initiate compost stabilization despite citrus-associated inhibitory compounds.
• Improve compost maturation rate and nutrient availability.
• Ensure ecological containment and prevent unintended environmental persistence. The intervention will initially be piloted within a single urban composting system, integrating municipal organic waste management. Rather than introducing foreign organisms, this approach reorganizes and amplifies naturally occurring citrus-associated microbiota using ecological engineering principles. Future exploratory phases may evaluate controlled molecular optimization within contained systems if ecological selection proves insufficient.

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.

Goal 1: Environmental Biosafety

• Prevent unintended ecological spread beyond compost systems.
• Ensure selected microbes do not displace native soil biodiversity.
• Monitor adaptation and ecological persistence over time.

Goal 2: Responsible Urban Deployment

• Integrate safely within municipal compost infrastructure.
• Maintain transparency about strain origin and function.
• Ensure community-level environmental benefit.

Goal 3: Precautionary Innovation

• Enable staged pilot testing before scale expansion.
• Avoid unnecessary regulatory burden for low-risk ecological interventions.
• Ensure reversibility during early implementation phases.

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

Option 1: Ecological Niche Containment (Technical Strategy)

Purpose: Current composting relies on unmanaged microbial succession, which may stall in citrus-rich systems. This strategy introduces consortia adapted specifically to citrus substrates and thermophilic compost conditions.

Design:

• Select strains dependent on citrus-derived compounds.
• Pilot small-scale compost reactors before municipal scaling.
• Require ecological monitoring during early deployment. Actors: Academic labs, municipal waste departments.

Assumptions:

• Citrus-specialized microbes will not thrive in ordinary soil conditions.
• Native strain origin reduces ecological disruption.

Risks:

• Failure: Adaptation beyond intended compost niche.
• Success risk: Dominance of selected strains reduces microbial diversity.

Option 2: Municipal Biological Additive Certification

Purpose: Introduce a city-level review mechanism for microbial compost accelerators.

Design:

• Pre-deployment risk assessment.
• Defined monitoring period.
• Public reporting of ecological impact. Actors: Municipal environmental agencies.

Assumptions:

• Certification builds public trust.
• Authorities have technical capacity.

Risks:

• Failure: Regulatory delay slows sustainable innovation.
• Success risk: Compliance burden limits smaller initiatives.

Option 3: Open Scientific Transparency Framework

Purpose: Complement regulation with community-based oversight.

Design:

• Open documentation of isolation methods.
• Public compost performance data.
• Independent academic peer review. Actors: Universities, environmental research groups.

Assumptions:

• Transparency reduces misuse.
• Scientific oversight remains rigorous.

Risks:

• Failure: Lack of enforcement authority.
• Success risk: Misinterpretation of biological data by the public.

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. The following is one framework but feel free to make your own:

Governance Scoring Matrix

(1 = strongest performance)

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.

For an initial urban pilot, I recommend prioritizing Option 1 (Ecological Niche Containment) supported by Option 3 (Open Scientific Transparency).

This approach:

• Embeds safety at the biological design level.
• Maintains flexibility for research iteration.
• Builds public trust through transparency.
• Minimizes unnecessary regulatory burden.

Primary uncertainty: Long-term ecological adaptation of selected consortia and potential impacts on compost microbial diversity.

Implementation should therefore include time-limited pilot phases with ecological monitoring and review checkpoints.

Ethical Reflection

This week’s discussions emphasized the ethical responsibility involved in modifying ecological systems, even when using native microbiota.

Key concerns:

• Does optimization risk ecological simplification?
• Could improving citrus compost efficiency alter natural microbial succession patterns?
• How do we balance efficiency with biodiversity?

Proposed governance measures:

• Mandatory biodiversity monitoring during pilot studies.
• Sunset clauses for field trials.
• Transparent publication of ecological data.
• Incremental scaling based on evidence.

The objective is not to dominate natural systems but to responsibly enhance their resilience and functionality within urban sustainability frameworks.

Week 2 Lecture Prep

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?

DNA polymerase makes mistakes: About 1 error for every 1,000,000 bases it copies. This would be a disaster for large genomes: Copying the 3.2-billion-base human genome with that error rate would create over 3,000 mutations every time a cell divides. Solution: Biology has proofreaders and repair crews. Special enzymes (like a 3’→5’ exonuclease and the MutS repair system) find and fix these mistakes. Result: The real-world error rate drops to about 1 in 1,000,000,000 per replication. The lesson is: you can build big, stable systems by pairing fast parts with excellent error-checking.

2.How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

You can write the “code” for a protein in a huge number of ways because many different DNA sequences (codons) can give you the same chain of amino acids. But not all DNA sequences work equally well because of real-world constraints: mRNA Folding: The sequence itself can make the mRNA molecule fold into a shape that blocks the ribosome. Stability: Some sequences make the mRNA less stable and more likely to be degraded. Codon Preference: Cells “prefer” certain codons for speed and accuracy. Using rare codons can slow things down. Repetitive Sequences: Long stretches of the same base (like CCCCCC) are hard to synthesize and can cause problems.

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

The most widely used method is solid-phase phosphoramidite chemistry, which builds DNA stepwise through repeated coupling, capping, oxidation, and deprotection cycles. It is highly automated and scalable but limited by stepwise efficiency.

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

Each nucleotide addition has a small failure rate, and these errors accumulate exponentially as length increases. Beyond ~200 nt, the proportion of full-length, error-free product drops dramatically and purification becomes inefficient.

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

Direct chemical synthesis cannot maintain high fidelity over thousands of coupling steps. Instead, long genes are assembled hierarchically from shorter oligos using enzymatic assembly methods such as Gibson Assembly or PCA.

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

The 10 essential amino acids in animals are phenylalanine, valine, threonine, tryptophan, isoleucine, methionine, histidine, arginine, leucine, and lysine. Because lysine is already diet-dependent in animals, the “Lysine Contingency” is biologically weak; a stronger containment strategy would involve dependence on synthetic or non-standard amino acids absent in nature. Rovner has showed experimental evidence that synthetic amino acid dependence creates organisms that cannot escape into the wild. Rovner et al., 2015 Nature

Week 3 HW: Lab Automation

1. Published Article on Automation: Apta-MIP Biosensors for Water Pathogen Detection

Title: Aptamer-Molecular Imprinted Polymer Hybrid Biosensors for Pathogen Detection in Water [citation:1]

Author: Meltem Agar

Journal/Institution: Doctoral Thesis, University of Bath, 2025 [citation:1]

Summary of the Work: This thesis addresses the critical global problem of detecting waterborne pathogens quickly and cost-effectively. Traditional methods like culturing take 2-4 days and require trained personnel. To solve this, the research developed novel electrochemical biosensors that combine two powerful technologies: aptamers and Molecularly Imprinted Polymers (MIPs) [citation:1].

This hybrid “Apta-MIP” sensor was designed to detect Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) in water samples. The sensor works by using the aptamer’s high specificity and the MIP’s ability to create precise, antibody-like cavities for the bacteria. This dual-recognition system makes the detection incredibly sensitive and selective [citation:1][citation:4][citation:9].

The performance was remarkable. For E. coli, the sensor achieved a detection limit as low as 2 CFU/mL in buffer solution and 3.5 CFU/mL in real tap water [citation:1]. It also successfully detected both bacteria simultaneously in a single test, making it a powerful tool for water quality monitoring [citation:1][citation:9].

Why Automation Was Essential (or could be): While the sensor itself is the novel achievement, the development and validation process highlight the need for automation. To move this technology from the lab to a real-world, point-of-care device, automation is key for several reasons:

• High-Throughput Screening: Testing the sensor’s performance against a wide range of bacterial concentrations and in different water matrices (tap water, river water, etc.) requires processing hundreds of samples. A liquid handler like the Opentrons OT-2 could automate the preparation of these samples and the application of bacteria to the sensor arrays [citation:1].
• Reproducibility: Automating the electrochemical measurements and the sensor fabrication process would ensure every sensor performs identically, a critical factor for regulatory approval and reliable commercial production.

This study is a perfect example of a “new biological application” enabled by advanced materials, and it represents an ideal candidate for future automation to make it a deployable, high-throughput diagnostic tool [citation:1][citation:9].

2. Proyecto de arte biológico con Opentrons

Diseño: Rana bioluminiscente

Creé un diseño artístico que representa una rana bioluminiscente utilizando dos colores:

• Verde: Cuerpo de la rana (usando ~200 puntos de coordenadas)
• Rojo: Ojos, lengua y detalles bioluminiscentes (~60 puntos)

El script de Python controla el Opentrons OT-2 para dispensar gotas de 1μL en coordenadas específicas (x, y) sobre una placa de agar, creando una imagen que simula la detección colorimétrica de bacterias.

https://colab.research.google.com/drive/1Z-UmyG9dBfAS33tIwYKGWMPEat7lgpKv#scrollTo=_CrnFLU2Zf7k

Also, you can see the result here:

featured-rana-design.png

3. Final Project Idea: Automated Colorimetric UTI Screening (U-ColorTest)

For my final project, I plan to design an automated workflow for the rapid, culture-free detection of E. coli in urine samples, inspired by the principles of the Apta-MIP sensor but adapted for a simpler, colorimetric readout.

Concept

The goal is to eliminate the 24-48 hour culture step for urinary tract infection (UTI) diagnosis. The system will detect E. coli directly in a urine sample using a colorimetric reaction. The intensity of the color will be proportional to the bacterial load (CFU/mL), providing a same-day result.

Why Automation?

Manual pipetting of 96-well plates with patient samples is tedious, error-prone, and a contamination risk. The Opentrons OT-2 will provide the precision, sterility, and throughput needed for a clinical lab setting.

Automated Workflow with Opentrons OT-2

The protocol will be divided into three main stages:

1. Sample Preparation:

   • The robot receives a 2 mL tube of urine (loaded in a custom 3D-printed tube rack).
   • It pipettes 1 mL of urine into a deep-well plate.
   • Pause: The user places the plate in a centrifuge.
   • The robot then resuspends the bacterial pellet in 100 μL of a reaction buffer.

2. Colorimetric Reaction:

   • The robot transfers 50 μL of the concentrated sample to a 96-well assay plate.
   • It adds 50 μL of a chromogenic substrate (e.g., a substrate for the enzyme β-glucuronidase, which is specific to E. coli).
   • The robot mixes the well contents.
   • The plate is then transferred to an on-deck thermal module for a 30-minute incubation at 37°C.

3. Detection and Interpretation:

   • After incubation, the plate is moved to a plate reader (or a simple camera setup within the robot’s deck).
   • The robot triggers the reader to measure absorbance at a specific wavelength (e.g., 405nm).
   • A Python script analyzes the data, comparing the optical density to a standard curve to estimate the CFU/mL.
   • Results are compiled into a report (e.g., “Negative,” “Low-Grade Infection,” “Positive UTI”).

Pseudocode Example

# Pseudocode for the U-ColorTest workflow on the OT-2

DEFINE PLATE_96, TIP_RACK, TUBE_RACK, RESERVOIR

LOAD pipette
LOAD thermal_module with assay_plate

# Sample Preparation
FOR EACH sample_tube IN TUBE_RACK:
    pipette.pick_up_tip()
    pipette.transfer(1000, sample_tube, deepwell_plate)  # Transfer urine
    pipette.drop_tip()

PROTOCOL.pause("Please centrifuge the deepwell plate, then place it back.")

FOR EACH sample_well IN deepwell_plate:
    pipette.pick_up_tip()
    pipette.transfer(50, sample_well, assay_plate)        # Transfer concentrated bacteria
    pipette.transfer(50, RESERVOIR['substrate'], assay_plate) # Add colorimetric substrate
    pipette.mix(3, 50, assay_plate)                      # Mix the reaction
    pipette.drop_tip()

# Incubation
thermal_module.set_temperature(37)
PROTOCOL.delay(minutes=30)

# Detection
trigger_plate_reader(assay_plate, wavelength=405)
results = get_data_from_plate_reader()

# Report Generation
FOR EACH result IN results:
    IF result.OD < 0.2:
        REPORT "Sample Negative"
    ELIF result.OD < 0.5:
        REPORT "Low Bacteria Count"
    ELSE:
        REPORT "POSITIVE for UTI"

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project