Week 1

Class Assignment — Week 1

1) Biological Engineering Application

I aim to develop a computational and experimental platform for engineering metabolically constrained microbial systems designed for responsible real-world use. Inspired by clinical exposure to preventable infectious disease and my research at the intersection of microbiology and computational biology, the platform integrates genomic design rules, programmed auxotrophies, and environmental sensing circuits that couple microbial survival to defined ecological contexts.

The central principle is ecological boundedness. Survival and function are conditional, not assumed. Outside intended environments, persistence becomes biologically untenable. This approach supports applications ranging from gut-targeted probiotics to agricultural symbionts and environmental remediation strains.

Rather than optimizing microbes solely for performance, I want to encode responsibility at the level of metabolism. The goal is to expand synthetic biology into high-need contexts while ensuring that safety, containment, and contextual awareness are intrinsic design features, not external corrections imposed after deployment.

2) Governance and Policy Goals

My overarching governance goal is to embed non-malfeasance directly into biological architecture rather than relying exclusively on downstream regulation.

First, intrinsic containment standards should become normative. This includes requiring conditional survival mechanisms such as auxotrophies or environmental dependency circuits prior to field deployment, alongside independent validation of escape potential and evolutionary stability.

Second, dual-use mitigation must be integrated into design pipelines. Sequence screening, risk-tiered access controls, and transparent but bounded documentation standards can reduce misuse without stifling legitimate research.

Third, equity should shape access and deployment. Safety-audited open frameworks should remain available to researchers in low-resource settings, and deployment priorities should align with public health and ecological need rather than purely commercial incentives.

Together, these goals move governance upstream. Ethical alignment becomes encoded in design logic, enabling innovation that is both socially responsive and technically responsible.

3) Governance Actions

Option 1 — Conditional Deployment Requirement

Purpose: Shift from voluntary containment to mandatory intrinsic safeguards for field-deployable microbes.
Design: Regulators require documented metabolic constraints and third-party validation before approval. Academic labs and companies must comply.
Assumptions: Safeguards remain evolutionarily stable and measurable.
Risks: Overregulation may slow beneficial innovation; success may create complacency about residual risk.

Option 2 — Integrated Design-Screening Infrastructure

Purpose: Embed sequence screening and risk assessment into computational design tools.
Design: Tool developers, funders, and journals require automated biosecurity checks as part of research workflows.
Assumptions: Screening algorithms remain adaptive to emerging threats.
Risks: False positives could burden researchers; sophisticated actors might bypass systems.

Option 3 — Incentivized Safety Certification

Purpose: Encourage responsible innovation through market and funding incentives.
Design: Grant agencies and industry consortia prioritize projects meeting certified intrinsic-containment standards.
Assumptions: Financial incentives shape behavior effectively.
Risks: Certification may become symbolic rather than substantive if poorly enforced.

4) Scoring Governance Actions

1 indicates strongest alignment.

5) Prioritization and Trade-offs

I would prioritize a combination of Option 2 and Option 3. Embedding screening directly into computational design tools makes safety habitual rather than exceptional, while incentive structures reinforce responsible norms without heavy-handed regulation.

Option 1 is powerful but risks slowing innovation in resource-constrained contexts where deployment urgency is high. My recommendation would target national research funders and international synthetic biology consortia, encouraging coordinated standards that scale globally.

Trade-offs include balancing speed with precaution and avoiding regulatory inequities that disadvantage researchers in low-income settings. Uncertainties remain regarding evolutionary stability of safeguards and adaptability of screening systems.

The central ethical concern that emerged for me is the illusion of control. Engineering containment does not eliminate uncertainty. Governance must remain adaptive, transparent, and humble, recognizing that biological systems are dynamic. Embedding responsibility into design is necessary, but continuous oversight and global dialogue remain essential.

Class Assignment — Week 2 Preparation

1) Essential Amino Acids and the Lysine Contingency

The ten essential amino acids in animals are histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine (essential in growing animals). Animals cannot synthesize these; survival depends on dietary supply.

This reframes the Lysine Contingency for me. It is not merely a clever containment device. Engineering microbes that require lysine creates a metabolic dependency aligned with a biological universal. Because animals cannot produce lysine, ecological persistence becomes tightly coupled to controlled supplementation. Survival becomes conditional, not autonomous.

I now see it less as a biosafety patch and more as a governance-embedded metabolic contract. The dependency encodes authority into biochemistry. Control is not enforced externally; it is written into the organism’s survival logic. That shift moves containment from policy language into molecular architecture.

2) Suggested Code for AA:AA Interactions

From the genetic code logic shown, base pairs have symmetry rules. Amino acids need something analogous. I would propose a layered interaction code:

First layer: chemical class (polar, nonpolar, charged, aromatic).
Second layer: interaction type (hydrophobic packing, hydrogen bonding, ionic pairing, pi stacking).
Third layer: geometry constraint (distance and orientation tolerance).

For example, NP-HYD-G1 could denote nonpolar hydrophobic packing within a defined geometric band. CH-ION-G2 could represent oppositely charged ionic interaction with specific spacing tolerance.

Such a code treats protein structure not as artistic folding but as readable and writable interaction grammar. If we can read polymers, we should also encode their interaction rules explicitly. That shift makes protein design less descriptive and more programmable.

3) Ethical Reflections

Biological systems do not respect borders. Political, institutional, even disciplinary lines dissolve in ecology. Framing safety as compliance feels incomplete because evolution does not comply. Good intentions are structurally irrelevant to selection pressures.

Governance must therefore treat evolution as a first-class design constraint. Safeguards must assume mutation, drift, and ecological leakage. Ethical assumptions should be embedded in design architectures, not appended through oversight committees.

I am increasingly drawn to resilience-based governance. Instead of trusting actors, we engineer systems that remain bounded even under failure. The goal is not perfect control but constrained adaptability. In living systems, humility is ethical. Governance must anticipate dynamics, not merely regulate behavior.

Key Takeaways

• Evolution is not theoretical. Population genetics, mutation rates, and selection coefficients are active in every gut. Any safeguard must assume adaptation under pressure.

• Biology is programmable matter. DNA is a chemically precise information system. If we can write sequence, responsibility must be encoded at that same molecular layer.

• Genetic recoding reshapes constraints. Codon reassignment and translational control can structurally limit horizontal gene transfer.

• Design capacity is accelerating. Sequencing and synthesis technologies now scale faster than the institutions meant to guide them.

• Design obeys physics. Protein folding, metabolic flux, and regulatory circuits follow thermodynamics and kinetics. Only systems stable under stress earn trust.

AI Prompts Employed

• Help me design a scientific but warm homepage visual, iterate fast, and fix what breaks
• Help me turn this from a messy course site into a coherent research story
• Help me debug under deadline without losing momentum
• Help me sound credible, grounded, and original — not speculative or sloppy
• Make contact details easy to find me without making it cringe

Week 2

Class Assignment — Week 2

Part 1 — Sequence Retrieval and Design Workflow

1) Sequence Retrieval and Benchling Initialization

The process began with obtaining a Lambda GenBank file from New England Biolabs. After confirming the correct format, I imported the file into Benchling as a DNA sequence. Care was taken to ensure that the file was not mistakenly uploaded as RNA and that annotations displayed properly within the platform.

This step established a stable working environment before any design modifications were introduced. Confirming correct topology and annotation structure prevented downstream formatting or visualization issues.

2) Genomic Exploration and Annotation Familiarization

Once imported, I explored the annotated regions of the Lambda genome within Benchling. This involved confirming gene orientation, identifying labeled regions, and understanding the graphical interface for both linear and circular visualization.

Although exploratory, this step reinforced familiarity with the design environment. It ensured that I could distinguish between expected gene clusters and annotation artifacts, and that I could confidently navigate the interface for subsequent editing.

3) Protein Selection and Sequence Acquisition

Furthermore, I selected Microcin M as the protein of interest. The choice aligned with my project, ÌṢỌ, which focuses on context-sensitive antimicrobial response within the gut ecosystem.

The selection criteria included:

• Narrow-spectrum antimicrobial activity
• Relevance to microbial competition
• Compatibility with a governed probiotic chassis

The amino acid sequence was retrieved in FASTA format from a reliable database (NCBI GenBank: CAE55705.1). I verified the header structure and ensured that the sequence corresponded exactly to the intended protein.

4) Reverse Translation

Using Benchling’s reverse translation functionality, I converted the amino acid sequence into a nucleotide sequence suitable for expression in Escherichia coli.

Key considerations included:

• Maintaining correct reading frame
• Ensuring inclusion of a start codon
• Confirming appropriate stop codon placement
• Selecting E. coli codon usage

The output DNA sequence was checked to ensure it translated back to the original protein sequence without truncation or frame shift.

5) Codon Optimization

Following reverse translation, codon optimization was performed for expression in E. coli. This step aimed to improve translational efficiency while minimizing expression burden and avoiding rare codons.

Optimization included:

• Aligning codon usage with host bias
• Avoiding problematic restriction sites
• Preserving protein sequence integrity

This stage reinforced that codon choice influences not only protein yield but also metabolic load and evolutionary stability.

Part 2 — Construct Assembly and Validation

6) Expression Cassette Assembly

The optimized coding sequence was integrated into a complete expression cassette using the assignment’s structural framework:

Promoter → Ribosome Binding Site → Start Codon → Codon-Optimized CDS → Optional His Tag → Stop Codon → Terminator

Each component was manually inserted and annotated within Benchling. Particular care was taken to ensure that the coding region replaced the example scaffold sequence rather than being appended to it.

Linear and circular map views were used to confirm structural continuity, annotation accuracy, and absence of unintended sequence artifacts.

7) Virtual Digest and Gel Simulation

To validate construct integrity, I performed a virtual digest within Benchling and obtained predicted fragment sizes. These fragment sizes were then visualized using an external gel simulation tool.

This step confirmed that the construct behaved as expected under restriction enzyme analysis and reinforced my understanding of plasmid verification workflows.

8) FASTA Export and Synthesis Preparation

The completed expression cassette was exported in FASTA format for potential synthesis ordering. Care was taken to ensure:

• Correct header formatting beginning with the greater-than symbol
• No extraneous spaces or formatting characters
• Proper file extension

Although synthesis ordering through Twist was initiated, access limitations prevented full completion. Instead of halting progress, I pivoted toward generating a complete plasmid visualization within Benchling.

9) Plasmid Map Generation

To simulate a complete plasmid construct, the sequence topology was converted to circular within Benchling. Circular map visualization confirmed clear annotation of promoter, ribosome binding site, coding sequence, and terminator.

This produced a plasmid map without requiring external synthesis confirmation. The visualization ensured structural coherence and clear representation of the engineered construct.

Technical Milestones Achieved

• Successful import and annotation of GenBank files
• Accurate reverse translation from protein to DNA
• Codon optimization aligned with host expression
• Proper construction of an annotated expression cassette
• Verified FASTA export formatting
• Simulated plasmid visualization in circular topology
• Integration of molecular workflow with ecological design philosophy

Design Integration

Throughout the experience, I maintained alignment with the core principles of ÌṢỌ:

• Fitness cost is a primary design variable
• Selection operates continuously
• Expression burden affects evolutionary stability
• Containment must be intrinsic to architecture
• Models inform design boundaries

This reframed it for me from a cloning exercise into a constraint-aware engineering process.

Process Reflections

The workflow required iterative verification at each stage. Formatting, reading frame integrity, codon usage, annotation accuracy, and topology conversion each presented potential points of error and addressing them incrementally reduced compounding mistakes.

More importantly, it reinforced that biological engineering is not simply about inserting genes. It requires contextual awareness, ecological humility, and structural foresight.

Sequence design is only the beginning. Stability under pressure determines whether a system is viable outside controlled conditions.

This process strengthened both my technical fluency and design discipline, linking molecular implementation to ecological responsibility.

Week 3

Class Assignment — Week 3

1) Opentrons Artwork

2) Published Papers Utilizing Automation

LabscriptAI — Autonomous Liquid-Handling Robotics Scripting

Gao et al., 2025 introduce LabscriptAI, a multi-agent framework that translates natural language experimental descriptions into validated Python scripts for heterogeneous liquid-handling robots, including Opentrons platforms.

The system integrates:

• Hierarchical task planning
• Platform-specific simulation validation
• A precise refactoring engine for targeted debugging
• Domain-specific knowledge retrieval
• Human-in-the-loop safety checkpoints

Experimental validation included:

• Cross-platform fluorescence calibration
• Automated cell-free expression and screening of 298 GFP variants
• Distributed enzyme engineering involving hazardous substrates

The central contribution is not pipetting precision alone. It is structured experimental execution with embedded validation and safety logic. Automation becomes reproducible, cross-platform, and governable.

Active Learning Directed Evolution (ALDE)

Active Learning Directed Evolution which integrates machine learning uncertainty estimation with iterative experimental screening to guide protein engineering efficiently was introduced by Yang, Lal, Arnold, et al. 2025.

ALDE automates experimental decision-making by:

• Training predictive sequence–function models
• Quantifying uncertainty across unexplored sequence space
• Selecting optimal next-round variants
• Iteratively refining search trajectories

Rather than brute-force screening, ALDE navigates design space intelligently, minimizing experimental waste while maximizing functional discovery.

Together, these systems represent complementary layers:

• ALDE enables intelligent experimental proposal
• Robotic scripting platforms enable validated execution

Automation becomes both cognitive and mechanical.

3) Automation Architecture for ÌṢỌ — Sentinel EcN

ÌṢỌ is a fitness-aware engineered probiotic system designed to sense gut context, produce targeted antimicrobial responses, and remain bounded through intrinsic containment.

Automation enables a structured Design–Build–Test–Learn loop.

A) Combinatorial Genetic Circuit Screening (requires automation)

Objective: Evaluate sensor–effector variants under growth constraints.

Automated workflow:

1. Dispense transformation master mix into 96-well plate
2. Add plasmid constructs into defined coordinates
3. Perform serial dilution plating
4. Inoculate colonies into induction gradient
5. Measure OD600 for growth
6. Measure fluorescence for reporter output
7. Normalize fluorescence by growth to assess fitness-aware performance

Example Opentrons pseudocode:

from opentrons import protocol_api

def run(protocol: protocol_api.ProtocolContext):
    plate = protocol.load_labware("corning_96_wellplate_360ul_flat", "1")
    tips = protocol.load_labware("opentrons_96_tiprack_300ul", "2")
    pipette = protocol.load_instrument("p300_single", "right", tip_racks=[tips])

    for well in plate.wells():
        pipette.pick_up_tip()
        pipette.transfer(50, transformation_mix, well)
        pipette.drop_tip()

This enables reproducible and remotely deployable transformation workflows.

B) Cell-Free Circuit Screening

To decouple metabolic burden from host growth:

• Echo transfer DNA constructs into 384-well plate
• Stamp CFPS master mix
• Dispense lysate to initiate expression
• Incubate at 37°C
• Measure fluorescence

This permits rapid high-throughput screening prior to in vivo validation.

C) Active Learning Integration

After first-round screening:

1. Fit sequence–function predictive model
2. Quantify uncertainty across design space
3. Propose next construct library
4. Upload variants for synthesis or robotic cloning
5. Repeat screening

This reduces combinatorial explosion and focuses experimentation where information gain is highest.

D) 3D Printed Hardware Integration (requires automation)

To approximate ecological realism:

• Custom 96-well anaerobic incubation adapter
• Microfluidic gradient diffusion holder
• Plate alignment fixtures for reproducible layout

These hardware additions introduce environmental constraint into automated pipelines rather than assuming ideal laboratory conditions.

E) Use of Ginkgo Nebula

For larger combinatorial libraries:

1. Upload sequence designs
2. Automated synthesis and cloning
3. High-throughput transformation
4. Automated phenotyping
5. Structured dataset return

Cloud laboratories enable distributed execution while preserving structured feedback into the design loop.

Summary

Automation within ÌṢỌ operates at two levels:

• Cognitive layer: uncertainty-aware experimental selection
• Execution layer: validated robotic implementation

Together, they form a closed-loop, governable engineering system that prioritizes stability under ecological pressure rather than maximal output under ideal conditions.

Works Cited

Yang, J., Lal, R. G., Bowden, J. C., et al. (2025). Active learning-assisted directed evolution. Nature Communications, 16, 714. https://doi.org/10.1038/s41467-025-55987-8

Gao, Y., Luo, Y., Li, W., Lan, Y., Jiang, H., Chen, Y., Yi, X., Li, B., Alinejad-Rokny, H., Wang, T., Fu, L., Yang, M., & Si, T. (2025). Autonomous liquid-handling robotics scripting for accessible and responsible protein engineering. bioRxiv. https://doi.org/10.1101/2025.09.30.679666

Proposed Final Project Ideas

Process Reflections

This week shifted my understanding of automation from technical convenience to systems architecture.

Initially, I approached the assignment by identifying a strong automation framework in LabscriptAI. However, as I explored complementary tools such as ALDE, it became clear that robotic precision alone is insufficient. Scalable biological engineering requires structured exploration, specifically uncertainty-aware active learning to navigate sequence and design space intelligently.

The key insight was recognizing that automation operates on two layers:

• Cognitive layer deciding what experiment to run next
• Execution layer safely and reproducibly running it

By combining both, my thinking moved beyond pipetting workflows toward a closed-loop, governable Design–Build–Test–Learn system. This reframing aligns directly with ÌṢỌ, which requires ecological realism, fitness awareness, and safety constraints.

Another important shift was recognizing the role of governance. Automation increases capability, but without structured safety checkpoints, biosecurity screening, and human oversight, it becomes fragile or irresponsible. Designing the automation architecture required explicit consideration of containment, ecological competition, and reproducibility.

This process strengthened three core skills:

1. Systems-level integration rather than tool-level selection
2. Designing for constraint rather than brute-force optimization
3. Framing automation as a platform rather than a procedure

Ultimately, I realized that my final project is not only an engineered probiotic. It is a structured, uncertainty-aware engineering pipeline for responsible biological deployment.

AI Prompts Employed

• Compare ALDE and LabscriptAI to see if they work well together as a system
• Design a closed-loop setup where AI chooses experiments and robots run them
• List what I would automate for ÌṢỌ (Sentinel EcN)
• Draft simple Opentrons-style pseudocode for running reactions
• Integrate 3D printed tools, cloud labs, and governance into the automation workflow

Week 4

Week 5

Week 6

Week 7

Week 9

Week 10

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Week 12

Week 13

Week 14