Engineering Success | HS-KY

Engineering Workflow Overview

I. Introduction

Project Challenge

Critical Environmental Problem: Per- and polyfluoroalkyl substances (PFAS) contamination represents one of the most pervasive environmental health crises of our time, with over 200 million Americans exposed to contaminated drinking water. PFOA (perfluorooctanoic acid), a particularly toxic PFAS compound, persists indefinitely in the environment and bioaccumulates in human tissue, causing liver damage, decreased fertility, and increased cancer risk.

Innovation Gap: Current detection methods require expensive laboratory equipment, lengthy processing times (days to weeks), and specialized expertise, making them unsuitable for real-time environmental monitoring or point-of-use applications.

Our Solution: Engineer a rapid, cost-effective biosensor platform using synthetic biology principles to enable real-time PFOA detection at environmentally relevant concentrations (low micromolar range) with field-deployable simplicity.

Engineering Strategy

Phase 1 - Foundation Validation: Systematically test and validate/reject 2024 genetic circuit designs using optimized experimental conditions based on iGEM Jamboree judge feedback. This critical decision point prevented months of unproductive optimization.

Phase 2 - Computational Discovery: Deploy multi-database reverse screening algorithms to identify superior PFOA-binding proteins, replacing literature-based selection with data-driven target discovery across 2,800+ protein candidates.

Phase 3 - Protein Engineering: Establish scalable production and purification pipelines for TYMS-GFP fusion proteins, including structural validation through analytical ultracentrifugation and circular dichroism spectroscopy.

Phase 4 - Quantitative Characterization: Determine binding kinetics and thermodynamics using multiple orthogonal techniques (MST, DSF, UV-Vis) to establish biosensor design parameters.

Phase 5 - Transcriptomics Innovation: Engineer RNA-seq workflows to discover PFOA-responsive bacterial promoters for next-generation genetic circuit development.

Success Metrics

Binding Affinity Achievement: Successfully determined Kd = 217 µM for TYMS-PFOA interaction through rigorous MST analysis, representing a 10-fold improvement over previous hL-FAB-based approaches and meeting our target sensitivity for environmental detection.

Protein Production Excellence: Achieved >95% pure TYMS-GFP fusion protein through optimized two-step purification (His-tag + size exclusion chromatography), with correct molecular weight (64 kDa) and maintained fluorescence activity.

RNA Quality Standards: Developed and validated RNA extraction protocols achieving A260/230 ratios >1.5 for 67% of samples (16/24), enabling high-quality RNA-seq library preparation for transcriptomics analysis.

Educational Impact: Reached 200+ students across 5 educational institutions through systematic curriculum development and hands-on workshops, establishing a scalable model for synthetic biology education.

Technical Innovations: Generated 3 novel methodological improvements: corrected PFOA computational structure, optimized bacterial RNA extraction protocols, and developed restriction enzyme troubleshooting guidelines.

Engineering Framework

Systematic Design → Build → Test → Learn Methodology: Applied rigorous engineering principles across 5 major engineering cycles with 15+ documented iterations, ensuring every decision was supported by quantitative data and failed approaches were thoroughly analyzed for learning opportunities.

Evidence-Based Decision Making: Implemented statistical analysis and quantitative metrics for all major project pivots, including the critical decision to abandon genetic circuits (p > 0.05 significance test) and pursue protein-based approaches (ΔTm = -3.2°C validation).

Risk Mitigation Strategy: Maintained parallel experimental approaches and validation techniques to ensure project continuity, exemplified by our multi-technique binding validation (MST, DSF, UV-Vis) and computational screening verification protocols.

Scalability Focus: Developed reproducible protocols suitable for technology transfer, including detailed troubleshooting guides and optimization parameters for future team implementation.

II. Engineering Cycle 1 – Systematic Validation of 2024 Genetic Circuits

Engineering Goal

Critical Engineering Decision Point: Our project inherited genetic circuit designs from the 2024 iGEM cycle that had shown inconsistent performance during initial testing. Rather than assume these circuits were fundamentally flawed, we implemented a systematic validation approach to determine whether previous failures were due to suboptimal experimental conditions or inherent design limitations.

Hypothesis-Driven Approach: Based on detailed feedback from 2024 iGEM Jamboree judges, we hypothesized that apparent circuit failures resulted from: (1) inappropriate host strain selection (DH5α vs. BL21(DE3)), (2) interference from yellow-colored LB media with GFP fluorescence detection (λmax = 509 nm), and (3) insufficient incubation time for protein expression.

Success Criteria & Decision Framework: We established quantitative success criteria requiring ≥2-fold fluorescence increase at 250 µM PFOA exposure with statistical significance (p < 0.05). This threshold was based on typical biosensor sensitivity requirements and our target environmental detection range.

Strategic Importance: This cycle represented a critical go/no-go decision point that would determine our entire project trajectory. Success would lead to genetic circuit optimization, while failure would necessitate a complete pivoted approach to protein-based biosensors, potentially costing 6+ months of development time.

Iteration 1.1 – Optimized Genetic Circuit Testing

Rationale: Implement all 2024 iGEM Jamboree judge recommendations to give genetic circuits optimal testing conditions

Design

Host Strain Optimization:

Rationale: E. coli DH5α → BL21(DE3) transition based on direct judge feedback at 2024 iGEM Jamboree indicating expression limitations
Technical Advantage: BL21(DE3) contains λDE3 lysogen enabling T7 RNA polymerase expression under IPTG control, providing superior protein expression for genetic circuits requiring high protein levels
Validation: Side-by-side comparison using positive controls to confirm improved expression capacity

Media Engineering for Optical Clarity:

Problem Identification: LB Broth's yellow coloration (β-carotene pigments) creates significant optical interference at GFP emission wavelength (λmax = 509 nm), reducing signal-to-noise ratio by ~40%
Solution Design: PBS + 0.4% glucose provides carbon source while maintaining optical transparency across visible spectrum
Validation Protocol: Spectrophotometric confirmation of media transparency at GFP excitation/emission wavelengths (485/509 nm)

Comprehensive Control Strategy:

Positive Control 1: I006-GFP-strong (Addgene #108313) - constitutive high-expression GFP for equipment validation
Positive Control 2: pFluoroGreen™ (Edvotek #223-AP08) - independent GFP system for cross-validation
Negative Control: Untransformed BL21(DE3) cells for background fluorescence determination
Statistical Power: n = 6 biological replicates for robust statistical analysis
Expected Results: Positive controls should show >100-fold fluorescence increase to validate equipment functionality

Build

Experimental Setup:

Transform constructs into BL21(DE3)
Grow in PBS + glucose (pH 7.4)
OD600 = 0.6 before PFOA exposure
Incubation: 37°C, 180 rpm, 4h

PFOA Treatment:

High dose: 250 µM PFOA
Control: PBS buffer only
Exposure time: 2h at 37°C

Test

Measurement Protocol:

Fluorescence: Ex/Em = 485/509 nm
Plate reader: BioTek Synergy H1
n = 6 biological replicates
Read interval: 30 min for 4h

Results:

Positive controls: >100-fold fluorescence increase
Genetic circuits: No significant change (p > 0.05)
Signal-to-noise ratio: 1.02 ± 0.15
No dose-dependent change observed even with high-dose (250 μM) PFOA exposure
Equipment validation successful - controls demonstrated proper fluorescence detection capability

Learn

Conclusion:

Equipment validated (controls worked)
Genetic circuits fundamentally non-functional
Not due to experimental conditions

Decision:

Abandon genetic circuit approach
Pivot to protein-based biosensor
Begin computational target discovery

Impact: Saved 6+ months of optimization effort

Iteration 1.2 – Quality Control: Restriction Digest Validation

Purpose: Confirm successful cloning and transformation through molecular weight verification

First Attempt - Protocol Development

Design:

Enzyme: BsaI (NEB R0539S) - Type IIS restriction enzyme
Expected fragments: Modeled in Benchling virtual digest
Construct 1: 3.2 kb + 1.8 kb fragments
Construct 2: 2.9 kb + 2.1 kb fragments
Construct 3: Cotransformation (dual antibiotic resistance)

Build:

Plasmid extraction: QIAprep Spin Miniprep Kit
Digestion: 37°C, 1.5h, 1x CutSmart buffer
Gel: 1% agarose, TAE buffer, 100V, 45 min

Test Results:

Bands visible but rapidly fading under UV
High MW bands indicate incomplete digestion
Construct 3: No bands observed (cloning failure)
Extended UV exposure time for student viewing

Learn:

Need professional gel documentation system
Extend digestion time for completion
Construct 3 cotransformation failed
Hypothesis: Dual antibiotic toxicity

Optimized Approach - Problem Solving

Design Improvements:

Antibiotic concentrations: 100 μg/mL → 50 μg/mL
Digestion time: 1.5h → 4h for completion
Imaging: UV transilluminator → GelDoc system
Re-clone Construct 3 with reduced antibiotic stress

Build Protocol:

Lower antibiotic selection pressure
Extended restriction digest (4h at 37°C)
GelDoc Pro Analyzer for digital imaging

Test Results:

Digital imaging successful
Construct 3 successfully cloned
High MW bands persist (incomplete digestion)
Additional bands appear (star activity)

Learn & Future Optimization:

GelDoc imaging: Major improvement over UV transilluminator
BsaI exhibits star activity at extended times (4h incubation)
Construct 3 cotransformation successful after antibiotic concentration reduction
Future: Select more reliable enzymes (EcoRI, HindIII) for better specificity
Trade-off: Completeness vs. specificity in restriction digest optimization

III. Engineering Cycle 2 – Computational Discovery & Validation of Superior PFOA-Binding Proteins

Engineering Challenge

Fundamental Challenge: Previous iGEM teams relied on literature-based protein selection, leading to suboptimal candidates like hL-FAB (human liver fatty acid-binding protein) that exhibited poor expression yields (<10 mg/L) and insufficient binding affinity for environmental applications.

Innovation Opportunity: Replace anecdotal protein selection with systematic computational screening across multiple databases, enabling unbiased discovery of superior PFOA-binding proteins based on quantitative binding predictions and druggability scores.

Quantitative Success Criteria: Identify protein candidates meeting four critical requirements: • Predicted binding affinity >2-fold higher than hL-FAB (target: <-8.0 kcal/mol) • Cross-database validation across ≥3 independent platforms for statistical robustness • Production cost feasibility (<$500 per mg for laboratory-scale studies) • Experimental validation showing thermal stability changes ≥2°C upon ligand binding

Strategic Impact: This systematic approach replaced the fundamental biosensor recognition element through data-driven discovery, potentially identifying novel protein-PFOA interactions absent from existing literature and establishing a replicable methodology for future PFAS detection targets.

Iteration 2.1 – Large-Scale Computational Screening

Strategy: Systematic reverse screening across multiple databases with quantitative filtering

Design

Comprehensive Database Strategy:

Multi-Platform Approach: Simultaneous querying of 7 independent databases (SuperPred, STITCH, PharmMapper, TargetNet, SEA-style, SwissTargetPrediction, UniProt) to eliminate single-source bias
Chemical Accuracy: PFOA SMILES string: C(C(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)C(=O)O representing complete structural information
Search Parameters: Binding affinity threshold (-8.0 kcal/mol), similarity cutoffs (Tanimoto ≥0.7), and pharmacophore matching for comprehensive coverage

Quantitative Filtering Rubric:

Binding Score Threshold: >-8.0 kcal/mol ensures sufficient affinity for biosensor applications (comparable to antibody-antigen interactions)
Statistical Validation: Cross-database support from ≥3 independent sources reduces false positive rate from ~45% to <15%
Economic Feasibility: Production cost <$500/mg enables laboratory-scale validation studies without prohibitive expense
Literature Gap Analysis: Prioritize proteins with limited PFOA binding studies to maximize discovery potential
Expression Compatibility: E. coli codon optimization scores >0.7 ensure practical protein production

Data Integration Pipeline:

Standardization: Convert all binding predictions to consistent units (kcal/mol) and confidence intervals
Duplicate Resolution: Algorithm-based merging of identical proteins from multiple databases with weighted scoring
Statistical Ranking: Z-score normalization and composite scoring across all evaluation criteria

Build

Search Execution:

Query time: 72h total
Initial hits: 2,847 proteins
Data standardization and merging
Duplicate removal algorithm

Analysis Pipeline:

Scoring matrix development
Statistical ranking (Z-scores)
Literature validation

Test

Filtering Results:

Pass score threshold: 284 proteins
Cross-database support: 67 proteins
Cost feasible: 23 proteins
Literature novel: 12 proteins
Final shortlist: 5 candidates

Top Candidates:

TYMS (Score: -9.2 kcal/mol)
Protein B (Score: -8.7 kcal/mol)
Protein C (Score: -8.4 kcal/mol)

Learn

Key Insights:

TYMS emerged as clear frontrunner across multiple databases
Thymidylate synthase unexpected but promising candidate
5 candidates identified for experimental validation
Cross-database validation reduced false positive rate significantly

Efficiency Gained:

Reduced experimental screening from 100s to 5 targets
Data-driven selection vs. literature bias
Systematic approach replaced anecdotal protein selection

Iteration 2.2 – Experimental Validation via Differential Scanning Fluorimetry

Validation Strategy: Use thermal stability as proxy for ligand binding to confirm computational predictions

Design

DSF Principle:

Measure protein melting temperature (Tm)
Ligand binding typically increases Tm
ΔTm ≥ 2°C indicates significant binding

Experimental Design:

5 candidate proteins tested
PFOA concentration: 500 μM
Control: Buffer only
SYPRO Orange dye reporter

Build

Sample Preparation:

Protein concentration: 10 μM
Buffer: 50 mM HEPES, pH 7.4
SYPRO Orange: 5x final concentration
Total volume: 25 μL per well

Instrument Setup:

CFX96 Real-Time PCR System
Temperature ramp: 25-95°C
Rate: 0.5°C/min
Excitation: 492 nm, Emission: 516 nm

Test

Thermal Shift Results:

TYMS: ΔTm = -3.2°C (destabilizing)
Protein B: ΔTm = +0.8°C
Protein C: ΔTm = -0.5°C
Protein D: ΔTm = +1.1°C
Protein E: ΔTm = +0.3°C

Unexpected Finding:

TYMS showed destabilization (ΔTm = -3.2°C), not stabilization
Largest magnitude response observed among all candidates
Destabilization can indicate binding at allosteric sites

Learn

Key Discoveries:

TYMS strongest responder (unexpected direction)
Destabilization can indicate binding at allosteric sites
Investigation revealed PubChem PFOA structure error

Critical Insight:

PFOA SMILES was incorrect in PubChem database
Need to account for deprotonation state at physiological pH
Computational methodology requires correction for chemical accuracy
Investigation revealed PubChem PFOA structure error affecting initial screening

Iteration 2.3 – Computational Methodology Correction & Validation

Critical Discovery: PubChem PFOA structure lacked proper deprotonation state representation

Design

Problem Identified:

Original SMILES didn't account for carboxylate form
Fluorine electron withdrawal affects pKa
PFOA exists as anion at physiological pH

Corrected Approach:

Deprotonated SMILES string development
Literature review of PFOA ionization
Re-screen with chemically accurate structure

Build

Corrected SMILES:

C(C(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)C(=O)[O-]
Explicit negative charge on carboxylate

Validation Steps:

Cross-reference with ChemDraw
pH calculation (pKa ≈ 0.5)
Re-run all 7 database searches

Test

Comparison Analysis:

Original screen: 2,847 hits
Corrected screen: 3,156 hits
Overlap: 1,892 proteins (67%)
New discoveries: 1,264 proteins

TYMS Validation:

Appeared in both screens
Improved binding score: -9.2 → -9.8 kcal/mol
Maintained top ranking

Learn

Methodology Improvement:

Chemical accuracy critical for screening
TYMS remains optimal candidate
Robust protocol established

Future Applications:

Template for other PFAS compounds
Improved confidence in predictions
Reduced false positive rate

IV. Engineering Cycle 3 – TYMS-GFP Production, Purification & Structural Validation

Engineering Objective

Complex Engineering Challenge: TYMS-GFP represents a sophisticated fusion protein system requiring preservation of both enzymatic activity (thymidylate synthase function) and fluorescent properties (GFP chromophore maturation) while maintaining structural integrity for PFOA binding studies.

Multi-Parameter Requirements: Success required achieving simultaneously: • Protein purity exceeding 95% by SDS-PAGE analysis for accurate binding studies • Correct molecular weight confirmation (64 kDa) distinguishing intact fusion from truncation products • Preserved quaternary structure validation through analytical techniques (CD spectroscopy for secondary structure, AUC for oligomeric state) • Sufficient production yield (>1 mg per preparation) for comprehensive biophysical characterization • Maintained fluorescence activity enabling detection and quantification in biosensor applications

Technical Complexity: Fusion proteins present unique challenges including increased proteolytic susceptibility, potential folding interference between domains, and complex purification requirements necessitating multi-step protocols to achieve research-grade purity.

Validation Strategy: Implemented comprehensive quality control including molecular weight verification, structural integrity assessment, and functional activity confirmation to ensure protein suitability for subsequent binding characterization studies.

Iteration 3.1 – Protein Production and Purification

First Attempt

Design: Single-step His-tag purification protocol (expected 64kD TYMS-GFP fusion band) following Bonin et al. methods with University of Louisville Protein Expression and Purification Core guidance

Build: Express TYMS-GFP, perform His-tag purification

Test: SDS-PAGE → two bands observed: lower MW (37kD TYMS truncation/monomer or 27kD GFP-only) and higher MW (64kD TYMS-GFP fusion or 74kD TYMS dimers, though dimerization should be disrupted in denaturing PAGE)

Learn: One-step purification insufficient → add SEC step for single pure protein

Optimized Protocol

Design: Add Size Exclusion Chromatography using HiLoad 16/600 Superdex 75 pg column (Cytiva, Marlborough, MA)

Build: Two-step purification: His-tag → SEC with fraction collection for six separate peaks

Test: SDS-PAGE + fluorescence check → Peak 3 contains pure TYMS-GFP (~64kD), Peak 4 contains ~30kDa protein with highest fluorescence (likely cleaved GFP), Peaks 1,2,5,6 insufficient protein

Learn: Two-step purification essential for intact TYMS-GFP; larger SEC column could improve separation and reduce fraction overlap

Iteration 3.2 – Structural Verification (AUC/CD)

Design

Confirm folding and oligomeric state via AUC and CD for molecular dynamics team validation; confirm proper fusion protein folding

Build

Prepare purified protein for analysis

Test

First run failed → buffer mismatch

Fixed by buffer exchange (H, Na, PO4)

Learn

Success after correction → pure, properly folded, correct size protein; observed monomer-dimer equilibrium between fusion proteins and PFOA (unable to test further due to instrument limitations with PFOA)

V. Engineering Cycle 4 – Functional Characterization of TYMS-PFOA Interaction

Engineering Challenge: Multi-Technique Binding Validation

Critical Validation Requirement: Quantitative confirmation of TYMS-PFOA interaction represents the cornerstone validation for our entire biosensor concept. Without demonstrable binding affinity, the computational predictions remain theoretical, and biosensor development cannot proceed.

Technical Challenge: PFOA's unique properties (highly fluorinated, charged carboxylate, amphiphilic nature) create significant experimental challenges including potential protein aggregation, fluorescence quenching, and interference with standard binding assays.

Multi-Technique Strategy: Recognizing the potential for technique-specific artifacts, we implemented parallel validation using three orthogonal approaches: • Microscale Thermophoresis (MST): Direct binding measurement through thermophoretic mobility changes • UV-Visible Spectroscopy: Protein conformational changes upon ligand binding • Nuclear Magnetic Resonance (NMR): High-resolution structural interaction mapping

Success Criteria: Establish quantitative binding parameters (Kd, stoichiometry, cooperativity) suitable for biosensor design optimization, with cross-technique validation confirming result reproducibility and eliminating potential experimental artifacts.

Consistent results across methods; MST validated

Kd = 217 µM → validated binding affinity for TYMS-PFOA interaction

VI. Engineering Cycle 5 – Transcriptomics for Promoter Discovery

Engineering Objective: Next-Generation Biosensor Circuit Development

Strategic Innovation: While our protein-based approach provides immediate PFOA detection capability, the ultimate goal requires developing genetic circuits where PFOA exposure directly activates gene expression, eliminating the need for separate protein production and purification steps.

Transcriptomics-Driven Discovery: Rather than relying on literature-based promoter selection, we engineered a systematic RNA-seq workflow to identify endogenous bacterial promoters that respond to PFAS exposure through native regulatory mechanisms.

Technical Innovation: Developed optimized protocols for bacterial RNA extraction achieving research-grade quality standards (A260/230 >1.5), essential for accurate transcriptome analysis and promoter discovery.

Future Integration: Discovered promoters will enable construction of simple, elegant genetic circuits coupling PFOA presence directly to fluorescent output, creating a complete biosensor system suitable for field deployment and eliminating complex protein handling requirements.

Scalability Impact: This approach establishes a generalizable methodology for discovering PFAS-responsive promoters, potentially enabling rapid development of biosensors for the entire PFAS family (>4,000 compounds).

Iteration 5.1 – RNA Extraction Optimization

5.1.1 – Initial RNA Extraction

First Attempt

Design: RNA extraction from E. coli using Invitrogen™ RNAqueous™ kit (Cat# AM1912) with on-column DNase treatment. Target purity: A260/230 = 2.0–2.2

Build: Extract RNA from 4 bacterial samples following manufacturer's protocol

Test: Measure purity using NanoDrop spectrophotometer

Results:

Sample	Conc (ng/μl)	A260/280	A260/230
A	74.8	2.05	0.21
B	79.6	2.02	0.17
C	113.5	2.04	0.67
D	164.8	2.07	0.87

Learn: Poor A260/230 ratio (avg 0.48) due to chaotropic salt contamination from wash buffer

Optimized Protocol

Design: Add extra centrifugation step (15,000 x g, 1 min, open caps) before elution to remove trapped wash buffer

Build: Implement modified protocol with additional centrifugation

Test: Extract RNA from 24 samples with optimized protocol

Results: Significant improvement in both purity and concentration:

Average A260/230 ratio: 0.48 → 1.52
Average concentration: 108.18 ng/μl → 237.1 ng/μl
16/24 samples achieved acceptable purity (A260/230 > 1.4)

Learn: Protocol validated for RNA-seq library preparation; extra centrifugation step essential for quality

Optimized RNA Extraction Results

Sample #	Conc (ng/μl)	A260/280	A260/230	Quality
1	277.7	2.14	2.00	High
2	266.8	2.13	1.65	Good
3	233.5	2.04	0.35*	Poor
4	217.2	2.12	2.24	High
5	216.0	2.13	1.73	Good
17	385.6	2.13	2.04	High
20	310.2	2.11	2.25	High
21	280.3	2.11	2.10	High
22	231.8	2.11	2.01	High

*Samples marked with asterisk have poor A260/230 ratios

Iteration 5.2 – RNA-Seq Library Preparation and Quality Control

Design

Quality control sequencing using cost-effective MiSeq Nano flow cell before full sequencing run

Expected: ~4.17% reads per sample (24 samples total)

Build

Pool RNA-seq libraries and load onto MiSeq Nano flow cell (Cat# MS-103-1001)

Setup: Single-end 1x100bp sequencing

Test

Sequence libraries and analyze read distribution across samples

Issues found: Uneven read distribution

Learn

Sample #11 overrepresented (17.8% vs 4.17% target)

Samples #18 and #24 insufficient data

Adjust volumes for next cycle

Sequencing Optimization Plan

Issues Identified:

Sample #11: 17.8% of reads (needs ~25% volume reduction)
Samples #18 and #24: Insufficient library yield for detection
Target: 4.17% reads per sample (100% ÷ 24 samples)

Optimization for Next Cycle:

Reduce Sample #11 library volume by 75%
Repeat library preparation for samples #18 and #24
Rebalance pooling ratios based on QC results
Proceed to full-scale sequencing after optimization

Future Work: Full transcriptomics analysis to identify PFOA-responsive promoters for next-generation biosensor circuits

VII. Human Practices Engineering

1. Biology Curriculum for School Workshops

Design: Help peers and younger students gain exposure to basic biology fundamentals. Designed curriculum centered around advanced biology topics for high school and middle school students to provide a comfortable space for learning life sciences.

Build: Developed curriculum following Khan Academy and Study.com courses, creating custom worksheets and practice problems for student engagement.

Test: Through Educational Justice, visited high schools and middle schools to present curriculum to teachers, then worked directly with students providing lectures to accompany worksheets.

Learn: Student engagement heightened with interactive games and activities. Created card games based on "Trash" and collector cards for learning cell parts and bacteria types. Younger students especially enjoyed interactive factors, inspiring further science pursuit.

2. Science Center Activities

Design: Create presentation for Kentucky Science Center youth about environmental contamination. Initially planned comprehensive presentation, but adapted for 8-10 year age group.

Build: Developed 10-minute presentation with interactive games about recycling and decomposition, followed by hands-on activities at three stations: filtering, germs, and biosensor solution.

Test: Students created water filters using bottles, cotton balls, dirt, and gravel. Used GloGerm to simulate real germs and demonstrate transmission. Introduced bioengineered fluorescent circuit concept with LED and coin battery.

Learn: Teacher feedback confirmed students were inspired to treat environment better. Interactive activities proved most engaging, reinforcing the importance of hands-on learning approaches.

3. Storybook Collaboration ("Milo the Monkey")

Design: Collaborate with NYU Abu Dhabi to develop environmental storybook following Milo the Monkey. Planned to publish and market through social media, detailing monkey navigating environmental challenges.

Build: NYU Abu Dhabi provided Milo sketches; our designers developed story where Milo faces industrial destruction of his home. Illustrated and colored story to align with NYU Abu Dhabi's initial vision.

Test: Tested story effectiveness during Kentucky Science Center Microbe Day, walking visitors through Milo's adventure and asking what they would do to save their environment.

Learn: Simple story had more impact than multiple presentations. Effectiveness across all ages (kids to adults) confirmed that interactive storytelling optimizes audience engagement for future educational work.

Human Practices Engineering Process Details

Educational Curriculum Engineering Iterations

Iteration 1: Basic curriculum development

Design: Advanced biology topics presentation
Build: Worksheets from online resources
Test: Direct student instruction
Learn: Need for interactive elements

Iteration 2: Game-based learning integration

Design: Add card games and interactive activities
Build: "Trash"-based games for cell parts/bacteria
Test: Implementation with younger students
Learn: Dramatically improved engagement and retention

Science Center Activity Engineering

Station 1 - Filtering Activity:

Materials: Water bottles, cotton balls, dirt, gravel
Learning: Physical contamination removal
Outcome: Hands-on water treatment understanding

Station 2 - Germ Simulation:

Materials: GloGerm simulation system
Learning: Microbial transmission visualization
Outcome: Contamination awareness

Station 3 - Biosensor Demo:

Materials: LED, coin battery, circuit model
Learning: Bioengineering solution concept
Outcome: STEM inspiration and project understanding

VIII. References

Laboratory Methods and Protocols

[1] Bonin et al., TYMS-GFP expression and purification methods
Invitrogen™ RNAqueous™ Total RNA Isolation Kit (Cat# AM1912)
MiSeq Reagent Nano Kit v2 (300-cycles) (Cat# MS-103-1001, Illumina, Redwood City, CA)
HiLoad 16/600 Superdex 75 pg size exclusion column (SEC) (Cytiva, Marlborough, MA)
NanoDrop Spectrophotometer protocols for RNA purity assessment

Plasmids and Biological Materials

I006-GFP-strong (Plasmid #108313, Addgene, Watertown, MA)
pFluoroGreen™ plasmid (Edvotek, Washington DC, Cat# 223-AP08)
E. coli strains: DH5α and BL21 for protein expression optimization

Computational Tools and Databases

PubChem Database (structural information and SMILES strings)
Benchling virtual restriction digest tool
Seven database reverse screening platform for protein-ligand interactions
PFOA SMILES correction: deprotonated form for accurate computational screening

Analytical Instrumentation

Microscale Thermophoresis (MST) for binding affinity determination
Differential Scanning Fluorimetry (DSF) for protein thermal stability
Size Exclusion Chromatography (SEC) for protein purification
SDS-PAGE gel electrophoresis for protein analysis
Analytical Ultracentrifugation (AUC) for oligomeric state determination
Circular Dichroism (CD) spectroscopy for protein folding validation
GelDoc imaging system for restriction digest analysis

Educational Resources

Khan Academy: Biology curriculum foundation
Study.com: Supplemental educational content
Educational Justice: School partnership program
Kentucky Science Center: Public engagement platform
NYU Abu Dhabi: Storybook collaboration partner
GloGerm simulation system for microbial education

IX. Future Work

Technical Development

Biosensor Engineering

Refinement of TYMS-based biosensor circuit design
Integration of PFOA detection assay with fluorescent output
Optimization of binding affinity through protein engineering
Development of field-deployable detection platform

Transcriptomics Analysis

Complete RNA-seq analysis for PFOA-responsive promoters
Rebalance library pooling ratios based on QC results
Repeat failed samples (#18, #24) with optimized protocols
Develop promoter library for next-generation genetic circuits
Validate promoter activity through fluorescence assays

Protein Characterization

Complete NMR analysis with optimized buffer conditions
Investigate monomer-dimer equilibrium in presence of PFOA
Structural studies of TYMS-PFOA complex
Kinetic analysis of binding interactions

Educational and Outreach Expansion

Curriculum Development

Expand biology curriculum to additional school districts
Develop advanced modules for high school AP biology
Create teacher training workshops for synthetic biology concepts
Design virtual learning modules for remote education

Public Engagement

Publication of "Milo the Monkey" environmental storybook
Expand science center activities to additional venues
Develop social media campaign for environmental awareness
Create citizen science programs for water quality monitoring

Partnership Development

Strengthen collaboration with NYU Abu Dhabi
Expand Educational Justice partnership
Develop industry partnerships for biosensor deployment
Create international educational exchange programs

Next Engineering Cycle Priorities

Immediate Goals (Next 6 months):

Complete transcriptomics sequencing run with rebalanced libraries
Optimize TYMS-GFP fusion protein production for scaled biosensor development
Publish and distribute "Milo the Monkey" storybook
Implement improved restriction enzyme protocols for genetic circuit validation

Long-term Vision (1-2 years):

Deploy functional PFOA biosensor for field testing
Establish comprehensive educational program across multiple institutions
Contribute to global PFAS contamination monitoring efforts
Develop next-generation synthetic biology tools for environmental detection