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I. Improved Functional Standard Parts for the "Pseudomonas aeruginosa Detection" Scenario — Contributions to Future iGEM Teams of Our University


1. Improved Standard Chassis Strain: EC1000 Δgus

Improvement Basis:

Based on the requirement that "standard vectors must be compatible with clean chassis," we used Escherichia coli EC1000 as the original chassis, which naturally lacks the lacZ gene and can provide the RepA protein for the pORI280 plasmid.

Core Improvement:

Through precise homologous recombination, the genomic gus gene was knocked out, constructing a ΔlacZ Δgus double-deletion strain. This completely eliminates endogenous β-galactosidase (encoded by lacZ) and β-glucuronidase (encoded by gus) activities, solving the problem of "carrier reporter signals being easily interfered with by host endogenous enzymes."

Additional Optimization:

The "lock-and-key" replication system of the pORI280 plasmid (replicating only in this chassis containing RepA) was retained, enhancing biosafety containment and meeting the design logic of "standardized parts balancing function and safety."


2. Improved Standard Selective Pressure Component: fabV-Mediated Triclosan Resistance System

Improvement Basis:

Based on the requirement that "standard components must have selectable markers," the resistance marker was upgraded to a functional selection system.

Core Improvements:

1>. The triclosan resistance gene fabV was introduced as the core selection element, replacing traditional antibiotic resistance. This allows only engineered bacteria and naturally tolerant Pseudomonas aeruginosa to grow in an environment containing triclosan (6 μg/mL), achieving dual enrichment of "target bacteria + engineered bacteria" and solving the problem of "insufficient specificity of standard selection markers."

2>. Upgraded to an arabinose-inducible system (pBADfabV): fabV is expressed only when arabinose is added; without induction, engineered bacteria cannot survive in triclosan-containing environments, enabling precise regulation of resistance expression and further reducing the risk of gene leakage.


3. Improved Standard Sensing Element: PYO/PQS-Specific Promoter-Regulatory Protein Modules

Improvement Basis:

Based on the requirement that "standard BioBricks must contain specific functional elements," the sensing elements were optimized for specificity and sensitivity for Pseudomonas aeruginosa detection.

Core Improvements:

1>. PQS-Specific Module: The Pseudomonas aeruginosa PpqsA promoter was validated to be activated only under induction by PQS (quorum-sensing signal molecule), achieving precise response to the unique signal of the target bacterium, consistent with the "signal specificity" requirement for functional elements.

2>. PYO-Optimized Module: From three candidate promoter-regulatory protein modules (SoxR–PmexG, MexL–PphzA1, BrlR–PbrlR), the SoxR–PmexG module was selected (molecular docking + experimental validation: strongest fluorescent signal, lowest background noise), solving the problem of "balancing theoretical binding affinity and actual response performance" and forming a standard functional module for PYO (pyocyanin) detection.


4. Improved Standard Reporter System: lacZgus Dual Reporter Module

Improvement Basis:

Upgrading from a single lacZ reporter gene to a dual-reporter system to meet the need for "dual-signal verification."

Core Improvements:

1>. The lacZ reporter gene (catalyzing ONPG to produce a yellow chromogenic signal) was retained for PQS response detection.

2>. A gus reporter gene (catalyzing MUG to produce blue fluorescent signal) was added for PYO response detection.

3>. Both were based on the ΔlacZ Δgus clean chassis, achieving "one strain, two signals, no mutual interference," breaking the limitation of "single reporter gene being unable to distinguish multiple target signals," and forming a reusable dual-functional reporter standard module.


Contributions to Future iGEM Teams
1. Chassis Strain Modification: Establishing the "Function Adaptation + Safety Redundancy" Design Principle

When developing biosensors with specific functions, future teams can adopt the logic of "targeted knockout of endogenous interfering genes + binding conditional replication systems." For example, when dual reporter systems (chromogenic + fluorescent) are required, the ΔlacZ Δgus double-deletion strategy can be referenced to eliminate endogenous enzyme activities in advance and avoid signal interference. The "host-plasmid" exclusive replication pairing (e.g., EC1000 with pORI280's RepA dependence) adds a biosafety "redundancy lock," reducing environmental spread risk, especially in medical, food, and environmental fields with high biosafety requirements.


2. Selective Pressure System: Promoting "Scenario-Specific Selection" to Replace "Universal Resistance"

Traditional antibiotic resistance selection easily leads to environmental spread of resistance genes and cannot distinguish between "engineered bacteria + target bacteria." The triclosan resistance system (fabV gene) and arabinose-inducible design provide a new concept of "functional selection" for future iGEM teams: based on the natural resistance of target microorganisms (e.g., Pseudomonas aeruginosa tolerance to triclosan), a selection environment allowing only engineered bacteria and target bacteria to survive can be customized, improving detection specificity in complex samples. The inducible regulation (pBADfabV) allows flexible control of the survival window, preventing gene leakage during non-detection phases. This concept can be extended to targeted detection in complex microbial communities such as soil and gut.


3. Sensing Element Development: Establishing a "Molecular Simulation + Experimental Validation" Dual-Screening System

To solve the problem of "selecting the optimal from multiple candidate functional elements," this study combined molecular docking (predicting protein-ligand binding affinity) with experimental detection (verifying actual signal output) to select the optimal PYO sensing module (SoxR–PmexG), breaking the inefficient "experiment-only trial-and-error" model. Future teams developing new sensing elements (e.g., for detecting other pathogens' signal molecules or environmental toxins) can first narrow candidates through molecular docking, then verify activity in vitro, significantly improving screening efficiency and performance reliability, especially when a number of components and costly.


4. Reporter System Design: Enhancing Accuracy through "Multi-Signal Cross-Validation"

Previous BioBrick standards often used single reporter genes, which are prone to false positives due to sample background interference. The lacZgus dual reporter system (PQS triggering chromogenic signal, PYO triggering fluorescent signal) reduces misjudgment through "independent dual-signal response and cross-validation." Future teams can reference this "signal separation" logic to design multi-channel reporter systems (e.g., different fluorescent wavelengths, color differences) for different detection targets, enabling "one experiment, multiple bacteria detection" in multiplex pathogen detection and improving efficiency and accuracy.


5. Practical Application: Constructing a "National Standard-Comparable" Translation Path

The core pain point in translating scientific research to industrial application is "lack of recognized accuracy." By comparing with the national standard method (GB/T 8538-2016), this study demonstrated that the engineered bacteria method has no difference in accuracy and significantly accelerates detection, providing a "laboratory technology–industry standard" docking paradigm. Future teams developing new detection technologies can anchor corresponding national/international standard methods in advance and conduct parallel validation during the R&D stage, shortening the translation cycle from laboratory to field application, especially in environmental monitoring and clinical diagnosis requiring strict compliance with industry standards.


II. Curve Fitting Software Based on High-Dimensional Search and Azimuth Statistics


1. Basic Information

Software Name: CFS_BHDS_AS (Curve Fitting Software Based on High-Dimensional Search and Azimuth Statistics)

Developer: Shengxuan BIAN

Contact Email: shixiu_yakuchi0324@qq.com

Dependencies:

Development: R-4.5.0, RStudio (2025.05.0+496)

Hardware: Intel(R) Core(TM) i7-8850H CPU @ 2.60GHz (2.59 GHz)


2. Core Functions

1>. Curve Fitting and Parameter Optimization: For kinetic models based on ordinary differential equations (ODEs), fits experimental data curves with predicted data curves and outputs optimal parameters matching experimental data, solving parameter search challenges for nonlinear models with transcendental functions.

2>. Real-Time Visual Monitoring: Provides a dynamic image window to observe fitting progress in real time, intuitively showing the approximation process between predicted and experimental curves.

3>. Multi-Dimensional Result Output: In addition to optimal parameters, outputs predicted curve images and parameter behavior analysis images to assist in evaluating model performance and parameter significance.


3. Core Algorithm Logic

1>. High-Dimensional Space Mapping: Treats all parameters of the ODE model as "prediction coordinates A" in high-dimensional space and the ideal parameters corresponding to the experimental data curve as "target coordinates B," transforming curve fitting into a high-dimensional space search problem of "guiding A to approach B to minimize Euclidean distance (curve difference)."

2>. Dynamic Vector Adjustment: Uses reinforcement learning to record historical performance of parameter directions, probabilistically generating movement vectors; when optimization efficiency decreases, generates orthogonal vectors perpendicular to the current direction to maintain search momentum; incorporates blacklist/whitelist mechanisms to avoid repeated ineffective searches and performs local fine-tuning on promising directions.

3>. Difference Measurement Standard: Uses R's base::outer(x, y, "-") function to calculate a distance information matrix between predicted and experimental curves, using mean squared error as the core metric for difference measurement.


Contributions to Future iGEM Teams
1>. Algorithm Design: Providing a "Low Data-Dependency" Path for Nonlinear ODE Parameter Search

Traditional parameter search methods (e.g., neural networks, Bayesian search) require large datasets for training, are time-consuming, and are difficult to adapt to nonlinear ODE models with transcendental functions (which constitute the majority of common scientific models). The proposed "high-dimensional space search + reinforcement learning" algorithm directly approaches the ideal solution by dynamically adjusting parameter vectors without relying on large training datasets, solving the pain point of "parameter optimization for nonlinear models with limited data." Future teams working on kinetic model fitting in physics, chemistry, and biology can reference this "problem space transformation + dynamic vector optimization" logic, especially in scenarios with scarce experimental data and high model complexity.


2>. Functional Design: Strengthening "Fitting-Analysis-Decision" Full-Process Support

Beyond basic fitting functions, the software adds parameter behavior analysis and multi-dimensional visualization modules to help interpret biological/physical significance behind parameters. For example, in Pseudomonas aeruginosa detection research, the software revealed that SoxR protein expression and degradation rates needed to be simultaneously upregulated, leading to the identification of SoxR as the optimal detection protein when combined with molecular weight data. This "fitting results directly inform experimental decisions" design provides a paradigm for future teams developing scientific software — software should serve not only as a "computational tool" but as a "bridge connecting dry experiments (modeling) and wet experiments (validation)," reducing data conversion and analysis costs in the research workflow.


3>. Application: Providing a Quantitative Modeling Tool for Synthetic Biology "Part Screening"

In synthetic biology, screening optimal components from multiple candidates (e.g., regulatory proteins, promoters) traditionally relies on repeated wet experiments, which are costly and time-consuming. By constructing ODE models matching experimental scenarios and using curve fitting to quantitatively evaluate component performance (e.g., comparing BrlR, SoxR, and MexL effects on detection signals), the software provides a "dry experiment prescreening" approach that significantly reduces wet experiment workload. Future teams can adopt this "ODE model + fitting software" strategy in component optimization and genetic circuit design to narrow candidates through dry experiments and improve screening accuracy and efficiency.


4>. Technical Accessibility: Lowering the Barrier for High-Dimensional Optimization Algorithms

High-dimensional space search algorithms are theoretically complex and traditionally require specialized programming and algorithm knowledge. By implementing the software in R with ready-to-use download and operation paths and simplifying workflows through visualization windows, non-algorithm-specialist researchers can quickly apply high-dimensional optimization techniques. Future teams developing specialized algorithm tools can adopt this "complex underlying algorithms, simple user interface" design philosophy to promote the application of advanced algorithms across various research fields.


III. Shared Optimization Strategies and Technical Methods


Reusable Detection Technologies and Method Optimizations

1. "Triple Verification + Dual Signal Output" Detection System: Combines "PQS signal molecule sensing + PYO virulence factor sensing + triclosan resistance selective enrichment" to enhance specificity, paired with lacZ (yellow chromogenic) and gus (blue fluorescent) dual reporter genes for intuitive, complementary signal output, solving false positive interference in complex water samples and applicable to specific detection design for other pathogens.

2. General Validation Strategy for Enzyme Substrate Methods: First validate the stability of the enzyme substrate method in multiple water sample types (sterile water, wild bacterial suspension, bottled water) using commercial kits, then develop a self-designed system based on this foundation, providing a standardized validation path from mature to innovative methods and avoiding later errors caused by unstable methodological foundations.

3. MPN Method Combined with 96-Well Plate Quantitative Detection: Integrates engineered bacteria, substrates, and triclosan into a 96-well quantitative plate, combined with an MPN (Most Probable Number) lookup table for rapid quantification, preserving the classical MPN quantitative logic while increasing detection throughput microplate design, suitable for on-site batch water sample testing.


Experimental Design Insights

1. "Multiple Candidates, One Selection" Component Screening Strategy: For PYO sensing circuits, three parallel circuits (SoxR–PmexG, MexL–PphzA1, BrlR–PbrlR) were constructed simultaneously, with dual evaluation through "molecular docking (theoretical affinity) + experimental validation (actual signal output)" to avoid the limitations of single plan and provide a paradigm for efficient functional component screening.

2. Phased Validation System Construction Logic: Progresses through five steps — "chassis modification → selective pressure system validation → vector component function testing → dual response verification → actual water sample evaluation" — with qualitative (e.g., blue-white screening) and quantitative (e.g., ONPG activity measurement) cross-validation at each stage to ensure final system stability, providing guidance for layered construction of complex biosensors.

3. Precise National Standard Comparison Framework: Selects five representative water sample types (tap water, drinking water, swimming pool water, etc.) and uses a "same sample split into two groups, tested by self-developed method and national standard method (GB/T 8538-2016)" design, with statistical analysis (p > 0.05) to verify accuracy, providing a standardized comparison template for credibility assessment of new detection methods.


Biosafety and Chassis Modification Contributions

1. "Dual Gene Deletion + Conditional Replication Origin" Chassis Design: Knocking out lacZ and gus genes in EC1000 eliminates endogenous enzyme interference, while using the pORI280 plasmid's "lock-and-key" replication mechanism (only in EC1000 containing RepA) prevents plasmid transfer to environmental strains, addressing both "background cleanliness" and "environmental safety" concerns and serving as a universal modification template for environmental monitoring engineered bacteria.

2.Safe-Selectable Marker:To ensure biosafety, the resistance gene fabV conferring resistance to triclosan (an antibacterial agent) was selected as the selection marker in this system. Notably, triclosan is not an antibiotic used in clinical practice, but rather a widely utilized antibacterial agent in consumer products. Consequently, the transmission risk of its associated resistance gene in clinical settings and the environment remains relatively low.