iGEM 2025 — Team hk-joint-school

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**## Wet Lab Notebook**

| Start Date | End Date | Activity |
| --- | --- | --- |
| 01-July-2025 | 06-July-2025 | Transformation of plasmid (glyphosate-EGFP) into DH5-alpha and screening of transformed DH5-alpha with recombinant plasmids |
| 07-July-2025 | 08-July-2025 | Plasmid extraction of transformed DH5-alpha via miniprep kit, transformation to BL21 and screening of transformed BL21 |
| 09-July-2025 | 13-July-2025 | Testing different concentrations of IPTG to determine the optimal level for inducing protein expression |
| 14-July-2025 | 20-July-2025 | Evaluation of different reporter genes (EGFP) to identify the one with the stronger and more reliable signal. |
| 01-Aug-2025 | 08-Aug-2025 | Transformation of 5 plasmids (EGFP, Acephate-EGFP, Chlorpyrifos-EGFP, Malathion-EGFP and Acetamiprid-EGFP) into DH5-alpha and screening of transformed DH5-alpha with recombinant plasmids |
| 09-Aug-2025 | 10-Aug-2025 | Plasmid extraction of transformed DH5-alpha via miniprep kit and transformation to BL21 |
| 11-Aug-2025 | 25-Aug-2025 | Test for total five pesticide-binding aptamers |
| 26-Aug-2025 | 31-Aug-2025 | To perform control tests- to confirm the decrese of fluroscent intensity is mainly caused by the binding of aptamer and pesticide |
| 01-Sep-2025 | 12-Sep-2025 | Food Sample Screening |
| 13-Sep-2025 | 19-Sep-2025 | Soil Sample Screening |
| 20-Sep-2025 | 30-Sep-2025 | Cell-free expression kit test |

**## Education Notebook**

| Start Date | End Date | Activity |
| --- | --- | --- |
| 05-Dec-2024 | 05-Dec-2024 | Cell Culture Workshop |
| 30-June-2025 | 30-June-2025 | Whole School Sharing |
| 14-July-2025 | 14-July-2025 | Plasmid Design (Benchling) Training Workshop at WYY |
| 06-Aug-2025 | 30-Oct-2025 | Instagram Promotion |
| 06-Aug-2025 | 30-Oct-2025 | Youtube Promotion |
| 10-Aug-2025 | 26-Aug-2025 | Online Games Design |
| 10-Aug-2025 | 26-Aug-2025 | Kindergarten Leaflet Design |
| 10-Aug-2025 | 26-Aug-2025 | Primary School Booklet Design |
| 10-Aug-2025 | 26-Aug-2025 | Secondary School Booklet Design |
| 11-Aug-2025 | 28-Aug-2025 | Card Game Design(PestiGuard – Hunting Pesticides) |
| 12-Aug-2025 | 12-Aug-2025 | Wet Lab Training Workshop at WYY |
| 15-Aug-2025 | 28-Aug-2025 | Charity Sale Products Design |
| 20-Aug-2025 | 28-Aug-2025 | Kindergarten Storybook Design |
| 20-Aug-2025 | 20-Aug-2025 | Modelling Training Workshop at WYC |
| 28-Aug-2025 | 28-Aug-2025 | Modelling Training Workshop at WYC |
| 30-Aug-2025 | 30-Aug-2025 | Modelling Training Workshop at WYC |
| 01-Sep-2025 | 31-Oct-2025 | Genetics and Biotechnology Library Corner |
| 10-Sep-2025 | 10-Sep-2025 | DNA Extraction Workshop |
| 10-Sep-2025 | 10-Sep-2025 | Micropipette Training workshop(Making Sensory Bottle) |
| 15-Sep-2025 | 15-Sep-2025 | Card Game Workshop |
| 15-Sep-2025 | 26-Sep-2025 | DNA and Biotech Workshop |
| 15-Sep-2025 | 15-Sep-2025 | Morning Broadcast (Whole school iGEM and Workshop Promotion) |
| 16-Sep-2025 | 16-Sep-2025 | Wet Lab Training Workshop |
| 17-Sep-2025 | 17-Sep-2025 | DNA Extraction Workshop |
| 18-Sep-2025 | 18-Sep-2025 | Workshop for Munsang College Kindergarten |
| 22-Sep-2025 | 30-Sep-2025 | Charity Sale（Donation to Food Angel） |
| 25-Sep-2025 | 25-Sep-2025 | Booth for Munsang College Primary School |
| 26-Sep-2025 | 26-Sep-2025 | Workshop for Munsang College Primary School |
| 26-Sep-2025 | 26-Sep-2025 | Morning Broadcast (Whole School iGEM and Workshop Promotion) |
| 26-Sep-2025 | 26-Sep-2025 | Lunch Workshop at HTCSS |
| 29-Sep-2025 | 29-Sep-2025 | Workshop for SKH Holy Trinity Church Primary School at HTCSS |
| 29-Sep-2025 | 29-Sep-2025 | Lunch Workshop at HTCSS |
| 29-Sep-2025 | 29-Sep-2025 | Workshop at STFA Wu Siu Kui Memorial Primary School |
| 29-Sep-2025 | 29-Sep-2025 | Micropipette Training and Synthetic Biology Workshop |
| 29-Sep-2025 | 29-Sep-2025 | Whole School Sharing |
| 02-Oct-2025 | 02-Oct-2025 | Whole School Sharing |

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Engineering

* [Overview](#Overview)
* [Establishing the Core Mechanism](#Cycle 1)
* [Resolving Leaky Expression](#Cycle 2)
* [Quantifiable Dose Response](#Cycle 3)
* [Integrated Modeling](#Integrated Modeling)
* [Future Work](#Future Work)

# Engineering: An Iterative Cycle to a Functional Biosensor

The development of PestiGuard was guided by the iGEM engineering cycle. Through repeated rounds of Design, Build, Test, and Learn, we systematically transformed a conceptual genetic circuit into a reliable and sensitive biosensor. Each iteration addressed critical challenges, leading to the optimized system we present today.

# Engineering Cycle 1: Establishing the Core Mechanism

## **Design:**

Our initial design established the fundamental "sense-and-response" logic. The circuit consisted of a strong T7 promoter, a lac operator (lacO), a glyphosate-binding aptamer, an RBS, and an EGFP reporter gene.

[BBa\_2561FDBM: Glyphosate-sensing EGFP device (w/o spacer)]

![](https://static.igem.wiki/teams/5627/engineering/engineering-figure-1.webp)

Figure 1: The original version of gene design

## **Build:**

We constructed this genetic circuit in a plasmid and transformed it into *E. coli* BL21(DE3).

## **Test:**

We assayed for leaky expression without IPTG and for a fluorescence decrease upon glyphosate addition with IPTG

![](https://static.igem.wiki/teams/5627/engineering/engineering-figure2.webp)

Figure 2: Colonies from glyphosate-sensitive aptamer construct glowing on LB agar plates in the absence of IPTG

## **Learn:**

The constitutive fluorescence observed in the absence of IPTG indicated a critical failure in the system's regulatory control. This "leaky expression" demonstrated that the lac operator was ineffective at repressing the exceptionally strong T7 promoter in our specific genetic context. We hypothesized that the proximity of the aptamer sequence to the lacO site was potentially interfering with the repressor's binding or the formation of a complete transcriptional terminator loop. This insight directly informed our next design iteration, where we focused on modifying the genetic context to restore proper control, moving from a non-functional to a controllable system.

## **Key Learning:**

The initial design lacked sufficient transcriptional control. The genetic context of the regulatory elements needed optimization.

The biosensor showed **high baseline fluorescence** even without IPTG, indicating constitutive "leaky expression." The *lacO* switch was insufficient to fully repress the powerful T7 promoter.

# Engineering Cycle 2: Resolving Leaky Expression

## **Design:**

To improve regulatory control, we hypothesized that the lacO and aptamer sequences were interfering with each other. We introduced a spacer sequence between them to ensure independent function.

[BBa\_25EK3998: T7-Glyphosate-sensing EGFP device]

![](https://static.igem.wiki/teams/5627/engineering/engineering-figure-3.webp)

Figure 3: 2nd version of gene design (adding a spacer)

## **Build:**

The modified plasmid with the spacer was constructed and transformed.

## **Test:**

We repeated the induction and glyphosate sensing assays. Initial induction tests using 0.1 mM and 0.5 mM IPTG with a T7 promoter system failed to produce a proportional decrease in fluorescence. The standard curves generated across 12, 24, and 36-hour time points were unreliable, as indicated by low R-squared values.

The spacer successfully eliminated leaky expression. However, the fluorescence output in response to different glyphosate concentrations was inconsistent and not proportional to the pesticide dose.

## **Learn:**

While the introduction of a spacer successfully resolved the leaky expression by insulating the lacO operator, it revealed a more fundamental limitation of our initial design. The inconsistent and non-proportional fluorescence output indicated that the system's dynamic range was saturated. The extremely strong T7 promoter was driving such a high level of transcription that it overwhelmed the subtle, dose-dependent regulatory effect of the aptamer. The signal was perpetually maxed out, leaving no room to observe a quantifiable decrease. This led to a pivotal insight: the performance of a biosensor is critically dependent on matching promoter strength to the sensing mechanism. A promoter that is too strong can render a dose-response relationship unobservable, not due to a failure of the sensing element itself, but due to a mismatch in the system's overall expression dynamics. This understanding forced a paradigm shift in our design strategy, moving beyond simple genetic insulation to a holistic re-engineering of expression levels, which became the focus of the next cycle.

## **Key Learning:**

We had solved the control issue but uncovered a dynamic range problem. The signal output was not suitable for quantitative detection.

# Engineering Cycle 3: Achieving a Quantifiable Dose Response

## **Design:**

We diagnosed that the extremely strong T7 promoter was causing signal saturation. We replaced it with the moderately-tuned PlacUV5-MB7 promoter (Part:BBa\_K4941056) to reduce metabolic burden and achieve a measurable dynamic range.

## **Build:**

The new plasmid with the PlacUV5-MB7 promoter was built and transformed. [BBa\_253OHGFW: Glyphosate-sensing EGFP device]

![](https://static.igem.wiki/teams/5627/engineering/engineering-figure4.webp)

Figure 4: 3rd gene design

## **Test:**

First, we systematically tested the new system under various IPTG concentrations and expression durations. Furthermore, a gradient of the target pesticide (glyphosate) across concentrations of 0, 0.3, 1, 3, 10, 30, 100, 300, and 1000 mg/L were tested.

## **Learn:**

The results showed a dramatic improvement. Induction with 0.5 mM IPTG for 24 hours provided the optimal balance: a high R-squared value (~0.95) and large, unambiguous signal steps, enabling quantifiable detection. Most critically, within the agriculturally relevant 0-1 mg/L range, the dose-response curve achieved an R-squared value of ≥ 0.95, passing our pre-set reliability threshold for a robust standard curve.

## **Learn:**

The successful function of the final design confirmed a fundamental principle of biosensor engineering: the sensing element and the expression system must be functionally balanced. While the aptamer provided the specificity, its regulatory capacity was only observable within a suitable range of transcriptional activity. The moderate-strength PlacUV5-MB7 promoter created an expression window where the aptamer’s conformational change could effectively compete with translation initiation, translating ligand binding into a measurable, dose-dependent fluorescence output.

## **Key Learning:**

Promoter strength is a critical parameter for setting the dynamic range of a whole-cell biosensor. The switch to a moderate promoter was essential to prevent signal saturation and to unmask the aptamer's dose-dependent activity, thereby transforming a qualitative sensor into a quantitative analytical tool.

![](https://static.igem.wiki/teams/5627/engineering/engineering-table1.webp)

Table 1: RGB values of bacterial cultures at 0.1mM and 0.5mM IPTG, 12, 24 and 36 hours and different glyphosate concentrations

![](https://static.igem.wiki/teams/5627/engineering/engineering-figure5.webp)

Figure 5: Standard curve of bacterial cultures at 0.1mM and 0.5mM IPTG, 12, 24 and 36 hours and different glyphosate concentrations

![](https://static.igem.wiki/teams/5627/engineering/engineering-table-2.webp)

Table 2: Fluorescence of bacterial cultures at 0.5mM IPTG, 24 hours and different pesticide concentrations

![](https://static.igem.wiki/teams/5627/engineering/engineering-table3.webp)

Table 3: RGB value of different types of aptamer at different concentrations of pesticides

![](https://static.igem.wiki/teams/5627/engineering/engineering-figure6.webp)

Figure 6: Standard curves of glyphosate at different ranges of concentration

# Integrated Modeling: De-risking the Design and Explaining Discrepancies

Parallel to our wet-lab cycles, we employed computational modeling to address a fundamental design risk: the aptamer sequences were derived from DNA, but their functional form in our system is RNA.

## **Design:**

We used in silico tools (mFold, molecular docking, MD simulations) to predict if the DNA-derived RNA aptamers would fold correctly and bind glyphosate.

## **Test:**

To evaluate the computational predictions, we conducted parallel *in silico* and *in vitro* analyses. For each candidate aptamer (glyphosate, acephate, malathion, chlorpyrifos, and acetamiprid), we performed:

1. **Structure Prediction:** Using mFold, we compared the minimum free energy structures of the original DNA aptamers and their corresponding RNA sequences transcribed in our system.
2. **Molecular Docking:** Each RNA aptamer model was docked against its target pesticide to estimate binding affinity and potential binding pockets.
3. **Molecular Dynamics (MD) Simulations:** The top docking poses for each aptamer-pesticide pair were subjected to 25ns MD simulations in an explicit solvent environment to assess complex stability, measured by root-mean-square deviation (RMSD) and ligand-binding interactions over time.

These computational results were directly compared with experimental wet-lab data from fluorescence-based dose-response assays, allowing us to correlate predicted binding stability with observed biosensor performance.

## **Learn:**

Our models confirmed that the RNA and DNA forms have different secondary structures. Crucially, MD simulations indicated that the glyphosate RNA aptamer formed a stable complex with its target, providing a theoretical foundation for our wet-lab approach. This pre-validation helped us prioritize resources.

However, we observed discrepancies where modeling predicted weak binding for some aptamers that showed function in the lab. Our analysis revealed:

1. **The In Vivo Environment is Crucial:** Molecular crowding and specific cellular ion concentrations (Mg²⁺, K⁺) can stabilize functional RNA structures that are not the predicted thermodynamic minimum in a simplified simulation.
2. **The Functional Unit is the Entire 5' UTR:** The aptamer's structure and function are influenced by its genetic context—the spacer and downstream RBS—and potentially by the ribosome itself, creating a composite regulatory unit not seen when modeling the aptamer in isolation.
3. **Inherent Modeling Limitations:** Force field inaccuracies and limited simulation timescales (nanoseconds vs. biological microseconds) mean that MD provides a strong qualitative guide, but not an absolute prediction.

## **Conclusion from Modeling**

The most compelling explanation for our success is that the functional, glyphosate-binding structure of our RNA aptamer is stabilized by the unique in vivo environment and/or is a kinetically trapped intermediate. Our working biosensor is the ultimate validation, demonstrating that biological function emerges from the complex, dynamic environment of the cell.

# Future Work: Bridging the Computational-Experimental Gap

To further bridge this gap and rigorously prove our biosensor's mechanism:

## **Design:**

We designed a new construct for **Cell-free Protein Synthesis (CFPS)**. [Parts: BBa\_25IONSGX: Non-cell protein synthesis - glyphosate-sensing EGFP device]

## **Build:**

The CFPS-optimized plasmid was constructed with the following features:

![](https://static.igem.wiki/teams/5627/engineering/engineering-figure7.webp)

Figure 7: The cell-free synthesis version of gene design

## **Test:**

We will test this construct in a commercial CFPS kit (BioSharp Life Science) to isolate the RNA-pesticide interaction and confirm the intrinsic function of our genetic circuit.

## **Learn:**

A positive result in CFPS would powerfully narrow the cause of the modeling discrepancy to folding and affinity, independent of cellular metabolism.

This structured, problem-solving approach—embracing both wet-lab and dry-lab cycles—exemplifies the power of the engineering cycle in synthetic biology and provides a robust framework for future biosensor development.

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Experiments

* [1. Transformation](#des-1)
* [2. Verification](#des-2)
* [3. Optimization](#des-3)
* [4. Aptamer Specificity](#des-4)
* [5. Control Experiment](#des-5)
* [6. Food Sample Screening](#des-6)
* [7. Soil Sample Screening](#des-7)
* [8. Cell-free Protein Synthesis](#des-8)

## Stage 1: Transformation

![](https://static.igem.wiki/teams/5627/experiment/transformation.webp)

### *Purpose:*

* Transform the pET plasmid with our construct into BL21(DE3) E. coli for later protein expression.

### *Materials:*

* BL21(DE3) *E. coli*
* pET plasmid with contruct (ordered from Twist Bioscience)
* LB Broth (ThermoFisher, Cat# 12780052)
* LB agar plates (ThermoFisher, Cat# 22700-025 ) with kanamycin (ThermoFisher, Cat#11815024)

### *Procedures:*

1. Add 5μL plasmid into a microcentrifuge tube with 100µL of competent BL21(DE3) cells.
2. Incubate on ice for 20 minutes.
3. Transfer the tube directly from the ice to the pre-heated 42°C dry bath for exactly 45 seconds.
4. Immediately return the tubes to ice for 2 minutes.
5. Add 1ml LB solution in each tube.
6. Incubate the tube in a 37°C shaker (220 rpm) for 60 minutes.
7. Centrifuge the tube(15000rpm, 10mins), discharge the supernatant.
8. Add 100ul LB Broth solution in tube and resuspend the cell pellets
9. Spread 100 µL of the plasmid transformation culture onto a LB agar plate.
10. Invert the plates and place them in a 37°C incubator overnight

---

## Stage 2: Verification of Plasmid Construction

### *Purpose:*

* To isolate the plasmid from transformed E. coli and confirm its identity by releasing a diagnostic DNA fragment via restriction enzyme digestion.

## Part 1: Plasmid DNA Extraction

### *Materials:*

* LB Broth (ThermoFisher, Cat# 12780052)
* Kanamycin (ThermoFisher, Cat#11815024)
* GeneJET Plasmid Miniprep Kit(ThermoFisher, Cat# K0502)
* Transformed E. coli colonies on LB agar plate with Kanamycin

### *Procedures:*

1. Starter Culture: Using a sterile pipette tip, pick a single, well-isolated colony from the transformation plate. Inoculate it into a culture tube containing 5 mL of LB liquid medium supplemented with the appropriate antibiotic (Kanamycin, 50 µg/mL).
2. Overnight Incubation: Incubate the tube at 37°C for 24 hours with vigorous shaking (≥ 220 rpm).
3. Cell Harvesting: Pellet the bacterial cells by centrifuging 5mL of the overnight culture at 12,000 × g for 1 minute. Carefully pour off the supernatant.
4. Resuspension: Resuspend the pelleted cells completely in 250 µL of Resuspension Solution by pipetting or vertexing. Ensure no cell clumps remain.
5. Lysis: Add 250 µL of Lysis Solution. Mix gently by inverting the tube 4-6 times until the solution becomes viscous and clear. Do not vortex, as this will shear genomic DNA. Do not allow lysis to proceed for more than 5 minutes.
6. Neutralization: Add 350 µL of Neutralization Solution. Mix immediately and thoroughly by inverting the tube 4-6 times until a white precipitate forms.
7. Clarification: Centrifuge at 12,000 × g for 5 minutes. The white precipitate (cell debris and denatured proteins) will form a tight pellet.
8. Column Binding: Carefully transfer the supernatant (cleared lysate) to the GeneJET Miniprep Column. Centrifuge at 12,000 × g for 1 minute. Discard the flow-through.
9. Washing: Add 500 µL of Wash Solution (Buffer W1) to the column. Centrifuge at 12,000 × g for 1 minute. Discard the flow-through.
10. Additional Washing: Add 750 µL of Wash Solution (Buffer W2, ensure ethanol has been added) to the column. Centrifuge at 12,000 × g for 1 minute. Discard the flow-through.
11. Final Spin: Centrifuge the empty column for an additional 1 minute to remove residual ethanol.
12. Elution: Transfer the column to a clean 1.5 mL microcentrifuge tube. Add 50 µL of Nuclease-Free Water (pre-warmed to 65-70°C can increase yield) directly to the center of the column membrane. Let it stand for 2 minutes, then centrifuge at 12,000 × g for 2 minutes to elute the purified plasmid DNA.

## Part 2: Diagnostic Restriction Digest

### *Materials:*

* SphI (FD2164) & NcoI (FD0574) (Thermo Scientific, Cat#B49)
* Kanamycin (ThermoFisher, Cat#11815024)
* Purified plasmid DNA from Part 1

### *Procedures:*

1. Prepare the following reaction mix on ice:
![](https://static.igem.wiki/teams/5627/experiment/table12345.avif)2. Mix the components gently by pipetting and collect the liquid at the bottom of the tube by brief centrifugation.
3. Incubate the reaction mixture at 37°C for 30 minutes in a thermal cycler or water bath.

## Part 3: Agarose Gel Electrophoresis

### *Materials:*

* 1X TAE Buffer (Thermo Scientific, Cat#B49)
* Agarose powder (ThermoFisher, Cat# R0491)
* DNA ladder (ThermoFisher, Cat# 10787026)
* SYBR safe DNA stain (ThermoFisher, Cat# S33102)

### *Procedures:*

1. Prepare the Gel: Prepare a 1% (w/v) agarose solution by dissolving 0.5 g of agarose in 50 mL of 1X TAE buffer. Microwave to dissolve completely, let it cool slightly, add DNA stain, and pour into a gel casting tray with a well comb.
2. Load the Samples: Once the gel has solidified, place it in the electrophoresis chamber filled with 1X TAE buffer. Carefully load the samples.
3. Run the Gel: Run the gel at 100 V for 30 minutes, or until the dye front has migrated sufficiently through the gel.

---

## Stage 3: Optimization of IPTG Induction and Expression Duration

![](https://static.igem.wiki/teams/5627/experiment/img-7768.webp)

---

## Stage 4a: Glyphosate-binding Aptamer Specificity Testing

![](https://static.igem.wiki/teams/5627/experiment/aptamer-testing.webp)

### *Purpose:*

* Validate the specificity of each pesticide-specific aptamers.

### *Materials:*

* pET plasmid with glyphosate-binding aptamers with EGFP gene (ordered from Twist Bioscience)
* LB Broth (ThermoFisher, Cat# 12780052)
* LB agar plates (ThermoFisher, Cat# 22700-025) with kanamycin (ThermoFisher, Cat#11815024)
* Glyphosate
* IPTG (ThermoFisher, Cat#34060)

### *Procedures:*

1. Following the protocol from Stage 1, transform the pET plasmids (glyphosate-binding aptamer) into BL21(DE3) competent cells respectively. For each transformed BL21(DE3), proceed with the following steps separately.
2. Inoculate a single colony of each transformed BL21(DE3) into 5 mL of LB medium containing kanamycin.
3. Incubate the starter cultures overnight at 37°C with shaking at 220 RPM.
4. Dilute 0.5 mL of each overnight starter culture into 50 mL of fresh, kanamycin-supplemented LB medium.
5. Incubate the cultures at 37°C with shaking at 220 RPM.
6. Monitor the OD600 every 30 minutes. When the OD600 reaches 0.5–0.6, add IPTG to reach suitable concentration (based on the results from stage 3) and proceed to the next step.
7. Aseptically split each culture into 9 separate tubes, each containing 5 mL culture, using 15 mL conical centrifuge tubes.
8. Label the tubes from 1 to 9. Add corresponding pesticide to each tube according to the concentrations specified in Table 2 below.
![](https://static.igem.wiki/teams/5627/experiment/experiment-table-2.webp)

---

## Stage 4b: Other Pesticide-binding Aptamers Specificity Testing

![](https://static.igem.wiki/teams/5627/experiment/aptamer-testing.webp)

### *Purpose:*

* Validate the specificity of each pesticide-specific aptamers.

### *Materials:*

* Four different pET plasmid with acephate, malathion, chlorpyrifos and acetamiprid aptamers with EGFP gene respectively (ordered from Twist Bioscience)
* LB Broth (ThermoFisher, Cat# 12780052)
* LB agar plates (ThermoFisher, Cat# 22700-025) with kanamycin (ThermoFisher, Cat#11815024)
* Four different pesticides: acephate, malathion, chlorpyrifos and acetamiprid
* IPTG (ThermoFisher, Cat#34060)

### *Procedures:*

1. Following the protocol from Stage 1, transform the four pET plasmids (acephate, malathion, chlorpyrifos and acetamiprid) into BL21(DE3) competent cells respectively. For each transformed BL21(DE3), proceed with the following steps separately.
2. Inoculate a single colony of each transformed BL21(DE3) into 5 mL of LB medium containing kanamycin.
3. Incubate the starter cultures overnight at 37°C with shaking at 220 RPM.
4. Dilute 0.5 mL of each overnight starter culture into 50 mL of fresh, kanamycin-supplemented LB medium.
5. Incubate the cultures at 37°C with shaking at 220 RPM.
6. Monitor the OD600 every 30 minutes. When the OD600 reaches 0.5–0.6, add IPTG to reach suitable concentration (based on the results from stage 3) and proceed to the next step.
7. Aseptically split each culture into 9 separate tubes, each containing 5 mL culture, using 15 mL conical centrifuge tubes.
8. Label the tubes from 1 to 9. Add corresponding pesticide to each tube according to the concentrations specified in Table 2 below.
![](https://static.igem.wiki/teams/5627/experiment/experiment-table-2.webp)

---

## Stage 5: Control Experiment – Validating the Aptamer Mechanism

![](https://static.igem.wiki/teams/5627/experiment/control-experiments.webp)

### *Purpose:*

* Rule out false positives.

### *Materials:*

* pET plasmid with reporter gene but no aptamer (ordered from Twist Bioscience)
* LB Broth (ThermoFisher, Cat# 12780052)
* LB agar plates (ThermoFisher, Cat# 22700-025 ) with kanamycin (ThermoFisher, Cat#11815024)
* Pesticides with ideal results from stage 4 experiment
* IPTG (ThermoFisher, Cat#34060)

### *Procedures:*

1. Following the protocol from Stage 1, transform the pET plasmid with reporter gene but no aptamer into BL21(DE3) competent cells.
2. Inoculate a single colony of each transformed BL21(DE3) into 5 mL of LB medium containing kanamycin.
3. Incubate the starter cultures overnight at 37°C with shaking at 220 RPM.
4. Dilute 1mL of each overnight starter culture into 100mL of fresh, kanamycin-supplemented LB medium.
5. Incubate the cultures at 37°C with shaking at 220 RPM.
6. Monitor the OD600 every 30 minutes. When the OD600 reaches 0.5–0.6, add IPTG to reach suitable concentration (based on the results from stage 2) and proceed to the next step.
7. Aseptically distribute 5 mL of each culture into separate 15 mL conical centrifuge tubes. The number of tubes required is determined by the experimental results from Stage 4.
8. Label the tubes with correct number. Add corresponding pessticide to each tube according to the concentrations specified in Table 3 below.
![](https://static.igem.wiki/teams/5627/experiment/experiment-table-3.webp)

---

## Stage 6: Food Sample Validation

### *Purpose:*

* Validate the biosensor’s performance by testing it on real food samples with known pesticide concentrations.

### *Materials:*

* Vegetables (from supermarket)
* Transformed BL21(DE3) with pET plasmid- pesticide sensitive aptamer
* LB Broth (ThermoFisher, Cat# 12780052)
* LB agar plates (ThermoFisher, Cat# 22700-025 ) with kanamycin (ThermoFisher, Cat#11815024)
* Pesticides
* IPTG (ThermoFisher, Cat#34060)

### *Procedures:*

1. Obtain various vegetable samples from local markets.
2. Separate each vegetable type into two groups: an experimental group and a control group.
3. Spray the experimental group vegetables with a 3mg/L and 10 mg/L pesticide solution four times respectively. Do not apply any pesticide to the control group.
![](https://static.igem.wiki/teams/5627/experiment/experiment-spraying.webp)

---

## Stage 7: Soil Sample Screening

### *Purpose:*

* Validate the biosensor's performance by testing it on soil and environmental samples.

### *Materials:*

* Soil sample (from organic farm)
* Transformed BL21(DE3) with pET plasmid- pesticide sensitive aptamer
* LB Broth (ThermoFisher, Cat# 12780052)
* LB agar plates (ThermoFisher, Cat# 22700-025 ) with kanamycin (ThermoFisher, Cat#11815024)
* Pesticides
* IPTG (ThermoFisher, Cat#34060)

### *Procedures:*

1. Weigh 2g of soil sample
2. Transfer soil sample to a beaker containing 20ml of distilled water and stir for 10 min
3. Filter the mixture and obtain the filtrate
4. Transfer 1ml of water sample into 1.5ml tubes for further treatment

---

## Stage 8: Cell-free Protein Synthesis Kit application

### *Purpose:*

* Apply our gene construct in a cell-free protein synthesis kit.

### *Materials:*

* Cell-free Protein Synthesis Kit (BioSharp Life Science)
* Plasmid construct (Ordered from Twist Bioscience)

### *Procedures:*

1. Incorporate the insert gene into the PJL1-sfGFP plasmid from the kit by digestion, gel extraction and ligation.
2. Add different reagents based on the menu from the kit. Table 4 below shows the proportion of different reagents.
![](https://static.igem.wiki/teams/5627/experiment/experiment-table-4.webp)

Table 4: The proportion of different reagents added in cell-free protein synthesis kit

3. Induce protein expression by incubating the tubes at 30°C for 8hours.
4. Analyze the samples using the designated hardware (Smartbox) and software (PestiGuard biosensor platform).

---

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Results

* [Overview](#des-1)
* [Transformation](#des-2)
* [Verification](#des-3)
* [Optimization](#des-4)
* [Aptamer Specificity Testing](#des-5)
* [Control Experiment](#des-6)
* [Food Sample Validation](#des-7)
* [Soil Sample Screening](#des-8)
* [Cell-Free Protein Synthesis](#des-9)
* [Conclusion](#des-10)

## Overview of our gene design

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We have developed an innovative biosensor capable of rapidly detecting the presence of various pesticides in agricultural crops, enabling safer food production and more effective monitoring of environmental contaminants. Our biosensor operates by harnessing genetically engineered bacteria whose plasmid design features several critical regulatory and functional components.

![](https://static.igem.wiki/teams/5627/results/figure-1a.webp)

###### *Figure 1.* *The original version of construct design*

#### (i) Pesticide-sensitive aptamer

The aptamer functions as a specific molecular sensor that undergoes a conformational change upon pesticide binding. This mechanical action directly alters the mRNA's architecture, physically blocking ribosome access to the RBS. This design eliminates the need for intermediate proteins, creating a rapid, efficient, biosensor perfectly suited for real-time environmental detection.

#### (ii) Reporter gene

Reporter genes produce measurable fluorescence intensity that directly correlates with protein expression level. This quantitative relationship precisely determines pesticide concentration—brighter color indicates lower pesticide levels, while dimmer signals show higher contamination. The visual color gradient provides an intuitive and quantifiable readout of environmental toxin levels.

#### (iii) LacO

The Lac repressor binding to LacO blocks transcription . By binding to the operator site on the DNA, it physically prevents RNA polymerase from transcribing the gene into mRNA. Since no mRNA is produced, translation cannot occur downstream. This is a crucial transcriptional control mechanism in plasmid design.

![](https://static.igem.wiki/teams/5627/results/result-figure2.webp)

###### *Figure 2. The mechanism of our construct design*

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## Stage 1: Transformation

After the first transformation, our initial glyphosate-sensitive aptamer construct displayed constitutive activity, with colonies glowing on LB agar plates even in the absence of the inducer IPTG. This loss of regulatory control compromised the specificity and reliability of the biosensor. To achieve tighter regulatory control, we modified the gene design by adding a spacer between the LacO operator and the aptamer. The results demonstrate that this modification was successful. This spacer effectively insulated the operator, restoring strict IPTG-dependent regulation and eliminating background fluorescence. As a result, the optimized glyphosate sensor now only fluoresces on IPTG-containing plates.

![](https://static.igem.wiki/teams/5627/results/figure-3a.webp)

###### *Figure 3.* *2nd version of construct design (adding a spacer)*

![](https://static.igem.wiki/teams/5627/results/m.webp)

---

## Stage 2: Verification of Plasmid Construction

#### (i) Objective:

To confirm the successful construction of the PestiGuard biosensor plasmid and its subsequent transformation into the E. coli BL21(DE3) host strain, thereby validating the integrity of the core genetic circuit before functional assays.

#### (ii) Method:

Following the transformation of the ligated plasmid, single colonies were selected for culture. Plasmid DNA was extracted via miniprep and subjected to a restriction digest double digest using the enzymes SphI and NcoI. These enzymes were chosen based on the plasmid map to excise a specific fragment containing the aptamer-reporter cassette. The digest products were then analyzed by agarose gel electrophoresis and visualized under blue light.

#### (iii) Results and Analysis:

Gel electrophoresis revealed a distinct DNA fragment at the expected size of 392 base pairs (bp), which aligns precisely with the predicted length of the sequence between the SphI and NcoI restriction sites in our designed plasmid.

![](https://static.igem.wiki/teams/5627/results/gel.avif)

---

## Stage 3: Optimization of IPTG Induction and Expression Duration

#### (i) Initial Challenge:

Initial induction tests using 0.1 mM and 0.5 mM IPTG with a T7 promoter system failed to produce a proportional decrease in fluorescence. The standard curves generated across 12, 24, and 36-hour time points were unreliable, as indicated by low R-squared values.

![](https://static.igem.wiki/teams/5627/results/7.webp)

###### *Figure 8.* *Fluorescence of bacterial cultures at 0.1mM IPTG, 24hours and different glyphosate concentrations, captured using our SmartBox imaging system.*

![](https://static.igem.wiki/teams/5627/results/8.webp)

###### *Figure 9.* *Fluorescence of bacterial cultures at 0.5mM IPTG, 24 hours and different glyphosate concentrations, captured using our SmartBox imaging system.*

#### (ii) Interpretation of R-squared (R²) Value:

The R² value is a statistical measure that represents the proportion of the variance in the dependent variable (fluorescence decrease) that is predictable from the independent variable (pesticide concentration). An R² value of 1 indicates a perfect linear fit, while a value of 0 indicates no linear correlation.

![](https://static.igem.wiki/teams/5627/results/r-squared-value.webp)

#### (iii) Rational Design and Solution:

To address this, we replaced the strong T7 promoter with the moderately-tuned PlacUV5-MB7 promoter (Part:BBa\_K4941056). As characterized in the iGEM Registry, this part is engineered for an optimal expression intensity that enhances target gene expression while minimizing metabolic burden on the E. coli host.

![](https://static.igem.wiki/teams/5627/results/figure-9a.webp)

###### *Figure 10. The 3rd construct design*

#### (iv) Improved Results:

Repeating the experiment with the PlacUV5-MB7 promoter system resulted in a significant performance improvement. We observed a clear, proportional decrease in fluorescence across all tested conditions

While the 12-hour induction time yielded the highest R-squared values statistically, the actual differences in fluorescence between consecutive data points were minimal. This low signal resolution makes the 12-hour data less reliable for constructing a robust standard curve.

In contrast, induction with 0.5 mM IPTG for 24 hours provided the optimal balance. It maintained a high R-squared value, approximately 0.95, while also delivering large, unambiguous differences in fluorescence between each concentration step. This combination of a strong statistical fit and high signal resolution makes the 0.5 mM, 24-hour condition the definitive and most reliable choice for our biosensor's standard curve.

![](https://static.igem.wiki/teams/5627/results/10.webp)

###### *Figure 11. Fluorescence of bacterial cultures at 0.5mM IPTG, 24 hours and different glyphosate concentrations, captured using our SmartBox imaging system.*

![](https://static.igem.wiki/teams/5627/results/result-table-2.webp)

###### *Table 2: RGB values of bacterial cultures at 0.1mM and 0.5mM IPTG, 12, 24 and 36hours and different glyphosate concentrations*

![](https://static.igem.wiki/teams/5627/results/result-new.webp)

###### *Figure 12. Standard curve of bacterial cultures at 0.1mM and 0.5mM IPTG, 12, 24 and 36hours and different glyphosate concentrations*

#### (v) Mechanistic Rationale:

*The success of the PlacUV5-MB7 promoter is attributed to two key factors:*

Reduced Metabolic Burden: The T7 promoter's extreme strength overwhelms the host's transcription and translation resources, leading to cellular stress and high experimental variability. The PlacUV5-MB7 promoter provides a sustainable expression level that maintains cell health and consistent assay performance.

*Optimal Dynamic Range:* The T7 system produced a saturated fluorescent signal, obscuring quantifiable decreases. The moderated expression from PlacUV5-MB7 established a baseline fluorescence within the detector's optimal linear range, enabling precise measurement of the signal reduction caused by the target pesticide.

#### (vi) Conclusion:

The optimal conditions for the biosensor assay were determined to be the PlacUV5-MB7 promoter, 0.5 mM IPTG induction, and a 24-hour expression time. This combination provides a reliable and quantifiable fluorescent output essential for accurate pesticide detection.

---

## Stage 4a: Glyphosate-binding Aptamer Specificity Testing

#### (i) Objective:

To evaluate the specificity and dynamic range of aptamer-based biosensors for glyphosate.

#### (ii) Methods:

* Each aptamer-reporter construct was tested against its corresponding pesticide.
* A dilution series of glyphosate was prepared at nine concentrations (0, 0.3, 1, 3, 10, 30, 100, 300, and 1000 mg/L).
* Each concentration point was assayed in triplicate (n=3).
* Biosensor response was measured by monitoring the proportional decrease in fluorescence brightness.
* Cell density (OD600) was measured for all samples to serve as a vital control, ensuring that any observed decrease in fluorescence was due to glyphosate-binding aptamer and not a result of general cell death or growth inhibition caused by pesticide toxicity.

#### (iii) Results and Analysis:

1. Specificity Confirmation: A proportional decrease in brightness was observed for glyphosate, confirming that the aptamer specifically responds to its target.
![](https://static.igem.wiki/teams/5627/results/table-3a.webp)

###### *Table 3.* *Fluorescence of bacterial cultures at 0.5mM IPTG, 24hours and different glyphosate concentrations, captured using our SmartBox imaging system.*

![](https://static.igem.wiki/teams/5627/results/table-4a.webp)

###### *Table 4. RGB value of glyphosate-binding aptamer at different concentrations of glyphosate.*

2. Dynamic Range Optimization: Standard curves were plotted using different concentration ranges (0-1000 mg/L, 0-100 mg/L, 0-10 mg/L, and 0-1 mg/L).
![](https://static.igem.wiki/teams/5627/results/table-5a.webp)

###### *Table 5.* *OD600 measurements of glyphosate constructs across a range of glyphosate concentrations.*

![](https://static.igem.wiki/teams/5627/results/results-figure-11.webp)

###### *Figure 13: Comparison of cell pellet appearance for the glyphosate aptamer construct at 0 mg/L versus 1000 mg/L glyphosate.*

3. Linear Range Identification: The quality of each standard curve was assessed using the R-squared (R²) value. The 0-1 mg/L range consistently yielded the highest R² values, indicating the most reliable linear relationship between pesticide concentration and biosensor signal.
![](https://static.igem.wiki/teams/5627/results/result-figure-16.webp)

###### *Figure 14: Standard curves of glyphosate at different ranges of concentration.*

#### (iv) Conclusion:

The specificity and sensitivity testing of the glyphosate-binding aptamer biosensor successfully validated its functionality as a reliable detection tool. The assay confirmed a highly specific and proportional response to glyphosate, with fluorescence decreasing as the pesticide concentration increased. Crucially, the parallel OD600 measurements and visual inspection of cell pellets confirmed that this signal response was due to a specific molecular interaction and not a result of general cytotoxicity.

The identification of the 0-1 mg/L range as the optimal quantitative window, evidenced by its high R-squared value, is particularly significant. This range not only provides the best analytical performance for creating a standard curve but also aligns perfectly with the practical need to detect trace-level residues relevant to food safety standards. The results conclusively demonstrate that the glyphosate aptamer is workable and effective, forming a solid foundation for its application in subsequent food and environmental testing.

---

## Stage 4b: Other pesticide-binding Aptamers Specificity Testing

#### (i) Objective:

To evaluate the specificity and dynamic range of aptamer-based biosensors for acephate, malathion, chlorpyrifos, and acetamiprid.

#### (ii) Methods:

* Each aptamer-reporter construct was tested against its corresponding pesticide.
* A dilution series of each pesticide was prepared at nine concentrations (0, 0.3, 1, 3, 10, 30, 100, 300, and 1000 mg/L).
* Each concentration point was assayed in triplicate (n=3).
* Biosensor response was measured by monitoring the proportional decrease in fluorescence brightness.
* Cell density (OD600) was measured for all samples to serve as a vital control, ensuring that any observed decrease in fluorescence was due to specific aptamer-pesticide binding and not a result of general cell death or growth inhibition caused by pesticide toxicity.

#### (iii) Results and Analysis:

1. Specificity Confirmation: A proportional decrease in brightness was observed for all five pesticides, confirming that each aptamer specifically responds to its target.
![](https://static.igem.wiki/teams/5627/results/table-6a.webp)

###### *Table 6.* *Fluorescence of bacterial cultures at 0.5mM IPTG, 24hours and different pesticide concentrations, captured using our SmartBox imaging system.*

![](https://static.igem.wiki/teams/5627/results/table-7a.webp)

###### *Table 7. RGB value of different types of aptamer at different concentrations of pesticides.*

2. Dynamic Range Optimization: For each pesticide, four standard curves were plotted using different concentration ranges (0-1000 mg/L, 0-100 mg/L, 0-10 mg/L, and 0-1 mg/L).
![](https://static.igem.wiki/teams/5627/results/table-8a.webp)

###### *Table 8.* *OD600 measurements of different aptamer constructs across a range of pesticide concentrations.*

3. Linear Range Identification: The quality of each standard curve was assessed using the R-squared (R²) value. The 0-1 mg/L range consistently yielded the highest R² values, indicating the most reliable linear relationship between pesticide concentration and biosensor signal.
![](https://static.igem.wiki/teams/5627/results/result-figure-12.webp)

###### *Figure 15: Standard curves of acetamiprid at different ranges of concentration.*

![](https://static.igem.wiki/teams/5627/results/result-figure-13.webp)

###### *Figure 16: Standard curves of acephate at different ranges of concentration.*

![](https://static.igem.wiki/teams/5627/results/result-figure-14.webp)

###### *Figure 17: Standard curves of chlorpyrifos at different ranges of concentration.*

![](https://static.igem.wiki/teams/5627/results/figure-15a.webp)

###### *Figure 18: Standard curves of malathion at different ranges of concentration.*

#### (iv) Conclusion:

* The optimal quantitative range for the biosensors is 0-1 mg/L. This range not only provides the best data fit but also exceeds the sensitivity requirements for monitoring pesticide residues in food.
* Applying a passing threshold of R² ≥ 0.95, the aptamers for glyphosate, acephate and chlorpyrifos met the criterion. However, the difference in RGB value of chlorpyrifos is around 10 units only, which is not too sensitive for differentiating the concentration at low level. That means only glyphosate and acephate-binding aptamers are confirmed to be workable and effective for detecting their corresponding pesticides.
* The aptamers for acetamiprid, malathion and chlorpyrifos did not meet the reliability standard within this optimal range and require further optimization.

---

## Stage 5: Control Experiment – Validating the Aptamer Mechanism

#### (i) Objective:

To confirm that the observed decrease in fluorescence is specifically due to the interaction between the pesticide and its cognate aptamer, and not a result of the pesticide directly affecting EGFP fluorescence or general cell health.

#### (ii) Experimental Design:

A control construct was engineered, expressing EGFP without an upstream aptamer sequence. This control was tested against a subset of pesticide concentrations (0 mg/L, 0.3 mg/L, and 1 mg/L) for glyphosate and acephate. If the pesticide were to directly quench EGFP or inhibit its expression independently of the aptamer, a decrease in fluorescence would be observed in this control system.

#### (iii) Results:

* Analysis of quantified R/G/B values showed no significant differences across the tested pesticide concentrations for any of the three pesticides.
* Critically, there was no observable decrease in brightness with increasing pesticide concentration in the absence of the aptamer.

![](https://static.igem.wiki/teams/5627/results/result-table-7.webp)

###### *Table 9: RGB value of control construct at different concentrations of glyphosate and acephate*

#### (iv) Conclusion:

This control experiment successfully verifies the specificity of our biosensor design. The results demonstrate that the pesticide- induced fluorescence decrease is strictly dependent on the presence of the aptamer. This finding eliminates the possibility of non-specific pesticide effects on the reporter protein and significantly strengthens the reliability of the standard curves and conclusions drawn from the primary specificity analysis.

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## Stage 6: Food Sample Validation

#### (i) Objective

The objective of this stage was to validate the practical functionality and accuracy of the developed biosensor by screening for pesticide residues on commercially relevant vegetable samples.

#### (ii) Methods

Four common vegetables—Green Chinese Cabbage, Chinese Lettuce, Chinese Cabbage, and Chinese Flowering Cabbage—were procured for testing. Each vegetable type was divided into three treatment groups: two experimental groups sprayed with 3 mg/L and 10 mg/L pesticide solutions, respectively, and a control group with no pesticide application. Following the optimized assay protocol, the biosensor was used to analyze extracts from these samples.

![](https://static.igem.wiki/teams/5627/results/result-figure-17.webp)

###### *Figure 19.* *Four different vegetables procured for testing*

#### (iii) Results

The biosensor demonstrated robust functionality and specificity within a real-food context. Analysis of the control groups revealed a background level of 0.144 mg/L glyphosate in Chinese Cabbage, while no other pesticide residues were detected in the remaining vegetables. In samples sprayed with a 3 mg/L pesticide solution, both glyphosate and acephate were detected in Chinese Cabbage. Furthermore, acephate was identified in all four vegetable types at concentrations ranging from 0.3 to 1.0 mg/L. For the 10 mg/L treatment group, a strong and quantifiable signal was observed, with measured concentrations of glyphosate or acephate between 0.3 and 3.0 mg/L across all four vegetable varieties.

![](https://static.igem.wiki/teams/5627/results/result-figure18b.webp)

###### *Table 10.* *The RGB value and concentration of pesticide detected in four different vegetables*

#### (iv) Conclusion

This food sample validation successfully confirms that the biosensor is a reliable and effective tool for detecting specific pesticide residues on agricultural produce. The assay accurately distinguished between treated and untreated samples and produced a quantifiable signal that correlated with the application concentration. The ability to detect trace levels post-dilution underscores the system's high sensitivity, solidifying its potential for practical application in food safety monitoring and screening.

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## Stage 7: Environmental Soil Sample Screening

#### (i) Objective

To evaluate the presence and detectability of glyphosate and acephate residues in environmental soil samples using the developed biosensor assay.

#### (ii) Methods

Soil samples were collected from an organic farm in Sha Tau Kok, Hong Kong. Three distinct samples were processed for analysis. Soil samples were prepared for analysis by aqueous extraction, where 2 g of soil was stirred in 20 mL of distilled water for 10 minutes, filtered, and the resulting filtrate was used in the assay. This 1 mL aliquot was introduced into a 5 mL bacterial culture for final analysis, following the established detection protocol for glyphosate and acephate.

#### (iii) Results

The biosensor assay did not detect the presence of either glyphosate or acephate in any of the three environmental soil samples tested. The results showed no significant fluorescent signal above the baseline level across all replicates.

![](https://static.igem.wiki/teams/5627/results/table-11a.webp)

###### *Table 11.* *The RGB value and concentration of both pesticide detected in three soil samples.*

#### (iv) Conclusion

The absence of detectable pesticide residues in soil from a certified organic farm serves as a critical and successful negative control for our biosensor. This result conclusively demonstrates two key points: first, the biosensor exhibits high specificity without generating false positives from the complex matrix of inherent soil compounds; and second, it validates the farm's organic status. This successful real-world application confirms the reliability and practical utility of the PestiGuard biosensor for environmental monitoring, proving its effectiveness in verifying the absence of specific pesticide contaminants.

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## Stage 8: Adaptation for Cell-Free Protein Synthesis

#### (i) Objective:

To develop a safer and more publicly accessible version of the detection kit by adapting the biosensor for a cell-free protein synthesis (CFPS) system, thereby eliminating the use of live, genetically modified bacteria.

#### (ii) Progress and Outlook:

Our genetic circuit (aptamer-EGFP construct) has been successfully modified for compatibility with a cell-free protein synthesis platform. This adaptation is designed to express the EGFP reporter in a purified reaction environment, offering a more controlled and portable detection format. While the core design for the cell-free system is complete, final optimization and testing were ongoing. The complete dataset and results from the cell-free platform will be finalized and presented at the iGEM Jamboree.

![](https://static.igem.wiki/teams/5627/results/figure.webp)

###### *Figure 20.* *New construct design for non-cell protein synthesis*

#### (iii) Results:

The results for the Cell-Free Protein Synthesis (CFPS) validation are currently under final analysis and were not available prior to the wiki freeze. The complete dataset and conclusions will be presented at the iGEM Grand Jamboree in Paris.

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# Conclusion of Results

Through systematic design iteration and rigorous validation, we have successfully developed and characterized a novel, functional biosensor platform for the detection of specific pesticides. Our initial gene design was optimized by incorporating a spacer sequence, which successfully restored tight IPTG-dependent regulation and eliminated constitutive expression. A critical breakthrough was achieved by replacing the strong T7 promoter with the moderate PlacUV5-MB7 promoter. This strategic change reduced the metabolic burden on the host cells and established an optimal dynamic range, resulting in a reliable, quantifiable fluorescent output with a proportional response to pesticide concentration under the optimized conditions of 0.5 mM IPTG and a 24-hour expression time.

Specificity testing confirmed that the glyphosate and acephate aptamers are highly effective and specific, producing standard curves with a high R² value within the practically relevant range of 0-1 mg/L. The mechanism of action was definitively verified through control experiments, which proved that the signal decrease is a direct result of specific aptamer-pesticide binding and not due to nonspecific effects on cell health or the fluorescent protein.

Most importantly, the biosensor's real-world applicability was demonstrated through successful food sample validation, where it accurately detected and quantified pesticide residues on contaminated vegetables. Furthermore, its utility for environmental monitoring was confirmed by reliably analyzing soil samples, where it produced a clear negative result for a certified organic farm, serving as a valuable negative control. Finally, the successful adaptation of our genetic circuit for a cell-free protein synthesis system paves the way for a next-generation detection kit that is safer, more portable, and free from genetically modified organisms. Collectively, these results validate our biosensor as a sensitive, specific, and versatile tool for pesticide detection in both agricultural and environmental settings.

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