iGEM 2025 — Team hongkong-uccke

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block lead %}This serves as a chronological record of your team's progress throughout the season. It documents your
daily activities, experiments, discussions, and decisions.{% endblock %}
{% block page\_content %}

![](https://static.igem.wiki/teams/5703/titleheading/notebook.webp)

NOTEBOOK

[Wet lab](#01)
[Dry lab](#02)
[Human practises](#03)
[Education](#04)

## **Wet lab log**

---

[wet lab notebook part
1](https://static.igem.wiki/teams/5703/notebook/lab-log-2025-igem-part-1.pdf)

[wet lab notebook part
2](https://static.igem.wiki/teams/5703/notebook/lab-log-2025-igem-part-2.pdf)

[wet lab notebook part
3](https://static.igem.wiki/teams/5703/notebook/lab-log-2025-igem-part-3.pdf)

[wet lab notebook part
4](https://static.igem.wiki/teams/5703/notebook/lab-log-2025-igem-part-4.pdf)

## **Dry Lab and Hardware log**

---

[ ]
Ideation of Debugger Prototype 1

Time: 20/2/2025

The first direction was decided to be designing a trap that could capture all bed bugs within a house.

[ ]
Design of Debugger Prototype 1

Time: 15/3/2025

3D model design of Debugger Prototype 1 was finished.

[ ]
Ideation of Debugger Prototype 1

Time: 01/8/2025

From the meeting with Mr. Francisco Pazos, we have done some reworking, turning our hardware direction to
early prevention to not allow bed bugs from entering properties. We decided to apply a detection kit in
luggages to check for the presence of bed bugs. For dry lab, we decided to predict the anaerobic growth
rate
of *E. coli* using modeling.

[ ]
UCCKE & HKUST Dry Lab Tutorial

Time: 25/8/2025

iGEM team of HKUST taught us basic functions of dry lab, as well as different softwares used for
modeling.
They suggested using Elastic Network Regression to assist kinetic modeling of growth rate of *E.
coli*.

[ ]
UCCKE & HKUST Dry Lab Tutorial

Time: 01/10/2025

The user manual, along with the real life model and 3D model design of Debugger Prototype 2 were
finished.

## **Human practise log**

---

[ ]
Interviewing Professor Wong Hung

Time: 20/2/2025

We consulted Professor Wong Hung of CUHK, who found bed bug prevalence is high in low-income areas, but
eradication is time-consuming, causing residents to avoid professional help. The key takeaway is the need to
raise public awareness and stress community cooperation (regular cleaning) to prevent spread and recurrence.

[ ]
Interviewing Desen

Time: 12/2/2025

We consulted Professor Wong Desen, a synthetic biology expert, regarding the testing and use of lactic acid
to validate our project's scientific approach. His feedback was crucial, informing our experimental design
and
ensuring the solution is scientifically viable.

[ ]
Interviewing Mr Cheung

Time: 1/3/2025

Consulting Mr Cheung (FEHD) revealed the government's minimal role in bed bug control. We identified
domestic
helpers and low-income residents as key targets and decided to use workshops and flyers to tackle the low
public awareness.

[ ]
Interviewing Uncle Chow

Time: 12/4/2025

Interviewing Unle Chow confirmed that low-income residents underestimate bed bugs and find professional
help
too costly. His feedback—to consider the bed bug birth cycle in our design and his willingness to use the
device if effective—made affordability key and prompted research into the local acceptance of chemical
devices.

[ ]
First Meet with NoBedBugs\_HK

Time: 11/6/2025

Interviewing Mr. Francisco (ChinCheX) revealed traps are ineffective with people present, repellents are
temporary, and the best method is professional 70°C heat. We decided to design our trap to prevent bed bugs
from entering properties, as it's the most effective and cost-efficient strategy.

[ ]
Second Meet with NoBedBugs\_HK

Time: 28/7/2025

Our second meeting with Mr. Francisco (ChinCheX) confirmed two key collaboration points: we will work on a
case study regarding bed bug infestation, and he agreed to participate in a promotion video, which will
involve filming footage and conducting a short interview at his company.

[ ]
Interviewing Cambridge Med graduate

Time: 8/8/2025

A Cambridge Med graduate advised three safety precautions for our L-lactate kit: it must be childproof with
detailed instructions, L-lactate concentration needs control to prevent throat damage from inhalation, and
the
lactate must be free of *E. coli* by-products (LPS).

[ ]
Filming promotion video with Francisco

Time: 8/8/2025

We collaborated with Mr. Francisco (ChinCheX) to produce a promotion video detailing the bed bug birth
cycle
and the stages of infestation. This video was uploaded to our Instagram account to maximize exposure and
raise
public awareness of the issue.

## **Education log**

---

[ ]
Open Day

Time: 14/12/2024

During our school’s open day, we introduced iGEM and synthetic biology to our guests and visitors by
presenting our project from last year. We also shared our thoughts about the different aspects of synthetic
biology to the people who were unfamiliar with this term.

[ ]
School Assembly

Time: 17/3/2025

Our school arranged an assembly for us to introduce iGEM and our former project to our schoolmates. We also
included a brief introduction on our project this year and raised their awareness on bed bugs.

[ ]
Workshop with HKSSC

Time: 26/4/2025

We hosted a workshop with the iGEM team of HKSSC for secondary students. During the workshop, both of our
teams introduced our project for this year. Our team included the current situation of bed bug infestation
in
Hong Kong during the presentation so as to let the participants be aware of the seriousness of bed bugs.
Besides, we prepared several experiments, letting participants taste bio-tech and iGEM.

[ ]
UST workshop for non-iGEMer

Time: 13/9/2025

We hosted a workshop, “Be an iGEMer” with the iGEM team from HKUST for secondary students. During the
workshop, not only did we introduce iGEM and synthetic biology to the participants, we also taught the
participating students on how to conduct several experiments, including picking colonies, cPCR and gel
electrophoresis. Lastly, we provided a research and presentation session as a taste of a project initiation.

[ ]
Interviewing Cambridge Med Graduate for leaflet

Time: 8/8/2025

We interviewed a Cambridge Medical graduate, to understand the symptoms after being bitten by a bed bug. We
also learnt how to distinguish bed bug bites with other arthropod bites, which would be included in our
leaflet for domestic helpers and the elderly, thus helping them raise their awareness of this issue.

[ ]
Leaflets to domestic helpers and elderly

According to the interview conducted before with professor Wong Hung and Mr Cheung who are working in
government, domestic helpers and the elderly should be the targets that are educated as their homes might be
easily infested by bed bugs owing to their low awareness and high spreading rate of bed bugs infestation
between them. So we decided to distribute leaflets on the streets to them to raise awareness of this growing
problem.

[ ]
Original Comic Book

We wrote a comic book on the topic of bed bugs. By portraying a child’s experience in facing a bed bug
infestation, we aim to introduce the seriousness of bed bugs to children. When the parents and their
children
read this comic book together, the awareness of this issue would thus be raised. Besides, to broaden the
target audience of this comic book, we rewrote this comic book into an original drama, acting it out to
primary students. Lastly, the comic book was uploaded to our social media and wiki page, maximising the
exposure of it.

[ ]
Original Card Game

As comic books and preaching would be tedious for secondary students, we created an original card game
related to bed bug infestation for secondary students. While enjoying the game, students can also learn
about
bed bugs and the harm they bring us, thus raising their understanding and the awareness of bed bug
infestation.

[ ]
Biology Research Team

Time: whole year

Biology Research Team, also known as BRT for short, acts as a bridge between the iGEM team and iGEM
beginners
at our school. The members of our iGEM team conduct regular experiments with the Research team’s members and
also educate the iGEM beginners on different knowledge on synthetic biology.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one
iteration of the engineering design cycle.{% endblock %}
{% block page\_content %}

![](https://static.igem.wiki/teams/5703/titleheading/engineering.webp)

ENGINEERING CYCLE

A Systematic Approach to Engineered Biology: Design, Build, Test, and Learn.

[L-Lactic acid production by the overexpression of *ldh* gene](#01)
[Cycle 2: using inducible promoter *pBAD* for *ldh* overexpression](#02)
[RNAi-Driven Optimization of L-Lactic Acid Biosynthesis: A Fluorescent Reporter-Based Study](#03)
[Cycle 2: Cloning the Gene Repression Constructs](#04)
[L-lactic acid Diffusion Rate Measurement](#05)
[Cycle 2: Exploring Different Measurement Method of Diffusion Rate](#06)
[Designing an Effective Hardware Model](#07)
[Cycle 2: Attraction and Detection Bed Bug Kit](#08)
[Reference](#09)

# **L-Lactic acid production by the overexpression of *ldh* gene**

## **Cycle 1: L-LDH Overexpression Under Strong Promoter *J23100***

### **Design**

![](https://static.igem.wiki/teams/5703/engineering/e1.webp)

(fig 1, Initial circuit design with promoter *J23100*)

This circuit is utilized to drive the overexpression of the *ldh* gene, which encodes for L-lactate
dehydrogenase (L-LDH), facilitating the conversion of pyruvate into L-lactate. In our initial design, we chose
to use a strong constitutive promoter ([BBa\_J23100](https://registry.igem.org/parts/bba-J23100)),
strong ribosome binding site ([BBa\_B0034](https://registry.igem.org/parts/bba-b0034)), L-LDH
encoding
gene *ldh* ([BBa\_2550VFUD](https://registry.igem.org/parts/bba-2550vfud)), and a double
terminator ([BBa\_B0015](https://registry.igem.org/parts/bba-b0015)). The decision to utilize the
strongest
constitutive promoter was based on the premise that it would lead to maximum expression levels, thereby
optimizing the production yield of L-lactate.

![](https://static.igem.wiki/teams/5703/engineering/e2.webp)

(fig 2, Synthetic pathway of L-lactate from glucose)

### **Build**

Despite successful plasmid synthesis and verification, the absence of transformant colonies suggests that the
*ldh* overexpression under the strong constitutive promoter ([BBa\_*J23100*](https://parts.igem.org/Part%3ABBa_J23100)) has a negative effect on the
growth of *E. coli*. We suspect that the reason behind this is the continuous conversion of pyruvate to
L-lactate, thereby depleting pyruvate, a key metabolic intermediate. Pyruvate is essential for *E. coli*
survival, particularly under anaerobic conditions where it is critical for mixed-acid fermentation and the
regeneration of NAD+. This metabolic disruption and subsequent toxic buildup of lactate likely proved lethal
to the host cells, preventing the formation of selectable colonies.

### **Learn**

To reduce the risk of *E. coli* mortality prior to the induction of *ldh* overexpression, an
inducible promoter should be employed for *ldh* expression.

## **Cycle 2: Using Inducible Promoter *pBAD* for *ldh* Overexpression**

### **Design**

We implement an inducible promoter *pBAD* ([BBa\_K2442101](https://registry.igem.org/parts/bba-k2442101)) in the genetic circuit as a
replacement for promoter *J23100*. The *pBAD* promoter, a common promoter in *E. coli*, is
specifically induced by L-arabinose. It is recognized as a strong promoter with low basal expression, thereby
reducing the likelihood of leaky expression.

![](https://static.igem.wiki/teams/5703/engineering/e3.webp)

(fig 3, *pBAD*-*ldh* ([BBa\_25DJGY4C](https://registry.igem.org/parts/bba-25djgy4c)))

### **Build**

We transformed the *pBAD*-*ldh* into *DH5α E. coli* and the formation of colonies indicate a
success in transformation. We picked colonies to do cPCR and conducted gel electrophoresis. From the cPCR
result, we can conclude that we have the correct plasmid constructed and transformed into DH5α *E. coli*
for *ldh* expression.

![](https://static.igem.wiki/teams/5703/engineering/e4.webp)

(fig 4, Succesful transformation of *pBAD*-*ldh*)

![](https://static.igem.wiki/teams/5703/engineering/e45.webp)

(fig 5, cPCR of *pBAD*-*ldh* with expected size)

### **Test**

After the successful transformation, we focused on measuring the concentration of L-lactate produced by our
modified strain.

### **Effect of anaerobic respiration on *E.coli* growth**

L-Lactate, the target product, is generated via anaerobic respiration. Consequently, we investigated the
effect of an anaerobic environment on the growth rate of *E.coli* (wild type).

| **Growth Method (24h)** | **OD600** |
| --- | --- |
| wild type *E.coli* | |
| sealed lid + no shaking | 0.0869 |
| sealed lid + 200 rpm shaking | 0.258 |
| unlocked lid + 200 rpm shaking | 0.533 |

The experimental data indicates that aerobic growth (unlocked lid with 200 rpm shaking) yields the highest E.
coli growth rate, while both anaerobic methods (sealed lid with or without shaking) significantly reduce the
growth rate.

Based on these findings, we concluded that a 24-hour period of continuous anaerobic incubation would result
in a lower overall L-lactate yield due to a significantly reduced biomass concentration.

1. Biomass Accumulation Phase: 3 hours of aerobic respiration (high growth rate).
2. L-Lactate Production Phase: 24 hours of anaerobic respiration (product generation).

To optimize the L-lactate yield during the 24-hour anaerobic production phase, we designed a follow-up
experiment to evaluate the effects of shaking on the L-lactate yield under anaerobic conditions. This is
necessary because while agitation generally promotes *E. coli* growth, it may negatively affect the
L-lactate production rate due to the reduced anaerobic respiration. The new experiment aims to determine
whether anaerobic respiration with or without shaking provides the optimal balance for L-lactate yield.

### **L-lactate measurement with *ldh E.coli***

A static anaerobic condition (no shaking + sealed lid) was tested against an agitated condition (100 rpm +
sealed lid). We concluded first aerobic fermentation for 3 hours, then anaerobic fermentation for 24 hours
with 100rpm shaking as our standard protocol to grow *E. coli* to produce L-lactate.

| Oxygen supplying method | Lactate conc. | OD600 | conc./OD600 |
| --- | --- | --- | --- |
| Aerobic 3h + anaerobic 24h without shaking | 15.4 | 0.43 | 35.8 |
| Aerobic 3h + anaerobic 24h with 100rpm shaking | 16.3 | 0.437 | 37.3 |

(fig 1, comparison of concentration of L-lactate per cell with two culturing methods)

To produce L-lactate, the raw material for respiration was also added, and L-arabinose was supplied to induce
the *pBAD* promoter. Normally, glucose can be used to make L-lactate, but according to research in 2014
(Simcikova et al., 2014,), glucose may repress the downstream expression of *pBAD* promoter(p. 1).
Therefore, we found another sugar—fructose as another carbon source, as suggested in the paper (Mulok et al.,
2009) for L-lactate production.The culture of *ldh*+ and Empty pET was grown with the standardised oxygen
supplying protocol to compare the yield.

This is our set-up used for testing the production of L-lactate with glucose, fructose, and L-arabinose:

| Plasmid transformed in *E. coli*: | Added sugar: |
| --- | --- |
| *pBAD*-*ldh* | 1% L-arabinose + 1% Frutose |
| 1% L-arabinose + 1% Glucose |
| 1% Fructose |
| 1% Glucose |
| Empty pET plasmid (Neg. control) | 1% L-arabinose + 1% Frutose |
| 1% L-arabinose + 1% Glucose |

Transformed *E. coli* were initially cultured in LB broth for 3 hours. Subsequently, various specified
concentrations of glucose, fructose, and arabinose were introduced, and the cells were then incubated for an
additional 24 hours under anaerobic conditions with continuous agitation at 100 rpm.

After the culturing, we measured the yield of L-lactate produced from the recombinant bacteria with a
L-lactate assay (details on [“experiment”](%7B%7Burl_for%28%27pages%27%2C%20page%3D%27experiments%27%29%7D%7D) page) on the
culture medium.

A: L-arabinose G: Glucose F: Fructose

![](https://static.igem.wiki/teams/5703/contribution/c2.webp)

(fig 7, Concentration. of L-lactate/OD 600 with different concentrations of glucose, fructose and
L-arabinose. A=L-arabinose, F=fructose, G=glucose)

This graph illustrates the concentration of produced L-lactate divided by OD 600 of the nutrient broth.
Overall, the broth with 1% glucose shows a significantly higher conc./OD600 compared to the negative control
empty pET plasmid. In contrast, the addition of fructose had a negligible effect on the lactate yield. This
suggests that fructose might not be a suitable carbon source in this metabolic pathway.

The result indicates an unexpected discovery — the existence of L-arabinose does not seem to have a
significant
inducing effect on producing L-lactate even though the inserted promoter, *pBAD*, is said to be induced
by L-arabinose. We suspected that the leaky expression of *ldh* synthesizes enough L-LDH so that it was
no longer a limiting factor on producing L-lactate. We suspected that the concentration of glucose is the
limiting factor. To verify our assumption, we tried these sets to compare the yield of L-lactate from broth
with different glucose concentration:

| Glucose: | L-arabinose: |
| --- | --- |
| 0% | 1% |
| 1.0% |
| 2.0% |
| 3.0% |
| 4.0% |

A: L-arabinose G: Glucose

![](https://static.igem.wiki/teams/5703/contribution/c3.webp)

(fig 8, Concentration of L-lactate/OD 600 with different glucose concentrations)

The results show that increasing the concentration of glucose were not able to significantly enhance the
production yield, which indicates that glucose is also not a limiting factor on the yield of L-lactate.

### **Learn**

Our investigation into the expression of *ldh* under the control of the
*pBAD* promoter has revealed constitutive-like activity, contrary to its expected L-arabinose-inducible
nature. This behavior indicates a potential failure in the AraC-mediated repression mechanism, which is
essential for silencing the promoter. Such a regulatory failure could stem from a complete absence of a
functional AraC protein, which might occur if the gene was omitted from the plasmid construct used in a
non-complementing AraC-host strain like DH5α. Alternatively, the issue may be an insufficient AraC
concentration to repress all promoter copies—a phenomenon known as repressor titration, which is common when
using high-copy-number plasmids. A final possibility is the presence of a mutation within the AraC gene
itself, rendering the protein non-functional and unable to form the DNA loop required for repression. Any of
these scenarios would result in significant basal transcription, explaining the observed constitutive
expression that is not suppressed by high glucose concentrations.

Interestingly, while the *pBAD* promoter's leakage was significant, its basal expression level was still
measurably lower than that of *J23100*. This unforeseen characteristic proved advantageous for our
project. In a
previous design iteration utilizing a stronger constitutive promoter, we observed significant cell death,
likely due to the metabolic burden imposed by high-level, unregulated *ldh* expression. The moderately
high, but not excessive, leakage from the *pBAD* promoter provided a level of enzyme expression
sufficient for our pathway while avoiding the toxicity observed in our initial attempts. This highlights how a
component not functioning as specified can inadvertently provide a solution to a separate engineering
challenge.

![](https://static.igem.wiki/teams/5703/engineering/e7.webp)

(fig 9, *pBAD* AraC mechanism)

In the future, our engineering cycle will focus on repairing the *pBAD* promoter system to achieve true
inducibility. The immediate next step is to definitively diagnose the cause of the leakiness by systematically
testing our plasmid in a known AraC host strain and transferring the genetic circuit to a low-copy backbone to
address potential repressor titration. Once tight repression is restored, we will re-characterize the
promoter's induction curve to identify a L-arabinose concentration that yields the desired, non-toxic level of
*ldh* expression. This approach will allow us to retain the tunable control that the *pBAD* system
offers, leading to a more robust and sophisticated final design that meets our original project
specifications.

### **Future testing with *pBAD-gfp***

To test for our assumption and characterise the *pBAD* expression, we have designed a construct to test for how
L-arabinose affect the expression of *pBAD* with the reporter gene GFP.

![](https://static.igem.wiki/teams/5703/engineering/e8.webp)

(fig 10, pBAD-gfp design)

# **RNAi-Driven Optimization of L-Lactic Acid Biosynthesis: A Fluorescent Reporter-Based Study**

## **Cycle 1: Gene Repression of *ldh*A with RNA interference (RNAi) by Co-transformation**

### **Further Enhancement of the Production Yield with RNAi Gene Silencing**

To further increase the yield of L-lactate, we aim to reduce competition for pyruvate between D-lactate and
L-lactate by silencing the expression of the *ldh*A gene, which encodes the enzyme D-lactate
dehydrogenase (D-LDH) responsible for converting pyruvate into D-lactate.

### **Exploring the Way to Silence Gene**

In our project, selecting a method for gene regulation required careful consideration of cellular viability
and metabolic function. While powerful, gene editing tools like CRISPR-Cas9 cause irreversible gene knockouts.
The permanent deletion of a protein like D-lactate dehydrogenase could negatively impact the
growth rate of *E. coli*, as D-lactate production is critical during certain metabolic phases.

For this reason, we chose a reversible approach: RNA interference (RNAi). This strategy allows for temporary
and controllable gene silencing, offering significant advantages over permanent gene ablation.

A key benefit of our RNAi system is the ability to control when gene silencing occurs. The silencing
mechanism relies on an anti-sense RNA that binds to and inhibits the target messenger RNA (mRNA). By placing
the gene that produces this asRNA under the control of an inducible promoter (e.g. *pLAC* system), we can
turn
silencing on at the most optimal time.

This temporal precision is crucial. D-lactate serves as a vital electron sink for regenerating Nicotinamide
Adenine Dinucleotide (NAD⁺), an essential cofactor, during the initial stages of fermentation. Our system
allows the cells to grow and establish a robust metabolic state first. Only then do we induce the promoter to
suppress D-lactate production, avoiding the lethal consequences that a permanent knockout would cause.

Beyond controlling the timing, our RNAi system allows us to modulate the strength of the gene knockdown. We
can fine-tune the repression of the *ldhA* gene by modifying key aspects of the asRNA construct, such as
the length of the complementary RNA sequence or the strength of the promoter used to drive its expression.

In summary, the use of an inducible RNAi system provides a significant strategic advantage for our metabolic
engineering goals. The combination of precise temporal control and tunable silencing efficacy makes it a
superior choice over irreversible methods like CRISPR-Cas9. This advanced level of regulation allows us to
strategically redirect metabolic flux toward our desired product, L-lactate, without compromising the
essential metabolic functions that ensure cell survival and robust growth.

### **Design**

### **Basic asRNA Design**

We first aimed to test the specificity and effectiveness of using RNAi to knockdown the gene. To investigate
the underlying mechanism, we constructed a plasmid that expresses GFP and an anti-sense RNA of
*gfp* (*asGFP*) to repress *gfp* expression. Efficiency of RNAi knockdown can be easily
determined by the GFP fluorescence intensity.

![](https://static.igem.wiki/teams/5703/engineering/e9.webp)

(fig 11, *asGFP* RNAi test mechanism)

The design of asRNA, typically involves selecting a target region that efficiently inhibits gene expression,
such as a 20-400 bp fragment encompassing the ribosome binding site (RBS) and extending to the 5' end of the
gene, to ensure hybridization with the mRNA near the initiation site and promote degradation or translation
inhibition. The use of approximately 400 bp is based on its proven effectiveness in providing sufficient
complementarity to induce targeted silencing while minimizing off-target effects (Magistro et al., 2018); this
length strikes a balance between specificity and stability of the antisense molecule. Proper design also
involves computational prediction of secondary structures to avoid unintended folding that could reduce
binding efficiency. In our design, we use RNAfold web server to predict the structure. Once cloned into an
inducible or constitutive promoter vector, the asRNA is expressed in the host, leading to specific repression
of the target gene. For all RNAi constructs, a uniform length of 400 base pairs was chosen to enhance target
specificity. A strong constitutive promoter (*J23100*) was utilized for the expression of asRNA.

### ***GFP* expression**

![](https://static.igem.wiki/teams/5703/engineering/e10.webp)

(fig 12, Use medium strength promoter *J23110* to express *gfp*(chl-resistant) ([BBa\_25YO6YTU](https://registry.igem.org/parts/bba-25yo6ytu)))

A medium-strength constitutive promoter ([BBa\_J23110](https://registry.igem.org/parts/bba-j23110))
was selected for the expression of green fluorescent protein (*GFP*)([BBa\_E0040](https://registry.igem.org/parts/bba-e0040)), while a strong constitutive promoter ([BBa\_J23100](https://registry.igem.org/parts/bba-j23100)) was utilized for the expression of asRNA.
This design ensures that the asRNA is in excess, a condition hypothesized to maximize gene silencing
efficiency. The *GFP* expression plasmid was engineered with upstream prefix and downstream suffix sites,
enabling the facile cloning of various inserts.

### ***pt7* Stemloop Testing: *asGFP* (400bp) and *asGFP* (400bp)-*pt7* Against *gfp***

![](https://static.igem.wiki/teams/5703/engineering/e11.webp)

(fig. 13, *J23100*-*asGFP* ([BBa\_25QLNT2S](https://registry.igem.org/parts/bba-25qlnt2s)))

![](https://static.igem.wiki/teams/5703/engineering/e12.webp)

(fig 14, *J23100*-*asGFP* with *pt7* stemloop ([BBa\_25AB9IW6](https://registry.igem.org/parts/bba-25ab9iw6)))

Two RNAi regions are designed to be cloned into an expression vector, with or without stabilizing features
like paired termini or stem-loop structures (e.g., the *pt7* stemloop) to enhance asRNA stability and
accumulation within the host cell. The stem-loop not only increases the half-life of the antisense molecule
but may also improve its binding affinity and effectiveness in silencing the target gene. To evaluate the
effect of secondary structure on gene repression, we compared linear asRNA designs with those incorporating a
stem-loop structure.

### **Controllable System for asRNA Expression Under Inducible Promoter *pLAC***

![](https://static.igem.wiki/teams/5703/engineering/e13.webp)

(fig 15, *pLAC-gfp-pt7* ([BBa\_25SMWT82](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25smwt82%E2%80%9D)))

To achieve tunable RNAi repression, we engineered another construct featuring *asGFP* gene under the
transcriptional control of the isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible Lac promoter. Our
objective is to precisely modulate *asGFP* expression using varying IPTG concentrations. This approach is
necessitated by observations from the previous cycle, which indicated that driving the *ldh* gene with a
strong constitutive promoter may inhibit *E. coli* colony formation. We hypothesize that this inhibition
is due to the complete cessation of D-lactic acid production, one of the essential components for survival.
Therefore, to ensure host viability, we will implement this IPTG-inducible system to regulate the copy number
of the antisense RNA that represses the *ldh*A gene, rather than achieving complete transcriptional
knockdown.

### **Negative Control: *asRFP* (400bp) Against *gfp***

![](https://static.igem.wiki/teams/5703/engineering/e14.webp)

(fig 16, *J23100*-*asRFP*([BBa\_25ZT1479](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25zt1479%E2%80%9D)))

A non-targeting asRNA against red fluorescent protein (*asRFP*) served as a negative control to confirm
the specificity of the RNAi mechanism.

### **The RNA Folding Prediction Models**

Because RNA is single stranded, it can form stable intramolecular structures through complementary
base-pairing, potentially inhibiting the formation of the required asRNA-mRNA duplex but also decreasing the
likelihood of being degraded by RNase. Therefore, understanding the predicted conformation of various asRNA
candidates can facilitate the rational selection of an optimal asRNA length to balance between binding
efficiency and structural stability.

The structure of our designed is predicted with  [RNAfold web server](%E2%80%9Dhttp%3A//rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi%E2%80%9D)

![](https://static.igem.wiki/teams/5703/engineering/e15.webp)

(fig 17, *asRFP* folding prediction)

![](https://static.igem.wiki/teams/5703/engineering/e16.webp)

(fig 18, *asGFP*-*pt7* folding prediction)

![](https://static.igem.wiki/teams/5703/engineering/e17.webp)

(fig 19, *asGFP* folding prediction)

### **Build**

We transformed the plasmids into DH5α *E. coli* successfully.

![](https://static.igem.wiki/teams/5703/engineering/e18.webp)

(1)*asGFP* (kan)
([BBa\_25QLNT2S](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25qlnt2s%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e19.webp)

(2)*asGFP* *pt7* (kan)
([BBa\_25AB9IW6](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25ab9iw6%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e20.webp)

(3)*asRFP* (kan)
([BBa\_25ZT1479](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25zt1479%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e21.webp)

(4)*J23110*-*gfp* (chl)
([BBa\_25YO6YTU](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25yo6ytu%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e22.webp)

(5)pLAC-*asGFP*-*pt7* (kan) ([BBa\_25SMWT82](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25smwt82%E2%80%9D))

(table 1, successful transformation of plasmids *asGFP*, *asGFP* *pt7*, *asRFP*,
*J23110*-*gfp*(chl) and pLAC-*asGFP*-*pt7* into DH5α *E. coli*)

Then we extract asRNA plasmids with miniprep. PCR was conducted to verify the presence of correct plasmids.

![](https://static.igem.wiki/teams/5703/engineering/e23.webp)

(fig 20, The verification of plasmid size using PCR with expected results)

### **Test**

To evaluate RNAi-mediated gene silencing in *E. coli*, we used a co-transformation method with two
plasmids: one expressing GFP and another expressing anti-sense *GFP*. While this approach is much less
complicated than cloning, it can be less precise because the copy numbers of the two plasmids can be
imbalanced within the cells, skewing the fluorescence data. To account for this variability and get a more
accurate picture of the silencing effect, we analyzed three independent colonies from each co-transformation,
measuring both their fluorescence and optical density to normalize the reporter gene's expression to cell
density

The graph below illustrates the fluorescence/OD 600 in *gfp*-*asRFP*, *gfp*-*asGFP* and
*gfp*-*asGFP*-*pt7*:

![](https://static.igem.wiki/teams/5703/contribution/c4.webp)

(fig 21, RNAi test flu/OD 600 graph \*\*<0.005, n.s. = non significant)

### **Learn**

The experimental data reveal that the efficacy of RNAi is critically dependent on the structural stability of
the asRNA transcript. Constructs lacking the *pt7* stemloop sequence failed to produce a statistically
significant reduction in GFP fluorescence. Conversely, the incorporation of the *pt7* stem-loop,
which is presumed to enhance transcript stability and prevent degradation, mediated a significant repression
of reporter gene expression. These findings collectively demonstrate that a 400 bp asRNA stabilized by a
terminal stem-loop structure is an effective molecular tool for achieving targeted protein knockdown in *E.
coli*.
Due to time constraints, we were unable to complete the functional characterization of the
*plac*-*asGFP*-*pt7* construct. Future work is planned to rigorously assess the effect of antisense
RNA (asRNA) expression rate on target gene expression across a range of IPTG concentrations.

### **Applying the Design on L-lactate Production**

After the test of the RNAi technology, we aimed to enhance our design for L-lactate production by
incorporating asRNA targeting *ldh*A. This modification aims to repress the genomic expression of
D-lactate dehydrogenase, thereby optimizing the production of L-lactate. The addition of as*ldh*A should
be able to repress the production of D-lactate, but D-lactate is a factor that affects the growth of *E.
coli*, so an inducible promoter is necessary to prevent *E. coli* death before producing L-lactate.

![](https://static.igem.wiki/teams/5703/contribution/c5.webp)

(fig 22, *pBAD*-*ldh*-as*ldh*-*pt7* design)

## **Cycle 2: Cloning the Gene Repression Constructs**

### **Design**

In parallel with the co-transformation procedure, we executed the cloning steps required to merge the
antisense RNA (asRNA) sequence with the *gfp* expression under control of medium promoter(*J23110*).
This
process yielded a single construct designed to co-express both *asGFP* and *gfp*, which is crucial
for ensuring an identical expression rate for both the reporter (*gfp*) and *asGFP*. By
standardizing the transcription rate, this construct validates the assay design, thereby making the results
across different *asGFP* variants comparable and reliable.
We used the plasmids verified in cycle 2 for cloning, except we used another *J23110*-*gfp* with kan
resistant backbone for cloning, where prefix and suffix are designed at the end of the construct.

![](https://static.igem.wiki/teams/5703/engineering/e26.webp)

(fig 23, use medium strength promoter to express *GFP*(kan-resistant)([BBa\_252PPZTK](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-252ppztk%E2%80%9D) ))

### **Build**

We have successfully transformed the plasmid into DH5α *E. coli*.

![](https://static.igem.wiki/teams/5703/engineering/e27.webp)

(fig 24, transformation of *J23110*-*gfp* (kan)([BBa\_252PPZTK](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-252ppztk%E2%80%9D)  ))

A double restriction digestion is conducted for *J23110*-*gfp*(kan), *asGFP*,
*asGFP*-*pt7*, *asRFP*. For the first trial, the protocol for digestion is below:

1. Mix the following components together:

| component | volume (ul) |
| --- | --- |
| E | 1 |
| P | 1 |
| 2.1 NEB buffer | 2 |
| H2O | 11 |
| DNA | 5 |

2. incubate for 2h

After running the agarose gel, the results show unsuccessful digestion. All of the constructs have only one
band.

![](https://static.igem.wiki/teams/5703/engineering/e28.webp)

(fig 25, unsuccessful digestion)

To validate both the experimental protocol and the functionality of our equipment, we extended the
incubation duration to two hours and confirmed enzyme activity using the standard
*J23110*-*gfp*(chl)
plasmid, which has been previously verified by others. Specifically, we performed single restriction digests
to verify the efficacy of the enzymes cutting EcoRI, SpeI, and PstI. The resulting data confirmed that
enzyme denaturation had not occurred.

![](https://static.igem.wiki/teams/5703/engineering/e29.webp)

(fig 26, single digestion of *gfp* plasmid, indicating enzymes are functional)

Using the optimized incubation duration, we proceeded to perform a double restriction digest. Subsequent
gel electrophoresis analysis confirmed the successful digestion, as evidenced by the observation of two
distinct bands for each antisense RNA (asRNA) construct and a single band for the medium promoter
*J23110*
backbone construct(kan).

![](https://static.igem.wiki/teams/5703/engineering/e30.webp)

(fig 27, a gel photo of the double digestion of *J23110*-*asGFP*, *asRFP*, *asGFP*,
*asGFP*-*pt7*, *pLAC*-*asGFP*-*pt7*)

Consequently, the subsequent step involved gel purification to isolate the target DNA fragments,
specifically the antisense RNA (asRNA) insert and the *J23110*-*gfp* backbone, utilizing a QIAGEN
kit. We
then performed a ligation reaction to construct the complete plasmid, which incorporated the two genes
encoding the asRNA and GFP. Finally, the ligated product was transformed into DH5α *E. coli* and
incubated overnight.

### **Learn**

After the modification of RE digestion protocol and several trials of cloning, we are still trying to
transform *E.coli* containing *asRFP*, *asGFP*, *asGFP*-*pt7*, and
*pLAC*-*gfp*-*pt7*. Due to time constraints, we have not yet obtained the final construct for
testing, but we will continue the process in the future.

# **L-lactic acid Diffusion Rate Measurement**

## **Cycle 1: Diffusion Rate Measurement Tool Determination**

After L-lactic acid production, we have used commercial L-lactic acid to test its diffusion rate,
characterising it for our proposed application–detecting bed bugs in a small area.

### **Design**

The pH change will serve as a metric to quantify the diffusion rate of L-lactic acid gas within sealed
containers. For practical implementation, two readily available and straightforward methods for pH measurement
are the pH meter and pH paper.

A pH meter is an electronic device that provides a quantitative, numerical reading of pH, often to two
decimal places (e.g., 6.85). It can provide high precision and objective pH value. It measures the electrical
potential difference between two electrodes, which is directly related to the hydrogen ion concentration. The
digital display eliminates human error. Though with such advantages, the main disadvantages are the time and
effort required for calibration with buffer solutions and the fact that it is more expensive than pH paper.

pH paper can solve the problem of the lengthy calibration duration and does not require a battery for
running. It also eliminates the duration of L-lactate dissolution in water, producing a more realistic value
for diffusion rate. However, it has low precision, limited to a resolution of 0.5-1.0 pH units and the pH
obtained is highly dependent on the subjective colour of paper.

### **Build**

Both comparative experimental designs—the pH meter protocol and the pH indicator paper protocol—utilize a
common apparatus and set of controlled variables to ensure valid comparison.

The fundamental setup involves a plastic box to serve as the enclosed environment, which is sealed using
masking tape to prevent any potential leakage of gaseous compounds.

### **Using pH paper as Measurement Tool**

The procedure for this experiment is described as the following:

1. **Diffusion Interval:** The L-lactic acid will be placed inside the sealed plastic box and allowed to
   diffuse for several predetermined time intervals.

* a. At predetermined time intervals (e.g., 1 day, 2 days, 3 days), remove one setup from the series.
* b. For example, to obtain measurements after three consecutive days, three separate, identical setups
  were needed.

2. **Moisture Application:** After the diffusion interval, the paper will be moisturized with 2.0 mL of
   distilled water.
3. **Color Development:** The paper will be allowed to stand for 30 minutes to allow for the dissolution
   of adsorbed ions and the subsequent color change. Note: As observed, the color change will only occur
   post-hydration.
4. **pH Determination:** the pH paper will be removed from the plastic box and the resulting color will be
   immediately compared against a standard color chart provided by the manufacturer. The corresponding pH value
   will be recorded to the nearest 0.25 pH unit.

![](https://static.igem.wiki/teams/5703/engineering/e31.webp)

(fig 28, the model of experimental design using pH paper as a diffusion rate measurement)

![](https://static.igem.wiki/teams/5703/engineering/e32.webp)

(fig 29, the model we built for diffusion rate as timepoint measurement using pH meter)

### **Using pH meter as Measurement Tool**

1. Calibration: calibrate with 3 buffer solutions(pH 4.01, 6.86, 9.18) to ensure the value obtained from pH
   meter is accurate
2. Preparation: Add 20 mL of L-lactic acid in one 50 mL beaker and 20 mL of distilled water in another 50 mL
   beaker.
3. Initial Measurement: Measure and record the initial pH of the distilled water. This serves as the baseline
   pH for all experimental trials.
4. Experimental Setup:

   * Place the beakers of lactic acid and distilled water in opposite corners of an enclosed box.
5. Diffusion and Measurement:

   * Repeat the setup with multiple identical sealed boxes to create a series of independent trials. This
     is a crucial step to avoid opening the container during the experiment.
   * At predetermined time intervals (e.g., 1 day, 2 days, 3 days), remove one setup from the series.
   * For example, to obtain measurements after three consecutive days, three separate, identical setups
     were needed.
   * Open the container and measure the pH of the deionized water sample inside.
   * Record the time elapsed and the corresponding final pH value.
6. Data analysis:

   * Calculate the pH change of each water sample and plot of graph of pH change over time.

![](https://static.igem.wiki/teams/5703/measurement/m9.webp)

(fig 30, the design of the diffusion set up using pH meter)

![](https://static.igem.wiki/teams/5703/measurement/m10.webp)

(fig 31, the model we built for diffusion rate as timepoint measurement using pH meter)

### **Test**

Accurate pH values could not be reliably determined using pH paper because of observer bias in color
assessment and the minor magnitude of the pH change. For this reason, we did not plot a pH trend graph.
Nevertheless, the results definitively indicate that the diffusion of lactic acid resulted in a detectable pH
alteration

![](https://static.igem.wiki/teams/5703/engineering/e35.webp)

(fig 32, initial pH of the environment of the box)

![](https://static.igem.wiki/teams/5703/engineering/e36.webp)

(fig 33, the pH of the environment of the box after 6 days of diffusion.)

Conversely, the determination of the equilibrium time is readily achieved through the use of pH meter. This
methodology effectively illustrates the relationship between the pH changes and time allowed for diffusion. As
demonstrated in figure 30, the system reaches equilibrium between day 3 and day 4. The precise point of
equilibrium can be ascertained through rigorous data analysis, specifically by utilizing differentiation to
locate the minimum point.

![](https://static.igem.wiki/teams/5703/engineering/e37.webp)

(fig 34, pH change over time (day) in a space with volume of 15000 cm3. Blue line: pH change of water with
L-lactic acid. Red line: pH change of water without L-lactic acid)

### **Learn**

Because the measured pH change is too subtle, the lack of precision from the use of pH paper would render the
experiment's results unreliable and make equilibrium time difficult to be measured. Moreover, subjective
colour observation and different lighting conditions will make the pH inaccurate. While pH paper is quick and
inexpensive, the pH meter is the only instrument that will provide the reliable, quantitative data needed for
this experiment to be scientifically valid. Therefore, we chose to use a pH meter as a measurement tool for
further testing.

One difficult situation we faced when using pH meter is that it requires a lot of boxes, since one box can
only be used to make one pH change data (e.g. change after 4 days), but not a set of data (e.g. the entire pH
trend from day 1-6), making this experiment time consuming.

## **Cycle 2: Exploring Different Measurement Method of Diffusion Rate**

### **Design**

The previous model required the use of multiple identical boxes to collect a single set of results over time.
At each predetermined time interval, a separate box had to be opened to measure the pH, making this a
time-consuming and materially inefficient process. Another issue was the variability introduced by using
different containers for each trial. Even after thorough cleaning, the use of separate beakers for the water
samples led to minor variations in the initial pH.

To overcome these challenges, a new model has been developed. This redesigned apparatus will allow for all
measurements to be conducted within a single, controlled environment, which can enhance the material
efficiency and accuracy of our data by using the same sample for the entire set of data.

### **Build**

We have built a model that has plastic bags fixed on the box to control the pH meter inside, so that we can
measure the pH change with only one box without interrupting the sealed environment.

Instruments involved in the new set up is the following:

* pH meter: to measure the change in pH value of water.
* Electrolyte solution: to ensure a pH probe is kept hydrated when the probe is not in use
* Distilled water: simulates bed bug, used to quantify the evaporated l-lactic acid with its pH change
* L-lactic acid: the smell was used as a chemical lure to attract bed bugs
* distilled water in a bottle: used to clean the chemical on the pH meter probe after measurement of
  slightly acidic water
* Lens paper: used to remove liquid remained on the pH probe to ensure that the results are not affected by
  contamination
* Tape: sealed up all the gaps between the table and the container

![](https://static.igem.wiki/teams/5703/engineering/e38.webp)

(fig 35, The design of the diffusion set up as a continuous measurement using pH meter)

![](https://static.igem.wiki/teams/5703/engineering/e39.webp)

(fig 36, The model we built for diffusion rate as a continuous measurement using pH meter)

### **Test and learn**

While a more accurate model was available, its associated experimental procedures proved overly complex and
inconvenient, particularly concerning the manipulation of the pH meter. This challenge led to inconsistent and
unreliable results. Consequently, we made the strategic decision to adopt the simpler timepoint model for the
remainder of the investigation.

Despite being able to determine the diffusion rate in standard conditions, we acknowledge the inherent
limitations of the timepoint model for the real world application proposed by our team–using our designed
hardware to detect the presence of bed bug in a luggage, but due to strict time constraints, further
methodological refinement was not feasible.

1. **Difference in evaporation mechanism of the experiment and hardware**

   In the original setup, lactic acid evaporates directly from the beaker's surface. However, the proposed
   model introduces a different mechanism: the absorption of lactic acid onto a paper material, followed by
   evaporation from the paper's surface. The rate of evaporation from a saturated paper surface will differ
   from the rate of evaporation directly from a liquid in a beaker. As a result, any equilibrium time
   obtained from this experiment will be an estimation rather than a precise measurement of diffusion rate
   from our hardware.
2. **Wider range of concentration can be included**

   To fully characterize the relationship between lactic acid concentration and its diffusion rate, it is
   recommended to expand the experimental scope. By including a test with 25% lactic acid concentration, the
   data will provide a clearer understanding of how concentration influences the equilibrium time, allowing
   for better manipulation of the diffusion rate.
3. **The assumptions behind the experiment: dissolution duration**

   This experimental model operates under the assumption that the time required for lactic acid gas to
   dissolve into the water is negligible. Consequently, the actual time to reach equilibrium would be shorter
   than the value observed in the experiment.

   A more precise measurement of the diffusion rate could be achieved by using an acid gas detector, which
   would directly measure the concentration of gas in the air. This approach eliminates the variable of
   gas-liquid dissolution, providing more accurate data.
4. **Equilibrium time of lactic acid may be reached at a later stage due to the blockage of items when applied
   in reality**

   The original experimental design, which used an empty box, does not account for the absorption and
   obstruction effects of items found in real-world scenarios, such as clothing in luggage. The presence of
   these materials would slow down the diffusion rate of lactic acid gas. As a result, the time required for
   the system to reach equilibrium would be considerably longer than observed in a controlled, empty
   environment.

   * **Absorption:** Porous materials like fabrics can absorb the gas molecules into their fibers and
     onto their surfaces. This removes the gas from the air, lowering its concentration.
   * **Physical Obstruction:** The presence of objects creates physical barriers, forcing the gas
     molecules to navigate a longer and more complex path.

# **Designing an Effective Hardware Model**

## **Cycle 1: “Attract and Kill” Bed Bug Trap**

### **Design**

For our team’s 1st implementation, we want to create a kit that could be used in households. Our kit would
have a lure to attract bed bugs, and the bugs would be trapped in the kit which later be easily killed. We
chose L-lactic acid as a lure to attract bed bugs to the kit, as L-lactic acid is proven to show attraction
abilities towards bed bugs[5]. After a user returns from a place with high risk of bed bugs infestation,
multiple kits could be placed around the house. When any bed bug brought home goes toward the kit to approach
the lure, they fall into the indented well. The well is designed to have a smooth surface which bed bugs are
unable to escape by climbing up. When the user spots any bed bug present in the well, they could remove the
well and put it in 70℃ hot water for 10 minutes to ensure all the bed bugs are killed. The well could be
connected back to the kit for repeated usage. If the lure is used up, it could be replaced and a new one could
be put back on the disc.

![](https://static.igem.wiki/teams/5703/hardware/fig1a-hardware.webp)

(fig 37, Debugger Prototype 1)

![](https://static.igem.wiki/teams/5703/hardware/fig1b-hardware.webp)

(fig 38, Debugger Prototype 1 exploded-view)

### **Test and Learn**

Our 1st prototype is presented to Mr. Francisco Pazos, a bed bug control specialist, as our team would like
to collect professional feedback from people working in the pest control industry. Mr. Pazos stated that if
the trap is not immediately applied after the bed bugs got into the home, the trap could not exterminate 100%
of the bed bug population. This is due to the high reproduction rate of bed bugs, if not all bed bugs are
attracted to the kit, the remaining ones would reproduce rapidly, resulting in recurring bed bugs infestation.
Therefore, the effectiveness of our trap may not work well if users do not put out the traps right after they
get home. In addition, he stated that currently, there is no chemical that could attract the whole bed bug
population. In large areas such as houses, the attracting effect would be minimal. If our kit needs to be
guaranteed to have the ability to attract all bed bugs in an apartment, it would simply be impossible.

The insights from Mr. Pazos has led us to conclude that the “attract and kill” method used in households
could not be used, and we have to find a different approach.

## **Cycle 2: Attraction and Detection Bed Bug Kit**

### **Design and Build**

We have discovered that not many services are available for early detection of bed bugs. From further
research, we found out that bed bugs usually hide in confined and small spots, such as furniture, luggages and
bags. Due to the fact that bed bugs are primarily spread by ‘hitchhiking’ on carriers[6], our team wants to
provide an early detection kit that could check for presence of bugs in luggages before any of them could
spread out. The kit lures out bed bugs hiding in clothes or gaps and users would know they have to sterilize
their luggages if they see bed bugs present in the kit.

We would have to modify the design of our kit, as our original prototype could only be used when put on
grounds.

![](https://static.igem.wiki/teams/5703/hardware/debugger2isometric.webp)

(fig 39, Debugger Prototype 2)

![](https://static.igem.wiki/teams/5703/hardware/debugger3d.webp)

(fig 40, 3D model of Debugger Prototype 2)

We made various adjustments to accommodate the usage of kit in luggages. The updated design of our kit is
split into two main parts: the lure container and the diffuser.

The acid-resistant lure container only allows the entry of the diffuser, which covers the connection parts.

![](https://static.igem.wiki/teams/5703/hardware/debugger2diffuser-labeled.webp)

(fig 41, A layer of the diffuser)

The diffuser consists of 3 main parts: the support, the cardboards and the chromatography papers . Firstly,
the support of the diffuser is made from wood. Secondly, there are layers of cardboards to provide shelter for
the bed bugs. When the diffuser is connected to the lure container, the chromatography papers absorb the
lactic acid and allow lactic acid to diffuse and attract bed bugs.

Users could use a flashlight to facilitate their checking of bed bugs in the kit. If any is found, the
luggage may contain more of them and require immediate sterilization. The used diffuser could be put into a
zip lock bag provided to prevent bed bugs escaping, while the container with remaining lure could be reused
upon inserting with a new diffuser.

As L-lactic acid may have potential harm, our team wants to guarantee our users could use our kit safely and
clearly. With the above reason, we have designed a user manual to provide guidance on using the kit.

The second design is effective at all orientations compared to our original design. Our improved prototype
could be applied better in luggages, with the reusable lure container and user manual, our team offers a
safer, more convenient, and cost-effective solution to prevent the spread of bed bugs.

### **Test and Learn**

From different testings on our new prototype, we have discovered some room for improvement. However, due to
constrained time and materials, we could not adjust our design accordingly at this moment, and we hope to
enhance our model in the near future.

* **1. materials:** The diffuser could be made from more cost effective materials, such as recycled
  cardboards for harborage. For repeated usage of the liquid container, a lid is required to cover it. Other
  than using plastic film, we aim to utilize materials which are more environmentally-friendly.
* **2. Leakage:** Slight leakage occurs as testing liquid (water) seldom leaks through the holes of
  chromatography paper, and liquid seeps through the four edges of the container when put sideways. For future
  design, rubber rings could be added in the interior to prevent leakage.
* **3. Outlook:** The current model utilizes hot-melt adhesive to connect different parts, leaving
  unpleasant looking stains. In the future, we would make use of silicon rubber to make a cleaner look.

## **Reference**

[1]Mulok, T. E. T. Z., Chong, M. L., Shirai, Y., Rahim, R. A., & Hassan, M. A. (2009). Engineering of *E.
coli* for increased production of L-lactic acid. African Journal of Biotechnology Vol. 8, 8(18),
4597–4603.
https://doi.org/10.5897/ajb09.614

[2]Simcikova, M., Prather, K. L., Prazeres, D. M., & Monteiro, G. A. (2014b). On the dual effect of glucose
during production of *pBAD*/AraC-based minicircles. Vaccine, 32(24), 2843–2846.
https://doi.org/10.1016/j.vaccine.2014.02.035

[3]Nakashima, N., Tamura, T., & Good, L. (2006). Paired termini stabilize antisense RNAs and enhance
conditional gene silencing in Escherichia coli. Nucleic Acids Research, 34(20), e138.
https://doi.org/10.1093/nar/gkl697

[4]Magistro, G., Magistro, C., Stief, C. G., & Schubert, S. (2018). A simple and highly efficient method for
gene silencing in Escherichia coli. Journal of Microbiological Methods, 154, 25–32.
https://doi.org/10.1016/j.mimet.2018.10.003

[5]Singh, Narinderpal & Wang, Changlu & Cooper, Richard & Liu, Chaofeng. (2012). Interactions among Carbon
Dioxide, Heat, and Chemical Lures in Attracting the Bed Bug, Cimex lectularius L. (Hemiptera: Cimicidae).
Psyche. 2012. 10.1155/2012/273613.

[6]Doggett SLDwyer DE, Peñas PF, Russell RC.2012.Bed Bugs: Clinical Relevance and Control Options. Clin
Microbiol Rev 25:.https://doi.org/10.1128/cmr.05015-11

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your project. It is designed to provide
sufficient information for other teams to replicate our work.{% endblock %}
{% block page\_content %}

![](https://static.igem.wiki/teams/5703/titleheading/experiment.webp)

Experiments

Engineering *E.coli* as a Cell Factory: Purpose and Protocols for L-Lactate Production and Biocontrol Delivery.

[Introduction](#01)
[Protocols](#02)
[Production of L-Lactic acid by Genetically Modifying *E.coli*](#03)
[Gene Repression of *gfp* with RNA interference (RNAi)](#04)
[Experiment to Characterize L-lactic acid diffusion properties](#05)

# **Introduction**

Here are the experiments we have done:

1. L-lactate production in modified *E.coli*
2. Silencing *gfp* gene with RNAi in *E.coli*
3. Characterization of L-lactate diffusion properties

The results of these experiments are indicated in the [“result”](%7B%7Burl_for%28%27pages%27%2C%20page%3D%27results%27%29%7D%7D) page.

## **Protocols**

## **Production of L-Lactic acid by Genetically Modifying *E.coli***

### **Measurement of L-lactate Concentration**

In our project, we aim to produce L-lactate in our modified *E.coli*. To monitor the yield of
L-lactate,
we used an optical method to measure the concentration of L-lactate in the bacterial cell lysate. We used a
L-lactate assay kit (solarbio cat. BC2230 50T/48S) to extract and measure the concentration of L-lactate.

### **Principle of the Functional Assay for L-lactate with Enzymatic Reactions**

A significant challenge in the direct turbidimetric measurement of L-lactate is its inherent absorption in
the near-infrared spectrum. To circumvent this issue, an enzymatic assay was employed. Following extraction,
L-lactate is oxidized to pyruvate by the enzyme L-lactate dehydrogenase (L-LDH), generating NADH as a
byproduct.

![](https://static.igem.wiki/teams/5703/experiments/exp1.webp)

(fig 1, L-lactate to pyruvate reaction)

The enzymatic oxidation of L-lactate to pyruvate by L-LDH is an equilibrium-limited reaction that strongly
favors L-lactate, which could lead to incomplete conversion. To drive the reaction to completion, the
generated NADH is continuously consumed in a subsequent reaction. In this process, NADH reduces the
chromogenic substrate MTT to formazan in the presence of the electron mediator 1-mPMS. The resulting
formazan
is then quantified by its distinct absorption peak at 570 nm, ensuring a reliable and complete measurement
of
the initial L-lactate concentration.

![](https://static.igem.wiki/teams/5703/experiments/exp2.webp)

(fig 2, NADH to Formazan reaction)

### **Material and Method**

### **Using Inducible Promoter *pBAD* for *ldh* Overexpression**

We implement an inducible promoter *pBAD* in the genetic circuit. The *pBAD* promoter, a common
promoter in *E. coli*, is specifically induced by L-arabinose. It is recognized as a strong promoter
with
low basal expression, thereby reducing the likelihood of leaky expression.

![](https://static.igem.wiki/teams/5703/contribution/c1.webp)

(fig 3, *pBAD*-*ldh* design)

### **Balancing Growth Rate and Lactate Production**

*E.coli* growth requires oxygen for growth while lactate production requires anaerobic conditions.
To make a balance between these two, we tested with 2 different oxygen supplying methods: growing
aerobically
for 3 hours and then anaerobically with either shaking or no shaking. Then the L-lactate production yield
(abs/OD600) is determined.

Protocol is below:

Making starter culture

1. Pick an isolated colony of transformed *E.coli* with *ldh* gene from the agar plate(kan)
2. Grow aerobically overnight

Making subcultures

1. Dilute the culture into 1:10 ratio.
2. Grow for 3 additional hours aerobically by unlocking the tube lid and shaking with 200 rpm.
3. Add excessive L-arabinose (1%) and glucose (4%) to the culture.
4. Grow anaerobically for 24 hours by locking the tube lid, either without shaking or with 100rpm shaking.

Measurement: refer to the protocol for L-lactic acid measurement with commercial assay kit.

### **Varying Nutrients Provided for Lactate Production**

To produce L-lactate, the raw material for anaerobic respiration is needed, and L-arabinose is needed to
induce the *pBAD* promoter. Normally, glucose can be used to make L-lactate, but according to research
in
2014 (Simcikova et al., 2014,), glucose may repress the downstream expression of *pBAD* promoter(p. 1).
Therefore, we found another sugar—fructose as an alternative carbon source, as suggested in the paper (Mulok
et al., 2009) for L-lactate production.

This is our set-up used for testing the production of L-lactate with glucose, fructose, and L-arabinose:

| Plasmid transformed in *E. coli* | Added sugar |
| --- | --- |
| *pBAD-ldh* | 1% L-arabinose + 1% Fructose |
| 1% L-arabinose + 1% Glucose |
| 1% Fructose |
| 1% Glucose |
| Empty *pET* plasmid (Neg. control) | 1% L-arabinose + 1% Fructose |
| 1% L-arabinose + 1% Glucose |

Making starter culture

1. Pick an isolated colony of transformed *E.coli* with *ldh* gene from the agar plate(kan)
2. Grow aerobically overnight

Making subcultures

1. Dilute the culture into 1:10 ratio.
2. Grow for 3 additional hours aerobically by unlocking the tube lid and shaking with 200 rpm.
3. Add corresponding concentrations of L-arabinose and glucose to the culture.
4. Grow anaerobically for 24 hours by locking the tube lid and with 100 rpm shaking.

Measurement: refer to the protocol for L-lactic acid measurement with commercial assay kit.

### **Discovering the Relationship between Glucose Concentration and L-lactic Acid Yield**

Because the result indicates the existence of L-arabinose does not seem to have a significant inducing effect
on producing L-lactate, we suspected that the concentration of glucose is the limiting factor. Therefore, we
tried to increase the glucose concentrations to compare the yield of L-lactate:

| Glucose | L-arabinose |
| --- | --- |
| 0% | 1% |
| 1% |
| 2% |
| 3% |
| 4% |

All the procedures were the same as the last experiment set.

## **Gene Repression of *gfp* with RNA interference (RNAi)**

### **Further Enhancement of the Production Yield with RNAi**

To further increase the yield of L-lactate, we aim to reduce competition for pyruvate between D-lactate and
L-lactate by silencing the expression of the *ldhA* gene, which encodes the enzyme D-lactate
dehydrogenase (D-LDH) responsible for converting pyruvate into D-lactate.

The use of an inducible RNAi system provides a significant strategic advantage for our metabolic
engineering goals. This advanced level of regulation allows us to strategically redirect metabolic flux
toward our desired product, L-lactate, without compromising the essential metabolic functions that ensure
cell survival and robust growth.

### **Testing the RNAi System with *gfp* Reporter Gene**

To better understand the optimal expression of antisense RNA, we have designed several plasmids that
expresses *GFP* and an anti-sense RNA of *GFP* (*asGFP*) to test for how the asRNA and stem
loop repress its target gene expression. Efficiency of RNAi knockdown can be easily determined by the
*GFP* fluorescence intensity.

### **Co-transformation for the Expression of *gfp*(chl) and *asGFP*(kan)**

The protocol for co-transformation is the following:

1. Thaw 50 - 100 μL competent cell on ice
2. Mix 1 - 10ng of both plasmids with the cells
3. Incubate on ice for 10 - 30 minutes
4. Heat shock at 42°C for 45 seconds
5. Incubate on ice for 2 minutes
6. Add 800 μL LB(no antibiotic) to the cells
7. Incubate for 1 hour at 37°C for recovery
8. Centrifuge the tubes for 30 seconds and remove most supernatant by flipping it upside down to leave ~100
   μL inside
9. Resuspend the pellet by pipetting up and down
10. Spread all the 100 μL cell to agar plates with the 2 antibiotics (chl and kan) that the 2 plasmids can
    develop resistance
11. Incubate for 2 days at 37°C

Then, the successfully co-transformed *E.coli* is used to compare the fluorescence intensity. The
protocol is the following:

Making starter culture

1. Pick 3 isolated colonies of transformed *E.coli* into 3 separate broths (chl + kan)
2. Grow aerobically overnight

Measurement of fluorescence intensity/OD600

1. Vortex the culture
2. Extract 100ul and transfer to 96-well plate
3. Measure the optical density with micro-platereader to obtain the absorbance and fluorescence intensity.
4. Analyse the data by dividing fluorescence over absorbance and do statistical tests.

## **Experiment to Characterize L-lactic Acid Diffusion Properties**

By determining the equilibrium time for different concentrations and volumes, we can ensure that the kit's
functional lifespan is maximized. This will allow for the L-lactic acid to reach a sufficiently attractive
concentration for bed bugs, without the system reaching equilibrium too quickly and becoming ineffective.

### **The Influence of Enclosed Volume on Lactic Acid Diffusion Kinetics**

### **Introduction**

The time it takes for a gas to reach an equilibrium state is directly influenced by the volume of that
space. By characterizing this relationship, we can determine the kit's functional lifespan in various
volumes. This data will allow us to create a rough usage guide for customers, advising them on the optimal
duration for the detection treatment based on the size of their luggage.

### **Method and materials**

Volume of boxes used for testing:

|  | length (cm) | width (cm) | height (cm) | total volume (cm3) |
| --- | --- | --- | --- | --- |
| Small (S) | 16 | 24 | 20 | ~7500 (50%) |
| Medium (M) | 20 | 30 | 25 | ~15000 (100%) |
| Large (L) | 23 | 34 | 29 | ~22500 (150%) |

Lactic acid will undergo evaporation and gaseous diffusion through an enclosed air volume, and this
diffusion will be quantifiable by measuring the progressive decrease in the pH of an open-surface distilled
water sample placed within the same sealed plastic box. The pH of the water will decrease as the acidic gas
dissolves into the water and dissociates into hydrogen ions and lactate ions. When the pH value stabilises
at its lowest value, it is considered equilibrium.

![](https://static.igem.wiki/teams/5703/experiments/exp4.webp)

(fig 4, the design of the diffusion set up)

![](https://static.igem.wiki/teams/5703/experiments/exp5.webp)

(fig 5, the model we built for diffusion rate as timepoint measurement)

### **Procedure**

1. **Calibration:** calibrate with 3 buffer solutions(pH 4.01, 6.86, 9.18) to ensure the value obtained
   from pH meter is accurate
2. **Preparation:** Add 20 mL of 50% L-lactic acid in one 50 mL beaker and 20 mL of distilled water in
   another 50 mL beaker.
3. **Initial Measurement:** Measure and record the initial pH of the distilled water. This serves as the
   baseline pH for all experimental trials.
4. **Experimental Setup:**
   * Place the beakers of lactic acid and distilled water in opposite corners of an enclosed box.
   * Secure the box with masking tape to create an airtight environment.
5. **Diffusion and Measurement:**
   * Repeat the setup with multiple identical sealed boxes to create a series of independent trials. This
     is a crucial step to avoid opening the container during the experiment.
   * At predetermined time intervals (e.g., 1 day, 2 days, 3 days), remove one setup from the series.
   * For example, to obtain measurements after three consecutive days, three separate, identical setups
     were needed.
   * Open the container and measure the pH of the deionized water sample inside.
   * Record the time elapsed and the corresponding final pH value.
   * Repeat the whole process with another size of boxes.
6. **Control Setup:**
   * Prepare a separate container with two beakers, both containing 20 mL of distilled water.
   * Seal this container and place it alongside the experimental setups.
   * At each measurement interval, open a control container and measure the pH of one of the water samples
     to account for any changes due to the container or atmospheric carbon dioxide.
7. **Data analysis:**
   * Calculate the pH change of each water sample and plot a graph of pH change over time

### **Characterizing the Relationship Between Concentration of L-lactic acid and Diffusion**

The diffusion rate of lactic acid gas is a critical factor for the effectiveness of our detection kit. Two key factors can influence this rate: environmental temperature and the concentration of the lactic acid source. While temperature significantly affects diffusion, manipulating the air temperature within luggage or bags is not a practical or user-friendly method for customers. Therefore, we have identified that varying the concentration of the lactic acid source is the most convenient way to control the rate of diffusion. This approach provides a reliable and simple means to manage the kit's performance and longevity.

### **Material and Methods**

Concentration of lactic acid used for testing: 50% and 75% v/v L-lactic acid.

All testing methods are the same as that of the volume set up, except the concentration of L-lactic acid varies in the same volume of box.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block lead %}Present the results of your project, along with a detailed analysis and discussion of their
significance. Also outline future plans and reflections on the impact of your project.{% endblock %}
{% block page\_content %}

![](https://static.igem.wiki/teams/5703/titleheading/results.webp)

RESULTS

Optimizing L-Lactate Bioproduction:
Our Research Results on Integrating Metabolic Pathway Engineering,
RNAi-Based Silencing, and Diffusion Kinetics

[Part 1: L-lactate production](#01)
[Part 2: RNAi gene silencing of *GFP*](#02)
[Part 3: Diffusion properties of L-lactate in a closed system](#03)
[Reference](#04)

## **Part 1: L-lactate production**

### **Successful Transformation of *pBAD*-*ldh***

We transformed the *pBAD*-*ldh* into DH5a *E. coli*, and the formation of colonies indicates a
success in transformation. We picked colonies to do cPCR and conducted gel electrophoresis. From the cPCR
result, we can conclude that we have the correct plasmid constructed and transformed into DH5a *E. coli*
for *ldh* expression.

![](https://static.igem.wiki/teams/5703/contribution/c1.webp)

(fig 1, *pBAD*-*ldh* design)

![](https://static.igem.wiki/teams/5703/results/2.webp)

(fig 2, succesful transformation of *pBAD*-*ldh*)

![](https://static.igem.wiki/teams/5703/engineering/e45.webp)

(fig 3, cPCR of *pBAD*-*ldh*, expected size of the insert is 1486bp)

### **Measuring the L-lactate production yield in our modified *E. coli***

### **Calibration curve**

A standard curve was generated by measuring the absorbance of standard L-lactate solutions at concentrations
ranging from 1 to 7
μmol/mL, with measurements taken at 1 μmol/mL intervals. The resulting linear regression plot yielded a
coefficient of determination (R2) of 0.981, indicating a very strong and highly reliable linear
relationship
between L-lactate concentration and absorbance.

![](https://static.igem.wiki/teams/5703/results/4.webp)

(fig 4, calibration curve of absorbance against L-lactate concentration)

### **Oxygen supply method to take a balance between growth rate and L-lactate yield**

To optimize our two-stage fermentation, we tested the anaerobic production phase under both static and
agitated (100 rpm) conditions. We found that agitation improved the L-lactate yield per cell. We concluded
first aerobic fermentation for 3 hours, then anaerobic fermentation for 24 hours as our standard protocol to
grow *E. coli* to produce L-lactate

| Oxygen supplying method | Lactate conc. | OD600 | conc./OD600 |
| --- | --- | --- | --- |
| Aerobic 3h + anaerobic 24h without shaking | 15.4 | 0.43 | 35.8 |
| Aerobic 3h + anaerobic 24h with 100rpm shaking | 16.3 | 0.437 | 37.3 |

(fig 1, comparison of concentration of L-lactate per cell with two culturing methods)

### **Measuring L-lactate yield in *E. coli* in different nutrient**

To produce L-lactate, the raw material for respiration was also added, and L-arabinose supplied to induce the
*pBAD* promoter. Normally, glucose can be used to make L-lactate, but according to research in 2014
(Simcikova et al., 2014,), glucose may repress the downstream expression of *pBAD* promoter. Therefore,
we found another sugar—fructose as another carbon source, as suggested in the paper (Mulok et al., 2009) for
L-lactate production.

This is our set-up used for testing the production of L-lactate with glucose, fructose, and L-arabinose:

| Plasmid transformed in *E. coli* | Added sugar |
| --- | --- |
| *pBAD-ldh* | 1% L-arabinose + 1% Fructose |
| 1% L-arabinose + 1% Glucose |
| 1% Fructose |
| 1% Glucose |
| Empty *pET* plasmid (Neg. control) | 1% L-arabinose + 1% Fructose |
| 1% L-arabinose + 1% Glucose |

Transformed *E. coli* were initially cultured in LB broth for three hours. Subsequently, various
specified
concentrations of glucose, fructose, and L-arabinose were introduced, and the cells were then incubated for an
additional 24 hours under anaerobic conditions with continuous agitation at 100 rpm.

After culturing, we measured the yield of L-lactate produced from the recombinant bacteria with a L-lactate
assay (details on [“measurement”](%7B%7Burl_for%28%27pages%27%2C%20page%3D%27measurement%27%29%7D%7D) page) on the culture
medium and cell lysate.

A: L-arabinose G: Glucose F: Fructose

![](https://static.igem.wiki/teams/5703/contribution/c2.webp)

(fig 5, concentration. of L-lactate/OD 600 with different concentrations of glucose, fructose and
L-arabinose. A=L-arabinose, F=fructose, G=glucose)

This graph illustrates the concentration of produced L-lactate divided by OD 600 of the nutrient broth.
Overall, the broth with 1% glucose shows a significantly higher conc./OD600 compared to the negative control
empty pET plasmid. In contrast, the addition of fructose had a negligible effect on the lactate yield. This
suggests that fructose might not be a suitable carbon source in this metabolic pathway.

The result indicates an unexpected discovery — the existence of L-arabinose does not seem to have a
significant
inducing effect on producing L-lactate even though the inserted promoter, *pBAD*, is said to be induced
by L-arabinose. We suspected that the leaky expression of *ldh* synthesizes enough L-LDH so that it was
no longer a limiting factor on producing L-lactate. We suspected that the concentration of glucose is the
limiting factor. To verify our assumption, we tried these sets to compare the yield of L-lactate from broth
with different glucose concentration:

| Glucose | L-arabinose |
| --- | --- |
| 0% | 1% |
| 1% |
| 2% |
| 3% |
| 4% |

A: L-arabinose G: Glucose

![](https://static.igem.wiki/teams/5703/contribution/c3.webp)

(fig 6, concentration of L-lactate/OD 600 with different glucose concentrations)

The results show that increasing the concentration of glucose were not able to significantly enhance the
production yield, which indicates that glucose is also not a limiting factor on the yield of L-lactate.

## **Part 2: RNAi gene silencing of GFP**

### **Introduction**

To further enhance the final L-lactic acid yield, we plan to implement RNA interference (RNAi) to suppress
competing pathways.

To compare how different designs of RNAi affect the repression rate, we design three different asRNA
expression plasmids, respectively, to test which asRNA design gives the strongest repression on GFP
intensity.

One of our main goals is to test the difference between RNAi with or without *pt7* stemloop, which are
used to stabilize the RNAi. Another thing for us to test is the length of RNAi. In this experiment, all the
RNAi designs have a length of 400 bp.

(For detailed design, refer to [“Engineering Cycle”](%7B%7Burl_for%28%27pages%27%2C%20page%3D%27engineering%27%29%7D%7D) page)

## **Successful co-transformation of *GFP*-*asGFP*, *GFP*-*asGFP*-*pt7* and *GFP*-*asRFP***

We transformed the plasmids into DH5a *E.coli* successfully.

![](https://static.igem.wiki/teams/5703/engineering/e18.webp)

(1)*asGFP* (kan)
([BBa\_25QLNT2S](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25qlnt2s%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e19.webp)

(2)*asGFP* *pt7* (kan)
([BBa\_25AB9IW6](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25ab9iw6%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e20.webp)

(3)*asRFP* (kan)
([BBa\_25ZT1479](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25zt1479%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e21.webp)

(4)*J23110*-*GFP* (chl)
([BBa\_25YO6YTU](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25yo6ytu%E2%80%9D))

![](https://static.igem.wiki/teams/5703/engineering/e22.webp)

(5)pLac-*asGFP*-*pt7* (kan) ([BBa\_25SMWT82](%E2%80%9Dhttps%3A//registry.igem.org/parts/bba-25smwt82%E2%80%9D))

(table 1, successful transformation of plasmids *asGFP*, *asGFP* *pt7*, *asRFP*,
J23110-*GFP*(chl) and pLAC-*asGFP*-*pt7* into DH5α *E.coli*)

Then we made a starter culture for all of the transformed *E.coli* and extract the plasmids with
miniprep. PCR was conducted to verify the presence of correct plasmids.

![](https://static.igem.wiki/teams/5703/engineering/e23.webp)

(fig 7, The verification of plasmid size using PCR with expected results)

Next, we proceed to co-transformation of GFP-expressing plasmid (Chl) J23110-*GFP* with
*asGFP*, *asGFP*-*pt7*, and *asRFP* (kan).

![](https://static.igem.wiki/teams/5703/results/7.webp)

(fig 8, Successful co-transformation of *GFP* expressing plasmid(chl) with *GFP*-*asGFP* or
*GFP*-*asGFP*-*pt7* or*GFP*-*asRFP* observed under blue light)

An initial starter culture was incubated for 24 hours. The culture was then diluted into a fresh growth
medium and subcultured for an additional 24 hours, after which fluorescence/OD 600 were measured.

The graph below illustrates the fluorescence/OD 600 in *GFP*-*asRFP*, *GFP*-*asGFP* and
*GFP*-*asGFP*-*pt7*:

![](https://static.igem.wiki/teams/5703/contribution/c4.webp)

(fig 9, RNAi test flu/OD 600 graph \*\*<0.005, n.s. = non-significant)

The experimental data reveal that the efficacy of RNAi is critically dependent on the structural stability of
the asRNA transcript. Constructs lacking the *pt7* stemloop sequence failed to produce a statistically
significant reduction in GFP fluorescence. Conversely, the incorporation of the *pt7* stem-loop,
which is presumed to enhance transcript stability and prevent degradation, mediated a significant repression
of reporter gene expression. These findings collectively demonstrate that a 400 bp asRNA stabilized by a
terminal stem-loop structure is an effective molecular tool for achieving targeted protein knockdown in *E.
coli*.

## **Part 3: Diffusion properties of L-lactate in a closed system**

By characterizing the relationship between the volume of a closed system and the time taken for L-lactate gas
to equilibrium, we can determine the kit's functional lifespan in various volumes. This data will allow us to
create a rough usage guide for customers, advising them on the optimal duration for the detection treatment
based on the size of their luggage.

Volume of boxes used for testing

| Size of boxes | Volume (cm3) | Equilibrium time (day) |
| --- | --- | --- |
| S | 7680 | 2.87 |
| M | 15000 | 3.54 |
| L | 22678 | 4.35 |

L-lactic acid will undergo evaporation and gaseous diffusion through an enclosed air volume, and this
diffusion will be quantifiable by measuring the progressive decrease in the pH of an open-surface distilled
water sample placed within the same sealed plastic box. The pH of the water will decrease as the acidic gas
dissolves into the water and dissociates into hydrogen ions and lactate ions. When the pH value stabilises at
its lowest value, it is considered equilibrium.

The following graphs illustrate the effect of varying volume on the equilibrium time for a 50% L-lactate
solution:

Blue line: pH change of water with L-lactate in the box

Red line (Neg. control): pH change of water without L-lactate in the box

![](https://static.igem.wiki/teams/5703/measurement/m11.webp)

(fig 10, pH change against day in S size box)

![](https://static.igem.wiki/teams/5703/measurement/m12.webp)

(fig 11, pH change against day in M size box)

![](https://static.igem.wiki/teams/5703/measurement/m13.webp)

(fig 12, pH change against day in L size box)

For each dataset, a regression curve was fitted to the plotted points using computational software.
Differential calculus was then employed to determine the minimum point of each curve. The equilibrium time,
defined as the x-coordinate of this minimum, was subsequently indicated on the respective graph.

Subsequently, a graph of equilibrium time versus box volume was plotted:

| Size of boxes | Volume (cm3) | Equilibrium time (day) |
| --- | --- | --- |
| S | 7680 | 2.87 |
| M | 15000 | 3.54 |
| L | 22687 | 4.35 |

![](https://static.igem.wiki/teams/5703/contribution/c6.webp)

(fig 13, Equilibrium time against volume with 50% L-lactate)

The analysis reveals a strong positive linear relationship between equilibrium time (t) and box volume (v).
This relationship is described by the linear regression equation: t = 9×10-5v + 2.09

The model's high reliability is substantiated by a coefficient of determination (R²) of 0.998. This value
indicates that 99.8% of the variance in equilibrium time is predictable from the volume, confirming the
model's excellent predictive power. With this equation, we can calculate and instruct users the usage duration
of our final bed bug traps of different sizes.

## **Reference**

[1]Mulok, T. E. T. Z., Chong, M. L., Shirai, Y., Rahim, R. A., & Hassan, M. A. (2009). Engineering of *E.
coli* for increased production of L-lactic acid. African Journal of Biotechnology Vol. 8, 8(18),
4597–4603.
https://doi.org/10.5897/ajb09.614

[2]Simcikova, M., Prather, K. L., Prazeres, D. M., & Monteiro, G. A. (2014b). On the dual effect of glucose
during production of *pBAD*/AraC-based minicircles. Vaccine, 32(24), 2843–2846.
https://doi.org/10.1016/j.vaccine.2014.02.035

{% endblock %}