iGEM 2023 — Team hainanu-china

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CLOSE

## Notebook

### Click Details to view PDF details

Details

![](https://static.igem.wiki/teams/4797/wiki/project/notebook/hardware.jpg)

## Hardware

Our hardware is about automatic culture media, and the notebook
records the progress of the hardware design. Please click Details to
view specific details.

Details

![](https://static.igem.wiki/teams/4797/wiki/project/notebook/software.jpg)

## Software

Our software is about three mini games related to synthetic biology,
and the notebook records the process of making the games. Please
click Details to view specific details.

Details

![](https://static.igem.wiki/teams/4797/wiki/project/notebook/model.jpg)

## Model

Our modeling proposed an innovative approach to screening
antimicrobial peptides, and the notebook documents the inspiration
for the approach and how it was refined into the final model. Please
click details to view specific details.

Details

![](https://static.igem.wiki/teams/4797/wiki/project/notebook/wetlab.jpg)

## Wet Lab

Our wet experiment mainly involves modifying engineered bacteria to
secrete specific antibacterial peptides and combining them with
optimized hydrogel materials to create an acne treatment patch.
Please click to see the specific details.

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![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/banner.png)

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### Catalog

# Engineering

## Small Games

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/cloud1.png)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/small-games/banner.png)

Inspired by the outstanding teams in the past, we have designed
three small games in order to achieve the purpose of popularizing
synthetic biology knowledge and let more people know and join us.
Small game production is divided into two parts at the same time.

1. Web version of mini-game 2048

Inspired by the outstanding teams of previous years, we
designed three mini-games to popularize the knowledge of
synthetic biology and let more people know and join us. We
first researched the relevant processes and regulations for
mini-game production online, and only started production after
we determined that it was safe, feasible, and non-infringing.
The original goal was to make a biological version of the 2048
mini-game. The source code was found on the open source
website GitHub as the original framework for development.
After many discussions in group meetings, we designed the
elements needed for the mini-game, paired with our team’s logo
and cards to reflect our team’s style. After continuous
modification and improvement, the biological version of the
mini-game 2048 was completed. It was first trialled within the
team, and then promoted through public accounts and tweets. It
was then modified and improved based on the feedback received.

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/small-games/molecule.png)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/small-games/fig1.png)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/small-games/2048.png)

2. Creature Jumping Rabbit & Puzzle

In the design stage, we used the WeChat mini game development
platform to select the game template of Leap Rabbit and
adapted it. However, during the production process, we found
that due to the limitations of the original framework of the
game, many elements of our team could not be integrated into
the game. At the same time, the game loop lacked challenge
during the clearance process. After consideration, we decided
to develop a creature puzzle game while retaining the Jumping
Rabbit mini-game. The latter has better integrated the
elements of our team, and at the same time has a certain
degree of challenge and fun, and can be used for PK with
friends. After the game is completed, it will be tested within
our team first. If there are no problems, it will be released
to the open source community. After the production of the
mini-game was completed, we wrote a copywriting summary,
published and promoted it in the form of a public account, so
that more people could participate and realize our original
intention of designing the mini-game.

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/small-games/jump-rabbit.png)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/small-games/fig2.jpg)

## Hardware

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/wood1.png)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/leave1.png)

### Multifunctional microbial automatic culture machine

#### Design phase:

From a service point of view, in order to allow team members to
better cultivate and detect microorganisms, we want to develop a
related product. Through the learning and thinking of the team's
own actual situation of the hardware BEST project in previous
years, combined with the difficulties and needs encountered in the
wet experiment, the team proposed a number of hardware project
directions, and decided to develop a multifunctional microbial
automatic culture machine in the process of discussion at several
meetings, relevant literature learning, and exploration after
practical testing. After determining the direction of the hardware
project, we began to strengthen team building and design
preparation in terms of learning and materials, and further
discussion, refine the product development content, and develop an
engineering practice plan, and design and manufacture this
hardware product together with the recommendation of the team
leader and the team.

#### Build phase:

According to the design planning flow chart, the team carried out
detailed development division and stage acceptance in terms of
theoretical construction and actual production. The product is
divided into three parts: first, the realization of the functions
of each module, including the selection and purchase of modules,
code run-through, physical assembly, etc. Secondly, the design of
the Internet of Things is carried out, including theoretical
learning and comparative selection of Bluetooth or WiFi based
communication and code function implementation. Finally, the shell
design and module integration are carried out, including shell
structure design, material selection, manufacturing process,
module layout and assembly.

![](https://static.igem.wiki/teams/4797/wiki/engin-hardware/1.png)

***Figure 1:**Hardware module design*

#### Test phase:

The test stage includes the function realization test of each
module and the integration test of the finished product in two
main stages. The stages are interconnected, so the error needs to
often adjust the overall situation. The difficulty is not small,
but in order to ensure product quality, serve the demanders, we
adhere to repeated rigorous stage testing, step by step, the
pursuit of better.

#### Learning Phase:

The engineering of this hardware product is a very practical
learning process, involving a wide range of knowledge and high
specialization. We need to continue to learn independently, give
play to innovative ideas, strengthen information retrieval
ability, hands-on production ability, team coordination and
communication ability, etc., in short, in this process, we have
gained a lot, it is a very meaningful learning stage.

#### Expected Phase:

At present, our products can basically meet the needs of the
team's internal experimental personnel, but can not achieve our
ideal effect, not enough for the broader, more professional group
needs, so we expect to further upgrade in the future material
selection, product function, quality and other aspects, so that
the multi-functional microbial automatic culture machine can
better play its utility for users.

[Hardware module details please click Hardware](%7B%7Burl_for%28%27pages%27%2C%20page%3D%27hardware%27%29%7D%7D)

### Housing

#### Design phase:

In order to achieve the integration of the automatic training
machine, we combined the target population, product functions,
market demand and other factors to consider, decided to make a
shell for the training mechanism.

#### Build phase:

By measuring the size of the specific parts, we first determined
the actual inner shell size of the incubator, and also designed
some grooves on the left, right and back three sides of the shell
according to the actual situation to fix the inner plate. Then we
use the drawing software to draw the specific model according to
the preliminary hand-drawn sketch, in which we standardize the
size and shape of the specific small parts, which can be used for
3D printing or to provide sheet cutting drawings for the factory.
Finally, in order to enable customers to have a more intuitive
understanding of our team's products, the model we drew was
rendered.

![](https://static.igem.wiki/teams/4797/wiki/engin-hardware/2.jpg)

***Figure 2:**Dimensions of the hardware shell*

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/sheep.png)

## MCF Skin worry-free web

### design phase:

Starting from the perspective of better serving patients with skin
diseases, after discussing and solving the actual situation, the
team decided to develop a popular science dermatology platform.
After having the general direction of development, the team
further discussed, refined the platform development content, and
formulated Developed a development arrangement plan, and connected
with the team’s art team to design the page preview renderings of
the platform.

### Build phase:

According to the page preview renderings, the team has carried out
detailed development division of labor, and the platform
construction and the collection of popular science materials have
been promoted simultaneously. The platform is built in two parts:
static page display and specific functional modules. After setting
up the platform framework, specific functions are embedded into
the platform. Finally, popular science materials are updated
simultaneously on the platform. The purpose of organizing popular
science materials is to provide platform users with Popular
science introduction to enrich the content of the platform.
Through meetings and discussions within the group, the performance
and details of the platform were further optimized, and the
development of the platform was completed.

### Testing phase:

The team held many discussions to test the operating performance
and usage experience of the Skin Care Intelligent Platform, and
further optimized the platform to better serve users.

### Learning phase:

A meaningful popular science platform should not only disseminate
popular science content, but also ensure the accuracy and
timeliness of the popular science content, as well as user
satisfaction of the platform. We update the latest science
popularization consultation in real time and take user
satisfaction evaluations seriously, and are committed to Further
improve the popularization, timeliness and user-friendliness of
the platform.

### Prospective stage:

While the platform plays a role in popularizing science, it can
develop other meaningful functions to make the platform more
diversified and further improve user stickiness. We will maintain
active and innovative research and development. Please continue to
pay attention to our future research!

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/mcf/intro.jpg)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/mcf/index.jpg)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/cloud2.png)

## Hardware interactive software

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/interact/circle.png)

In the later stages of designing and implementing a hardware
product - a multi-functional automatic microbial culture machine,
in order to give full play to the functions of the product itself
and provide users with a more efficient and convenient culture
experience, after team discussions and combined with the actual
situation, we put the idea Transformed into an entity, I decided
to design an IoT software that can be automatically controlled and
intelligently interacted. I started several discussions with
another member and initially determined the production direction
of the software. Afterwards, the team members discussed with the
team leader to further refine the software requirements and make
the production direction clearer. After clarifying the production
direction, I began to look for some available resources online and
learn about software design and production. After communicating
with the team members based on the available resources, it was
finally determined that the software was a Bluetooth-based IoT
method and named Zhipei.

After the preparation work was completed, I started software
production. During the production process, I used the Bluetooth
debugger to set module components suitable for the team's own
hardware products, searched for and learned suitable control
codes, and made many modifications, additions and deletions,
constantly improving the code, and at the same time conducting
actual control tests. When discovering problems Solve problems,
conduct team discussions when encountering major difficulties,
consult mentors, search for information, etc., and constantly make
the software mature. During the process of software production and
testing, I received many suggestions, such as: how to reasonably
layout component locations, supplementary function settings,
simple output feedback channel settings for code, etc. These
internal testing suggestions have improved the software. After the
internal testing function is completed, we will install this
software on users' mobile devices and hope to get your experience
and suggestions on this software. Fortunately, the software
experience has been well received.

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/interact/1.jpg)

*Software operating interface*

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/interact/2.jpg)

*Software output debugging process*

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/mount1.png)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/cloud2.png)

## Modeling Engineering

Our modeling was inspired by a paper published in Nature
Communication, which reasonably used machine learning methods
involving a machine learning framework, and applied it to the
entire small molecule peptide sequence space to mine Potential
antimicrobial peptides, we were extremely surprised to see this,
because our experiments were very eager to find a method that can
quickly and accurately find antimicrobial peptides. We immediately
adjusted our research direction and started to learn machine
learning-related knowledge from scratch, but Due to insufficient
computer performance, we quickly gave up the work of reproducing
the original paper, and instead adjusted the research purpose from
mining the entire small molecule peptide sequence space to mining
the candidate peptide sequence sample space. This not only
improved the implementability of the solution sex, and more in
line with the needs of biological workers. We learned the core
idea in the original paper, which is to build a funnel-shaped
machine learning pipeline. In terms of the specific design of the
pipeline, we conducted more detailed explorations, discovered some
shortcomings of the original solution, and made appropriate
adjustments. Improvement, the specific plan is as follows:

### 1.1 Feature extraction

We found that the original solution incorporated a large number of
feature encoding schemes, making the training features quite
large, with a total of 676 features. We thought there might be
some irrelevant redundant features, so we rebuilt the features.
Specifically, We first tried the impact of different feature
encoding schemes on model performance. We selected seven feature
encoding schemes and used XGBoost as the classifier. The
experimental results are shown in Figure 1. It can be found that
AAC, CTDD, DP, DPC These four coding schemes have obvious
advantages (the corresponding colors in the picture are darker),
so we chose to fuse the above four coding schemes, but the number
of features after fusion reached 635, which is not much different
from the original scheme. Improvement, after analyzing the
reasons, we found that there is still some redundant information
between these four coding schemes, so we used F-score to calculate
the ability of features to distinguish between the two categories,
and finally selected 352 valid ones with higher quality. features,
shortening the calculation amount by nearly 50%, and avoiding the
impact of some redundant features on training, the performance of
the model has been improved.

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/model/1.jpg)

***Figure 1:**Performance comparison of different
encoding schemes*

### 1.2 Classifier selection

Since the classification module handles the first step of the
machine learning pipeline, the accuracy of its predictions is
crucial. Misclassification errors in the first step may miss some
very promising antimicrobial peptides, which is important to us.
is unsatisfactory, so we chose to stack multiple base learners,
and experimental verification showed that the stacked model has a
certain improvement over the original model. Specifically, we
tried a total of six machine learning methods: XGBoost, Random
Forest (RF), Gradiant Boosting Decision True (GBDT), Logistic
Regression (LR), Adaboost, and Decision True (DT). We found that
The three methods have obvious advantages over the others, so we
use them as the base learner, use stacking fusion technology, and
use Logistic Regression as the second layer classifier, and
finally get a model with better performance.

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/model/2.png)

***Figure 2:***

### 1.3 Introduce deep learning methods to improve the accuracy of regression modules

With the rapid development of artificial intelligence, more and
more cases show that deep learning can better learn complex
relationships and has greater development prospects, so we also
tried to build different deep model frameworks ourselves. We
initially built We have built a more complex deep neural network,
but we found that no matter how we adjust the hyperparameters, the
model always suffers from overfitting. Figure 3 shows some failure
cases, from which we can see that with the training rounds With
the increase, the model error always develops in unexpected
directions.

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/model/3.png)

***Figure 3:**Test mean square error change curves
in different rounds before adjustment*

After analyzing the reasons, we believe that our training data set
is not difficult to learn, and only needs some relatively
lightweight deep learning models to complete the task well, so we
rebuilt the deep learning model. This time, the model Developing
in a good direction, the prediction error shows an overall
downward trend and gradually stabilizes after about 70 rounds,
indicating that the model has completed learning.

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/model/4.png)

***Figure 4:**Test mean square error change curve
in different rounds after adjustment*

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/cloud2.png)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/seed.png)

## Wet Engineering

Due to some force majeure factors, our experimental team
experienced some personnel changes, and in the end only a few
students were left persisting in conducting experiments. This
undoubtedly had a certain impact on the progress of our
experiments, making our progress slower than expected. At the same
time, due to policy changes at Hainan University, our laboratory
needed to move to a new campus. During this process, we
encountered many unexpected difficulties, which made the conduct
of experiments extremely difficult. Despite facing such huge
challenges, our team did not give up. On the contrary, we actively
seek solutions to overcome the impact of laboratory changes as
much as possible so that subsequent experiments can proceed more
smoothly. In order to better promote the experiment, we chose to
enrich our experimental methods by reviewing relevant papers,
asking teachers for advice, and using biological information, in
order to achieve better results in the experiment.

![](https://static.igem.wiki/teams/4797/wiki/wet-lab/wet-engineering/banner.png)

For details, please click
[Moisture Experiment Project](%7B%7Burl_for%28%27pages%27%2Cpage%3D%27wet-engineering%27%29%7D%7D)

![](https://static.igem.wiki/teams/4797/wiki/project/engineering/art/pd.png)

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## What should this page contain?

---

Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).

## Inspirations

---

* [2019 Nantes](https://2019.igem.org/Team%3ANantes/Experiments)
* [2019 TU Eindhoven](https://2019.igem.org/Team%3ATU_Eindhoven/Experiments)
* [2019 Mingdao](https://2019.igem.org/Team%3AMingdao/Demonstrate)
* [2020 Amsterdam](https://2020.igem.org/Team%3AAmsterdam/Experiments)
* [2020 NCTU Formosa](https://2020.igem.org/Team%3ANCTU_Formosa/Experiments)
* [2020 USAFA](https://2020.igem.org/Team%3AUSAFA/Experiments)

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# Results

Hainan University

![](https://static.igem.wiki/teams/4797/wiki/777.png)

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### Catalog

* [Modeling](#model)
* [Hardware](#hardware)
* [Software](#software)

# Results

![](https://static.igem.wiki/teams/4797/wiki/project/results/1.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/2.png)

## Modeling

Antimicrobial peptides are the main approach we use to treat acne, and finding suitable and
effective antimicrobial peptides is crucial in wet lab experiments. It is also a pressing issue
for all biologists. Our modeling aims to utilize artificial intelligence techniques to propose a
multistage model ensemble pipeline (MMEP) consisting of classification, ranking, and regression
modules. This method can efficiently identify antimicrobial peptides that meet our requirements
while ensuring a high level of accuracy. Experimental results have shown that our approach
achieves an accuracy rate of 94.8% in identifying antimicrobial peptides and accurately predicts
the corresponding minimum inhibitory concentration. To extend its benefits to a broader
audience, we have developed a user-friendly website that allows biologists with no programming
background to quickly grasp and experience this method. For more detailed information about this
method, please [refer to the model page.](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D)

![](https://static.igem.wiki/teams/4797/wiki/project/results/3.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/4.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/5.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/1.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/2.png)

## Hardware

In order to help wet experiments cultivate relevant microbial experimental strains with higher
quality, we have designed a multi-functional automatic microbial culture machine. This product
is mainly based on modern biotechnology and automatic control concepts and aims to provide an
efficient, accurate, safe and easy-to-use microbial culture machine. Useful, cheap microbial
culture equipment to meet the needs of different users.

The multi-functional automatic microbial culture machine is an intelligent hardware product that
integrates intelligent cultivation, environmental monitoring, automated control, intelligent
interaction and other functions. It can provide users with an efficient, intelligent, fast and
healthy culture environment and provide users with one-stop microbial culture services. At the
same time, it can also be interconnected with users' mobile phones, computers and other devices
through intelligent cloud connections, so it has the characteristics of high automation,
flexibility and diversification, quick response, efficiency and safety, which can reduce manual
operation errors and labor intensity, while achieving Fast and accurate cultivation and
monitoring improves cultivation efficiency, saves resources, reduces costs, and increases
output.

In short, the multifunctional automatic microbial culture machine can serve the consumer groups
of academic scientific research or microbial companies, including those who love microorganisms
but cannot provide sufficient management time for microorganisms due to busy work schedules, and
those who are full of pursuit of environmental protection, health, and technology. It is a
high-quality product with wide application, mature technology and broad market prospects, and is
expected to play an important role in the biological field. Please [see the Hardware section
for details.](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27hardware%27%29%20%7D%7D)

![](https://static.igem.wiki/teams/4797/wiki/project/results/4.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/5.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/1.png)

![](https://static.igem.wiki/teams/4797/wiki/project/results/2.png)

## Software

### 一、Zhipei

In order to give full play to the functions of the product itself and provide users with a more
efficient and convenient experience in cultivating bacterial strains, we have developed software
based on the Bluetooth debugger that matches our team's multi-functional automatic microbial
culture machine.

This software allows users to reduce unnecessary time in the process of cultivating bacterial
strains and better cultivate bacterial strains. It has great autonomous operability and can set
the button functions in accordance with the module functions of a multi-functional automatic
microbial culture machine. Determine, perform short-range control and receive information
visually. In the interface of the debugging project, we can even control the microbial growth
environment and required nutrients based on the feedback window that our code has run, and at
the same time feedback the growth curve of the microorganism to the user.

In short, this software allows cultivators to enjoy the convenience of strain cultivation and
allows users to obtain high-quality strain cultivation results. Please [see the Software section
for details.](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27software%27%29%20%7D%7D)

![](https://static.igem.wiki/teams/4797/wiki/project/results/6.png)

### 二、MCF(milky&flawless confident)

The MCF (milky&flawless confident) Skin Care Intelligent Platform aims to provide a popular
science service platform for people who are troubled by skin diseases (especially acne) and
other groups who are concerned about their skin conditions. The MCF Skin Careless Confidence
Intelligent Platform includes five major functional sections: Popular science information, AI
intelligent question and answer (not yet fully developed), questionnaire assessment and testing,
disease identification and testing, and product introduction. This platform aims to provide
popular science services to the groups in need. More information about the MCF Skin Intelligent
Worry-Free Platform can be viewed in the [software](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27software%27%29%20%7D%7D)
.

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