<p>Leveraging its immense computational power and intelligent algorithms, AI provides researchers with
unprecedented insights. AI can handle vast amounts of biological data, such as genomes and protein
interaction networks, accelerating drug development and disease diagnosis. The impact is staggering,
with AI speeding up <astyle="font-weight: bolder">gene identification and analysis</a> by at least <astyle="font-weight: bolder">100 times</a>. In protein folding
prediction, AI achieves an accuracy rate of 90%, greatly reducing the time and resources required
compared to traditional methods. Additionally, AI excels in medical imaging diagnostics, with an
impressive <astyle="font-weight: bolder">96%</a> accuracy in breast cancer detection, surpassing human doctors' assessment
capabilities. These remarkable numbers highlight the enormous potential of AI in biology,
revolutionizing medical research and healthcare management, and make our team to think, how can AI
<pclass="tuzhu">Figure 4. Obstacles of learning AI</p>
<p>These results show that <astyle="font-weight: bolder">the application of AI is still relatively limited</a>, and further research about
AI technology is needed to effectively solve problems in specific fields, and we believe this is
also true in the field of synthetic biology. In addition, <astyle="font-weight: bolder">the high difficulty of professional
knowledge</a> limits people's further study of the application of AI in specific fields, which also
reminds us that it is necessary and helpful to carry out popular science and education activities
about AI for synthetic biology to better promote them to the public.</p>
<p>Then we asked users and developers of AI tools respectively that which aspect of AI they will focus
on. About 70% of them have the request of accuracy, 61% of them pursue ease operation of AI and
about 50% of them expect AI to have data privacy and fast processing speed, which shows that if AI
want to be widespread, it must both to be <astyle="font-weight: bolder">accurate and easy to understand</a>, just like web page
technology. Developers concentrated most on the <astyle="font-weight: bolder">efficiency and accuracy</a>, as well as their <astyle="font-weight: bolder">ability to
transfer to multiple problems</a>, which provides a guidance on our AI model design.</p>
<pclass="tuzhu">Figure 6. Focus as a developer on aspects of AI models or algorithms</p>
<p>In terms of the function of AI, we found it widely used in various aspects, and nearly <astyle="font-weight: bolder">92%</a> of people
believe AI will have <astyle="font-weight: bolder">positive effect</a> on the fields they work in. However, it has relatively <astyle="font-weight: bolder">few
applications in scientific research</a>, thus we believe it is our value to improve the new application
of AI technology in synthetic biology research.</p>
<pclass="tuzhu">Figure 8. The impact AI have on the field you work in</p>
<p>In the next part, we aimed to further explore the public perceptions of the promise of AI for
synthetic biology. We were upset to find that <astyle="font-weight: bolder">half</a> the respondents have <astyle="font-weight: bolder">never heard of synthetic biology</a>
before, and over <astyle="font-weight: bolder">16%</a> of people who know synthetic biology still have <astyle="font-weight: bolder">little knowledge about the
application of AI in the field</a>. Obviously, it is necessary for us to introduce and propagate AI for
synthetic biology to the public in a more efficient and suitable way.</p>
<pclass="xiaobiaoti"id="s41">1. The Visit to Nanjing Yiweisen Biotechnology Co., LTD</p>
<p>After gaining recognition from relevant researchers, we wanted to further explore the industrial significance of our project. We visited Nanjing Yiweisen Biotechnology Co. and had a discussion with Professor Zhongchang Wang. Professor Wang established Nanjing Yiwesen Biotechnology Co., Ltd as the main founder. The company is a high-tech enterprise based on artificial intelligence technology in the field of synthetic biology, incubated by Artificial Intelligence Biomedical Research of Nanjing University. It primarily focuses on microbial genetic modification and downstream industrial applications, researching and developing high-value natural active substances for use in sectors such as food, cosmetics, plant protection, and biopharmaceuticals. Professor Wang acknowledged the significance of our project and pointed out that the current traditional screening methods for selecting target strains from a large number of randomly mutated strains are often costly, time-consuming, and limited in scope. The application of artificial intelligence in synthetic biology has greatly improved the accuracy of mutagenic strains and reduced the cost of screening. Additionally, he provided us with some potential application directions from a business perspective.</p>
<p>We are pleasant to work with several different iGEM teams on various kinds of collaborations,
which we benefit a lot and they can also learn something from us. Through collaborations, we
provide and get peer support and review, enhance creativity and advanced our project.<br>
Please click the <ahref="{{ url_for('pages', page='partnership') }}"style="color:#5271FF">partnership
link</a>
to learn more about our communications and collaborations with other teams.
</p>
<p>After achieving some experimental results, we engaged in discussions with AI pharmaceutical
companies to seek guidance on the subsequent industrialization process. They recognized the
potential utility of our model in industrial production but also highlighted certain
considerations. Firstly, for yeast production and larger-scale experiments, it is crucial to pay
special attention to <astyle="font-weight: bolder">preventing biological contamination</a>. Secondly, due to the high level of
pollution associated with yeast production, <astyle="font-weight: bolder">wastewater treatment</a> should be prioritized. In fact,
the production of <astyle="font-weight: bolder">1 ton</a> of dry yeast can generate <astyle="font-weight: bolder">150 tons</a> of wastewater. Building upon these
considerations, Zhang Xiaotong, the supervisor of the wet lab experiments, guided the innovative
development of a <astyle="font-weight: bolder">biological wastewater treatment device</a> (Patent No: CN215886592U) and a
<astyle="font-weight: bolder">bioreactor</a> to prevent contamination from miscellaneous bacteria (Patent No: CN216141527U). These
developments have prepared us for the industrial production phase of our project. </p>
<p>Based on Pymaker, we plan to develop <astyle="font-weight: bolder">a software for designing cis-regulatory elements in yeast promoter regions based on deep learning</a>. We have already reached intentions for collaboration and signed agreements with Nanjing LianDu Biological Technology Co., Ltd. and Nanjing YiWeiSen Biological Technology Co., Ltd. Through the collaboration, our research results and developed models can be used to <astyle="font-weight: bolder">guide the design and optimization of yeast fermentation production lines</a>, significantly reducing the cost of industrial production screening and validation, and ultimately bringing high-quality products to the market. </p>
