HUMAN PRACTICE

2. Redefining the Value Proposition: From "Energy Saving" to "Insurance"

The team's initial value proposition was energy savings for the fermentation industry. Prof. Li keenly pointed out that saving on water and electricity might not be the core pain point for factories, and its economic value could be limited. He creatively proposed a more compelling direction: positioning the project as a "genetic insurance" mechanism.

The core value of the project is not to enable yeast to work efficiently at high temperatures for extended periods but to address sudden incidents (such as power outages or cooling system failures causing temperature spikes). This prevents the complete loss of entire yeast batches worth hundreds of thousands of yuan, thereby avoiding major production accidents and economic losses. The market logic of the project lies in the fact that this "insurance" function can save factories the costly investment in backup emergency systems (such as self-provided generators) or compensate for the lack of corresponding insurance markets. Its economic value is easier to quantify and more likely to be accepted by the market.

3. Industrialization Path and Suggestions

Professor Li outlined the essential tasks the team must complete to move toward industrialization：

It is necessary to quantitatively verify through experiments how much the survival and recovery rates of the modified yeast have improved compared to ordinary yeast under specific high temperatures (e.g., 39°C). This data is key to persuading the market. It is essential to verify whether the introduction of foreign genes affects the normal production efficiency and final product quality (e.g., carotenoids) of the yeast.The team is advised to investigate the market size, number of factories, and the average loss from a typical production accident in the target industry (e.g., carotenoid or alcohol production) to estimate the potential market value of this "insurance" technology.

Prof. Li encouraged the team, noting that a good idea, like "boiled water is beneficial for health," derives its value from universal applicability. He advised the team to focus on the clear value proposition of "risk prevention" and support this story with solid data. This dialogue provided the Tsinghua-M team with a crucial business thinking framework for transitioning from lab research to potential market application.

2. Small-Sample Learning: Drawing on TCM Classification Wisdom to Address Data Scarcity

In response to the team's issue of limited wet-lab data, which is insufficient for traditional machine learning models, Professor Ma suggested leveraging the classification approach of TCM under small-sample conditions. TCM uses theoretical frameworks and expert experience to categorize complex pulse conditions into a limited number of classes. This "rules + judgment" model is more suitable for the team's current data scale. She further indicated that small-sample learning methods from image classification and reinforcement learning could be incorporated to enhance the model's generalization ability without relying on big data.

3. Modeling Methods and Algorithm Optimization

Regarding the problem of slope fluctuation (delta parameter) caused by noise in parameter fitting, Professor Ma advised the team to conduct extensive research on various fitting algorithms and adopt a phased, multi-algorithm integration strategy to enhance model robustness. Additionally, in response to concerns about vague classification boundaries and reliance on manual annotation, she encouraged the team to define clear classification criteria and draw on semi-supervised and weakly-supervised learning ideas to reduce dependence on large amounts of labeled data.

Professor Ma concluded by noting that the "i" in iGEM could also stand for "Intelligence." She encouraged the team to genuinely integrate artificial intelligence into the project narrative while maintaining clarity in scientific concepts and logical rigor, thereby achieving a leap from cultural inspiration to technical feasibility. This exchange provided valuable technical perspectives and methodological guidance for the team in dry-lab modeling and interdisciplinary integration.

First, we engaged in a detailed exchange with Professor Xie regarding the project overview and wet-lab design, specifically introducing the triple oscillator and the two-strain system, while paying close attention to the feasibility of practical application. Professor Xie gave full affirmation to our project and provided two highly valuable suggestions.

Firstly, Professor Xie suggested we focus closely on the genetic components used, especially those related to transcription, to measure amplitude with higher precision and reduce uncontrollable random errors. Secondly, he introduced us to a novel oscillator design. As an application of a "sponge effect," this oscillator incorporates a buffer plasmid to provide more binding sites for transcription factors, thereby reducing the instability of the oscillator. Both of these new perspectives provided significant inspiration for our team.

Next, we presented some of our current confusions to Professor Xie.

Q: If our product is to be implemented, what key aspects should we consider?

A: The most important consideration is industrial demand. While the current project is an excellent conceptual design for the iGEM competition, practical implementation requires considering real-world scenarios, such as the relatively low probability of stress incidents in industry and whether the design might affect production yield.

Q: Our project currently faces the issue of limited regulatory methods. What suggestions do you have in this regard?

A: CRISPR is an excellent molecular tool that can be used to introduce genes or regulatory elements.

Q: The fitting results of our current dry-lab work are not ideal due to the small sample size of data. What suggestions do you have regarding this? Also, what are your views on the prospects of AI and synthetic biology integration?

A: I believe a small amount of data can be sufficient. You could try simulations under various conditions and use precisely measured real data for modeling, for instance, accounting for differences in the properties of different fluorescent proteins. AI currently has many uses, such as designing small molecules, proteins, and RNA, all of which could be applied in the field of synthetic biology in the future.

This exchange with Associate Professor Xie Zhen provided crucial guidance for our project. On the technical level, his suggestions regarding the precision of genetic components and the design of novel oscillators pointed the way toward optimizing system performance. On the application level, his insights into practical industrial needs and diverse regulatory tools like CRISPR prompted the team to think more deeply about the core challenges of translating the project from concept to reality. This discussion deepened our understanding of the engineered essence of synthetic biology and emphasized the importance of application-oriented research.