1.1 Utilization of Red Algae as a Novel Carbon Source

To mitigate the drawbacks of using fast-carbon glucose for terpenoid production, our project innovatively introduces red algae as a novel alternative carbon source. The core of this project is to endow Saccharomyces cerevisiae(S. cerevisiae) with red algal polysaccharide degradation capability through heterologous expression of key enzymes. Specifically, we introduced and overexpressed the agarase gene AqAga (BBa_258028F8) from Aquimarina agarilytica ZC1 and the neoagarobiose hydrolase gene agaNash (BBa_257L75AN) from Cellvibrio sp. OA-2007 in S. cerevisiae, combined with the S. cerevisiae α-mating factor secretion peptide gene (BBa_258RYIFY) to guide extracellular secretion of the enzymes. Agarase specifically hydrolyzes β-1,4 glycosidic bonds in agar, initially degrading red algal polysaccharides into neoagarobiose; neoagarobiose hydrolase further hydrolyzes neoagarobiose into galactose, providing an accessible carbon source for S. cerevisiae. Meanwhile, the most suitable promoter combination for the expression of agarasea and neoagarobiose hydrolase was selected from the 18 promoter combinations, providing a basis for the selection of promoters for the subsequent expression of polysaccharide hydrolases in yeast.

1.2 Construction of Rh1 Synthesis Pathway in Saccharomyces cerevisiae

As a high-value tetracyclic triterpenoid, rare ginsenoside Rh1 is still very difficult to obtain from natural sources and from chemical production methods. Our project achieved efficient microbial synthesis of Rh1 through genetic engineering by heterologously integrating key enzymes for Rh1 synthesis into the S. cerevisiae chassis. We introduced five exogenous genes into the S. cerevisiae genome: dammarenediol synthase gene (PgDDS, BBa_255LOGT6), protopanaxadiol synthase gene (CYP716A47, BBa_25RY2RMX), cytochrome P450 reductase gene (PgCPR1), protopanaxatriol synthase gene (CYP716A53v2, BBa_25NSM6TW), and glycosyltransferase gene (UGTPg100, BBa_255ROLW2). Experimental results showed that the engineered strain produced up to 141.78 mg/L of Rh1 after 144 hours of fermentation under optimal conditions, providing an efficient and green microbial cell factory solution for industrial production of rare ginsenosides.

2.1 Open-Source FBA Computational Software Tool

Our newly developed visualized Flux Balance Analysis (FBA) software tool seamlessly combines an agent-based Q&A system with operational assistance, offering two key advantages:

• It eliminates the need for programming and provides powerful visualization tools to facilitate result interpretation.
• It allows users, including those with no background in the field, to design experiments through intuitive natural language interaction.

Currently, most FBA tools still require programming by users and lack intuitive visualization and graphical interaction capabilities. Visualization is often limited to results presentation, whereas the computational process itself remains code-dependent. Under the traditional research paradigm, conducting metabolic network analysis demands proficiency in programming languages such as MATLAB or Python. To address this, our software aims to provide a low-threshold, flexible, and user-friendly platform with graphical interaction support for metabolic modeling and analysis, tailored for synthetic biology research teams. The platform utilizes COBRApy for core FBA computations and offers a web-based user interface for visualization. It also incorporates an agent system with a dedicated FBA knowledge base, allowing users to interact with the software using natural language.

The complete workflow and implementation details are thoroughly described in the Software section.

2.2 Protein Molecular Docking Methods

This study achieved efficient screening of target enzymes through protein structure modeling and molecular docking techniques. During the project, we discovered that red algae cannot be directly utilized by yeast cells and require the synergistic action of two specific enzymes. We established a computational screening pipeline: first predicting protein structures of candidate enzymes from amino acid sequences using AlphaFold2, and then performing molecular docking with the Vina force field to evaluate the interaction efficiency of different enzyme combinations, thereby identifying the optimal enzyme system. Experimental validation of the model predictions partially confirmed the reliability of the docking results, indicating that the model is accurate and practically useful.

The full methodology is elaborated in Model Part 3.

2.3 Promoter Combination Prediction Methods

We developed a promoter combination prediction model based on ODE kinetic analysis. In the biosynthetic pathway of ginsenoside Rh1 in S. cerevisiae, the yield of squalene directly determines the synthesis efficiency of Rh1. This process involves multiple coupled factors, including promoter regulation, enzyme expression levels, energy allocation, and cell growth. With theoretically thousands of possible promoter combinations, conventional experimental screening poses significant challenges. Our model effectively addresses the need for efficient selection of optimal promoter combinations. After expansion, the model demonstrated significantly improved predictive performance, achieving an overall R² of 0.8384 and a K-S test p-value of 0.7864. Predictions for specific combinations such as “P_GAL1+P_GPD” were consistent with experimental rankings, demonstrating high reliability.

The complete modeling process and validation results are provided in Model Part 4.

3.1 Self-improvement Mechanism Based on Suggestion Boards

To break through the limitations of traditional science popularization lectures, which are characterized by "one-way output and delayed feedback," we have constructed a dynamic improvement system centered around a "post-it suggestion board" as an innovative self-improvement tool for optimizing lecture content. In cross-grade lectures covering primary schools, middle schools, and universities, we set up a "thought collection session" at the end of each activity: providing different colored post-it notes and guiding participants to record feedback around four dimensions: "difficulties in understanding content," "interesting knowledge points," "desired forms of supplementation," and "misunderstandings about synthetic biology," which are then pasted onto a dedicated suggestion board on-site. Through the suggestion board, a closed loop of "lecture - feedback - optimization" is achieved, significantly improving the accuracy of audience understanding of content during the second lecture, achieving remarkable self-improvement effects, and also making the lectures more tailored to the cognitive needs of different grade levels.For details, see ihp 5.2.2

3.2 Knowledge Dissemination Method Using Drama as a Medium

In response to the dissemination pain points of synthetic biology knowledge, which include "a large number of technical terms and a high threshold for cross-age understanding," we have innovatively adopted a "dramatized drama interpretation" form of knowledge dissemination. Based on the core technical logic of the project, we create exclusive scripts, using personification of characters and technical scenarios to create a science popularization medium suitable for all ages. The script is centered around the "awakening of the ability of yeast," designing a highly fitting character system for the project. At the same time, we design adaptation details for different age groups: simplifying complex logic for primary school students, highlighting the "growth of yeast"; supplementing "role-technology correspondence cards" for junior and senior high schools, clarifying the actual functions of the three major genes and squalene; and conveying deep thinking about "innovative technology breaking stereotypes" to college students.For details, see ihp 5.2.3