2.1 Why Synthesize Rare Ginsenoside Rh1?

Ginseng, a treasure in traditional East Asian medicine, has a history of application spanning thousands of years in China. As the core pharmacologically active components of ginseng, ginsenosides are a class of terpenoid with complex structures. Among them, rare ginsenosides have become key substances in applications due to their superior bioavailability and targeted activity [1].

However, the acquisition of rare ginsenosides has long faced dual dilemmas. In terms of plant extraction, the extremely low content of rare ginsenosides in ginseng, the long growth cycle of years, the vulnerability of ginseng to environmental factors and pathogens lead to very low production efficiency of rare ginsenosides. Additionally, the extraction process consumes a large amount of organic solvents, which is neither economical nor environmentally friendly [2]. In terms of chemical synthesis: the complex molecular structure of rare ginsenosides leads to cumbersome synthesis process and high costs, which is difficult to realize the large-scale production [3]. Therefore, microbial synthesis using synthetic biology technology is a promising alternative to meet the growing demand for such botanical natural products.

Based on differences in aglycone structure, dammarane-type ginsenosides are divided into two major categories: protopanaxadiol (PPD)-type and protopanaxatriol (PPT)-type ginsenosides [4]. As a typical representative of PPT-type rare ginsenosides, Rh1 has unique and significant advantages in the skincare field and is the core active component of high-end anti-aging products. Studies have shown that ginsenoside Rh1 can reduce melanin synthesis by inhibiting the activity of tyrosinase (a key enzyme in melanin synthesis) and downregulating the expression of microphthalmia-associated transcription factor (MITF) [5][6]. Moreover, it is able to alleviate skin inflammatory responses by inhibiting TLR2/TLR4-mediated inflammatory pathways, such as reducing the release of LPS-induced inflammatory factors [7][8]. Its excellent skincare activity can meet consumers' dual demands for "effective anti-aging" and "safe and natural", demonstrating great potential.

2.2 Why Choose Saccharomyces cerevisiae as the Chassis Cell?

Saccharomyces cerevisiae has become a highly promising platform cell factory and exhibits significant advantages in the field of biological manufacturing. The reasons for selecting it as the chassis cell are as follows: Firstly, it is the first eukaryote with a completely sequenced genome, with clear and accessible genetic information, facilitating precise genetic manipulation. Secondly, it is a generally recognized as safe (GRAS) strain and is approved for use in the production of biological products and food, which ensures the reliable safety. More importantly, it inherently possesses the MVA pathway for endogenous terpenoid synthesis, providing a foundation for the synthesis of specific metabolites. In addition, the P450 membrane proteins involved in saponin synthesis are more likely to achieve compatible expression in eukaryotes such as Saccharomyces cerevisiae. Meanwhile, its complex organelle structure gives it unique advantages in the post-translational modification of enzymes, contributing to the production of products with higher functional activity [9].

2.3 Why Choose Red Algae as the Carbon Source?

Red algae have significant advantages in multiple aspects as the carbon source for ginsenoside Rh1 synthesis. The short growth cycle, high photosynthetic efficiency, and independence of artificial fertilization during growth, suggest the high environmentally friendliness of red algae. Moreover, the intrinsic low lignin content (< 10%) and abundant polysaccharides, with carbohydrates content over 60%, indicate their prominent potential for raw material utilization. The sugars in red algae are mainly composed of agar as the core component, which can be hydrolyzed to produce monosaccharides such as galactose, 3,6-anhydro-L-galactose, and glucose [10].

Using such marine biomass as the carbon source not only efficiently enables the resource utilization of the accumulated red algae residues in coastal areas, but also mitigate the competition for raw materials between fermentation production and food crop cultivation, which alleviates the global food shortage pressure. More importantly, using galactose and 3,6-anhydro-L-galactose from red algae as the fermentation substrates can effectively circumvent the Crabtree effect induced by glucose in traditional fermentation systems. Rapidly fermentable sugars such as glucose are prone to induce Crabtree effect, which diverts metabolic flux towards ethanol production, inhibits the tricarboxylic acid (TCA) cycle and mitochondrial function, which finally hinders the synthesis of target products [11][12].

On contrast, the adoption of a new red algae-derived carbon source as the fermentation substrate can significantly enhance the respiratory efficiency of Saccharomyces cerevisiae and provide a favorable metabolic environment for terpenoid synthesis. Unlike previous approaches that enhance yield through downstream regulation such as pathway optimization and enzyme engineering, this strategy starts from the source of the carbon source substrate, creating basic conditions for the efficient synthesis of rare ginsenoside Rh1 by Saccharomyces cerevisiae.

2.4 Design of Our Metabolic Pathway

With "sustainable utilization of marine resources" as the entry point, this project aims to develop high-end anti-aging cosmetics centered on Rh1, featuring the dual advantages of "high-purity de novo synthesis" and "non-arable land occupation". The project deeply integrates the "top-down" systematic design thinking and "bottom-up" component optimization logic of synthetic biology to precisely regulate the biosynthetic process and achieve efficient and green production of Rh1.

3.1 Why Choose FBA as Feasibility Analysis Tool?

This project aims to utilize red algae extract as a non-traditional carbon source to overcome potential limitations associated with glucose metabolism. Thus, the cellular uptake and conversion efficiency of unconventional carbon sources and their impact on the biosynthetic pathway of ginsenosides should be evaluated, which requires a quantitative analysis of metabolic flux. To be more specific, the core scientific question is outperform the traditional single carbon sources glucose, in terms of theoretical yield and sustainability.

Accurate quantification of metabolic flux typically relies on ¹³C-labeled metabolic flux analysis (¹³C-MFA) [13]. This method tracks the distribution of ¹³C-labeled substrates within the metabolic network, detects isotopic labeling patterns using mass spectrometry or nuclear magnetic resonance techniques, and employs computational models to fit and obtain intracellular flux distributions. Although ¹³C-MFA yields precise results, it is costly and involves a lengthy experimental process that includes cell culture, sample preparation, data acquisition, and modeling analysis.

To rapidly evaluate the feasibility of this project, we adopted flux balance analysis (FBA), a more efficient method that offers reasonable accuracy. FBA is a constraint-based mathematical modeling approach used to predict flux distributions of metabolites in metabolic networks [14]. Based on mass conservation and steady-state assumptions, it constructs a system of constraints through stoichiometric relationships and optimizes flux distributions using an objective function (e.g., biomass maximization). This project employs the consensus genome-scale metabolic model of Saccharomyces cerevisiae, GEM-Yeast9, which includes 3,928 reactions, 2,666 metabolites, and 1,133 genes, and integrates 163 condition-specific single-cell models (csGEMs) and 1,229 strain-specific models (ssGEMs) [15]. This model can incorporate constraints based on single-cell transcriptomic and proteomic data, enabling accurate prediction of metabolic reprogramming under osmotic stress and nitrogen source limitation.

At the tool level, we use the COBRApy framework, which effectively represents complex biological metabolism and gene regulation processes. It features a robust open-source ecosystem, high usability, and strong integration capabilities. By incorporating metabolic models validated through large-scale experimental data, FBA can provide relatively reliable predictions of metabolic fluxes.

3.2 Why Introduce an Agent Based on FBA?

Currently, there is a lack of dedicated FBA computational software with visualization and graphical interactive capabilities. Most tools still require users to perform operations through programming, and visualization is typically limited to final result presentation, while modeling and computational processes still involve coding. In traditional research settings, conducting metabolic network analysis demands that researchers possess both biological expertise and programming skills (e.g., using MATLAB with the COBRA Toolbox or Python with COBRApy), including knowledge of syntax, environment configuration, and debugging, which presents a high-threshold obstacle especially for beginners.

Our software is designed to be high user-friendly by integrating full-process computational visualization and graphical interactive operation, which does not require prior programming knowledge. Users can complete all modeling steps via a web interface without writing command-line code. Additionally, by leveraging the Escher tool to generate interactive metabolic flux maps, flux distributions become immediately clear, greatly enhancing the intuitiveness of result interpretation and research efficiency.

To further minimize the user-entrance threshold, we developed a specialized Agent based on DeepSeek-R1:7B. Users only need to provide natural language instructions, and the Agent can invoke the corresponding tools to perform software operations, thereby eliminating the reliance on coding. Moreover, the system integrates a dedicated knowledge base for FBA. This knowledge base incorporates the practical experience of five synthetic biology researchers, systematically collating common issues, frequent operations, and typical application scenarios, ensuring that the Agent can more accurately understand user intent and provide effective responses.

The approach exemplifies a new human-computer interaction paradigm that can be extended to various research software in synthetic biology, systems biology, and biotechnology. By abstracting technical operations into conversational exchanges, such agent-assisted systems not only enhance accessibility but also accelerate iterative design-validation cycles. They provide scalable and adaptable support for a range of tasks—from metabolic network debugging to genetic parts assembly—enabling more researchers to translate innovative ideas into computable workflows with ease. This represents a step toward more democratized and widely accessible computational research infrastructures.