Figure 1: Three Research Paradigms in Quantitative Synthetic Biology

1. Principle-Based Design (Fig. 1a)

Rational design requires models capable of precise system prediction. Typically, models abstract the internal mechanisms of biological systems, helping to understand the logical architecture (topology) behind functions. For relatively simple biological functions, well-established theoretical models exist. Thus, many early classic works in synthetic biology adopted this "top-down" paradigm: first, mathematical models are developed to explore the principles of functional emergence, identifying the topological structures that yield target functions; then, specific biological components are designed based on these structures.

2. Bottom-Up Design (Fig. 1b)

As synthetic biology advances, systems have grown increasingly complex, making "top-down" design highly challenging. Consequently, many studies have adopted a "bottom-up" strategy. This approach starts with components and initially involves trial and error—exploring possible functions by testing different assembly methods. While "serendipitous" discoveries of interesting functions may occur, quantitative synthetic biology begins at this stage: once a system with the expected function is obtained, its known components allow inference of the topology, enabling the construction of mathematical models and validation through synthetic systems to elucidate functional principles. Unexpected functions emerging during synthesis—often overlooked in traditional synthetic biology—can guide the discovery of new principles in quantitative synthetic biology. Understanding these principles enables the design of synthetic systems with similar or more complex functions. The emergent principles discovered often apply to both natural and synthetic biological systems, thereby advancing fundamental life sciences.

3. AI-Assisted Design (Fig. 1c)

Advances in artificial intelligence (AI) offer new pathways for quantitative prediction of biological systems. AI-based algorithms do not require understanding the internal workings of biological systems but instead identify hidden patterns between components and functions through large-scale data analysis, predicting how to design components to achieve specific functions. This paradigm relies on vast amounts of high-quality, standardized data. Thus, future synthetic biology will require automated, high-throughput equipment platforms and standardized experimental methods.

Currently, there is a global surge in the establishment of automated biofoundries, which use automation technology to efficiently construct and test synthetic biological systems. These facilities not only provide AI with standardized, quantitative, large-scale data generated through machine-automated experiments (free from human operational errors) under guided system design (including essential controls), enabling rapid "design-build-test-learn" iterations to achieve target functions, but also enhance the quality of trial-and-error in Paradigm 2, facilitating the discovery of new principles through machine learning on high-quality big data.

All three design paradigms emphasize close integration with quantitative analysis methods, using mathematical logic and quantitative relationships to make predictive assessments of biological systems, thereby providing a foundation for rational design of synthetic biological systems. Thus, the authors propose quantitative synthetic biology as a developmental direction for the field. Quantitative synthetic biology incorporates the thinking and methods of quantitative biology and systems biology, establishing mathematical or AI models capable of quantitatively predicting biological systems to guide the design and construction of synthetic systems, thereby addressing the bottleneck of "rational design" in synthetic biology. Advancing quantitative synthetic biology will transform the field from qualitative, descriptive, and fragmented research to quantitative, theoretical, and systematic studies. Simultaneously, it will deepen fundamental understanding of life systems and the principles governing their design, elevating synthetic biology from an engineering discipline to a driving force for basic biological science. The synergistic progress between fundamental life science research and synthetic biology will truly open the door to a revolution in life sciences research while guiding the development of new-generation biotechnology and engineering biology.

Researcher Liu Chenli from the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, and Researcher Zhao Guoping from the Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, are the corresponding authors. Assistant Researcher Luo Nan from Liu Chenli's team is the first author. This work was supported by multiple projects from the National Natural Science Foundation of China.