On July 24, 2024, Dr. Liu Chenli from the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and Dr. Zhao Guoping from the Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, published a review article titled "Quantitative Synthetic Biology" in Nature Reviews Bioengineering. This article is the first in the international community to elucidate the research paradigm and academic connotations of the new field direction of "Quantitative Synthetic Biology," and it offers suggestions for the next steps in the development of synthetic biology.

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Original Article Link: https://www.nature.com/articles/s44222-024-00224-y

Synthetic biology is becoming a powerful engine for driving the next generation of biomanufacturing and bioeconomic development. Over the past two decades, with the continuous innovation of technologies such as DNA synthesis and gene editing, the ability to construct synthetic biological systems has rapidly improved. However, the foundational design capabilities remain very limited. Due to the complexity of biological systems, even if the functions of individual components are known, the system they form together may not necessarily exhibit the expected functions. To rationally design synthetic systems with specific functions, a deep understanding of the principles of emergent functions in natural systems is required, which has been rarely addressed in synthetic biology research to date. Currently, most synthetic biological systems are built mainly through trial and error, which is slow and inefficient, greatly limiting the development of synthetic biology. Therefore, one of the biggest challenges synthetic biology currently faces is how to improve the ability for rational design. Only when design capabilities effectively synergize with synthetic capabilities, with synthesis providing verification for design and design providing guidance for synthesis, forming a closed loop of "design-synthesizing-testing-learning," can we hope to reliably and efficiently construct more sophisticated and complex biological systems.

Therefore, synthetic biology needs to develop a more mature theoretical and methodological system to guide the rational design of biological systems—synthetic biology needs to rise to the new height of quantitative synthetic biology. The authors propose that rational design is design based on "prediction." When combining biomolecules, genes, and circuits into synthetic biological systems, if one can make accurate predictions about the system's behavior and functions, it is possible to foresee how to construct the system to achieve the desired functions, thus avoiding repeated trial and error.

The authors summarize three research paradigms for achieving rational design in quantitative synthetic biology:

Figure 1: Three Research Paradigms of Quantitative Synthetic Biology

Design based on principles (Figure 1a). To rationally design a system, a model that can make accurate predictions about the system is needed. Typically, a model is an abstraction of the internal mechanisms of a biological system and helps us understand the logical architecture (topology) behind the functions. For simpler biological functions, we have mature theoretical models. Therefore, many classic works in the early days of synthetic biology adopted this paradigm. This "top-down" paradigm first explores the principles of function generation through the establishment of mathematical models, obtains the system topology that can produce the target functions, and then designs specific biological components based on the topology.

Design from the bottom up (Figure 1b). As synthetic biology evolves and synthetic biological systems become increasingly complex, establishing theoretical models from functions becomes a great challenge, and "top-down" design becomes very difficult. Therefore, many studies have adopted a "bottom-up" strategy. This strategy starts with components, and the initial stage is trial and error: exploring possible functions by trying different assembly methods of components. In the process of "relying on luck," we may obtain the functions we are interested in. In the past, synthetic biology research often stopped here, but entering the field of quantitative synthetic biology, the work has just begun: after obtaining the system with the expected function, since the components inside the system are known, we can infer its topology, establish mathematical models, and then use synthetic systems to verify the models and clarify the principles of function generation. Another common situation is that "unintended functions" appear in this "synthesis"-"attempt" process. In previous synthetic biology research, these findings were often ignored, but for quantitative synthetic biology, they often guide the discovery of new principles. Once the principles are understood, we can design synthetic systems that produce similar or more complex functions based on these principles. In this process, the emergent principles discovered are generally rules that both natural and synthetic biological systems follow. Therefore, the discovery of these principles will also promote the progress of basic life sciences.

Artificial intelligence (AI)-assisted design (Figure 1c). The development of AI has provided a new path for quantitative prediction of biological systems. AI-based algorithms do not need to understand the internal working principles of biological systems but are based on big data to find hidden patterns between components and functions, thus predicting how to design components to produce specific functions. This paradigm relies on a vast amount of high-quality, standardized data. Therefore, future synthetic biology will require automated, high-throughput equipment platforms, and standardized experimental methods. Currently, there is a global trend in building automated biofoundries, using automated technology to efficiently construct and test synthetic biological systems. This not only provides AI with standardized quantitative data produced by machine automation experiments under the guidance of system design (excluding human operational errors), quickly completing the "design-synthesizing-testing-learning" iteration to quickly obtain target functions; it can also truly improve the level of manual trial and error in paradigm two, truly achieving the discovery of new principles guided by machine learning with large models based on high-quality big data.

The three design paradigms all emphasize a close integration with quantitative analysis methods, using mathematical logic and quantitative relationships to make quantitative predictions of biological systems and provide a basis for the rational design of synthetic biological systems. Therefore, the authors propose the development direction of "quantitative synthetic biology" in synthetic biology. Quantitative synthetic biology absorbs the thinking and methods of quantitative biology and systems biology, establishes mathematical or AI models that can quantitatively predict biological systems, guides the design and construction of synthetic biological systems, and thus solves the bottleneck problem of "rational design" in synthetic biology. The development of quantitative synthetic biology will promote synthetic biology from qualitative, descriptive, and local research to quantitative, theoretical, and systematic transformation. At the same time, quantitative synthetic biology will enhance people's basic understanding of life systems, better understand the basic laws and design principles of living organisms, so that synthetic biology is no longer just an engineering discipline but becomes an important force in promoting basic biological sciences. The spiral rise of basic life science research and synthetic biology research will truly open the door to the life science research revolution and lead the development of the new generation of biotechnology and engineering biology.

In 2017, the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, established the Center for Quantitative Synthetic Biology, first proposing the concept of the interdisciplinary discipline of quantitative synthetic biology. In 2020, the center was approved as an innovative cross-team and key laboratory of quantitative engineering biology by the Chinese Academy of Sciences. In 2021, China held the Quantitative Synthetic Biology Fragrant Hill Science Conference. In June 2023, the Shenzhen Advanced Institute was approved to establish a key laboratory of quantitative synthetic biology (Chinese Academy of Sciences). In this process, the new direction of quantitative synthetic biology has gradually gained recognition and attention from peers in the field. Domestic and international academic journals such as ACS Synthetic Biology, Quantitative Biology, "Science Bulletin," and "Synthetic Biology" have successively published special issues on "Quantitative Synthetic Biology"; the latest synthetic biology international conference—Synthesis, Engineering, Evolution, and Design (SEED)—has specially set up a symposium on "Modeling and Quantitative Synthetic Biology"; international research institutions such as Duke University in the United States and the TIGEM Institute in Italy have also begun to layout in the direction of "Quantitative Synthetic Biology."

Dr. Liu Chenli from the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and Dr. Zhao Guoping from the Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, are the co-corresponding authors of this article. Dr. Luo Nan, an associate researcher in Dr. Liu Chenli's group at the Shenzhen Institutes of Advanced Technology, is the first author. This work was supported by funding from multiple projects of the National Natural Science Foundation.