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On July 24, 2024, researcher Liu Chenli from the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences and researcher Zhao Guoping from the Center for Excellence in Molecular Plant Science of the Chinese Academy of Sciences published a review article entitled "Quantitative Synthetic Biology" online in the journal Nature Reviews Bioengineering, explaining the research paradigm and discipline connotation of "quantitative synthetic biology". Suggestions are made for the next development of synthetic biology.
Synthetic biology is becoming a powerful engine for the next generation of biomanufacturing and the development of the bioeconomy. In the past two decades, with the continuous innovation of DNA synthesis and gene editing technologies, people's ability to build synthetic biological systems has increased rapidly, but the design ability as the basis for construction is still very limited. Due to the complexity of biological systems, even if the function of the individual components is known, the resulting system when combined does not necessarily perform the intended function. The rational design of synthetic systems with specific functions requires a deep understanding of the principles of functional emergences of natural systems, which have been rarely covered in synthetic biology research to date. At present, the construction of most synthetic biology systems mainly relies on manual trial and error, which is slow and inefficient, which greatly limits the development of synthetic biology. Therefore, one of the biggest challenges facing synthetic biology today is how to improve the ability of rational design. Only when the design capability and the synthesis capability are effectively synergized, the synthesis provides verification for the design, and the design provides guidance for the synthesis, forming a closed loop of "design-synthesis-testing-learning", which is expected to reliably and efficiently build more sophisticated and complex biological systems.
Therefore, synthetic biology needs to develop more mature theories and methodologies to provide guidance for the rational design of biological systems—synthetic biology needs to rise to a new level of quantitative synthetic biology. The author proposes that the so-called rational design is based on "prediction" design. When biomolecules, genes, and circuits are combined into a synthetic biological system, if the behavior and function of the system can be accurately predicted, it is possible to predict how to build the system to obtain the expected function, thus avoiding repeated trial and error.
The authors summarize three research paradigms for quantitative synthetic biology to achieve rational design:
图1：定量合成生物学的三种研究范式(Credit: Nature Reviews Bioengineering)
1. Principle-based design (Fig. 1a). To rationally design a system, it is necessary to build a model that can accurately predict the system. In general, a model is an abstraction of the internal mechanisms of a biological system that helps us understand the logical architecture (topology) of the system behind the function. For simpler biological functions, we have well-established theoretical models. Therefore, many of the classical works in the early days of synthetic biology adopted this paradigm. This "top-down" paradigm first explores the principle of function generation through the establishment of mathematical models, obtains the system topology that can produce the target function, and then designs specific biological components according to the topology.
2. Bottom-up design (Fig. 1b). With the development of synthetic biology, synthetic biology systems have become more and more complex, and it has become a great challenge to build theoretical models based on function, and "top-down" design has become very difficult. As a result, many studies have adopted a "bottom-up" strategy. This strategy starts with the components, and the initial stage is trial and error: exploring possible functions by experimenting with different ways in which the components are assembled. In the process of "trying our luck", it is possible to get a feature that interests us. In the past, synthetic biology research tended to stop there, but in the field of quantitative synthetic biology, the work has just begun: after obtaining the intended functioning system, because the internal elements of the system are known, we can speculate its topology, build a mathematical model, and then use the synthetic system to verify the model and elucidate the principle of its functional generation. Another common scenario is that "unintended functions" appear in this "synthesis"-"trying" process. These findings have often been overlooked in previous synthetic biology studies, but in quantitative synthetic biology, they often lead to the discovery of new principles. Once we understand the principles, we can design synthetic systems with similar or more complex functions based on the principles. In this process, the emergent principles discovered are generally rules that are followed by both natural and synthetic biological systems. Therefore, the discovery of these principles will also advance the basic life sciences.
3. Artificial intelligence (AI)-assisted design (Figure 1c). The development of AI provides a new path for the quantitative prediction of biological systems. Instead of understanding the inner workings of biological systems, AI-based algorithms are based on big data to find hidden patterns between components and functions, so as to predict how components should be designed to produce specific functions. This paradigm relies on massive amounts of high-quality, standardized data, so the future of synthetic biology requires automated, high-throughput equipment platforms and standardized experimental methods. At present, there has been a boom in the construction of automated biofoundries around the world, using automation technology to efficiently build and test synthetic biological systems, not only providing AI with standardized and quantitative massive data generated by machine automation experiments (excluding human factor operation errors) under the guidance of system design (including various important controls), but also quickly completing the iteration of "design-synthesis-testing-learning" and quickly obtaining target functions; It can also really improve the level of manual trial and error in Paradigm 2, and truly realize the discovery of new principles guided by machine learning of large models on the basis of high-quality big data.
The above three design paradigms emphasize the close integration with quantitative analysis methods, and use mathematical logic and quantitative relationships to make quantitative predictions of biological systems, so as to provide a basis for the rational design of synthetic biological systems. Therefore, the authors propose "quantitative synthetic biology", which is the direction of 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, and guides the design and construction of synthetic biological systems, so as to solve the bottleneck problem of "rational design" in synthetic biology. The development of quantitative synthetic biology will promote the transformation of synthetic biology from qualitative, descriptive and local research to quantitative, theoretical and systematic. At the same time, quantitative synthetic biology will enable people to enhance their basic understanding of living systems and better understand the basic laws and design principles of living organisms, so that synthetic biology is no longer just an engineering and technical discipline, but an important force to promote basic biological science. The spiral of basic life science research and synthetic biology research will truly open the door to a revolution in life science research, and at the same time lead the development of a new generation of biotechnology and engineering biology.
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https://www.nature.com/articles/s44222-024-00224-y
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