The current application of AI technology has extended from the “learning” stage of synthetic biology to the entire process of DBLT cycle and engineering.

Bio-AI model cultivation reaches a certain level or emerges from “ape” to “human” evolution, and with the optimization of the algorithm, the dependence on training computing power is reduced, and finally an artificial intelligence-based “synthetic biologist” is built. Greatly increase the rate of target engineering strain construction and target product scale production.

In this process, the core gap of the Bio-AI system may lie in the accumulation of data, and the first-mover advantage is obvious.

AI brings new opportunities for the development of synthetic biology

OpenAI’s Gpt-4 has aroused people’s attention to artificial intelligence (AI), and the rapid development of AI technology has greatly benefited the field of synthetic biology, and even derived intelligent synthetic biology (bio-AI).

Bio-AI is the integration of artificial intelligence and synthetic biology. It transforms the traditional biosynthesis process, which is time-consuming, labor-intensive, long-term, and dependent on the operator’s level of operation, into an intelligent fully automatic integrated biosynthesis process.

At first, AI technology in the field of synthetic biology was mainly practiced in the “learning” stage; gradually, the influence of AI technology has been extended to the entire DBLT cycle, and it is expected to play an important role in fields such as engineering amplification, thereby greatly improving the field of synthetic biology. efficiency.

Specifically, AI can greatly improve the gene editing efficiency, metabolic pathway optimization, production process optimization, and protein design of synthetic biology:

1.Improving the accuracy and efficiency of gene editing: The application of AI in gene editing is mainly reflected in improving editing efficiency, reducing non-specific cuts, and predicting editing results. By utilizing machine learning techniques, models can be trained to predict the activity and specificity of gene editing tools such as CRISPR-Cas9, and guide synthetic biologists to select more appropriate sgRNA sequences to achieve higher editing efficiency and reduce side effects. At the same time, AI can analyze existing gene editing experimental data to provide more accurate predictions and guidance for future experiments.

2.Guide protein design: The application of AI in the field of protein design mainly focuses on predicting the three-dimensional structure of proteins, designing proteins with specific functions, and optimizing protein stability and biological activity. Through deep learning techniques, models such as AlphaFold can predict the three-dimensional structure of proteins and provide synthetic biologists with information about protein functions. In addition, AI can be used in protein affinity design to improve the biological activity of proteins by optimizing the interaction between proteins and target molecules. These technologies have important applications in the fields of drug design, biosensors and industrial enzymes.

3.Optimizing metabolic pathways and biological production processes: In the field of metabolic engineering, AI is mainly used to optimize metabolic pathways, regulate gene expression, and predict microbial production performance. Using machine learning techniques, metabolic network models can be constructed and the impact of gene knockout or overexpression on product yield can be predicted. This helps researchers screen and optimize production strains and improve the yield and purity of biological products. In addition, AI can also guide synthetic biologists to design gene regulatory elements (such as promoters, ribosome binding sites, etc.) to achieve fine regulation of biological system functions.

Not only that, AI can also help optimize the fermentation process and amplification process. There is a complex interaction between environmental factors and cell metabolism. The traditional fermentation amplification method uses statistical methods combined with trial and error and the experience of fermentation engineers.

With the help of artificial intelligence technology, using the massive fermentation process data formed by micro-reactor clusters, combined with the theory of experimental design, the work of fermentation strain verification and fermentation process development can be realized more efficiently, greatly shortening the time for synthetic biology innovation strains from the laboratory. time to industrialize.

In particular, AI could help synthetic biology overcome a fundamental challenge of predicting the impact of bioengineering methods on the host and environment. Previously, because synthetic biology could not predict the results of bioengineering, even combined with computer assistance, it could only achieve engineering goals through a lot of trial and error, and consumed a lot of computing resources.

But AI provides the opportunity to use public data and experimental data to predict the impact on the host and the environment, bringing the development of synthetic biology to a new stage.

In recent years, AI technology has developed rapidly, especially in deep learning and big data processing, making the application of AI in the field of synthetic biology more extensive and in-depth.