Synthetic biology, recognized as one of the most revolutionary interdisciplinary fields in the 21st century, has established innovative strategies for the genetic improvement of rice through the integration of multidisciplinary technologies including genome editing, genetic circuit design, metabolic engineering, and artificial intelligence. This review systematically summarizes recent research advancements and breakthrough achievements in the application of synthetic biology in the genetic improvement of rice, focusing on three critical domains: yield improvement, nutritional quality fortification, and reinforcement of disease resistance and abiotic stress tolerance. It elucidates that synthetic biology enables precise genomic and metabolic pathway engineering through modular, standard, and systematic approaches, effectively overcoming the limitations of conventional breeding methods characterized by prolonged cycles and restricted trait modification capabilities. The implementation of synthetic biology has facilitated synergistic improvement of multi-traits, thereby providing critical technical references for developing elite rice cultivars with superior productivity and nutritional value. These technological breakthroughs hold significant implications for ensuring global food security and promoting green and sustainable development of agriculture.
Oryza/growth & development* ; Synthetic Biology/methods* ; Metabolic Engineering ; Plant Breeding/methods* ; Gene Editing ; Genetic Engineering/methods* ; Plants, Genetically Modified/genetics* ; Disease Resistance/genetics*

Gene synthesis is an enabling technology that supports the development of synthetic biology. The existing approaches for de novo gene synthesis generally have tedious operation, low efficiency, high error rates, and limited product lengths, being difficult to support the huge demand of synthetic biology. The assembly and error correction are the keys in gene synthesis. This study first designed the oligonucleotide sequences by reasonably splitting the virus genome of approximately 10 kb by balancing the parameters of sequence design software ability, PCR amplification ability, and assembly enzyme assembly ability. Then, two-step PCR was performed with high-fidelity polymerase to complete the de novo synthesis of 3.0 kb DNA fragments, and error correction reactions were performed with T7 endonuclease Ⅰ for the products from different stages of PCR. Finally, the virus genome was assembled by 3.0 kb DNA fragments from de novo synthesis and error correction and then sequenced. The experimental results showed that the proposed method successfully produced the DNA fragment of about 10 kb and reduced the probability of large fragment mutations during the assembly process, with the lowest error rate reaching 0.36 errors/kb. In summary, this study developed an efficient de novo method for synthesizing a viral genome of about 10 kb with T7 endonuclease Ⅰ-mediated error correction. This method enabled the synthesis of a 10 kb viral genome in one day and the correct plasmid of the viral genome in five days. This study optimized the de novo gene synthesis process, reduced the error rate, simplified the synthesis and assembly steps, and reduced the cost of viral genome assembly.
Genome, Viral/genetics* ; Polymerase Chain Reaction/methods* ; DNA, Viral/genetics* ; Bacteriophage T7/enzymology* ; Synthetic Biology/methods*

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO₂ emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.
Biotechnology/methods* ; Knowledge Bases ; Synthetic Biology ; Databases, Factual ; Artificial Intelligence ; Systems Biology ; Computational Biology ; Fermentation

With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potential in various fields, including medicine, agriculture, and industrial biotechnology. This review comprehensively summarizes the significant progress in applying artificial intelligence (AI) technologies to the design, mining, and modification of CRISPR-Cas systems. AI technologies, especially machine learning, have revolutionized sgRNA design by analyzing high-throughput sequencing data, thereby improving the editing efficiency and predicting off-target effects with high accuracy. Furthermore, this paper explores the role of AI in sgRNA design and evaluation, highlighting its contributions to the annotation and mining of CRISPR arrays and Cas proteins, as well as its potential for modifying key proteins involved in gene editing. These advancements have not only improved the efficiency and precision of gene editing but also expanded the horizons of genome engineering, paving the way for intelligent and precise genome editing.
CRISPR-Cas Systems/genetics* ; Artificial Intelligence ; Gene Editing/methods* ; RNA, Guide, CRISPR-Cas Systems/genetics* ; Machine Learning ; Humans ; Genetic Engineering/methods* ; Synthetic Biology

Nucleic acid elements are essential functional sequences that play critical roles in regulating gene expression, optimizing pathways, and enabling gene editing to enhance the production of target products in biomanufacturing. Therefore, the design and optimization of these elements are crucial in constructing efficient cell factories. Artificial intelligence (AI) provides robust support for biomanufacturing by accurately predicting functional nucleic acid elements, designing and optimizing sequences with quantified functions, and elucidating the operating mechanisms of these elements. In recent years, AI has significantly accelerated the progress in biomanufacturing by reducing experimental workloads through the design and optimization of promoters, ribosome-binding sites, terminators, and their combinations. Despite these advancements, the application of AI in biomanufacturing remains limited due to the complexity of biological systems and the lack of highly quantified training data. This review summarizes the various nucleic acid elements utilized in biomanufacturing, the tools developed for predicting and designing these elements based on AI algorithms, and the case studies showcasing the applications of AI in biomanufacturing. By integrating AI with synthetic biology and high-throughput techniques, we anticipate the development of more efficient tools for designing nucleic acid elements and accelerating the application of AI in biomanufacturing.
Artificial Intelligence ; Synthetic Biology ; Nucleic Acids/genetics* ; Algorithms ; Gene Editing ; Promoter Regions, Genetic ; Biotechnology/methods*

Transcription factor (TF)-based biosensors have been widely applied in metabolic engineering, synthetic biology, metabolites monitoring, etc. These biosensors are praised for the high orthogonality, modularity, and operability. However, most natural TFs with weak responses and low specificity still demand optimization for desired performance in applications. Herein, we comprehensively summarize the recent advances in the engineering and optimization of TF-based biosensors with the assistance of computational simulation and artificial intelligence. This review includes the regulatory protein engineering aided by protein structure prediction and ligand binding simulation and the regulatory protein responses predicted by a mathematical model obtained from machine learning of mutagenesis data. In comparison with conventional tools, computational simulation and artificial intelligence enable more accurate and rapid design and construction of biosensors. Thus, these technologies will greatly promote the development of novel biosensors for applications.
Biosensing Techniques/methods* ; Transcription Factors/metabolism* ; Artificial Intelligence ; Protein Engineering/methods* ; Computer Simulation ; Synthetic Biology ; Machine Learning

Synthetic biology is an emerging interdisciplinary field at the convergence of biology, engineering, and computer science. It employs a bottom-up approach to progressively design biological parts, devices, and circuits, aiming to create artificial biological systems not found in nature or to redesign existing biological systems for specific purposes. With the rapid development of the synthetic biology industry, there is an increasing demand for large complex genetic circuits. However, the traditional trial-and-error methods, heavily reliant on empirical knowledge, have limited efficiency and success rates of parts/circuits construction, thereby impeding the innovation and technology translation for synthetic biology. These limitations have prompted a paradigm shift from labor-intensive, experience-driven trial-and-error models towards standardized, intelligent engineering approaches. Machine learning, capable of uncovering hidden structures and relationships within biological data, offers robust support for the intelligent design of synthetic biological parts and genetic circuits. Here, we review commonly used machine learning algorithms and analyze their typical applications in designing biological parts (e.g., synthetic promoters, RNA regulatory elements, and transcription factors) and simple genetic circuits. Additionally, we discuss the primary challenges in machine learning-aided design and propose potential solutions. Lastly, we envision the future trend of integrating machine learning with synthetic biological system design, highlighting the importance of interdisciplinary collaboration.
Synthetic Biology/methods* ; Machine Learning ; Gene Regulatory Networks ; Algorithms

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.
Metabolic Networks and Pathways/physiology* ; Models, Biological ; Artificial Intelligence ; Systems Biology ; Kinetics ; Cell Physiological Phenomena ; Computational Biology ; Synthetic Biology ; Humans

The ability of cells to sense and adapt to metabolic changes and environmental variations is essential for their functions. Recent advances in synthetic biology have uncovered increasing mechanisms through which cells detect changes in metabolism and environmental conditions, leading to broader applications. However, a systematic review on the regulation of cellular metabolism and environmental adaption is currently lacking. This article presents a comprehensive overview of this field from three perspectives. First, it introduces key transmembrane and sensor proteins involved in the cellular perception of metabolic and environmental changes. Next, it summarizes the adaptive regulation mechanisms that natural cells employ when confronted with intracellular and extracellular metabolic changes. Finally, the review explores the application scenarios based on cellular adaptive regulation in three aspects: dynamic control, rational metabolic engineering, and adaptive evolution and makes an outlook on the future development directions in this field. This review not only provides a comprehensive perspective on the mechanisms by which cells sense metabolic and environmental variations, but also lays a theoretical foundation for further innovations in the field of synthetic biology. With the continuous advancement of future technologies, a deeper understanding of cellular adaptive regulation mechanisms holds great potential to drive the development and application of novel biomanufacturing platforms.
Adaptation, Physiological ; Synthetic Biology ; Metabolic Engineering/methods* ; Environment ; Bacteria/genetics*

Microbiomes in natural environments have diverse functions and harbor vast exploitable potential of modifying the nature and hosts, being significant resources for development. The inherent high complexity and uncontrollability of natural microbiomes, as well as the selection by the nature and hosts, impose significant constraints on practical applications. Synthetic microbiomes, serving as precisely defined engineered microbiomes, demonstrate enhanced functionality, stability, and controllability compared with natural microbiomes. These engineered microbiomes emerge as a prominent research focus and are potentially having applications across various fields including environmental bioremediation and host health management. Nevertheless, substantial challenges persist in both fundamental research and practical application of synthetic microbiomes. This review systematically summarizes three core design principles for synthetic microbiomes, introduces current construction strategies including top-down, bottom-up, and integrated approaches, and comprehensively lists their applications in environmental remediation, agricultural innovation, industrial biotechnology, and healthcare. Furthermore, it critically examines existing technical and conceptual challenges while proposing strategic recommendations, thereby providing theoretical guidance for future advancements in the design, engineering, and application of synthetic microbiomes.
Microbiota/genetics* ; Synthetic Biology/methods* ; Biotechnology/methods* ; Biodegradation, Environmental ; Humans