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Table of Content

    30 June 2025, Volume 6 Issue 3
    Contents in Chinese and English
    2025, 6(3):  0. 
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    Invited Review
    Cytoplasmic concentration: an old question and a new parameter in cell biology
    LI Qian, FERRELL JR. James E., CHEN Yuping
    2025, 6(3):  497-515.  doi:10.12211/2096-8280.2024-086
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    Cytoplasmic concentration is an important parameter in cell physiology, influencing almost all biochemical reactions, playing key roles in regulating various cellular biological processes. However, studying cytoplasmic concentration has been particularly challenging due to its inherent complexity, the difficulty of direct manipulation, and the lack of precise measurement techniques for intact cells. In recent years, advances in microscopy, microfluidics, and synthetic biology have led to the development of novel tools for studying cytoplasmic concentration, such as Quantitative Phase Microscopy, Stimulated Raman Scattering Microscopy, and Genetically Encoded Multimers for single-particle tracking, etc. These advanced tools have enabled researchers to explore the regulation of cytoplasmic concentration, mechanism of the concentration homeostasis, and their influences on cellular physiology, providing deeper insights into their roles in physiological regulations. In this review, we explore both historical and recent advances in methods and overview data regarding cytoplasmic concentration, summarize the molecular and systematic mechanisms that govern its homeostatic regulation, and highlight its roles in physiological and biochemical processes. Specifically, we discuss the key biological processes that influence cytoplasmic concentration, including mitotic swelling, genome dilution, protein synthesis and degradation, and importantly, the heterogeneity of cytoplasmic concentration that arises from local subcellular structure and thermodynamic fluctuation. Furthermore, we expand on connections between cytoplasmic concentration, cellular aging, signal transduction, cellular differentiation, and microtubule assembly dynamics. Additionally, we explore the theoretical interpretation of cytoplasmic concentration in reaction kinetics and its homeostatic regulation, providing evidence from both experimental and theoretical studies on the prevalence of diffusion-limited reactions in biological systems. Despite these advances, significant challenges remain in fully understanding underlying mechanisms of cytoplasmic concentration homeostasis, its complex interactions with other physiological processes, and its potential applications in synthetic biology. Research on cytoplasmic concentration is rapidly evolving into an active field of study, promising major breakthroughs in understanding fundamentals of cellular life, improvement of human health, and engineering of synthetic cells.

    Design principles and artificial synthesis of biological oscillators
    JIANG Yuanxu, FAN Yingying, WEI Ping
    2025, 6(3):  516-531.  doi:10.12211/2096-8280.2024-096
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    Oscillation plays a crucial role in functioning properly for various biological systems, including circadian regulation, cell cycle, neuron activity and intracellular signal transduction. Ever since the first discovery of glycolysis oscillation back to the 1950s, more and more researchers have been engaged in the theoretical exploration of criteria for biological oscillations. At the turn of this century, the artificial synthesis of the repressilator system, which composed of the prokaryotic transcriptional repressors LacI, TetR and λcI, marked the beginning of modern synthetic biology and leading to a golden age for research on artificially synthesized biological oscillators. This article reviews the development in this field that was achieved within the past two decades, discussing them from three aspects: design principles, experimental synthesis, and practical applications. The three main conditions for generating biological oscillations are a negative feedback network structure, sufficient delay over long time, and nonlinear regulatory relationship. Time delay can be achieved by directly introducing biochemical interactions with slow timescales, adding multiple intermediate reaction steps, or forming interlinked positive feedback loops. By adjusting the network topology or introducing external periodic signals, the tunability and stability of oscillations can be enhanced. With computational approaches, researchers are able to scan all the possible network topologies with less than three nodes for robust oscillation emergence and noise resistance. The earliest synthetic oscillatory systems were entirely based on transcriptional regulation in E.coli, yet now synthetic oscillators have been developed at the protein, metabolite, and even multicellular population levels as well as in biological chassis ranging from bacteria, yeast and mammalian cells. These artificially synthesized oscillatory systems have been demonstrated to be able to potentiate the precise regulation of population growth and drug delivery, improve fermentation efficiency in engineered strains, reprogram cell aging, and potentially offer new perspectives for immunotherapy and beyond.

    “Economics Paradox” with cells in synthetic gene circuits
    TIAN Xiao-jun, ZHANG Rixin
    2025, 6(3):  532-546.  doi:10.12211/2096-8280.2024-083
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    In synthetic biology, gene modules are fundamental components that facilitate the execution of various biological functions. “Modularity” refers to the property where known genetic elements maintain their relatively independent functions after being assembled into specific gene circuits. Unlike traditional engineering systems, which often possess independent and stable characteristics, gene circuits must navigate the complexities of dynamically fluctuating cellular environments. This inherent variability means that the effectiveness of gene circuits in producing functional proteins is highly contingent upon the availability of intracellular resources. When these resources are scarce, it can create significant bottlenecks that impede the overall functionality of the gene circuits. Moreover, gene modules do not typically operate in isolation; rather, they are integrated into complicated network systems that interact with other modules to achieve multifaceted regulatory objectives. This interconnectedness leads to competition among various modules for limited intracellular resources, which disrupts the basic principle of modular design. Restoring the modularity of gene circuits is crucial for constructing universal models of life systems, which can further promote the intelligent development of artificial life systems. Recently, increasing studies have focused on how this resource competition impacts the performance of gene circuits, which have deepened our understanding of the underlying mechanisms and have paved the way for optimizing gene circuit designs to enhance their modularity and functionality. This review aims to comment on the influences of cellular resource competition on gene circuit functions, through exploring various aspects, including the fluctuations in noise levels within gene circuits, the coupling relationships among different gene modules, and the emergent “winner-takes-all” phenomenon. Additionally, we summarize existing strategies for controlling these challenges, such as the orthogonal design of cellular resources, the regulation of single gene modules, and the coordinated control of multiple gene modules. With the rapid development of synthetic biology, artificially designed gene circuits are becoming increasingly complicated in both structures and functions. This trend suggests that future research will no longer be limited to simple resource competition control systems, but instead will need to expand to larger-scale research areas. At the same time, research directions should extend from basic research to practical applications, ultimately aiming to construct precisely controllable artificial life systems.

    Protein structural bioinformatics empowered by statistical physics and artificial intelligence
    XIA Chenliang, ZHANG Zecheng, GUAN Xingyue, TANG Qianyuan
    2025, 6(3):  547-565.  doi:10.12211/2096-8280.2025-016
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    Structural bioinformatics focuses on the computational study of three-dimensional biomolecular structures and their functions, with protein structures as its core research object. Traditional research in this field relied on protein structure databases of experimentally determined proteins but was constrained by the high cost and low-throughput nature of experimental methods. The revolution in protein structure prediction driven by deep learning, particularly AlphaFold2’s breakthrough, has fundamentally transformed the field’s data landscape by achieving atomic-level prediction accuracy from amino acid sequences alone. The deep integration of statistical physics with big data analysis methodologies has enabled researchers to overcome limitations of traditional case-by-case studies, systematically revealing universal principles of protein design from massive datasets. The accumulation of extensive protein structure data provides a crucial foundation for quantifying long-range correlations in protein dynamics and their evolutionary correspondence, revealing universal principles rooted in the interplay between sequence variability, structural constraint, and functional optimization. These principles not only offer a unified framework for understanding protein structure, dynamics, function, and evolution but also serve as the basis for predictive models and de novo protein design in engineering applications. Building upon this foundation, statistical analyses based on the AlphaFold Database highlight the crucial role of data-driven methods in uncovering universal statistical laws and dimensionality reduction principles in protein evolution across increasing organismal complexity, offering fresh perspectives on the fundamental constraints and convergent patterns driving molecular evolution. Recognizing that protein functions often depends on transitions between multiple conformational states, precise prediction of protein dynamics has become a core research direction. These advances are propelling protein engineering into an era of precise rational design where researchers can predict and manipulate conformational change pathways to regulate enzyme activity, optimize ligand specificity, and design allosteric responses with unprecedented precision. The research paradigm combining statistical physics and artificial intelligence continues to drive innovation in protein science, enhancing high-throughput screening and rational design efficiency to accelerate translation from basic discoveries to practical applications. As computational capabilities advance and AI models evolve, the field progresses from single protein design toward complex biological system construction, opening new frontiers in synthetic biology, precision medicine, and other applications.

    Applications of machine learning in the reconstruction and curation of genome-scale metabolic models
    WU Ke, LUO Jiahao, LI Feiran
    2025, 6(3):  566-584.  doi:10.12211/2096-8280.2024-090
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    Since the publication of the first genome-scale metabolic model (GEM) in 1999, GEMs have become an essential tool for analyzing metabolism. The models integrate genes, metabolites, and reactions for combining stoichiometric matrices with constraint-based optimization to systematically describe and simulate metabolic processes in organisms. The development of automated pipelines for reconstructing GEMs has expanded their applicability to organisms from all kingdoms of life. Additionally, GEMs can integrate kinetic parameters, thermodynamic parameters, multi-omics data and multi-cellular processes to reconstruct more accurate models, thereby improving prediction accuracy. However, the reconstruction of GEMs remains heavily dependent on pre-existing knowledge, inherently limiting their scope to currently available information. This dependency restricts our ability to fully unravel the complexity and dynamic nature of metabolism. Recent advances in machine learning have demonstrated extraordinary capabilities for biological tasks such as protein structure prediction, disease identification and GEM reconstruction with functional annotation and large-scale data integration, showcasing its power in identifying patterns and uncovering hidden relationships within biological systems. Machine learning provides a promising pathway to overcome the limitations of GEMs by expanding their applicability to areas previously constrained by data availability and complexity. This review summarizes the traditional reconstruction methods of GEMs and their applications in integrating multi-dimensional data to build multi-constraint and multi-process models. The review also focuses on key applications of machine learning in gene function annotation, pathway analysis, gap-filling prediction in the reconstruction of GEMs. Additionally, the potential of machine learning in predicting kinetic, thermodynamic, and other key biochemical parameters in the reconstruction of multi-constraint and multi-process models is discussed. By combining GEMs with machine learning innovations, researchers can improve model accuracy, enhance scalability, and gain new insights into previously elusive metabolic mechanisms, bridging gaps in metabolic knowledge, and underscoring its importance as a cornerstone for future development in systems biology and biotechnology.

    Advances and prospects in genome-scale models of yeast
    LI Yongzhu, CHEN Yu
    2025, 6(3):  585-602.  doi:10.12211/2096-8280.2024-084
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    Yeasts, particularly Saccharomyces cerevisiae, are widely used eukaryotic organisms with relatively clear cellular structures and metabolic networks, and their cellular processes exhibit a certain degree of conservation among eukaryotes. These organisms play a crucial role in research with synthetic biology and systems biology as well. However, due to the complexity of their metabolic networks and the variability of cellular activities, study and design of pathways for yeasts still present considerable challenges. To address these issues, researchers have developed genome-scale models, which are mathematical framework that integrates genomic, biochemical, and physiological data to simulate cellular processes and predict the relationship between genotype and phenotype, which are further used to simulate cellular functions and predict cell behaviors under different conditions, providing a systematic approach for understanding and engineering biological systems. This review introduces methods for building and analyzing genome-scale models of yeasts, including traditional metabolic models and their derived multi-constraint and multi-process models. It also traces the development of yeast models over time. Furthermore, this article discusses recent applications of yeast models in areas such as designing yeasts as cell factories for producing valuable compounds, studying microbial physiology, optimizing cultivation conditions, and simulating microbial community interactions. These models also provide insights into identifying potential metabolic engineering targets for optimizing cellular functions. Despite the advantages of the genome-scale models, their development and application are still limited in several aspects, such as incomplete data on metabolic pathways, limited focus on secondary metabolism, and high barriers to use, particularly for users without programming backgrounds. This review proposes several strategies to address these challenges. To enhance the development of traditional models, it is crucial to incorporate more comprehensive datasets, with a particular emphasis on secondary metabolism and metabolic dark matters. Additionally, improving the accessibility of models requires the development of user-friendly platforms, the provision of clear and standardized tutorials. These strategies can lower barriers for users, and promote applications of the genome-scale models.

    Challenges and opportunities in text mining-based protein function annotation
    ZHANG Chengxin
    2025, 6(3):  603-616.  doi:10.12211/2096-8280.2025-002
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    Understanding the biological function of proteins is crucial for advancing quantitative synthetic biology. Except for a small number of model organisms, most species contain many proteins whose functions have not been experimentally verified, necessitating the development of accurate, automated protein function annotation methods. Recent progress in protein bioinformatics, particularly in predicting protein structures and functions, has been driven significantly by the application of artificial intelligence (AI) algorithms, with a notable emphasis on deep learning models. For instance, the top-ranked methods in recent Critical Assessment of Function Annotation (CAFA) challenge have used deep learning models, primarily large language models, to perform text mining-based protein function annotation. These methods either predict Gene Ontology (GO) terms directly from text features extracted from scientific literatures or from template proteins with databases. Despite the extensive work in developing increasingly powerful deep learning models for text mining-based protein function annotation, several major challenges have been overlooked when parsing scientific literature data. This manuscript reviews existing methods and challenges in protein function annotation. First, many text mining-based protein function predictors rely exclusively on PubMed abstracts collected by UniProt curators for the query protein, ignoring literatures that have not been reviewed by biocurators. Consequently, protein functions predicted by text mining might overlap with those from manual curation of the UniProt Gene Ontology Annotation. Second, nearly all methods only parse PubMed abstracts, ignoring the more informative full-text documents often available in the PubMed Central and Europe PMC repositories. Third, few studies have been proposed to automatically differentiate between different categories of literatures, such as low and high throughput experiments, and computational predictions. This manuscript also proposes promising approaches to enhance text mining-based protein function annotation using the latest development in AI, which is expected to contribute to the development of next-generation text mining tools for more accurate function annotation.

    AI-enabled directed evolution for protein engineering and optimization
    SONG Chengzhi, LIN Yihan
    2025, 6(3):  617-635.  doi:10.12211/2096-8280.2025-044
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    Directed evolution is one of the core enabling technologies in synthetic biology. By recapitulating evolutionary processes that occur in nature within the laboratories, directed evolution employs functional screening to continually isolate variants with improved performance from large mutant libraries for functions that are difficult to achieve with wild-type proteins. In recent years, rapidly advancing artificial intelligence (AI) approaches—such as machine learning and protein language models—have further expanded both the range of applications and the operational efficiency of directed evolution, yielding unprecedented achievements in the engineering of enzymes, antibodies, biosensors, and more. In this review, we first outline classic strategies and emerging techniques for mutagenesis and functional selection in traditional directed evolution, followed by an in-depth examination of various continuous directed evolution systems. We highlight common limitations of directed evolution, emphasizing issues such as constrained search space and susceptibility to local optima. Combining rapidly iterated AI methods with directed evolution offers promising solutions to these challenges. Protein language models, in particular, leverage learned patterns from experimental variants alongside fundamental protein properties, providing superior predictive accuracy for unexplored mutants and facilitating the extrapolation of sequence-function relationships to broader sequence space. AI-based methods enhance directed evolution workflows from multiple perspectives. De novo protein design and unsupervised protein language models aid in generating functional starting sequences with targeted sequence diversity. Machine learning models trained on experimental data enable the construction of optimized mutant libraries tailored for subsequent selection rounds. Additionally, models derived from statistical physics and dynamical systems help extract detailed functional information from data acquired across multiple selection rounds. Collectively, these machine learning approaches significantly enhance the overall efficiency of directed evolution. To illustrate the transformative potential of machine learning-assisted directed evolution, we discuss exemplary cases of protein function improvement and modification. Lastly, we briefly address ongoing challenges and future directions in this rapidly evolving and promising research area.

    DeepSeek model analysis and its applications in AI-assistant protein engineering
    LI Mingchen, ZHONG Bozitao, YU Yuanxi, JIANG Fan, ZHANG Liang, TAN Yang, YU Huiqun, FAN Guisheng, HONG Liang
    2025, 6(3):  636-650.  doi:10.12211/2096-8280.2025-041
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    In early 2025, Hangzhou DeepSeek AI Foundation Technology Research Co., Ltd. released and open-sourced its independently developed DeepSeek-R1 conversational large language model. This model exhibits extremely low inference costs and outstanding chain-of-thought reasoning capabilities, performing comparably to, and in some tasks surpassing, proprietary models like GPT-4o and o1. This achievement has garnered significant international attention. Furthermore, DeepSeek’s excellent performance in Chinese conversations and its free-for-commercial-use strategy have ignited a wave of deployment and application within China, thereby promoting the widespread adoption and development of AI technology. This work systematically analyzes the architectural design, training methodology, and inference mechanisms of the DeepSeek model, exploring the transfer potential and application prospects of its core technologies in AI-assistant protein research. The DeepSeek model integrates several cutting-edge, independently innovated technologies, including a multi-head latent attention mechanism, mixture-of-experts (MoE) with load balancing, and low-precision training. These innovations have substantially reduced the training and inference costs for Transformer models. Although DeepSeek was originally designed for human language understanding and generation, its optimization techniques hold significant reference value for pre-trained language models with proteins, which are also based on the Transformer architecture. By leveraging the key technologies employed in DeepSeek, protein language models are expected to achieve substantial reductions in training and inference costs.

    Application and prospect of live cell DNA-based molecular recorders in cell lineage tracing
    JIANG Baiyi, QIAN Long
    2025, 6(3):  651-668.  doi:10.12211/2096-8280.2024-082
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    Tracing the division and differentiation history of cells is a critical issue in organismal development and cancer research. Live cell DNA-based molecular recorders, a synthetic system that induces heritable DNA variations, offers an innovative approach for reconstructing cell lineage histories. As a representative for the new generation of cell lineage tracing method, this system can be integrated with high-throughput single-cell sequencing and multi-omics analysis, enabling the reconstruction of developmental differentiation pathways of cells and the phylogenetic trees of tumorigenesis as well. Live cell DNA-based molecular recorders serve as an effective platform for exploring these core biological processes. This review systematically analyzes the technological evolution of Cas9-based molecular recorders in lineage tracing since 2016 and its applications, while also analyzing the research trends of some novel molecular recorders and evaluating their advantages and limitations. Since 2016, molecular recorders based on the CRISPR-Cas9 system have made significant progress and gradually become the mainstream technology in this field. However, Cas9-based molecular recorders still suffer from several inherent limitations, such as the low lineage resolution due to insufficient editing efficiencies, the loss of recorded information caused by DNA double-strand breaks, and potential lineage merging due to barcode homoplasy. These limitations pose challenges for researchers to explore and develop new types of molecular recorders as more efficient and precise tools for cell lineage tracing. Novel molecular recorders based on new principles, such as prime editors, DNA-binding protein-fused base editors, and T7 RNA polymerase-fused base editors, can avoid DNA double-strand breaks and record information through base substitutions rather than deletions. Compared to the Cas9 system, they exhibit unique advantages but also come with potential risks and challenges. Prime editors can record information in a temporal sequential manner, but off-target effects remain a concern. DNA-binding protein-fused base editors offer high editing efficiencies and specificities, but their effectiveness across different cell types requires further exploration. T7 RNA polymerase-fused base editors have achieved success in in vivo directed evolution systems, but their application in mammalian systems is still limited. In the future, the research of DNA-based molecular recorders should focus on optimizing editing efficiency, reducing information loss, improving lineage recovery efficiency, and exploring their application potentials in complicated biological systems.

    Applications of synthetic biology to stem-cell-derived modeling of early embryonic development
    YANG Ying, LI Xia, LIU Lizhong
    2025, 6(3):  669-684.  doi:10.12211/2096-8280.2025-013
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    Understanding how a fertilized egg develops from a single cell into complex tissues and organs remains a central question in developmental biology. However, in mammals, especially in humans, technical and ethical constraints limit in utero investigation of the post-implantation development and ex utero culture beyond organogenesis as well. As a result, the molecular and cellular mechanisms underpinning spatiotemporal regulation during these stages remain poorly understand. This knowledge gap underscores the urgent need for high-fidelity in vitro models that not only recapitulate in vivo developmental processes but also allow for precise experimental perturbations. Recent advances in stem cell-based embryo models and organoids leverage the developmental potential and intrinsic self-organizing capabilities of pluripotent stem cells to mimic aspects of early embryonic and organ development, offering new platforms for studying those complex processes. Concurrently, synthetic biology provides powerful tools, such as programmable gene circuits, optogenetics, and engineered signaling pathways, to control gene expression, cell differentiation, intercellular communications, and tissue patterning with unprecedented precision. This review highlights recent progress in integrating synthetic biology with in vitro models to dissect and reconstitute fundamental mechanisms of embryonic development. By harnessing synthetic biology tools, researchers can now modulate specific pathways with temporal and spatial precision, enabling a deeper understanding of processes such as signal transduction dynamics, cellular adhesion networks, symmetry breaking, and the establishment of polarity. This bottom-up “build-to-learn” approach shifts the paradigm from observational to predictive developmental biology. Such innovations have collectively given rise to the emerging field of synthetic developmental biology. This field not only provides mechanistic insights into developmental events that were previously inaccessible but also opens new avenues for building artificial tissues and structures with tailored functions. We also discuss current limitations in mimicking the morphology and function of natural embryonic structures, emphasizing the need for robust evaluation systems and refined strategies to precisely control cell behavior. Finally, we explore how synthetic developmental biology can elucidate key principles of embryogenesis and accelerate future applications in regenerative medicine.

    Exploration of gene functions and library construction for engineering strains from a synthetic biology perspective
    ZHANG Yiqing, LIU Gaowen
    2025, 6(3):  685-700.  doi:10.12211/2096-8280.2024-079
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    Synthetic biology, as a discipline that designs, constructs, and modifies biological systems to achieve specific functions, is widely applied in biomanufacturing, the biodegradation of environmental pollutants, and drug synthesis. Systematic exploration of gene functions and construction of libraries for engineered strains are driving forces of the development of synthetic biology. These libraries serve as foundational tools for understanding complex biological processes and engineering microorganisms for potential applications. This review focuses on the construction methods and application prospects of various yeast libraries in synthetic biology. With the rapid advancement of genome sequencing and high-throughput screening technologies, microbial libraries, such as those of Saccharomycescerevisiae and Schizosaccharomycespombe, play a pivotal role in systematic research. Yeast libraries, including gene knockout libraries, overexpression libraries, and transposon insertion libraries, provide valuable tools for optimizing gene combinations and designing metabolic pathways, thus promoting applications in metabolic engineering and synthetic biology. These libraries facilitate the development of robust industrial strains, driving improvements in biofuel production, chemical synthesis, and other biotechnological processes. In the environmental field, the screening of modified genes generates strains with pollutant degradation capabilities, contributing to ecological restoration. In drug synthesis, these libraries aid in constructing strains for the efficient production of pharmaceutical compounds, advancing the development of biopharmaceuticals. Despite these successes, there remain challenges in library construction and application, such as the high cost of library generation, difficulty in precise genome editing, and limitation in screening efficiency. In the future, advances in automation, digitization, and novel screening technologies are expected to overcome these barriers, facilitating the rapid construction and efficient screening of yeast libraries. No doubt, synthetic biology holds immense promise, with improvements in library construction and screening processes expected to accelerate the development of sustainable solutions in industrial production, environmental protection, and healthcare, thereby driving innovations in biotechnology.

    Standardization for biomanufacturing: global landscape, critical challenges, and pathways forward
    HUANG Yi, SI Tong, LU Anjing
    2025, 6(3):  701-714.  doi:10.12211/2096-8280.2025-040
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    Biomanufacturing represents a strategic frontier in the global technological revolution and industrial transformation, disruptively reshaping how we produce various products such as biofuels, bioenergy, biobased chemicals, and biomaterials through the convergence of synthetic biology, artificial intelligence, and other cutting-edge technologies. However, the persistent lack of comprehensive standardization frameworks in this emerging field poses significant challenges. Standardization in biomanufacturing is essential for accelerating scientific discovery, enhancing production efficiency, and ensuring sustainable industry growth. Therefore, major global economies have prioritized biomanufacturing standardization as a critical element of national competitiveness. The United States, through the National Institute of Standards and Technology (NIST), has established itself as a global leader in developing standards for data, metrology, intelligent algorithms, and automated facilities. NIST spearheads numerous ISO standards in biotechnology and biomanufacturing that shape international practices. In the United Kingdom, the Centre for Engineering Biology Metrology and Standards formulates roadmaps to guide the development of metrology and standards for engineering biological species. The European Union fosters the standards in metrology, chassis, yeast, and other eukaryotic systems through the International Cooperation for Synthetic Biology Standardization Project (BioRoBoost). The European Committee for Standardization has been particularly active in developing and updating standards for biomaterials and biobased products including wood-derived products, while establishing corresponding product classification rules. Since launching its 14th Five-Year Plan, China has strategically prioritized the development of standardization for biomanufacturing across the entire value chain, including key components such as sensors, production equipment like bioreactors, and operational processes such as production technical specifications. Furthermore, China has implemented a series of standards for product quality control, testing methods, and evaluation procedures across various biomanufacturing application sectors, including food, pharmaceuticals, fine chemicals, and others. This article presents a comprehensive assessment on the development of biomanufacturing standardization worldwide and in China. At the international level, we focus on standards issued by major international organizations in three major categories: basic commonalities, enabling technologies, and application fields. At the domestic level, our analysis is based on systematic data mining from China Standards Service Network. Our findings reveal several notable patterns. First, the distribution of standard types shows a clear hierarchical structure, with association standards comprising the majority, which highlights the pivotal role of professional organizations in driving technical integration and standardization. Second, we observed substantial variations in standardization maturity across application sectors. Biobased materials currently possess the most comprehensive portfolio with standards for standardization, followed by rapid progress in biobanking and active pharmaceutical ingredient manufacturing. Based on the current status, we identifies major challenges including the lagging of standard formulation, the obstruction of cross-disciplinary standard coordination, and the insufficiency of mutual recognition for international standards, through multi-dimensional analysis encompassing technological development, industrial ecosystem, and international collaboration. To address these challenges, we propose a strategic framework for developing biomanufacturing standards, including the construction of a dynamic standard transformation mechanism, the establishment of a cross-sector standard coordination platform, and the implementation of a standard internationalization plan. These recommendations provide both theoretical foundations and decision-making references for accelerating the development of biomanufacturing standardization system in China. By facilitating the transition from technology-driven to standard-led development in biomanufacturing, this framework aims to help secure China’s competitiveness in the global economy.