This article reviews the review articles and research papers related to biomanufacturing driven by engineered organisms published in the Chinese Journal of Biotechnology from 2023 to 2024. The content covers 26 aspects, including chassis cells; gene (genome) editing; facilities, tools and methods; biosensors; protein design and engineering; peptides and proteins; screening, expression, characterization and modification of enzymes; biocatalysis; bioactive substances; plant natural products; microbial natural products; development of microbial resources and biopesticides; steroidal compounds; amino acids and their derivatives; vitamins and their derivatives; nucleosides; sugars, sugar alcohols, oligosaccharides, polysaccharides and glycolipids; organic acids and monomers of bio-based materials; biodegradation of polymeric materials and biodegradable materials; intestinal microorganisms, live bacterial drugs and synthetic microbiomes; microbial stress resistance engineering; biodegradation and conversion utilization of lignocellulose; C1 biotechnology; bioelectron transfer and biooxidation-reduction; biotechnological environmental protection; risks and regulation of biomanufacturing driven by engineered organisms, with hundreds of technologies and products commented. It is expected to provide a reference for readers to understand the latest progress in research, development and commercialization related to biomanufacturing driven by engineered organisms.
Biotechnology/methods* ; Gene Editing ; Genetic Engineering ; Metabolic Engineering ; Protein Engineering ; Biosensing Techniques

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

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*

Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.
Fermentation ; Genomics/methods* ; Biotechnology/methods* ; Proteomics/methods* ; Models, Biological ; Metabolomics/methods* ; Bioreactors ; Multiomics

As a strategic emerging industry, biomanufacturing faces core challenges in achieving precise optimization and efficient scale-up of fermentation processes. This review focuses on two critical aspects of fermentation-real-time sensing and intelligent control-and systematically summarizes the advancements in online monitoring technologies, artificial intelligence (AI)-driven optimization strategies, and digital twin applications. First, online monitoring technologies, ranging from conventional parameters (e.g., temperature, pH, and dissolved oxygen) to advanced sensing systems (e.g., online viable cell sensors, spectroscopy, and exhaust gas analysis), provide a data foundation for real-time microbial metabolic state characterization. Second, conventional static control relying on expert experience is evolving toward AI-driven dynamic optimization. The integration of machine learning technologies (e.g., artificial neural networks and support vector machines) and genetic algorithms significantly enhances the regulation efficiency of feeding strategies and process parameters. Finally, digital twin technology, integrating real-time sensing data with multi-scale models (e.g., cellular metabolic kinetics and reactor hydrodynamics), offers a novel paradigm for lifecycle optimization and rational scale-up of fermentation. Future advancements in closed-loop control systems based on intelligent sensing and digital twin are expected to accelerate the industrialization of innovative achievements in synthetic biology and drive biomanufacturing toward higher efficiency, intelligence, and sustainability.
Artificial Intelligence ; Fermentation ; Bioreactors/microbiology* ; Neural Networks, Computer ; Algorithms ; Biotechnology/methods*

As green, sustainable, and environmentally friendly material processing processes using biological cells or enzymes to achieve substance conversion, bioprocesses play an increasingly important role in biomanufacturing. It is difficult to optimize bioprocesses because of the complex relationship at multiple levels and multiple scales. The knowledge of mesoscale behaviors is the key to understanding the dynamics of bioprocesses and to sort out the complex relationships of parameter variations in the spatial-temporal domain. Mesoscale numerical simulation paves a way for understanding these phenomena, and the integration of artificial intelligence (AI) and mesoscale simulation offers new vitality into the optimization of bioprocesses. This article reviews the progress in mesoscale simulation and AI optimization of bioprocesses and discusses the possible development directions, aiming to promote the development of this field.
Artificial Intelligence ; Biotechnology/trends* ; Computer Simulation

Microbiome engineering is an emerging interdisciplinary field that systematically investigates and applies engineering methods to uncover the functions, structures, and interaction mechanisms of microbial communities with their environments, offering critical insights into global challenges. To showcase the latest advancements and achievements in this field, Chinese Journal of biotechnology has specially organized a special issue, inviting experts and scholars from multiple domestic institutions to elaborate on the practical applications and potential of microbiome engineering in agriculture and industrial production, environmental and ecological restoration, and health and medical treatment, from perspectives of fundamental research, technological innovation, and engineering applications. Additionally, this issue explores future trends in the field, providing valuable references to promote innovation and contribute to the sustainable development of human society.
Humans ; Bioengineering ; Biotechnology ; Microbiota

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

Agriculture, the strategic cornerstone of national long-term stability, is undergoing a fundamental shift from resource-dependent to technology-driven, driven by global food security and ecological conservation needs. Traditional agriculture can no longer sustain the growing food demand. Scientific and technological advancements are fundamental guarantees for ensuring food supply security and are the primary driver for future agricultural development. This special issue compiles the latest research advancements from diverse experts, covering fields such as microbe-driven green agriculture, pesticide technology innovation, intelligent agricultural machinery, smart manufacturing, and molecular design breeding fundamentals. It aims to inspire researchers to explore cutting-edge directions in future agriculture, promote interdisciplinary collaboration and technological integration, and thereby drive innovative breakthroughs and industrial transformation in agricultural modernization.
Agriculture/methods* ; Crops, Agricultural/genetics* ; Food Supply ; Biotechnology ; Pesticides

The Principle of Biotechnology is a compulsory course for undergraduates majoring in bioengineering at Zhejiang University of Technology. In response to the "Double First-Class" initiative and in order to improve the teaching effect of this course and the quality of talent training, we reformed the teaching of Principle of Biotechnology, the core course in bioengineering. Specifically, we reorganized the teaching contents, improved the process management of teaching and learning, and implemented multi-dimensional teaching practice. These measures improved teaching quality and promoted the achievement of training goals, which was of great significance for developing "First-Class" disciplines.
Biotechnology/education* ; Teaching ; China ; Curriculum ; Bioengineering/education* ; Universities