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*

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

OBJECTIVES: This study analyzed the health technology trends and sentiments of users using Twitter data in an attempt to examine the public's opinions and identify their needs. METHODS: Twitter data related to health technology, from January 2010 to October 2016, were collected. An ontology related to health technology was developed. Frequently occurring keywords were analyzed and visualized with the word cloud technique. The keywords were then reclassified and analyzed using the developed ontology and sentiment dictionary. Python and the R program were used for crawling, natural language processing, and sentiment analysis. RESULTS: In the developed ontology, the keywords are divided into ‘health technology‘ and ‘health information‘. Under health technology, there are are six subcategories, namely, health technology, wearable technology, biotechnology, mobile health, medical technology, and telemedicine. Under health information, there are four subcategories, namely, health information, privacy, clinical informatics, and consumer health informatics. The number of tweets about health technology has consistently increased since 2010; the number of posts in 2014 was double that in 2010, which was about 150 thousand posts. Posts about mHealth accounted for the majority, and the dominant words were ‘care‘, ‘new‘, ‘mental‘, and ‘fitness‘. Sentiment analysis by subcategory showed that most of the posts in nearly all subcategories had a positive tone with a positive score. CONCLUSIONS: Interests in mHealth have risen recently, and consequently, posts about mHealth were the most frequent. Examining social media users' responses to new health technology can be a useful method to understand the trends in rapidly evolving fields.
Biomedical Technology ; Biotechnology ; Boidae ; Data Mining ; Informatics ; Medical Informatics ; Methods ; Natural Language Processing ; Privacy ; Public Opinion ; Social Media ; Telemedicine

Construction of artificial cell factory to produce specific compounds of interest needs wild strain to be genetically engineered. In recent years, with the reconstruction of many genome-scale metabolic networks, a number of methods have been proposed based on metabolic network analysis for predicting genetic modification targets that lead to overproduction of compounds of interest. These approaches use constraints of stoichiometry and reaction reversibility in genome-scale models of metabolism and adopt different mathematical algorithms to predict modification targets, and thus can discover new targets that are difficult to find through traditional intuitive methods. In this review, we introduce the principle, merit, demerit and application of various strain optimization methods in detail. The main problems in existing methods and perspectives on this emerging research field are also discussed, aiming to provide guidance to choose the appropriate methods according to different types of products and the reliability of the predicted results.
Algorithms ; Biotechnology ; methods ; Computer Simulation ; Genome ; Industrial Microbiology ; Metabolic Engineering ; methods ; Metabolic Networks and Pathways ; Models, Theoretical ; Reproducibility of Results

Breeding is not only an important area of medicinal plants research but also the foundation for the superior varieties acquirement of medicinal plants. The rise of modern biotechnology provides good opportunities and new means for medicinal plants breeding research in China. Biotechnology shows its technical advantages and new development prospects in breeding of new medicinal plants varieties with high and stable yield, good quality, as well as stress-resistance. In this paper, we describe recent advances, problems, and development prospects about the application of modern biotechnology in medicinal plants breeding research in China.
Biotechnology ; methods ; Breeding ; China ; Plants, Medicinal ; genetics ; Research ; Tissue Culture Techniques