Sheng wu gong cheng xue bao = Chinese journal of biotechnology最新文献

[Artificial intelligence-enhanced physics-based computational modeling technologies for proteins]. [基于人工智能增强物理的蛋白质计算建模技术]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240604

Baoyan Liu, Shuai Li, Hao Su, Xiang Sheng

{"title":"[Artificial intelligence-enhanced physics-based computational modeling technologies for proteins].","authors":"Baoyan Liu, Shuai Li, Hao Su, Xiang Sheng","doi":"10.13345/j.cjb.240604","DOIUrl":"https://doi.org/10.13345/j.cjb.240604","url":null,"abstract":"Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challenges at different levels and has become a central focus of research in biomanufacturing. In the computational modeling of proteins, which are the key parts in biological systems, the traditional physics-based methods (computer software and mathematical model) have been widely used to study the physical and chemical processes in the functioning of proteins, and have thus been recognized as a powerful tool for understanding complex biological systems and guiding experimental designs. As the scale of computational modeling continues to expand, traditional modeling techniques face difficulties in balancing computational accuracy and speed. In recent years, the explosive growth of biological data has made it possible to construct high-performance artificial intelligence (AI) models, which brings new opportunities to the computational modeling of proteins, and the AI-enhanced physics-based computational modeling technologies have emerged. This combined strategy not only incorporates the chemical knowledge and established physical principles but also is powerful in data processing and pattern recognition, which greatly improves the computational efficiency and prediction accuracy, as well as possesses stronger interpretation ability, transferability, and robustness. The AI-enhanced physics-based computational modeling technologies have already shown great potential and value in biocatalysis, paving a new way for the future development of biomanufacturing.","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"917-933"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Machine learning-aided design of synthetic biological parts and circuits]. [合成生物部件和电路的机器学习辅助设计]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240605

Ruichao Mao, Baojun Wang

{"title":"[Machine learning-aided design of synthetic biological parts and circuits].","authors":"Ruichao Mao, Baojun Wang","doi":"10.13345/j.cjb.240605","DOIUrl":"https://doi.org/10.13345/j.cjb.240605","url":null,"abstract":"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.","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1023-1051"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Mathematical modelling for cellular processes]. [细胞过程的数学建模]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.250061

Yan Zhu, Jibin Sun

{"title":"[Mathematical modelling for cellular processes].","authors":"Yan Zhu, Jibin Sun","doi":"10.13345/j.cjb.250061","DOIUrl":"https://doi.org/10.13345/j.cjb.250061","url":null,"abstract":"Biomanufacturing harnesses engineered cells for the large-scale production of biochemicals, biopharmaceuticals, biofuels, and biomaterials, playing a vital role in mitigating global environmental crises, achieving carbon peaking and neutrality, and driving the green transformation of the economy and society. The effective design and construction of these engineered cells require precise and comprehensive computational models. Recent technological breakthroughs including high-throughput sequencing, mass spectrometry, spectroscopy, and microfluidic devices, coupled with advances in data science, artificial intelligence, and automation, have enabled the rapid acquisition of large-scale biological datasets, thereby facilitating a deeper understanding of cellular dynamics and the construction of mechanism-based models with enhanced accuracy. This review systematically summarises the mathematical frameworks employed in cellular modelling. It begins by evaluating prevalent mathematical paradigms, such as network topology analyses, stochastic processes, and kinetic equations, critically assessing their applicability across various contexts. The discussion then categorises modelling strategies for specific cellular processes, including cellular growth and division, morphogenesis, DNA replication, transcriptional regulation, metabolism, signal transduction, and quorum sensing. We also examine the recent progress in developing whole-cell models through the integration of diverse cellular processes. The review concludes by addressing key challenges such as data scarcity, unknown mechanisms, multi-dimensional data integration, and exponentially escalating computational complexity. Overall, this work consolidates the mathematical models for the precise simulation of cellular processes, thereby enhancing our understanding of the molecular mechanisms governing cellular functions and contributing to the future design and optimisation of engineered organisms.","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1052-1078"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Advances in the regulation of microbial cell metabolism and environmental adaptation]. 微生物细胞代谢与环境适应调控研究进展

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240937

Yuan Liu, Guipeng Hu, Xiaomin Li, Jia Liu, Cong Gao, Liming Liu

{"title":"[Advances in the regulation of microbial cell metabolism and environmental adaptation].","authors":"Yuan Liu, Guipeng Hu, Xiaomin Li, Jia Liu, Cong Gao, Liming Liu","doi":"10.13345/j.cjb.240937","DOIUrl":"https://doi.org/10.13345/j.cjb.240937","url":null,"abstract":"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.","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1133-1151"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Data-driven multi-omics analyses and modelling for bioprocesses]. [数据驱动的多组学分析和生物过程建模]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.250065

Yan Zhu, Zhidan Zhang, Peibin Qin, Jie Shen, Jibin Sun

{"title":"[Data-driven multi-omics analyses and modelling for bioprocesses].","authors":"Yan Zhu, Zhidan Zhang, Peibin Qin, Jie Shen, Jibin Sun","doi":"10.13345/j.cjb.250065","DOIUrl":"https://doi.org/10.13345/j.cjb.250065","url":null,"abstract":"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.","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1152-1178"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Intelligent mining, engineering, and de novo design of proteins]. [蛋白质的智能挖掘、工程和从头设计]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240629

Cui Liu, Zhenkun Shi, Hongwu Ma, Xiaoping Liao

{"title":"[Intelligent mining, engineering, and de novo design of proteins].","authors":"Cui Liu, Zhenkun Shi, Hongwu Ma, Xiaoping Liao","doi":"10.13345/j.cjb.240629","DOIUrl":"https://doi.org/10.13345/j.cjb.240629","url":null,"abstract":"Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"993-1010"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Artificial intelligence-assisted design, mining, and modification of CRISPR-Cas systems]. [人工智能辅助CRISPR-Cas系统的设计、挖掘和修改]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240865

Yufeng Mao, Guangyun Chu, Qingling Liang, Ye Liu, Yi Yang, Xiaoping Liao, Meng Wang

{"title":"[Artificial intelligence-assisted design, mining, and modification of CRISPR-Cas systems].","authors":"Yufeng Mao, Guangyun Chu, Qingling Liang, Ye Liu, Yi Yang, Xiaoping Liao, Meng Wang","doi":"10.13345/j.cjb.240865","DOIUrl":"https://doi.org/10.13345/j.cjb.240865","url":null,"abstract":"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.","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"949-967"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Mesoscale simulation and AI optimization of bioprocesses]. [生物过程的中尺度模拟和人工智能优化]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240598

Zhihui Wang, Cong Wang, Qinghua Zhang, Jianye Xia, Wei Cong, Chao Yang

引用次数: 0

[Research progress in energy metabolism design of cell factories]. [细胞工厂能量代谢设计研究进展]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240565

Yiqun Yang, Qingqing Liu, Shuo Tian, Tao Yu

引用次数: 0

[Research progress in mutation effect prediction based on protein language models]. [基于蛋白质语言模型的突变效应预测研究进展]。

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Pub Date : 2025-03-25 DOI: 10.13345/j.cjb.240683

Liang Zhang, Pan Tan, Liang Hong

引用次数: 0