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

{"title":"[Advances in reconstruction and optimization of cellular physiological metabolic network models].","authors":"Luchi Xiao, Hongzhong Lu","doi":"10.13345/j.cjb.240966","DOIUrl":"https://doi.org/10.13345/j.cjb.240966","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1112-1132"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764964","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}

{"title":"[Research progress in mechanism models and artificial intelligence models for protein expression systems].","authors":"Yi Yang, Jun DU, Chunhe Yang, Hongwu Ma","doi":"10.13345/j.cjb.240935","DOIUrl":"https://doi.org/10.13345/j.cjb.240935","url":null,"abstract":"<p><p>Proteins are the basic building blocks of life. Studying the protein expression mechanism is essential for understanding the cellular organization principles and the development of biotechnology. Protein expression, involving transcription, translation, folding, and post-translational modification, is a complicatedly regulated process affected by various cellular components and sequence features of the expressed protein. Establishing protein expression models based on expression data is of great significance for probing into the regulatory factors and mechanisms of protein expression. Here we review the recent research progress in the mechanism models for quantitatively simulating the protein expression process and the prediction algorithms based on artificial intelligence for analyzing the regulatory factors. Chemical reaction network models have been developed to mathematically describe the elementary processes in protein expression and simulate the influences of various cellular components such as RNA polymerase and tRNA. However, the experimental determination of the huge number of model parameters is a big challenge. The main objective of data-driven AI models is to study the effects of protein/DNA sequences of the target protein on its expression, and subsequently optimize the sequences to improve protein expression. Methods combining mechanism models and AI models have the potential to deepen our understanding of protein expression processes, providing theoretical and technical support for the efficient production of high-value proteins and coordinate the regulation of different proteins.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1079-1097"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764978","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}

{"title":"[Databases, knowledge bases, and large models for biomanufacturing].","authors":"Zhitao Mao, Xiaoping Liao, Hongwu Ma","doi":"10.13345/j.cjb.240653","DOIUrl":"https://doi.org/10.13345/j.cjb.240653","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"901-916"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764989","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}

{"title":"[Preface for special issue on AI-driven biomanufacturing].","authors":"Qinhong Wang, Hongwu Ma, Jianye Xia","doi":"10.13345/j.cjb.250197","DOIUrl":"https://doi.org/10.13345/j.cjb.250197","url":null,"abstract":"<p><p>Biomanufacturing is one of important strategies for sustainable development, China places significant emphasis on the development of biomanufacturing, and the national and local governments have successively introduced special policies for biomanufacturing, and vigorously developing biomanufacturing has become an unstoppable trend. At present, with the rapid development of systems biology and synthetic biology, biological big data and information technology are deeply integrating with biotechnology. Novel theories, methods and technologies for the design, creation and application of biological systems are constantly emerging, which promoted the development of biomanufacturing into the era of artificial intelligence (AI). In order to grasp the innovation and development of AI-driven biomanufacturing, we publish this special issue to review the opportunities, challenges, and development status of AI-driven biomanufacturing from aspects such as AI-driven enabling technologies, intelligent design and construction of biological parts, circuits and artificial cells, as well as intelligent bioprocess control and optimization, and look forward to the future developments. This will provide valuable references for effectively promoting technological innovation and industrial development in the field of biomanufacturing.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"I-VIII"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765030","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}

{"title":"[Intelligent design of nucleic acid elements in biomanufacturing].","authors":"Jinsheng Wang, Zhe Sun, Xueli Zhang","doi":"10.13345/j.cjb.240599","DOIUrl":"https://doi.org/10.13345/j.cjb.240599","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"968-992"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764994","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}

{"title":"[Optimization of fermentation processes in intelligent biomanufacturing: on online monitoring, artificial intelligence, and digital twin technologies].","authors":"Jianye Xia, Dongjiao Long, Min Chen, Anxiang Chen","doi":"10.13345/j.cjb.250032","DOIUrl":"https://doi.org/10.13345/j.cjb.250032","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1179-1196"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765027","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}

{"title":"[Intelligent design of transcription factor-based biosensors].","authors":"Chaoning Liang, La Xiang, Shuangyan Tang","doi":"10.13345/j.cjb.240603","DOIUrl":"https://doi.org/10.13345/j.cjb.240603","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 3","pages":"1011-1022"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764997","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}

{"title":"[Physiological responses and transcriptional regulation of <i>Prunus mume</i> 'Meiren' under drought stress].","authors":"Zixu Wang, Chunyan Luo, Yuhang Tong, Weijun Zheng, Qingwei Li","doi":"10.13345/j.cjb.240524","DOIUrl":"https://doi.org/10.13345/j.cjb.240524","url":null,"abstract":"<p><p><i>Prunus mume</i> is an ecologically and economically valuable plant with both medicinal and edible values. However, drought severely limits the promotion and cultivation of <i>P</i>. <i>mume</i> in the arid and semi-arid areas in northern China. In this study, we treated <i>P</i>. <i>mume</i> 'Meiren' with natural drought and then assessed photosynthetic and physiological indexes such as osmoregulatory substances, photosynthetic parameters, and antioxidant enzyme activities. Furthermore, we employed transcriptome sequencing to explore the internal regulatory mechanism of <i>P</i>. <i>mume</i> under drought stress. As the drought stress aggravated, the levels of chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (a+b)[Chl(a+b)], and soluble protein (SP) in <i>P</i>. <i>mume</i> first elevated and then declined. The net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), maximum photochemical efficiency (Fv/Fm), effective photochemical quantum yield [Y(Ⅱ)], photochemical quenching (qP), and relative electron transport rate (ETR) all kept decreasing, while the levels of malondialdehyde, superoxide dismutase (SOD), peroxidase (POD), and osmoregulatory substances rose. Transcriptome sequencing revealed a total of 24 853 high-quality genes. Gene ontology (GO) enrichment showed that differentially expressed genes (DEGs) were the most under severe drought. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis showed that the DEGs during the four drought periods were mainly involved in the biosynthesis of secondary metabolites, plant-pathogen interaction, plant hormone signal transduction, starch and sucrose metabolism, and mitogen-activated protein kinase signaling pathways. Furthermore, we identified 16 key genes associated with the drought tolerance of <i>P</i>. <i>mume</i> 'Meiren'. This study discovered that <i>P</i>. <i>mume</i> might up-regulate or down-regulate the expression of drought tolerance-related genes such as <i>SUS</i>, <i>P5CS</i>, <i>LEA</i>, <i>SOD</i>, <i>POD</i>, <i>SOD1</i>, <i>TPPD</i>, and <i>TPPA</i> <i>via</i> transcription factors like MYB, ERF, bHLH, NAC, and WRKY to promote the accumulation of osmoregulatory substances like sucrose and enhance the activities of antioxidant enzymes such as SOD and POD, thus reducing the harm of reactive oxygen species and protecting the structure and function of the membrane system under drought stress. The findings provide theoretical references for further exploration of candidate genes of <i>P</i>. <i>mume</i> in response to drought stress and breeding of drought-tolerant varieties.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 2","pages":"618-638"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143483988","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}

{"title":"[Arbuscular mycorrhizal fungi improve physiological metabolism and ameliorate root damage of <i>Coleus scutellarioides</i> under cadmium stress].","authors":"Yanan Hou, Fan Jiang, Shuyang Zhou, Dingyin Chen, Yijie Zhu, Yining Miao, Kai Ceng, Yifang Wang, Min Wu, Peng Liu","doi":"10.13345/j.cjb.240299","DOIUrl":"https://doi.org/10.13345/j.cjb.240299","url":null,"abstract":"<p><p>Soil cadmium pollution can adversely affect the cultivation of the ornamental plant, <i>Coleus scutellarioides</i>. Upon cadmium contamination of the soil, the growth of <i>C</i>. <i>scutellarioides</i> is impeded, and it may even succumb to the toxic accumulation of cadmium. In this study, we investigated the effects of arbuscular mycorrhizal fungi (AMF) on the adaptation of <i>C</i>. <i>scutellarioides</i> to cadmium stress, by measuring the physiological metabolism and the degree of root damage of <i>C</i>. <i>scutellarioides</i>, with <i>Aspergillus oryzae</i> as the test fungi. The results indicated that cadmium stress increased the activity of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and the content of malondialdehyde (MDA) and proline (Pro) within the cells of <i>C</i>. <i>scutellarioides</i>, but inhibited mycorrhizal infestation rate, root vigour and growth rate to a great degree. With the same cadmium concentration, the inoculation of AMF significantly improved the physiological indexes of <i>C</i>. <i>scutellarioides</i>. The maximum decrease of MDA content was 42.16%, and the content of secondary metabolites rosemarinic acid and anthocyanosides could be increased by up to 27.43% and 25.72%, respectively. Meanwhile, the increase of root vigour was as high as 35.35%, and the DNA damage of the root system was obviously repaired. In conclusion, the inoculation of AMF can promote the accumulation of secondary metabolites, alleviate root damage, and enhance the tolerance to cadmium stress in <i>C</i>. <i>scutellarioides</i>.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 2","pages":"680-692"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143483851","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}

{"title":"[LBD gene family in <i>Hippophae rhamnoides</i>: identification and expression pattern during flower bud development].","authors":"Xinjuan Li, Panpan Yang, Tian Zhang, Qiandan Ren, Wu Zhou","doi":"10.13345/j.cjb.240157","DOIUrl":"https://doi.org/10.13345/j.cjb.240157","url":null,"abstract":"<p><p>Lateral organ boundaries (LOB) domain (LBD) genes encode a family of transcription factors ubiquitous in higher plants, playing crucial roles in the growth, development, and stress responses. <i>Hippophae rhamnoides</i>, known for its drought, cold, and saline-alkali tolerance, offers significant economic benefits and ecological values. Utilizing the whole genome data and bioinformatics approaches, this study identified and analyzed the LBD gene family in <i>H</i>. <i>rhamnoides</i>. Additionally, we examined the expression pattern of HrLBD genes by integrating the transcriptome data from male and female flower buds in development. Eleven LBD genes were identified in <i>H</i>. <i>rhamnoides</i>, and these genes were distributed on five chromosomes. The HrLBD proteins showed the lengths ranging from 159 aa to 302 aa, the molecular weights between 18 249.91 Da and 33 202.01 Da, and the subcellular localization in the nucleus or chloroplasts. LBD protein domains and gene structures were highly conserved, featuring similar motifs. The phylogenetic analysis of HrLBD genes and the LBD genes in <i>Arabidopsis thaliana</i> and <i>Hordeum vulgare</i> revealed that HrLBD genes falled into two major categories: Class Ⅰ and Class Ⅱ. The transcriptome data and RT-qPCR showed that HrLBD genes were highly expressed in male flower buds, with up-regulated expression levels throughout bud development, indicating a role in the specific stage of male flower bud development. This study lays a theoretical foundation for exploring the roles of HrLBD genes in the growth, development, and sex differentiation of <i>H</i>. <i>rhamnoides</i> flower buds.</p>","PeriodicalId":21778,"journal":{"name":"Sheng wu gong cheng xue bao = Chinese journal of biotechnology","volume":"41 2","pages":"753-770"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143483971","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}