[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119945-en":3,"doc-seo-119945-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119945,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Optimizing the Production of Valuable Metabolites using a Hybrid of Constraint-based Model and Machine Learning Algorithms: A Review","Advances in genome sequencing and metabolic engineering enable reengineering of cellular functions, while the growth of omics data shifts molecular biology toward data-driven workflows. This review consolidates research published from 2020 to 2023 that combines constraint-based modeling with machine learning to optimize valuable metabolites. Studies are collected from Web of Science and Scopus using terms such as machine learning, flux balance analysis, and metabolic engineering, yielding 13 records. The paper summarizes current in silico approaches, discusses integration methodology, highlights research gaps, and proposes future directions for hybrid optimization strategies.","(IJACSA) International Journal of Advanced Computer Science and Applications,  \nVol. 14, No. 10, 2023  \nOptimizing the Production of Valuable Metabolites using a Hybrid of Constraint-based Model and Machine Learning Algorithms: A Review  \nKauthar Mohd Daud 1 , Ridho Ananda2 , Suhaila Zainudin3 , Chan Weng Howe4 , Kohbalan Moorthy5 , Nurul Izrin Binti Md Saleh6  \nCenter for Artificial Intelligence Technology, Faculty of Information Science and Technology,  \nUniversiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia 1 , 2 , 3  \nInstitut Teknologi Telkom Purwokerto, Indonesia2  \nUTM Big Data Centre, Faculty of Computing, Universiti Teknologi Malaysia,  \n81310 UTM Johor Bahru, Johor Malaysia4  \nFaculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang Malaysia5  \nFaculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka,  \nHang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia6  \nAbstract—The advances in genome sequencing and metabolic engineering have allowed the reengineering of the cellular function of an organism. Furthermore, given the abundance of omics data, data collection has increased considerably, thus shifting the perspective of molecular biology. Therefore, researchers have recently used artificial intelligence and machine learning tools to simulate and improve the reconstruction and analysis by identifying meaningful features from the large multi-omics dataset. This review paper summarizes research on the hybrid of constraint-based models and machine learning algorithms in optimizing valuable metabolites. The research articles published between 2020 and 2023 on machine learning and constraintbased modeling have been collected, synthesized, and analyzed. The articles are obtained from the Web of Science and Scopus databases using the keywords: “Machine learning”,“flux balance analysis”, and “metabolic engineering”. At the end of the search, this review contained 13 records. This review paper aims to provide current trends and approaches in in silico metabolic engineering while providing research directions by highlighting the research gaps. In addition, we have discussed the methodology for integrating machine learning and constraint-based modeling approaches.  \nKeywords—Flux balance analysis; genome-scale metabolic model; machine learning; metabolic engineering  \nI. INTRODUCTION  \nMicroorganisms have been used in industrial sectors such as food processing, chemical manufacturing, pharmaceuticals, fermentation, and others. Advances in genome sequencing have resulted in several innovations that allow researchers to gain in-depth knowledge and information about an organism. One of these advancements is metabolic engineering, which reengineers the cellular function of an organism. In the 1990s, metabolic engineering was introduced to describe recombinant DNA technology for optimizing microbial activity [1] . Metabolic engineering aims to optimize the synthesis of desired metabolites by directing the metabolic flow and the fluxes toward the desired metabolites. The designs are categorized into two types: [1] targeting metabolic network components, such as gene/reaction knockout/knock-in, and [2]  \nenhancing the metabolic network by altering it using network reconstruction tools or incorporating new non-native pathways into the host.  \nOver the previous few decades, there has been a noticeable breakthrough, such as incorporating adenosylcobinamide phosphate biosynthesis from Rhadobacter capsulatus into the E.coli strain, which improves the vitamin B 12 to 307 µg/g [2] . In another case, the yeast was engineered to improve the production of rubusoside and rebaudiosides, leading to 1368.6 mg/L and 132.7 mg/L, respectively [3] . Although metabolic pathway optimization technologies have shown promise, an incomplete understanding of the connection between target cell phenotype and genotype impedes their further development. This results in the prevalent utilizati","cbCaiudDaSFRXH3u","https://ap.wps.com/l/cbCaiudDaSFRXH3u","pdf",1137521,1,15,"English","en",105,"# Introduction\n## Metabolic engineering and metabolite optimization\n## Constraint-based modeling (CBM) and its variants\n## Challenges in CBM and traditional optimization\n# Hybrid approaches: machine learning with CBM\n## Role of omics data and big-data driven analysis","[{\"question\":\"What problem does this review address about producing valuable metabolites?\",\"answer\":\"It addresses how to optimize the production of valuable metabolites using hybrid strategies that integrate constraint-based modeling with machine learning to improve reconstruction and analysis from large multi-omics datasets.\"},{\"question\":\"How were the reviewed studies selected and how many records were included?\",\"answer\":\"The studies were collected from Web of Science and Scopus using keywords such as machine learning, flux balance analysis, and metabolic engineering, and the review ended with 13 records.\"},{\"question\":\"What are the main challenge(s) in constraint-based modeling that motivate hybrid methods?\",\"answer\":\"A key issue is that desired fluxes may not be limited to a single solution due to network redundancy, and selecting appropriate reactions/genes for knockout is difficult, laborious, and time-consuming.\"}]","Optimizing the Production of Valuable Metabolites using a Hybrid of Constraint-based Model and Machine Learning Algorithms: A Review | 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problem does this review address about producing valuable metabolites?","Question",{"text":75,"@type":76},"It addresses how to optimize the production of valuable metabolites using hybrid strategies that integrate constraint-based modeling with machine learning to improve reconstruction and analysis from large multi-omics datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the reviewed studies selected and how many records were included?",{"text":80,"@type":76},"The studies were collected from Web of Science and Scopus using keywords such as machine learning, flux balance analysis, and metabolic engineering, and the review ended with 13 records.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main challenge(s) in constraint-based modeling that motivate hybrid methods?",{"text":84,"@type":76},"A key issue is that desired fluxes may not be limited to a single solution due to network redundancy, and selecting appropriate reactions/genes for knockout is 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