Vinoj Chamilka Liyanaarachchi, Gannoru Kankanamalage Sanuji Hasara Nishshanka, P H Viraj Nimarshana, Jo-Shu Chang, Thilini U Ariyadasa, Dillirani Nagarajan
{"title":"通过机器学习、数学和代谢网络建模建立虾青素生物合成模型。","authors":"Vinoj Chamilka Liyanaarachchi, Gannoru Kankanamalage Sanuji Hasara Nishshanka, P H Viraj Nimarshana, Jo-Shu Chang, Thilini U Ariyadasa, Dillirani Nagarajan","doi":"10.1080/07388551.2023.2237183","DOIUrl":null,"url":null,"abstract":"<p><p>Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.</p>","PeriodicalId":10752,"journal":{"name":"Critical Reviews in Biotechnology","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling.\",\"authors\":\"Vinoj Chamilka Liyanaarachchi, Gannoru Kankanamalage Sanuji Hasara Nishshanka, P H Viraj Nimarshana, Jo-Shu Chang, Thilini U Ariyadasa, Dillirani Nagarajan\",\"doi\":\"10.1080/07388551.2023.2237183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.</p>\",\"PeriodicalId\":10752,\"journal\":{\"name\":\"Critical Reviews in Biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Critical Reviews in Biotechnology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/07388551.2023.2237183\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical Reviews in Biotechnology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/07388551.2023.2237183","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/16 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling.
Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.
期刊介绍:
Biotechnological techniques, from fermentation to genetic manipulation, have become increasingly relevant to the food and beverage, fuel production, chemical and pharmaceutical, and waste management industries. Consequently, academic as well as industrial institutions need to keep abreast of the concepts, data, and methodologies evolved by continuing research. This journal provides a forum of critical evaluation of recent and current publications and, periodically, for state-of-the-art reports from various geographic areas around the world. Contributing authors are recognized experts in their fields, and each article is reviewed by an objective expert to ensure accuracy and objectivity of the presentation.