Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 January 2024 V1 Latest version Share on Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production Authors : Nipon Sarmah , Vazida Mehtab , Pratyusha Bugata , James Tardio , Suresh Bhargava 0000-0002-3127-8166 , Rajarathinam Parthasarathy , and Sumana Chenna 0000-0003-0283-8723 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.170670211.12836928/v1 Published Bioresource Technology Version of record Peer review timeline 263 views 135 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Lipase derived from Candida antractica is the most widely used enzyme for catalyzing various reactions. This paper reports the growth and enzyme kinetics of Candida antarctica MTCC-2706 for lipase production, which is one of the fundamental steps in bioprocess design, optimization, and scale-up studies. A hybrid machine learning (ML) aided experimental approach is proposed here to evaluate growth kinetics in which, different ML models were built to predict the growth curves at various substrate concentrations using a substantially smaller set of experimental samples. Comparative assessment of model performances revealed the superiority of gradient boosting regression (GBR) in predicting the growth curves. GBR-based growth kinetics was found to be fitted well with Monod’s model. Further, the activity and enzyme kinetics of lipase produced was investigated by considering the hydrolysis of p-nitrophenyl butyrate. The maximum lipase activity resulted was 24.07 U at 44 hrs. The deviation and R2 values of Monod’s and Michaelis-Menten’s models were 1.4% and 2.25%, and 0.96 and 0.99, respectively. The proposed ML-based approach is found to be efficient in predicting the growth kinetics with reduced experimental effort, time and resources (~50%) as compared to conventional method and its application can be extended to any other microbial processes. Supplementary Material File (manuscript.docx) Download 126.84 KB Information & Authors Information Version history V1 Version 1 31 January 2024 Peer review timeline Published Bioresource Technology Version of Record 1 May 2022 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords biocatalysis biochemical engineering candida antarctica fermentation gradient boosting regression machine learning microbiology modeling yeast Authors Affiliations Nipon Sarmah Indian Institute of Chemical Technology CSIR View all articles by this author Vazida Mehtab Indian Institute of Chemical Technology CSIR View all articles by this author Pratyusha Bugata Indian Institute of Chemical Technology CSIR View all articles by this author James Tardio Royal Melbourne Institute of Technology View all articles by this author Suresh Bhargava 0000-0002-3127-8166 Royal Melbourne Institute of Technology View all articles by this author Rajarathinam Parthasarathy Royal Melbourne Institute of Technology View all articles by this author Sumana Chenna 0000-0003-0283-8723 [email protected] Indian Institute of Chemical Technology CSIR View all articles by this author Metrics & Citations Metrics Article Usage 263 views 135 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Nipon Sarmah, Vazida Mehtab, Pratyusha Bugata, et al. Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production. Authorea . 31 January 2024. DOI: https://doi.org/10.22541/au.170670211.12836928/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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