@article{3e367be04f83469ba401e143f65d3f34,
title = "A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403",
abstract = "The cyanobacterial phycobiliproteins (PBPs) are an important natural colorant for nutraceutical industries. Here, a multi-objective hybrid machine learning-based optimization approach was used for enhanced cell biomass and PBPs production simultaneously in Nostoc sp. CCC-403. A central composite design (CCD) was employed to design an experimental setup for four input parameters, including three BG-11 medium components and pH. We achieved a 61.76% increase in total PBPs production and an almost 90% increase in cell biomass by our prediction model. We also established a test genome-scale metabolic network (GSMN) for Nostoc sp. and identified potential metabolic fluxes contributing to PBPs enhanced production. This study highlights the advantage of the hybrid machine learning approach and GSMN to achieve optimization for more than one objective and serves as the foundation for future efforts to convert cyanobacteria as an economically viable source for biofuels and natural products.",
author = "Saini, {Dinesh Kumar} and Amit Rai and Alka Devi and Sunil Pabbi and Deepak Chhabra and Chang, {Jo Shu} and Pratyoosh Shukla",
note = "Funding Information: SP acknowledges the support from Department of Biotechnology, Govt. of India (BT/PR15686/AAQ/3/811/2016). PS acknowledges the infrastructural support from Department of Science and Technology, Govt. of India, New Delhi, through FIST grant (Grant No. 1196 SR/FST/LS-I/2017/4) and Department of Biotechnology, Government of India (Grant no. BT/PR27437/BCE/8/1433/2018). AR was supported by Grant-in-Aid for Scientific Research (S), JSPS (19H05652). DKS acknowledges minor technical help from Ms Diya Roy in performing purification steps. We would also like to express our gratitude to Megha Rai, Assistant Professor, Chiba University, Japan, and Dr. Gourvendu Saxena, National University of Singapore, Singapore for offering us valuable inputs to improve the content of this manuscript. PS also acknowledges, the Lab Infrastructure grant by BHU, Varanasi (F(C)/XVIII-Spl.Fund/Misc/Infrastructure/Instt.Sc/ 2019-2020/10290) and BTISNET- Sub-Distributed Information Centre, funded by DBT, Govt. of India at the School of Biotechnology, Banaras Hindu University, Varanasi, India. Funding Information: SP acknowledges the support from Department of Biotechnology, Govt. of India (BT/PR15686/AAQ/3/811/2016). PS acknowledges the infrastructural support from Department of Science and Technology, Govt. of India, New Delhi, through FIST grant (Grant No. 1196 SR/FST/LS-I/2017/4) and Department of Biotechnology, Government of India (Grant no. BT/PR27437/BCE/8/1433/2018). AR was supported by Grant-in-Aid for Scientific Research (S), JSPS (19H05652). DKS acknowledges minor technical help from Ms Diya Roy in performing purification steps. We would also like to express our gratitude to Megha Rai, Assistant Professor, Chiba University, Japan, and Dr. Gourvendu Saxena, National University of Singapore, Singapore for offering us valuable inputs to improve the content of this manuscript. PS also acknowledges, the Lab Infrastructure grant by BHU, Varanasi (F(C)/XVIII-Spl.Fund/Misc/Infrastructure/Instt.Sc/ 2019-2020/10290) and BTISNET- Sub-Distributed Information Centre, funded by DBT, Govt. of India at the School of Biotechnology, Banaras Hindu University, Varanasi, India. Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",
year = "2021",
month = jun,
doi = "10.1016/j.biortech.2021.124908",
language = "English",
volume = "329",
journal = "Agricultural Wastes",
issn = "0960-8524",
publisher = "Elsevier Limited",
}