Machine Learning Modeling for Ultrasonication-Mediated Fermentation of Penicillium brevicompactum to Enhance the Release of Mycophenolic Acid

Patel, Gopal; Patil, Mahesh D.; Tangadpalliwar, Sujit R.; Nile, Shivraj Hariram; Garg, Prabha; Kai, Guoyin; Banerjee, Uttam Chand

doi:10.1016/j.ultrasmedbio.2020.11.018

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…[ 122 ] Recently, using the k‐nearest‐neighbor (KNN) method, a 1.64‐fold improvement in Penicillium brevicompactum fermentation producing mycophenolic acid (MPA) has been obtained. [ 123 ]…”

Section: Ml: a Data‐driven Approachmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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Section: Ml: a Data‐driven Approachmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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show abstract

“…For submerged fermentation, the yield of MPA was increased from 1.2 to 1.7 g/L using the wild strain of P. brevicompactum (MTCC 8010) with central composite design (CCD) and one-at-a-time variation factors method at the shake flask level [ 17 ]. In the submerged fermentation process, the maximum MPA production was reached 3.1 g/L using P. brevicompactum with ultrasonicated batch fermentation at the shake flask level, which is 1.64-fold higher than without ultrasonicated batch fermentation [ 18 ]. It was reported that a titer of 6.7 g/L MPA was reached using strain of NRRL864 by increasing the intracellular Ca 2+ concentration of MPA high-producing strain NRRL864 and medium optimization at the shake flask level [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Production of Mycophenolic Acid from Penicillium brevicompactum via Optimized Fermentation Strategy

Bilal

et al. 2022

Appl Biochem Biotechnol

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Mycophenolic acid (MPA) is an occurring antibiotic produced through Penicillium brevicompactum . Its production was achieved by systematic process optimization under submerged fermentation. In shake flask, single-factor test experiments, Box–Behnken design (BBD) experiments, and fermentation strategy were determined, and the MPA yield was reached at 3002 ± 47, 3610 ± 51, and 4748 ± 59 µg/mL, respectively. For fermentation strategy, MPA production was 58.1% higher than that initial fermentation condition without optimization. Then, the optimized medium was further carried out in 5-L stirred fermenter for 180 h; MPA titer was increased from 3712 ± 65 µg/mL to 5786 ± 76 µg/mL, 55.9% higher than that of single-factor optimized medium. The results of this investigation will provide a vital step toward industrial-scale production of MPA.

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Artificial Intelligence and Discovery of Microbial Natural Products

Hamdy,

Altaie,

El-Labbad

et al. 2023

Drug Discovery and Design Using Natural Products

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Machine Learning Modeling for Ultrasonication-Mediated Fermentation of Penicillium brevicompactum to Enhance the Release of Mycophenolic Acid

Cited by 4 publications

References 47 publications

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Enhanced Production of Mycophenolic Acid from Penicillium brevicompactum via Optimized Fermentation Strategy

Artificial Intelligence and Discovery of Microbial Natural Products

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