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

Sarmah, Nipon; Mehtab, Vazida; Bugata, Pratyusha; Tardio, James; Bhargava, Suresh K.; Parthasarathy, Rajarathinam; Sumana, C.

doi:10.1016/j.biortech.2022.127087

Cited by 12 publications

(7 citation statements)

References 45 publications

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“…114 Sarmah et al utilized ML technology to assess the growth kinetics of lipase production by Candida antarctica. 47 Different ML-based models were constructed, and a final AI model of gradient boosting regression (GBR) was obtained with good agreement with the results from conventional experimental methods. After arriving at an ideal model, the experimental effort, time, and resources were reduced by approximately 50%.…”

Section: Ai-aided Lipase Productionmentioning

confidence: 77%

“…46 Sarmah et al applied a hybrid ML-assisted experimental approach to assess the growth kinetics of lipase production by Mycobacterium antracis; various ML models were developed and refined to anticipate the growth rates at different concentrations of substrate with good enzyme yields. 47 Gradient boosting regression (GBR)-based growth kinetics was found to closely match the outcomes of traditional experimental methods. The ML-assisted experimental workload was reduced by approximately 50% when matched with traditional methods.…”

Section: Models and Algorithms For Ai In Lipase Technologymentioning

confidence: 92%

“…Not only do the digital assistants of large Internet companies rely heavily on ML but also the biological field is also gradually integrating with ML, e.g., in aspects of enzyme engineering . Sarmah et al applied a hybrid ML-assisted experimental approach to assess the growth kinetics of lipase production by Mycobacterium antracis; various ML models were developed and refined to anticipate the growth rates at different concentrations of substrate with good enzyme yields . Gradient boosting regression (GBR)-based growth kinetics was found to closely match the outcomes of traditional experimental methods.…”

Section: Models and Algorithms For Ai In Lipase Technologymentioning

confidence: 99%

“…Future improvements in computing power may make it possible to routinely use bioreactor and dynamic models that can mechanically describe mass and heat transfer as well as particle phenomena at the macroscale . Sarmah et al utilized ML technology to assess the growth kinetics of lipase production by Candida antarctica . Different ML-based models were constructed, and a final AI model of gradient boosting regression (GBR) was obtained with good agreement with the results from conventional experimental methods.…”

Section: Ai-aided Lipase Productionmentioning

confidence: 99%

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Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Ge,

Chen,

Qian

et al. 2023

J. Agric. Food Chem.

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With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

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Section: Ai-aided Lipase Productionmentioning

confidence: 77%

Section: Models and Algorithms For Ai In Lipase Technologymentioning

confidence: 92%

Section: Models and Algorithms For Ai In Lipase Technologymentioning

confidence: 99%

Section: Ai-aided Lipase Productionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Ge,

Chen,

Qian

et al. 2023

J. Agric. Food Chem.

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“…The Logistic model is fitted to the growth data of Proteus mirabilis and MLA were used to train and validate the model, so that it can accurately predict various unseen data of Proteus mirabilis. 28…”

Section: Introductionmentioning

confidence: 99%

Predicting Single Cell Lag Time and Maximum Specific Growth Rate of Proteus mirabilis using Curve Fitting Machine Learning Algorithm (MLA)

Ramona¹,

Dharmawan²

2023

J. Pure Appl. Microbiol.

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The lack of adequate assessment methods for pathogens especially in food is a critical problem in microbiology. Traditional predictive methods are not able to accurately describe the trend of low-density bacterial growth behavior observed in the laboratory. The purpose of this study was to leverage state-of-the-art of machine learning algorithms (MLA) to develop a predictive model for bacterial growth of Proteus mirabilis after treatment of bay leaf extract. The experimental data are fitted to three models, namely logistic, Gompertz, and Richard models. These models are trained using simulation data and a curve-fitting optimization algorithm in MATLAB called fminsearch is applied to the data to obtain the optimal parameters of the models. The results show that this method provides a breakthrough in bacterial growth modeling. Various forms of mathematical models such as Gompertz, Richard, and others are no longer necessary to model bacterial behavior. Additionally, the generated model can help microbiologists in understanding the growth characteristics of bacteria after disinfectant treatment, and provides a theoretical reference and a method of risk management for better assessment of pathogens in food.

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Screening, identification, and characterization of lipase-producing halotolerant Bacillus altitudinis Ant19 from Antarctic soil

2023

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

Cited by 12 publications

References 45 publications

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Predicting Single Cell Lag Time and Maximum Specific Growth Rate of Proteus mirabilis using Curve Fitting Machine Learning Algorithm (MLA)

Screening, identification, and characterization of lipase-producing halotolerant Bacillus altitudinis Ant19 from Antarctic soil

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