Simultaneous modeling of enzyme production and biomass growth in recombinant Escherichia coli using artificial neural networks

Günay, M. Erdem; Nikerel, Emrah; Öner, Ebru Toksoy; Kırdar, Betül; Yıldırım, Ramazan

doi:10.1016/j.bej.2008.08.002

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…Artificial neural networks, based on biological neurons, have superior abilities for modeling and data mining in nonlinear systems. − With recent developments in hardware and software, the use of artificial neural networks has become widespread in almost every scientific area. There have been several successful applications of neural networks in the field of catalysis, − including some of our recent works. − This technique seems to be suitable for extracting knowledge from published data as well.…”

Section: Introductionmentioning

confidence: 99%

Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature

Günay

Yıldırım

2011

Ind. Eng. Chem. Res.

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In this work, a database containing 1337 data points for selective CO oxidation over Cu-based catalysts was constructed from 20 research publications and used for knowledge extraction by artificial neural networks. The experimental CO conversions reported in each publication were successfully predicted by a neural network trained using the data from the remaining 19 publications unless that one publication contained unique variables. The effects and relative significances of the catalyst preparation variables (such as Cu loading, second metal additive, support type, and preparation method) and operating variables (such as reaction temperature, feed composition, and feed flow rate/catalyst weight ratio) were also determined quite successfully by the artificial neural networks. We conclude that neural network modeling can be used to extract valuable experience and knowledge accumulated in the published data and can help researchers to plan new experiments in a more effective manner.

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Section: Introductionmentioning

confidence: 99%

Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature

Günay

Yıldırım

2011

Ind. Eng. Chem. Res.

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“…The processing units in a network typically include input units, output units, and hidden units . ANN is a powerful tool for biological modeling, e.g., biomass growth was modeled on different network topologies by using the complete data set to measure the training error and the multiplicative cross-validation analysis to test its generalization accuracy . In general, most of the data are used to train the model and a small portion are applied to test the accuracy of the training model.…”

Section: Models and Algorithms For Ai In Lipase Technologymentioning

confidence: 99%

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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“…Among the various technique that have been applied successfully, ANN has drawn much attention (Torrecilla et al. 2005; Günay et al. 2008) as it does not require any prior knowledge about the existed relationship and easily model nonlinear relationships between the dependent and the independent variables.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence, such as fuzzy logic (Perrot et al 2006;Lu and Tsai 2007) and artificial neural network (ANN), was adopted as new and powerful tools. Among the various technique that have been applied successfully, ANN has drawn much attention (Torrecilla et al 2005;Günay et al 2008) as it does not require any prior knowledge about the existed relationship and easily model nonlinear relationships between the dependent and the independent variables. Many kinds of networks are developed with different properties and applications.…”

Section: Introductionmentioning

confidence: 99%

APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PREDICTION OF CANTONESE SOY SAUCE BREWING AND CHANGING PATTERN CONCERNING TOTAL NITROGEN AND α‐AMINO ACID NITROGEN

Liu

Zhao

et al. 2011

J Food Process Engineering

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In the present study, the artificial neural network was used to predict the brewing of traditional Cantonese soy sauce and its changing pattern with regard to total nitrogen (TN) andα‐amino acidic nitrogen (AN). The effects of parameters during brewing on the contents of TN and AN were modeled by various networks. After the 7‐40‐2 network was determined statistically, it was used to extract the possible correlations between TN and AN, and the relative significance of some factors. It was found that the change of TN and AN contents with the change of time and acidic protease activity was strongly correlated with an R value of 0.9848 and 0.9916, respectively. As for the relative significances of inputs, aging time is the key factor for both outputs during the moromi aging. And for other inputs, it was temperature > pH > neutral protease activity > acid protease activity for TN, and temperature > acidic protease activity > neutral protease activity > pH for AN, respectively. PRACTICAL APPLICATIONS Because of its open‐air brewing and lengthy brewing time of Cantonese soy sauce, it was difficult to predict its quality indices, the contents of total nitrogen and α‐amino acidic nitrogen, which would change with outside factors (time and temperature) and inside factors (protease activity and pH values). So the study on brewing‐state predication and changing pattern of quality indices were performed by using the artificial neural network on an experimental scale. And results from this preliminary study would help manufacturers to foresee the change and contents of quality indices accurately in the brewing process on an industrial scale. Furthermore, it was anticipated that more effective control and improvement on the quality of traditional Cantonese would be achieved in the future study. Finally, the trained network also can be used in the research of online prediction for other time‐consuming natural fermentation.

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Simultaneous modeling of enzyme production and biomass growth in recombinant Escherichia coli using artificial neural networks

Cited by 12 publications

References 20 publications

Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature

Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PREDICTION OF CANTONESE SOY SAUCE BREWING AND CHANGING PATTERN CONCERNING TOTAL NITROGEN AND α‐AMINO ACID NITROGEN

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