Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models

Bai, Lidan; You, Qidong; Zhang, Chenyang; Sun, Jun; Liu, Long; Lu, Hengyang; Chen, Qidong

doi:10.1007/s43393-022-00115-6

Cited by 6 publications

(4 citation statements)

References 74 publications

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“…The application of machine learning is not limited to enzyme engineering; it is expected to be widely used and developed in the field of metabolic engineering, including the optimization of metabolic fluxes, multigenetic pathways, and fermentation parameters . Machine learning includes numerous algorithms, such as deep learning, decision trees, artificial neural networks (ANN), and support vector machines (SVMs) . Regarding altering metabolic fluxes, ANN can identify the simpler and more available substrates for the target bioproducts, which are beneficial to the reconstruction of pathways and have the potential to save production costs.…”

Section: Prospectivesmentioning

confidence: 99%

“…118 Machine learning includes numerous algorithms, such as deep learning, decision trees, artificial neural networks (ANN), and support vector machines (SVMs). 119 Regarding altering metabolic fluxes, ANN can identify the simpler and more available substrates for the target bioproducts, which are beneficial to the reconstruction of pathways and have the potential to save production costs. SelProm, based on partial least-squares regression, has been used to optimize the concentration of inducers and induction time of E. 120 In another study, ANN and decision trees were used to optimize the parameters of the production of biological hydrogen and other bioproducts after collecting a large amount of data.…”

Section: Prospectivesmentioning

confidence: 99%

See 1 more Smart Citation

Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review

Qu,

Liu,

et al. 2023

J. Agric. Food Chem.

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To achieve an adequate sense of sweetness with a healthy low-sugar diet, it is necessary to explore and produce sugar alternatives. Recently, glycoside sweeteners and their biosynthetic approaches have attracted the attention of researchers. In this review, we first outlined the synthetic pathways of glycoside sweeteners, including the key enzymes and rate-limiting steps. Next, we reviewed the progress in engineered microorganisms producing glycoside sweeteners, including de novo synthesis, whole-cell catalysis synthesis, and in vitro synthesis. The applications of metabolic engineering strategies, such as cofactor engineering and enzyme modification, in the optimization of glycoside sweetener biosynthesis were summarized. Finally, the prospects of combining enzyme engineering and machine learning strategies to enhance the production of glycoside sweeteners were discussed. This review provides a perspective on synthesizing glycoside sweeteners in microbial cells, theoretically guiding the bioproduction of glycoside sweeteners.

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Section: Prospectivesmentioning

confidence: 99%

Section: Prospectivesmentioning

confidence: 99%

Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review

Qu,

Liu,

et al. 2023

J. Agric. Food Chem.

Self Cite

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“…GEMs represent a major progress in the understanding of the reaction structure of a given organism, but they are incomplete and difficult to deploy in practice. Recently, the combination of ML and constraint-based modeling (mechanistic) has been proposed to fill this gap [100][101][102][103]. Faure and coauthors proposed a hybrid ANN method that combines mechanistic layers, based on flux balance analysis (FBA), and ANN layers, trained on experimental data and/or FBA-generated data [104].…”

Section: Narrowing the Gap Between Hybrid Modeling And Systems Biologymentioning

confidence: 99%

From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives

Agharafeie,

Ramos,

Mendes

et al. 2023

Fermentation

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Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging compared to other industries. A promising approach is to combine deep neural networks (DNN) with prior knowledge in hybrid neural network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It reveals that HNNs have been applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs have been applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies have combined shallow feedforward neural networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, convolution neural networks (CNN), long short-term memory (LSTM) networks and physics-informed neural networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

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“…Genetic algorithms, simulated annealing, and particle swarm optimization are capable of performing global searches within the parameter space. During the model's iterative training process, intelligent optimization algorithms continuously adjust its fitness value, thereby evaluating and enhancing selected parameters, reducing prediction errors, and enhancing accuracy [20]. The integration of intelligent optimization algorithms with traditional machine learning models has produced excellent outcomes in regression prediction research.…”

Section: Introductionmentioning

confidence: 99%

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Wang,

Zhang

et al. 2024

Remote Sensing

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Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.

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Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models

Cited by 6 publications

References 74 publications

Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review

Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review

From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

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