Analysis of the tendency for the electronic conductivity to change during alcoholic fermentation

Li, Chongwei; Wang, Yue; Sha, Shuang; Yin, Hui; Zhang, Huilin; Wang, Yongsheng; Zhao, Bo; Song, Fuqiang

doi:10.1038/s41598-019-41225-x

Cited by 18 publications

(13 citation statements)

References 19 publications

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“…The conductivity values showed a slight tendency to increase with the duration of the experiment and can be correlated with the cell growth stages of the yeast strains used, the highest differences being recorded for S. cerevisiae fermentative strains ( S. cerevisiae CM6B70 (P3) and S. cerevisiae CMGB234 (P4)) in the case of lead and lead and cadmium double contaminations, respectively. The higher values of the conductivity observed at the end of the experiment might be an indication of the increase in the number of dead cells and the change of the ion flow (i.e., the release of ions by breaking the structure of cell membranes) [ 53 ].…”

Section: Resultsmentioning

confidence: 99%

Bioremediation of Wastewater Using Yeast Strains: An Assessment of Contaminant Removal Efficiency

Nicula

Lungulescu

Rimbu

et al. 2023

IJERPH

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The main goal of wastewater treatment is to significantly reduce organic compounds, micronutrients (nitrogen and phosphorus) and heavy metals and other contaminants (pathogens, pharmaceuticals and industrial chemicals). In this work, the efficiency of removing different contaminants (COD, NO3−, NO2−, NH4+, PO43−, SO42−, Pb2+, Cd2+) from synthetic wastewater was tested using five different yeast strains: Kluyveromyces marxianus CMGBP16 (P1), Saccharomyces cerevisiae S228C (P2), Saccharomyces cerevisiae CM6B70 (P3), Saccharomyces cerevisiae CMGB234 (P4) and Pichia anomala CMGB88 (P5). The results showed a removal efficiency of up to 70% of COD, 97% of nitrate, 80% of nitrite, 93% of phosphate and 70% of sulfate ions for synthetic wastewater contaminated with Pb2+ (43 mg/L) and Cd2+ ions (39 mg/L). In contrast, the results showed an increase in ammonium ions, especially in the presence of Pb2+ ions. The yeast strains showed a high capacity to reduce Pb2+ (up to 96%) and Cd2+ (up to 40%) ions compared to the initial concentrations. In presence of a crude biosurfactant, the removal efficiency increased up to 99% for Pb2+ and 56% for Cd2+ simultaneously with an increase in yeast biomass of up to 11 times. The results, which were obtained in the absence of aeration and in neutral pH conditions, proved a high potential for practical applications in the biotreatment of the wastewater and the recovery of Pb and Cd ions, with a high benefit–cost ratio.

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Section: Resultsmentioning

confidence: 99%

Bioremediation of Wastewater Using Yeast Strains: An Assessment of Contaminant Removal Efficiency

Nicula

Lungulescu

Rimbu

et al. 2023

IJERPH

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“…One study employing structure additive regression (STAR) illustrates a model that can be created gradually, making it easier to analyze and adjust for operators . Furthermore, novel biosensor development strategies have been explored to build new soft sensors with potentially higher predictive ability over significant offline variables. , …”

Section: Applications Of ML In Biosystems Designmentioning

confidence: 99%

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Goshisht

2024

ACS Omega

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Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

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“…A recent example using structure additive regression (STAR) demonstrates a model that can be built in a stepwise fashion lending it to easier interpretation and adjustability for operators . Additionally, novel techniques for biosensor development have been explored to create new types of soft sensors that can potentially have more predictive power over important offline variables. , More complex models including neural networks have started to gain popularity for modeling nonlinear bioprocesses and in 2018 the first deep neural network applied to nonlinear bioprocess was used to predict penicillin concentration evolution in a bioreactor. ,, More complex neural network models have shown improved predictive power and generalizability, but they suffer from a lack of interpretability. , …”

Section: At the Process Levelmentioning

confidence: 99%

“…132 In summary, the most popular area of study in bioprocess engineering related to ML is in the development of biosensors, since their upside promises reduced manual labor for testing offline variables and more accurate predictions of cellular performance. 127 Studies related to bioprocess engineering will likely gain momentum as researchers find more effective methods for data extraction and adopt standardized data collection practices. The general workflow for ML on the process level is largely focused on improving data collection and processing techniques to allow for robust, standard ML models to work consistently well (Figure 8).…”

Section: ■ At the Genome Levelmentioning

confidence: 99%

Biosystems Design by Machine Learning

et al. 2020

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Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

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Analysis of the tendency for the electronic conductivity to change during alcoholic fermentation

Cited by 18 publications

References 19 publications

Bioremediation of Wastewater Using Yeast Strains: An Assessment of Contaminant Removal Efficiency

Bioremediation of Wastewater Using Yeast Strains: An Assessment of Contaminant Removal Efficiency

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Biosystems Design by Machine Learning

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