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

Latest Trends in Lipase-Catalyzed Synthesis of Ester Carbohydrate Surfactants: From Key Parameters to Opportunities and Future Development

Carbohydrate-based surfactants are amphiphilic compounds containing hydrophilic moieties linked to hydrophobic aglycones. More specifically, carbohydrate esters are biosourced and biocompatible surfactants derived from inexpensive renewable raw materials (sugars and fatty acids). Their unique properties allow them to be used in various areas, such as the cosmetic, food, and medicine industries. These multi-applications have created a worldwide market for biobased surfactants and consequently expectations for their production. Biobased surfactants can be obtained from various processes, such as chemical synthesis or microorganism culture and surfactant purification. In accordance with the need for more sustainable and greener processes, the synthesis of these molecules by enzymatic pathways is an opportunity. This work presents a state-of-the-art lipase action mode, with a focus on the active sites of these proteins, and then on four essential parameters for optimizing the reaction: type of lipase, reaction medium, temperature, and ratio of substrates. Finally, this review discusses the latest trends and recent developments, showing the unlimited potential for optimization of such enzymatic syntheses.

A kinetic-assisted growth curve prediction method for Chlamydomonas reinhardtii incorporating transfer learning

Traditional predictions of microalgal growth states rely on empirical or easily implementable kinetic models, leading to significant biases and elevated cost. This study proposes a kinetic-assisted machine learning method for predicting the growth curve of microalgal biomass under small sample conditions. Firstly, a microalgae growth kinetic model is constructed based on the logistic model. A two-stage kinetic fitting strategy is specified to account for the light-dark ratio. The Box-Behnken method is employed for experimental design. Then, using Two-stage TrAdaboost.R2 algorithm, the kinetic model is utilized as the source domain, and the experimental design data serves as the target domain for training machine learning models. The results indicate that the proposed method outperforms a single machine learning model in terms of prediction and has the potential to rapidly estimate microalgal growth trends under different conditions and accurately predict harvested biomass, potentially reducing the need for laborious, expensive, and time-consuming laboratory trials.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

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.

Enhanced production of acetic acid through bioprocess optimization employing response surface methodology and artificial neural network

In this study, acetic acid bacteria (AAB) are isolated from fruit waste and cow dung on the basis of acetic acid production potential. The AAB were identified based on halo-zones produced in the Glucose-Yeast extract-Calcium carbonate (GYC media) agar plates. In the current study, maximum acetic acid yield is reported to be 4.88 g/100 ml from the bacterial strain isolated from apple waste. With the help of RSM (Response surface methodology) tool, glucose and ethanol concentration and incubation period, as independent variable showed the significant effect of glucose concentration and incubation period and their interaction on the AA yield. A hypothetical model of artificial neural network (ANN) was also used to compare the predicted value from RSM. Acetic acid production through the biological route can be the sustainable and clean approach to utilizing food waste in circular economy approach.

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

Potent lipase-producing and halotolerant Bacillus altitudinis Ant19 strain was screened and isolated from Antarctic soil. The isolate showed broad-range lipase activity against different lipid substrates. Presence of lipase activity was confirmed by PCR amplification and sequencing of the lipase gene from Ant19. The study attempted to establish the use of crude extracellular lipase extract as cheap alternative to purified enzyme by characterizing the crude lipase activity and testing it in certain practical applications. Crude lipase extract from Ant19 showed high stability at 5-28 ℃ (> 97%), while lipase activity was noted in a wide temperature range of 20-60 ℃ (> 69%), with optimum activity at 40 ℃ (117.6%). The optimum lipolytic activity was noted at pH 8 with good activity and stability in alkaline conditions (pH 7-10). Moreover, the lipase activity was substantially stable in various solvents, commercial detergents, and surfactants. It retained 97.4% activity in 1% solution of commercial Nirma detergent. Besides, it was non-regiospecific, and active against substrates having different fatty acid chain lengths with preference for shorter chain length. Further, the crude lipase enhanced the oil stain removal efficiency of commercial detergent from 52 to 77.9%, while 66% oil stain was removed using crude lipase alone. Immobilization process improved the storage stability of crude lipase for 90 days. In our knowledge, it is the first study on characterization of lipase activity from B. altitudinis, which has promising applications in various fields.

Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review

By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.