Artificial neural network-based model for the prediction of optimal growth and culture conditions for maximum biomass accumulation in multiple shoot cultures of Centella asiatica

Using Mathematical Optimization Models to Improve the Efficiency of Duckweeds (Wolffia arrhiza and Lemna minor) Micropropagation

The method of plant micropropagation is widely used to obtain genetically homogeneous and infection-free plants for the needs of various industries and agriculture. Optimization of plant growth and development conditions plays a key role in economically successful micropropagation. Computer technologies have provided researchers with new approaches for modeling and a better understanding of the role of the factors involved in plant growth in vitro. To develop new models for optimizing growth conditions, we used plants with a high speed of vegetative in vitro reproduction, such as duckweed (Wolffia arrhiza and Lemna minor). Using the development of the optimal modeling of the biological processes, we have obtained the prescriptions for an individually balanced culture medium that enabled us to obtain 1.5-2.0 times more duckweed biomass with a 1.5 times higher protein concentration in the dry mass. Thus, we have demonstrated that the method of optimization modeling of the biological processes based on solving multinomial tasks from the series of quadratic equations can be used for the optimization of trophic needs of plants, specifically for micropropagation of duckweeds in vitro.

Computational approaches for modeling and structural design of biological systems: A comprehensive review

The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.

Application of Data Modeling, Instrument Engineering and Nanomaterials in Selected Medid the Scientific Recinal Plant Tissue Culture

At present, most precious compounds are still obtained by plant cultivation such as ginsenosides, glycyrrhizic acid, and paclitaxel, which cannot be easily obtained by artificial synthesis. Plant tissue culture technology is the most commonly used biotechnology tool, which can be used for a variety of studies such as the production of natural compounds, functional gene research, plant micropropagation, plant breeding, and crop improvement. Tissue culture material is a basic and important part of this issue. The formation of different plant tissues and natural products is affected by growth conditions and endogenous substances. The accumulation of secondary metabolites are affected by plant tissue type, culture method, and environmental stress. Multi-domain technologies are developing rapidly, and they have made outstanding contributions to the application of plant tissue culture. The modes of action have their own characteristics, covering the whole process of plant tissue from the induction, culture, and production of natural secondary metabolites. This paper reviews the induction mechanism of different plant tissues and the application of multi-domain technologies such as artificial intelligence, biosensors, bioreactors, multi-omics monitoring, and nanomaterials in plant tissue culture and the production of secondary metabolites. This will help to improve the tissue culture technology of medicinal plants and increase the availability and the yield of natural metabolites.

Biotechnological and endophytic-mediated production of centellosides in Centella asiatica

In vitro culture of a plant cell, tissue and organ is a marvellous, eco-friendly biotechnological strategy for the production of phytochemicals. With the emergence of recent biotechnological tools, genetic engineering is now widely practiced enhancing the quality and quantity of plant metabolites. Triterpenoid saponins especially asiaticoside and madecassoside of Centella asiatica (L.) Urb. are popularly known for their neuroprotective activity. It has become necessary to increase the production of asiaticoside and madecassoside because of their high pharmaceutical and industrial demand. Thus, the review aims to provide efficient biotechnological tools along with proper strategies. This review also included a comparative analysis of various carbon sources and biotic and abiotic elicitors. The vital roles of a variety of plant growth regulators and their combinations have also been evaluated at different in vitro growth stages of Centella asiatica. Selection of explants, direct and callus-mediated organogenesis, root organogenesis, somatic embryogenesis, synthetic seed production etc. are also highlighted in this study. In a nutshell, this review will present the research outcomes of different biotechnological interventions used to increase the yield of triterpenoid saponins in C. asiatica. KEY POINTS: • Critical and updated assessment on in vitro biotechnology in C. asiatica. • In vitro propagation of C. asiatica and elicitation of triterpenoid saponins production. • Methods for mass producing C. asiatica.

Assessment of major centelloside ratios in Centella asiatica accessions grown under identical ecological conditions, bioconversion clues and identification of elite lines

Centellosides viz., asiatic acid, madecassic acid, asiaticoside, madecassoside, are the major bioactive molecules in Centella asiatica. In this study madecassic acid:asiatic acid, madecassoside:asiaticoside (C6-hydroxylation versus non-hydroxylation) and asiaticoside:asiatic acid, madecassoside:madecassic acid (C28-glycoside versus aglycone) ratios in 50 C. asiatica accessions originally collected from their natural habitats in south India and grown under identical ecological conditions for six generations were determined using validated HPTLC-densitometry protocols. Asiatic acid, madecassic acid, asiaticoside and madecassoside contents ranged from 0.00-0.29% (average 0.03 ± 0.06%; 28 accessions recorded asiatic acid content as zero), 0.02-0.72% (0.12 ± 0.13%), 0.04-2.41% (0.44 ± 0.52%) and 0.15-5.27% (1.59 ± 1.26%), respectively. Distinctly, C6-hydroxylated (madecassic acid:asiatic acid 4.00, madecassoside:asiaticoside 3.61) and C28-glycosylated (asiaticoside:asiatic acid 14.67, madecassoside: madecassic acid 13.25) centellosides dominated over the respective non-derivatized entities. Our results infer that both C6-hydroxylation by CYP450-dependent monooxygenases and C28-glycosylation by UDP-Glc glucosyltransferases are dominant bioconversion steps in C. asiatica. Besides, this study discovered six elite lines of C. asiatica, with their (asiaticoside + madecassoside) contents above the industrial benchmark (≥ 4%) from south India. Two elite clones with asiaticoside contents ≥ 2% were also identified. Standardization of the agrotechniques of these elite lines could lead to their industrial applications. Further, this study emphasizes the need for standardizing all four centellosides as biomarkers in C. asiatica raw drugs, pharmaceutical and cosmetic products.

Treasuring the computational approach in medicinal plant research

Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

The Past, Present and Future of Cannabis sativa Tissue Culture

The recent legalization of Cannabis sativa L. in many regions has revealed a need for effective propagation and biotechnologies for the species. Micropropagation affords researchers and producers methods to rapidly propagate insect-/disease-/virus-free clonal plants and store germplasm and forms the basis for other biotechnologies. Despite this need, research in the area is limited due to the long history of prohibitions and restrictions. Existing literature has multiple limitations: many publications use hemp as a proxy for drug-type Cannabis when it is well established that there is significant genotype specificity; studies using drug-type cultivars are predominantly optimized using a single cultivar; most protocols have not been replicated by independent groups, and some attempts demonstrate a lack of reproducibility across genotypes. Due to culture decline and other problems, the multiplication phase of micropropagation (Stage 2) has not been fully developed in many reports. This review will provide a brief background on the history and botany of Cannabis as well as a comprehensive and critical summary of Cannabis tissue culture. Special attention will be paid to current challenges faced by researchers, the limitations of existing Cannabis micropropagation studies, and recent developments and future directions of Cannabis tissue culture technologies.

Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture

Artificial intelligence (AI) models and optimization algorithms (OA) are broadly employed in different fields of technology and science and have recently been applied to improve different stages of plant tissue culture. The usefulness of the application of AI-OA has been demonstrated in the prediction and optimization of length and number of microshoots or roots, biomass in plant cell cultures or hairy root culture, and optimization of environmental conditions to achieve maximum productivity and efficiency, as well as classification of microshoots and somatic embryos. Despite its potential, the use of AI and OA in this field has been limited due to complex definition terms and computational algorithms. Therefore, a systematic review to unravel modeling and optimizing methods is important for plant researchers and has been acknowledged in this study. First, the main steps for AI-OA development (from data selection to evaluation of prediction and classification models), as well as several AI models such as artificial neural networks (ANNs), neurofuzzy logic, support vector machines (SVMs), decision trees, random forest (FR), and genetic algorithms (GA), have been represented. Then, the application of AI-OA models in different steps of plant tissue culture has been discussed and highlighted. This review also points out limitations in the application of AI-OA in different plant tissue culture processes and provides a new view for future study objectives. KEY POINTS: • Artificial intelligence models and optimization algorithms can be considered a novel and reliable computational method in plant tissue culture. • This review provides the main steps and concepts for model development. • The application of machine learning algorithms in different steps of plant tissue culture has been discussed and highlighted.

Using artificial neural network in determining postharvest LIFE of kiwifruit

BACKGROUND

Artificial intelligence systems have been employed for the development of predictive models that estimate many agricultural processes.

RESULTS

In present study, the predictive capabilities of artificial neural networks (ANNs) were evaluated with respect to assessing fruit firmness as a postharvest life index, with determinations made at four stages of storage: 1, 60, 120 and 180 days after harvesting. Single concentrations of nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg) on fruit (D₁ ), all of these nutrient concentrations (D₂ ), the ratios of the nutrient concentrations alone (D₃ ), and a combination of nutrient concentrations and their ratios (D₄ ), were considered.

CONCLUSION

The results obtained showed that fruit firmness at 1 and 60 days after harvesting was not influenced by nutrients. However, the ANN model estimated fruit firmness of 120 and 180 days, respectively, for D₁ and D₃ more accurately than for the D₂ and D₄ datasets. Application of D₃ (nitrogen/calcium ratio) as the input dataset improved predictions of fruit firmness, with a correlation coefficient of 0.85 between the measured and estimated data. © 2019 Society of Chemical Industry.

Modeling callus induction and regeneration in an anther culture of tomato (Lycopersicon esculentum L.) using image processing and artificial neural network method

Doubled haploids, subsequent to haploid induction, have wide range of applications in basic and applied plant studies. Various parameters can affect the efficiency of haploid induction through an anther culture of tomato. The hybrid system of image processing-artificial neural network (ANN) was used to better understand callus induction and regeneration in an anther culture of tomato. The effect of parameters such as plant genotype, the concentrations of 2,4-dichlorophenoxyacetic acid (2,4-D) and kinetin (Kin) plant growth regulators, the concentration of gum arabic (GA) additive, the cold pretreatment duration, and flower length on callus induction percentage and number of regenerated calli in an anther culture of tomato were studied using multiple linear regression (MLR) and ANN models. The precise flower bud length was measured using an image processing technique. The 4',6-diamidino-2-phenylindole (DAPI) analysis showed that the flowers with 5-6.9 mm length had the highest percentage of the mid- to late-uninucleate microspore stage. The best ANN model for both callus induction percentage and number of regenerated calli was a model with one hidden layer, 12-15 neurons in the first hidden layer, Levenberg-Marquardt learning algorithm, and Tan-Sigmoid transfer function in hidden layer, based on the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) statistics. The scatter plot of measured values versus the predicted values showed the superiority of the ANN to MLR model to predict the callus induction percentage in an anther culture of tomato. The sensitivity analysis of MLR and ANN models revealed the plant genotype and 2,4-D concentration as the most important factors affecting both callus induction percentage and number of regenerated calli. Since tomato is a recalcitrant plant to androgenesis-based pathway of haploid induction, therefore the results of the present study can be helpful to develop an efficient haploid induction protocol in tomato through an anther culture pathway.

A design of experiments approach for the rapid formulation of a chemically defined medium for metabolic profiling of industrially important microbes

Geobacillus thermoglucosidans DSM2542 is an industrially important microbe, however the complex nutritional requirements of Geobacilli confound metabolic engineering efforts. Previous studies have utilised semi-defined media recipes that contain complex, undefined, biologically derived nutrients which have unknown ingredients that cannot be quantified during metabolic profiling. This study used design of experiments to investigate how individual nutrients and interactions between these nutrients contribute to growth. A mathematically derived defined medium has been formulated that has been shown to robustly support growth of G. thermoglucosidans in two different environmental conditions (96-well plate and shake flask) and with a variety of lignocellulose-based carbohydrates. This enabled the catabolism of industrially relevant carbohydrates to be investigated.