Mathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm

Synthetic biology advances towards a bio-based society in the era of artificial intelligence

Synthetic biology is a rapidly emerging field with broad underlying applications in health, industry, agriculture, or environment, enabling sustainable solutions for unmet needs of modern society. With the very recent addition of artificial intelligence (AI) approaches, this field is now growing at a rate that can help reach the envisioned goals of bio-based society within the next few decades. Integrating AI with plant-based technologies, such as protein engineering, phytochemicals production, plant system engineering, or microbiome engineering, potentially disruptive applications have already been reported. These include enzymatic synthesis of new-to-nature molecules, bioelectricity generation, or biomass applications as construction material. Thus, in the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society.

Culture-based diversity of endophytic fungi of three species of Ferula grown in Iran

A total of 1,348 endophytic fungal strains were isolated from Ferula ovina, F. galbaniflua, and F. persica. They included Eurotiales (16 species), Pleosporales (11 species), Botryosphaeriales (1 species), Cladosporiales (2 species), Helotiales (6 species), Hypocreales (31 species), Sordariales (7 species), Glomerellales (2 species), and Polyporales (1 species). F. ovina had the richest species composition of endophytic fungi, and the endophytic fungi were most abundant in their roots compared to shoots. Chao, Margalef, Shannon, Simpson, Berger-Parker, Menhinick, and Camargo indices showed that F. ovina roots had the most endophytic fungal species. The frequency distribution of fungal species isolated from Ferula spp. fell into the log-series model, and F. ovina roots had the highest Fisher alpha. The dominance indices showed that there are no dominant species in the endophytic fungal community isolated from Ferula spp., indicating community stability. Evenness values were 0.69, 0.90, 0.94, and 0.57 for endophytic fungi isolated from F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots, respectively, indicating a species distribution that tends toward evenness. The fungal species community isolated from each of F. ovina roots, F. ovina shoots, F. galbaniflua roots, and F. persica roots was a diverse species group originating from a homogeneous habitat. Their distribution followed a log-normal distribution, suggesting that the interactions of numerous independent environmental factors multiplicatively control species abundances. Principal component analysis showed that the highest species diversity and dominance were observed in the endophytic fungal community isolated from F. ovina and F. persica roots, respectively.

Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint

Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide. They are a potential source of food and medicine because they are rich in nutrients and contain various minerals, vitamins, essential macro- and micronutrients, and bioactive compounds. The reuse of agricultural and industrial residues is crucial from an ecological and economic perspective. In this study, the running length (RL) of L. edodes cultured on 64 substrate compositions obtained from different ratios of bagasse (B), wheat bran (WB), and beech sawdust (BS) was recorded at intervals of 5 days after cultivation until the 40th day. Multilayer perceptron-genetic algorithm (MLP-GA), multiple linear regression, stepwise regression, principal component regression, ordinary least squares regression, and partial least squares regression were used to predict and optimize the RL and running rate (RR) of L. edodes. The statistical values showed higher prediction accuracies of the MLP-GA models (92% and 97%, respectively) compared with those of the regression models (52% and 71%, respectively) for RL and RR. The high degree of fit between the forecasted and actual values of the RL and RR of L. edodes confirmed the superior performance of the developed MLP-GA models. An optimization analysis on the established MLP-GA models showed that a substrate containing 15.1% B, 45.1% WB, and 10.16% BS and a running time of 28 days and 10 h could result in the maximum L. edodes RL (10.69 cm). Moreover, the highest RR of L. edodes (0.44 cm d-1) could be obtained by a substrate containing 30.7% B, 90.4% WB, and 0.0% BS. MLP-GA was observed to be an effective method for predicting and consequently selecting the best substrate composition for the maximal RL and RR of L. edodes.

Machine learning-mediated Passiflora caerulea callogenesis optimization

Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of callogenesis and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. In the present investigation, the callogenesis responses (i.e., callogenesis rate and callus fresh weight) of P. caerulea were predicted based on different types and concentrations of plant growth regulators (PGRs) (i.e., 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), 1-naphthaleneacetic acid (NAA), and indole-3-Butyric Acid (IBA)) as well as explant types (i.e., leaf, node, and internode) using multilayer perceptron (MLP). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and explant types for maximizing callogenesis responses. Furthermore, sensitivity analysis was conducted to assess the importance of each input variable on the callogenesis responses. The results showed that MLP had high predictive accuracy (R2 > 0.81) in both training and testing sets for modeling all studied parameters. Based on the results of the optimization process, the highest callogenesis rate (100%) would be obtained from the leaf explant cultured in the medium supplemented with 0.52 mg/L IBA plus 0.43 mg/L NAA plus 1.4 mg/L 2,4-D plus 0.2 mg/L BAP. The results of the sensitivity analysis showed the explant-dependent impact of the exogenous application of PGRs on callogenesis. Generally, the results showed that a combination of MLP and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.

Plants and endophytes interaction: a "secret wedlock" for sustainable biosynthesis of pharmaceutically important secondary metabolites

Many plants possess immense pharmacological properties because of the presence of various therapeutic bioactive secondary metabolites that are of great importance in many pharmaceutical industries. Therefore, to strike a balance between meeting industry demands and conserving natural habitats, medicinal plants are being cultivated on a large scale. However, to enhance the yield and simultaneously manage the various pest infestations, agrochemicals are being routinely used that have a detrimental impact on the whole ecosystem, ranging from biodiversity loss to water pollution, soil degradation, nutrient imbalance and enormous health hazards to both consumers and agricultural workers. To address the challenges, biological eco-friendly alternatives are being looked upon with high hopes where endophytes pitch in as key players due to their tight association with the host plants. The intricate interplay between plants and endophytic microorganisms has emerged as a captivating subject of scientific investigation, with profound implications for the sustainable biosynthesis of pharmaceutically important secondary metabolites. This review delves into the hidden world of the "secret wedlock" between plants and endophytes, elucidating their multifaceted interactions that underpin the synthesis of bioactive compounds with medicinal significance in their plant hosts. Here, we briefly review endophytic diversity association with medicinal plants and highlight the potential role of core endomicrobiome. We also propose that successful implementation of in situ microbiome manipulation through high-end techniques can pave the way towards a more sustainable and pharmaceutically enriched future.

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.

Multilayer perceptron-genetic algorithm as a promising tool for modeling cultivation substrate of Auricularia cornea Native to Iran

Auricularia cornea Ehrenb (syn. A. polytricha) is a wood-decaying fungi known as black ear mushroom. Earlike gelatinous fruiting body distinguishes them from other fungi. Industrial wastes have the potential to be used as the basic substrate to produce mushrooms. Therefore, 16 substrate formulations were prepared from different ratios of beech (BS) and hornbeam sawdust (HS) supplemented with wheat (WB) and rice brans (RB). The pH and initial moisture content of substrate mixtures were adjusted to 6.5 and 70%, respectively. The comparison of in vitro growth characteristics of the fungal mycelia under the different temperatures (25, 28, and 30°C), and culture media [yeast extract agar (YEA), potato extract agar (PEA), malt extract agar (MEA), and also HS and BS extract agar media supplemented with maltose, dextrose, and fructose revealed that the highest mycelial growth rate (MGR; 7.5 mm/day) belonged to HS and BS extract agar media supplemented with three mentioned sugar at 28°C. In A. cornea spawn study, the substrate combination of BS (70%) + WB (30%) at 28°C and moisture contents of 75% displayed the highest mean MGR (9.3 mm/day) and lowest spawn run period (9.0 days). In the bag test, "BS (70%) + WB (30%)" was the best substrate displaying the shortest spawn run period (19.7 days), and the highest fresh sporophore yield (131.7 g/bag), biological efficiency (53.1%) and number of basidiocarp (9.0/bag) of A. cornea. Also, A. cornea cultivation was processed to model yield, biological efficiency (BE), spawn run period (SRP), days for pinhead formation (DPHF), days for the first harvest (DFFH), and total cultivation period (TCP) by multilayer perceptron-genetic algorithm (MLP-GA). MLP-GA (0.81-0.99) exhibited a higher predictive ability than stepwise regression (0.06-0.58). The forecasted values of the output variables were in good accordance with their observed ones corroborating the good competency of established MLP-GA models. MLP-GA modeling exhibited a powerful tool for forecasting and thus selecting the optimal substrate for maximum A. cornea production.

Modeling and optimizing in vitro percentage and speed callus induction of carrot via Multilayer Perceptron-Single point discrete GA and radial basis function

Background

Callus induction is the first step in optimizing plant regeneration. Fit embryogenesis and shooting rely on callus induction. In addition, using artificial intelligence models in combination with an algorithm can be helpful in the optimization of in vitro culture. The present study aimed to evaluate the percentage and speed of callus induction optimization in carrot with a Multilayer Perceptron-Single point discrete genetic algorithm (GA).

Materials and methods

In this study, the outputs included callus induction percentage and speed, while inputs were different types and concentrations of plant growth regulator (0. 5, 0.2 mg/l 2,4-D, 0.3, 0.2, 0.5 mg/l BAP, 1, 0.2 mg/l Kin, and 2 mg/l NAA), different explants (shoot, root, leaf, and nodal), a different concentration compound of MS medium (1 × MS, 4× MS, and 8× MS) and time of sampling. The data were obtained in the laboratory, and multilayer perceptron (MLP) and radial basis function (RBF), two well-known ANNs, were employed to model. Then, GA was used for optimization, and sensitivity analysis was performed to indicate the inputs’ importance.

Results

The results showed that MLP had better prediction efficiency than RBF. Based on the results, R2 in training and testing data was 95 and 95% for the percentage of callus induction, while it was 94 and 95% for the speed of callus induction, respectively. In addition, a concentration compound of MS had high sensitivity, while times of sampling had low sensitivity. Based on the MLP-Single point discrete GA, the best results were obtained for shoot explants, 1× MS media, and 0.5 mg/l 2, 4-D + 0.5 mg/l BAP. Further, a non-significant difference was observed between the test result and predicted MLP.

Conclusions

Generally, MLP-Single point discrete GA is considered a potent tool for predicting treatment and fit model results used in plant tissue culture and selecting the best medium for callus induction.

Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate

Recently, γ-Aminobutyric acid (GABA) has been introduced as a treatment with high physiological activity induction to enhance the ability of plants against drought and salinity stress, which led to a decline in plant growth. Since changes in morphological traits to drought and salinity stress are influenced by multiple factors, advanced computational analysis has great potential for computing nonlinear and multivariate data. In this work, the effect of four input variables including GABA concentration, pomegranate cultivars, days of treatment, and drought and salinity stress evaluated to predict and modeling of morphological traits using artificial neural network (ANN) models including multilayer perceptron (MLP) and radial basis function (RBF). Image processing technique was used to measure the LLI, LWI, and LAI parameters. Among the ANNs applied, the MLP algorithm was chosen as the best model based on the highest accuracy. Furthermore, to predict and estimate the optimal values of input variables for achieving the best morphological parameters, the MLP algorithm was linked to a non-dominated sorting genetic algorithm-II (NSGA-II). Based on the results of MLP-NSGA-II, the best values of crown diameter (18.42 cm), plant height (151.82 cm), leaf length index (5.67 cm), leaf width index (1.76 cm), and leaf area index (13.82 cm) could be achieved with applying 10.57 mM GABA on ‘Atabaki’ cultivar under control (non-stress) condition after 20.8 days. The results of modeling and optimization can be helpful to predict the morphological responses to drought and salinity conditions.

Fungi Classification in Various Growth Stages Using Shortwave Infrared (SWIR) Spectroscopy and Machine Learning

Dark septate endophytes (DSEs) fungi are beneficial to host plants with regard to abiotic stress. Here, we examined the capability of SWIR spectroscopy to classify fungus types and detected the growth stages of DSEs fungi in a timely, non-destructive and time-saving manner. The SWIR spectral data of five DSEs fungi in six growth stages were collected, and three pre-processing methods and sensitivity analysis (SA) variable selection methods were performed using a machine learning model. The results showed that the De-trending + first Derivative (DET_FST) processing spectra combined with the support vector machine (SVM) model yielded the best classification accuracy for fungi classification at different growth stages and growth stage detection on different fungus types. The mean accuracy of generic model for fungi classification and growth stage detection are 0.92 and 0.99 on the calibration set, respectively. Seven important bands, 1164, 1456, 2081, 2272, 2278, 2448 and 2481 nm, were found to be related to the SVM fungi classification. This study provides a rapid and efficient method for the classification of fungi in different growth stages and the detection of fungi growth stage of various types of fungi and could serve as a tool for fungi study.

Mathematical modeling and optimizing the in vitro shoot proliferation of wallflower using multilayer perceptron non-dominated sorting genetic algorithm-II (MLP-NSGAII)

Novel computational methods such as artificial neural networks (ANNs) can facilitate modeling and predicting results of tissue culture experiments and thereby decrease the number of experimental treatments and combinations. The objective of the current study is modeling and predicting in vitro shoot proliferation of Erysimum cheiri (L.) Crantz, which is an important bedding flower and medicinal plant. Its micropropagation has not been investigated before and as a case study multilayer perceptron- non-dominated sorting genetic algorithm-II (MLP-NSGAII) can be applied. MLP was used for modeling three outputs including shoots number (SN), shoots length (SL), and callus weight (CW) based on four variables including 6-benzylaminopurine (BAP), kinetin (Kin), 1-naphthalene acetic acid (NAA) and gibberellic acid (GA₃). The R² correlation values of 0.84, 0.99 and 0.93 between experimental and predicted data were obtained for SN, SL, and CW, respectively. These results proved the high accuracy of MLP model. Afterwards the model connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to optimize input variables for obtaining the best predicted outputs. The results of sensitivity analysis indicated that SN and CW were more sensitive to BA, followed by Kin, NAA and GA. For SL, more sensitivity was obtained for GA₃ than NAA. The validation experiment indicated that the difference between the validation data and MLP-NSGAII predicted data were negligible. Generally, MLP-NSGAII can be considered as a powerful method for modeling and optimizing in vitro studies.

Insights into the control of taxane metabolism: Molecular, cellular, and metabolic changes induced by elicitation in Taxus baccata cell suspensions

More knowledge is needed about the molecular/cellular control of paclitaxel (PTX) production in Taxus spp. cell cultures. In this study, the yield of this anticancer agent in Taxus baccata cell suspensions was improved 11-fold after elicitation with coronatine (COR) compared to the untreated cells, and 18-fold when co-supplemented with methyl-β-cyclodextrins (β-CDs). In the dual treatment, the release of taxanes from the producer cells was greatly enhanced, with 81.6% of the total taxane content being found in the medium at the end of the experiment. The experimental conditions that caused the highest PTX production also induced its maximum excretion, and increased the expression of taxane biosynthetic genes, especially the flux-limiting BAPT and DBTNBT. The application of COR, which activates PTX biosynthesis, together with β - CDs, which form inclusion complexes with PTX and related taxanes, is evidently an efficient strategy for enhancing PTX production and release to the culture medium. Due to the recently described role of lipid droplets (LDs) in the trafficking and accumulation of hydrophobic taxanes in Taxus spp. cell cultures, the structure, number and taxane storage capacity of these organelles was also studied. In elicited cultures, the number of LDs increased and they mainly accumulated taxanes with a side chain, especially PTX. Thus, PTX constituted up to 50-70% of the total taxanes found in LDs throughout the experiment in the COR + β - CD-treated cultures. These results confirm that LDs can store taxanes and distribute them inside and outside cells.

Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models

BACKGROUND

Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied.

RESULTS

Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R² of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability.

CONCLUSION

Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher's desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.

Engineering Considerations to Produce Bioactive Compounds from Plant Cell Suspension Culture in Bioreactors

The large-scale production of plant-derived secondary metabolites (PDSM) in bioreactors to meet the increasing demand for bioactive compounds for the treatment and prevention of degenerative diseases is nowadays considered an engineering challenge due to the large number of operational factors that need to be considered during their design and scale-up. The plant cell suspension culture (CSC) has presented numerous benefits over other technologies, such as the conventional whole-plant extraction, not only for avoiding the overexploitation of plant species, but also for achieving better yields and having excellent scaling-up attributes. The selection of the bioreactor configuration depends on intrinsic cell culture properties and engineering considerations related to the effect of operating conditions on thermodynamics, kinetics, and transport phenomena, which together are essential for accomplishing the large-scale production of PDSM. To this end, this review, firstly, provides a comprehensive appraisement of PDSM, essentially those with demonstrated importance and utilization in pharmaceutical industries. Then, special attention is given to PDSM obtained out of CSC. Finally, engineering aspects related to the bioreactor configuration for CSC stating the effect of the operating conditions on kinetics and transport phenomena and, hence, on the cell viability and production of PDSM are presented accordingly. The engineering analysis of the reviewed bioreactor configurations for CSC will pave the way for future research focused on their scaling up, to produce high value-added PDSM.

Traditional in vitro strategies for sustainable production of bioactive compounds and manipulation of metabolomic profile in medicinal, aromatic and ornamental plants

Precursor feeding, elicitation and culture medium parameters are traditional in vitro strategies to enhance bioactive compounds of medicinal, aromatic, and ornamental plants (MAOPs). Machine learning can help researchers find the best combination of these strategies to increase the secondary metabolites content of MAOPs. Many requirements for human life, from food, pharmaceuticals and cosmetics to clothes, fuel and building materials depend on plant-derived natural products. Essential oils, methanolic and ethanolic extracts of in vitro undifferentiated callus and organogenic cultures of medicinal, aromatic, and ornamental plants (MAOPs) contain bioactive compounds that have several applications for various industries, including food and pharmaceutical. In vitro culture systems provide opportunities to manipulate the metabolomic profile of MAOPs. Precursors feeding, elicitation and culture media optimization are the traditional strategies to enhance in vitro accumulation of favorable bioactive compounds. The stimulation of plant defense mechanisms through biotic and abiotic elicitors is a simple way to increase the production of secondary metabolites in different in vitro culture systems. Different elicitors have been applied to stimulate defense machinery and change the metabolomic profile of MAOPs in in vitro cultures. Plant growth regulators (PGRs), stress hormones, chitosan, microbial extracts and physical stresses are the most applied elicitors in this regard. Many other chemical tolerance-enhancer additives, such as melatonin and proline, have been applied along with stress response-inducing elicitors. The use of stress-inducing materials such as PEG and NaCl activates stress tolerance elicitors with the potential of increasing secondary metabolites content of MAOPs. The present study reviewed the state-of-the-art traditional in vitro strategies to manipulate bioactive compounds of MAOPs. The objective is to provide insights to researchers involved in in vitro production of plant-derived natural compounds. The present review provided a wide range of traditional strategies to increase the accumulation of valuable bioactive compounds of MAOPs in different in vitro systems. Traditional strategies are faster, simpler, and cost-effective than other biotechnology-based breeding methods such as genetic transformation, genome editing, metabolic pathways engineering, and synthetic biology. The integrate application of precursors and elicitors along with culture media optimization and the interpretation of their interactions through machine learning algorithms could provide an excellent opportunity for large-scale in vitro production of pharmaceutical bioactive compounds.

Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm

Plant callus is generally considered to be a mass of undifferentiated cells and can be used for secondary metabolite production, physiological studies, and plant transformation/regeneration. However, there are several types of callus with different morphological and developmental characteristics and not all are suitable for all applications. Callogenesis is a multivariable developmental process affected by several intrinsic and extrinsic factors, but the most important driver is plant growth regulator (PGRs) levels and type. Since callogenesis is a non-linear process influenced by many different factors, robust computational methods such as machine learning algorithms have great potential to model, predict, and optimize callus growth and development. The current study was conducted to evaluate the effect of PGRs on callus morphology in drug-type Cannabis sativa to maximize callus growth and promote embryogenic callus production. For this aim, random forest (RF) and support vector machine (SVM) were applied in conjunction with image processing to model and predict callus morphological and physical traits. The results showed that SVM was more accurate than RF. In order to find the optimal level of PGRs for optimizing callus growth and development, the SVM was linked to a genetic algorithm (GA). To confirm the reliability of SVM-GA, the optimized-predicted outcomes were tested in a validation experiment that revealed SVM-GA was able to accurately model and optimize the system. Moreover, our results showed that there is a significant correlation between embryogenic callus production and the true density of callus. KEY POINTS: • The effect of PGRs on callus growth and development of cannabis was studied. • The predictive accuracy of SVM and RF was evaluated and compared. • GA was linked to the SVM for optimizing the callus growth and development.

Advances and Perspectives in Tissue Culture and Genetic Engineering of Cannabis

For a long time, Cannabis sativa has been used for therapeutic and industrial purposes. Due to its increasing demand in medicine, recreation, and industry, there is a dire need to apply new biotechnological tools to introduce new genotypes with desirable traits and enhanced secondary metabolite production. Micropropagation, conservation, cell suspension culture, hairy root culture, polyploidy manipulation, and Agrobacterium-mediated gene transformation have been studied and used in cannabis. However, some obstacles such as the low rate of transgenic plant regeneration and low efficiency of secondary metabolite production in hairy root culture and cell suspension culture have restricted the application of these approaches in cannabis. In the current review, in vitro culture and genetic engineering methods in cannabis along with other promising techniques such as morphogenic genes, new computational approaches, clustered regularly interspaced short palindromic repeats (CRISPR), CRISPR/Cas9-equipped Agrobacterium-mediated genome editing, and hairy root culture, that can help improve gene transformation and plant regeneration, as well as enhance secondary metabolite production, have been highlighted and discussed.

A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

BACKGROUND

Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time.

RESULTS

GRNN-FOA models (0.88-0.97) showed the superior prediction performances as compared to regression models (0.57-0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l^-1).

CONCLUSIONS

Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

Modeling Agrobacterium-Mediated Gene Transformation of Tobacco (Nicotiana tabacum)-A Model Plant for Gene Transformation Studies

The multilayer perceptron (MLP) topology of an artificial neural network (ANN) was applied to create two predictor models in Agrobacterium-mediated gene transformation of tobacco. Agrobacterium-mediated transformation parameters, including Agrobacterium strain, Agrobacterium cell density, acetosyringone concentration, and inoculation duration, were assigned as inputs for ANN-MLP, and their effects on the percentage of putative and PCR-verified transgenic plants were investigated. The best ANN models for predicting the percentage of putative and PCR-verified transgenic plants were selected based on basic network quality statistics. Ex-post error calculations of the relative approximation error (RAE), the mean absolute error (MAE), the root mean square error (RMS), and the mean absolute percentage error (MAPE) demonstrated the prediction quality of the developed models when compared to stepwise multiple regression. Moreover, significant correlations between the ANN-predicted and the actual values of the percentage of putative transgenes (R ² = 0.956) and the percentage of PCR-verified transgenic plants (R ² = 0.671) indicate the superiority of the established ANN models over the classical stepwise multiple regression in predicting the percentage of putative (R ² = 0.313) and PCR-verified (R ²= 0.213) transgenic plants. The best combination of the multiple inputs analyzed in this investigation, to achieve maximum actual and predicted transgenic plants, was at OD ₆₀₀ = 0.8 for the LB4404 strain of Agrobacterium × 300 μmol/L acetosyringone × 20 min immersion time. According to the sensitivity analysis of ANN models, the Agrobacterium strain was the most important influential parameter in Agrobacterium-mediated transformation of tobacco. The prediction efficiency of the developed model was confirmed by the data series of Agrobacterium-mediated transformation of an important medicinal plant with low transformation efficiency. The results of this study are pivotal to model and predict the transformation of other important Agrobacterium-recalcitrant plant genotypes and to increase the transformation efficiency by identifying critical parameters. This approach can substantially reduce the time and cost required to optimize multi-factorial Agrobacterium-mediated transformation strategies.

Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study

Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)-non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R² had superb performance in comparison with all other individual models. According to DF-NSGAII, the highest ER and SEN can be obtained from the medium containing 14.27 mM NH₄⁺, 38.92 mM NO₃^-, 22.79 mM K⁺, 5.08 mM Cl^-, 3.34 mM Ca²⁺, 1.67 mM Mg²⁺, 2.17 mM SO₄^2-, and 1.44 mM H₂PO₄^-. Based on the DF-GA model, the maximum SEN can be obtained from a medium containing 0.61 μM thiamine, 5.93 μM nicotinic acid, 0.25 μM biotin, and 0.26 μM riboflavin. The efficiency of the established-optimized medium was experimentally compared to Murashige and Skoog medium (MS) for embryogenesis of five chrysanthemum cultivars, and results indicated the efficiency of optimized medium over MS medium.Key points• MLP, SVR, and ANFIS were fused by a bagging method to develop a data fusion model.• NSGA-II and GA were linked to the data fusion model for establishing and optimizing a new embryogenesis medium.• The new culture medium (HNT) had better efficiency than MS medium.

Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases

Optimizing the gene transformation factors can be considered as the first and foremost step in successful genetic engineering and genome editing studies. However, it is usually difficult to achieve an optimized gene transformation protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach such as machine learning models for analyzing gene transformation data. In the current study, three individual machine learning models including Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Radial Basis Function (RBF) were developed for forecasting Agrobacterium-mediated gene transformation in chrysanthemum based on eleven input variables including Agrobacterium strain, optical density (OD), co-culture period (CCP), and different antibiotics including kanamycin (K), vancomycin (VA), cefotaxime (CF), hygromycin (H), carbenicillin (CA), geneticin (G), ticarcillin (TI), and paromomycin (P). Consequently, best-obtained results were used in the fusion process by bagging method. Results showed that ensemble model with the highest R² (0.83) had superb performance in comparison with all other individual models (MLP:063, RBF:0.69, and ANFIS: 0.74) in the validation set. Also, ensemble model was linked to Fruit fly optimization algorithm (FOA) for optimizing gene transformation, and the results showed that the maximum gene transformation efficiency (37.54%) can be achieved from EHA105 strain with 0.9 OD₆₀₀, for 3.8 days CCP, 46.43 mg/l P, 9.54 mg/l K, 18.62 mg/l H, and 4.79 mg/l G as selection antibiotics and 109.74 μg/ml VA, 287.63 μg/ml CF, 334.07 μg/ml CA and 87.36 μg/ml TI as antibiotics in the selection medium. Moreover, sensitivity analysis demonstrated that input variables have a different degree of importance in gene transformation system in the order of Agrobacterium strain > CCP > K > CF > VA > P > OD > CA > H > TI > G. Generally, the developed hybrid model in this study (ensemble model-FOA) can be employed as an accurate and reliable approach in future genetic engineering and genome editing studies.

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.

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

BACKGROUND

Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy.

RESULTS

The results showed that SVR (R2 > 0.92) had better performance accuracy than MLP (R2 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum's somatic embryogenesis accurately.

CONCLUSIONS

SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

Optimizing in vitro shoot regeneration conditions in wheat is one of the important steps in successful micropropagation and gene transformation. Various factors such as genotypes, explants, and phytohormones affect in vitro regeneration of wheat, hindering the ability to tailor genotype-independent protocols. Novel computational approaches such as artificial neural networks (ANNs) can facilitate modeling and predicting outcomes of tissue culture experiments and thereby reduce large experimental treatments and combinations. In this study, generalized regression neural network (GRNN) were used to model and forecast in vitro shoot regeneration outcomes of wheat on the basis of 10 factors including genotypes, explants, and different concentrations of 6-benzylaminopurine (BAP), kinetin (Kin), 2,4-dichlorophenoxyacetic acid (2,4-D), indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NAA), zeatin, and CuSO4. In addition, GRNN was linked to a genetic algorithm (GA) to identify an optimized solution for maximum shoot regeneration. Results indicated that GRNN could accurately predict the shoot regeneration frequency in the validation set with a coefficient determination of 0.78. Sensitivity analysis demonstrated that shoot regeneration frequency was more sensitive to variables in the order of 2,4-D > explant > genotype < zeatin < NAA. Results of this study suggest that GRNN-GA can be used as a tool, besides experimental approaches, to develop and optimize in vitro genotype-independent regeneration protocols.