Efficiency of neural network-based combinatorial model predicting optimal culture conditions for maximum biomass yields in hairy root cultures

Hairy root culture technology: applications, constraints and prospect

Hairy root (HR) culture, a successful biotechnology combining in vitro tissue culture with recombinant DNA machinery, is intended for the genetic improvement of plants. This technology has been put to use since the last three decades for genetic advancement of medicinal and aromatic plants and also to harvest the economical products in the form of secondary metabolites that are significantly important for their ethnobotanical and pharmacological properties. It also provides an efficient way out for the quicker extraction and quantification of the valuable phytochemicals. The current review provides an account of the in vitro HR culture technology and its wide-scale applications in the field of research as well as in pharmaceutical industries. Different facets of HR with respect to the culture establishment, phytochemical production as well as research investigations concerning the areas of gene manipulation, biotransformation of the secondary metabolites, phytoremediation, their industrial utilisations and different problems encountered during the application of this technology have been covered in this appraisal. Eventually, an idea has been provided on HR about the recent trends on the progress of this technology that may open up newer prospects in near future and calls for further research and explorations in this field. KEY POINTS: • Genetic engineering-based HR culture aims towards enhanced secondary metabolite production. • This review explores an insight in the HR technology and its multi-faceted approaches. • Up-to-date ground-breaking research applications and constraints of HR culture are discussed.

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.

Enhancement of Phenolic Compounds and Antioxidative Activities by the Combination of Culture Medium and Methyl Jasmonate Elicitation in Hairy Root Cultures of Lactuca indica L

Lactuca indica L. has been traditionally used as a wild vegetable and as a medicinal plant for centuries. The various compounds present in it and their biological activities have been extensively reported. Hairy-root culture combined with agrobacterium-meditated metabolic engineering is a useful technique to achieve stable production of biologically active plant compounds. Here, we evaluated the enhancement of secondary metabolites in L. indica L. and their bioactivities by testing culture media composition and the use of an elicitor. Hairy roots were induced and cultured in MS or SH liquid media for 2 weeks prior to treatment with various concentrations of MeJa, for different periods. The resulting phenolic contents and physiological activities were analyzed. Higher total phenolic, flavonoid, and hydroxycinnamic acids contents were attained by elicitation with MeJa. Metabolite accumulation, especially in SH media and in the presence of MeJa, was time dependent. Particularly, accumulation of chicoric acid increased markedly with time. Similarly, we observed time dependent positive and negative responses of antioxidant activity in DPPH and ABTS assays, respectively. As in previous studies, the highest correlation was found between total phenolic content and total flavonoid content. Further, 3,5-DCQA showed the highest correlation with total phenolic content, total flavonoid content, and antioxidant activities in hydroxycinnamic acids. Our data effectively identified optimal culture conditions to increase the accumulation of secondary metabolites and antioxidant activity in hairy roots cultures of L. indica L.

Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

The main aim of the present investigation is modeling and optimization of a new culture medium for in vitro rooting of G×N15 rootstock using an artificial neural network-genetic algorithm (ANN-GA). Six experiments for assessing different media culture, various concentrations of Indole - 3- butyric acid, different concentrations of Thiamine and Fe-EDDHA were designed. The effects of five ionic macronutrients (NH4+, NO3-, Ca2+, K+ and Cl-) on five growth parameters [root number (RN), root length (RL), root percentage (R%), fresh (FW) and dry weight (DW)] were evaluated using the ANN-GA method. The R2 correlation values of 0.88, 0.88, 0.98, 0.94 and 0.87 between observed and predicted values were acquired for all five growth parameters, respectively. The ANN-GA results indicated that among the input variables, K+ (7.6) and NH4+ (4.4), K+ (7.7) and Ca2+ (2.8), K+ (36.7) and NH4+ (4.3), K+ (14.7) and NH4+ (4.4) and K+ (7.6) and NH4+ (4.3) had the highest values of variable sensitivity ratio (VSR) in the data set, for RN, RL, R%, FW and DW, respectively. ANN-GA optimized LS medium for G×N15 rooting contained optimized amounts of 1 mg L-1 IBA, 100, 150, or 200 mg L-1 Fe-EDDHA and 1.6 mg L-1 Thiamine. The efficiency of the optimized culture media was compared to other standard media for Prunus rooting and the results indicated that the optimized medium is more efficient than the others.

Production of Bacalin, Bacalein and Wogonin in Hairy Root Culture of American Skullcap (Scutellaria lateriflora)by Auxin Treatment

ABSTRACT: The hairy root culture of American Skullcap (Scutellaria lateriflora) was studied to investigate the biomass and flavonoids content (baicalin, baicalein and wogonin) in response of various auxin concentrations.The growth rates of the hairy roots varied significantly only at IBA 0.1 mg/L and for all other auxin treatments did not vary significantly. The biomass of hairy roots was 8% higher when treated with IBA 0.1 mg/L and biomass was almost similar and slightly lower levels when treated with various IAA concentration and NAA, respectively. However, the auxins treatments responsed positively to increase flavone production in American Skullcaphairy root culture. The auxin indole-3-butyric acid (IBA) at 1 mg/L performed the best for the accumulation of baicalin and wogonin. The auxin IBA at 1 mg/L accumulated 1.64 and 2.92 times higher baicalin and wogonin, respectively compared to control treatment. Meanwhile, the highest levels of baicalein were observed for hair root cultures in the presence of 1-naphthaleneacetic acid (NAA) at 0.1 mg/L achieving 2.38 times higher than that of accumulated in the control. These findings indicate that hairy root cultures of S. lateriflorausing liquid 1/2MS medium supplemented with auxin could be a valuable alternative approach for flavonoid production.

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

An artificial neural network (ANN)-based modelling approach is used to determine the synergistic effect of five major components of growth medium (Mg, Cu, Zn, nitrate and sucrose) on improved in vitro biomass yield in multiple shoot cultures of Centella asiatica. The back propagation neural network (BPNN) was employed to predict optimal biomass accumulation in terms of growth index over a defined culture duration of 35 days. The four variable concentrations of five media components, i.e. MgSO₄ (0, 0.75, 1.5, 3.0 mM), ZnSO₄ (0, 15, 30, 60 μM), CuSO₄ (0, 0.05, 0.1, 0.2 μM), NO₃ (20, 30, 40, 60 mM) and sucrose (1, 3, 5, 7 %, w/v) were taken as inputs for the ANN model. The designed model was evaluated by performing three different sets of validation experiments that indicated a greater similarity between the target and predicted dataset. The results of the modelling experiment suggested that 1.5 mM Mg, 30 μM Zn, 0.1 μM Cu, 40 mM NO₃ and 6 % (w/v) sucrose were the respective optimal concentrations of the tested medium components for achieving maximum growth index of 1654.46 with high centelloside yield (62.37 mg DW/culture) in the cultured multiple shoots. This study can facilitate the generation of higher biomass of uniform, clean, good quality C. asiatica herb that can efficiently be utilized by pharmaceutical industries.

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts ([Formula: see text], [Formula: see text], [Formula: see text], Ca2+, K+, [Formula: see text], Mg2+, and Cl-) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R2 correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, [Formula: see text] and [Formula: see text] had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 [Formula: see text], 14 [Formula: see text], 5 Ca2+, 25.9 K+, 0.7 Mg2+, 1.1 [Formula: see text], 4.7 [Formula: see text], and 0.96 Cl-. The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity.

Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO₄, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

Hairy root biotechnology--indicative timeline to understand missing links and future outlook

Agrobacterium rhizogenes-mediated hairy roots (HR) were developed in the laboratory to mimic the natural phenomenon of bacterial gene transfer and occurrence of disease syndrome. The timeline analysis revealed that during 90 s, the research expanded to the hairy root-based secondary metabolite production and different yield enhancement strategies like media optimization, up-scaling, metabolic engineering etc. An outlook indicates that much emphasis has been given to the strategies that are helpful in making this technology more practical in terms of high productivity at low cost. However, a sequential analysis of literature shows that this technique is upgraded to a biotechnology platform where different intra- and interdisciplinary work areas were established, progressed, and diverged to provide scientific benefits of various hairy root-based applications like phytoremediation, molecular farming, biotransformation, etc. In the present scenario, this biotechnology research platform includes (a) elemental research like hairy root-mediated secondary metabolite production coupled with productivity enhancement strategies and (b) HR-based functional research. The latter comprised of hairy root-based applied aspects such as generation of agro-economical traits in plants, production of high value as well as less hazardous molecules through biotransformation/farming and remediation, respectively. This review presents an indicative timeline portrayal of hairy root research reflected by a chronology of research outputs. The timeline also reveals a progressive trend in the state-of-art global advances in hairy root biotechnology. Furthermore, the review also discusses ideas to explore missing links and to deal with the challenges in future progression and prospects of research in all related fields of this important area of plant biotechnology.

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology

BACKGROUND

Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology.

METHODOLOGY AND PRINCIPAL FINDINGS

In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1).

CONCLUSIONS

Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.