Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

Viswanathan, Pavitra; Gosukonda, Jaabili S.; Sherman, Samantha H.; Joshee, Nirmal; Gosukonda, Ramana

doi:10.1016/j.heliyon.2022.e11969

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…By employing sensitivity analysis, we were able to identify the relative importance of the different phytohormones in regulating callogenesis [50,67]. This information can guide the optimization of tissue culture protocols by focusing on the most influential variables.…”

Section: Discussionmentioning

confidence: 99%

Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis

Rezaei,

Mirzaie-asl,

Abdollahi

et al. 2023

PLoS ONE

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The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture processes. The study aimed to develop a predictive model for callogenesis in petunia using ML algorithms and to optimize the concentrations of phytohormones to enhance callus formation rate (CFR) and callus fresh weight (CFW). The inputs for the model were BAP, KIN, IBA, and NAA, while the outputs were CFR and CFW. Three ML algorithms, namely MLP, RBF, and GRNN, were compared, and the results revealed that GRNN (R2≥83) outperformed MLP and RBF in terms of accuracy. Furthermore, a sensitivity analysis was conducted to determine the relative importance of the four phytohormones. IBA exhibited the highest importance, followed by NAA, BAP, and KIN. Leveraging the superior performance of the GRNN model, a genetic algorithm (GA) was integrated to optimize the concentration of phytohormones for maximizing CFR and CFW. The genetic algorithm identified an optimized combination of phytohormones consisting of 1.31 mg/L BAP, 1.02 mg/L KIN, 1.44 mg/L NAA, and 1.70 mg/L IBA, resulting in 95.83% CFR. To validate the reliability of the predicted results, optimized combinations of phytohormones were tested in a laboratory experiment. The results of the validation experiment indicated no significant difference between the experimental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogenesis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation and potential applications in plant tissue culture and genetic engineering.

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Section: Discussionmentioning

confidence: 99%

Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis

Rezaei,

Mirzaie-asl,

Abdollahi

et al. 2023

PLoS ONE

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“…It is a small creeping perennial herb with numerous branches, small oblong leaves, and light purple flowers (Figure 1) growing in wetlands. It has been used in many parts of the world as a medicine, particularly for treating various neurological disorders such as improving memory, thinking skills, insomnia, seizures, and anxiety [37][38][39][40][41]. In a review performed by Fatima et al [40], the authors studied the molecular pathways in therapeutic interventions of BM in several metabolic disorders and also in Alzheimer's and Parkinson´s diseases.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Neuroprotective and Cognitive-Enhancing Effects of Bacopa monnieri: A Systematic Review Focused on Inflammation, Oxidative Stress, Mitochondrial Dysfunction, and Apoptosis

Valotto Neto,

Reverete de Araujo,

Moretti Junior

et al. 2024

Antioxidants

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The aging of the global population has increased the prevalence of neurodegenerative conditions. Bacopa monnieri (BM), an herb with active compounds, such as bacosides A and B, betulinic acid, loliolide, asiatic acid, and quercetin, demonstrates the potential for brain health. Limited research has been conducted on the therapeutic applications of BM in neurodegenerative conditions. This systematic review aims to project BM’s beneficial role in brain disorders. BM has anti-apoptotic and antioxidant actions and can repair damaged neurons, stimulate kinase activity, restore synaptic function, improve nerve transmission, and increase neuroprotection. The included twenty-two clinical trials demonstrated that BM can reduce Nuclear Factor-κB phosphorylation, improve emotional function, cognitive functions, anhedonia, hyperactivity, sleep routine, depression, attention deficit, learning problems, memory retention, impulsivity, and psychiatric problems. Moreover, BM can reduce the levels of pro-inflammatory biomarkers and oxidative stress. Here, we highlight that BM provides notable therapeutic benefits and can serve as a complementary approach for the care of patients with neurodegenerative conditions associated with brain disorders. This review adds to the growing interest in natural products and their potential therapeutic applications by improving our understanding of the mechanisms underlying cognitive function and neurodegeneration and informing the development of new therapeutic strategies for neurodegenerative diseases.

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“…In vitro micropropagation is a multifactorial and complex biological process, because it is influenced by genotypes/cultivar and many keys interacting factors that would be required for optimizing this mentioned process [ 39 – 42 ]. Commonly, revealing all the information encrypted over the large datasets of biological interactions by traditional statistical techniques is a highly challenging task, particularly when datasets are nonlinear in nature, complex, noisy and ambiguous such as in multifactorial processes of in vitro culture [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia

et al. 2023

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The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study investigated the role of various types and concentrations of disinfectants (i.e., NaOCl, Ca(ClO)2, HgCl2, H2O2, NWCN-Fe, MWCNT) as well as immersion time in successful in vitro seed sterilization and germination of petunia. Also, the utility of three artificial neural networks (ANNs) (e.g., multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN)) as modeling tools were evaluated to analyze the effect of disinfectants and immersion time on in vitro seed sterilization and germination. Moreover, non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the selected prediction model. The GRNN algorithm displayed superior predictive accuracy in comparison to MLP and RBF models. Also, the results showed that NSGA‑II can be considered as a reliable multi-objective optimization algorithm for finding the optimal level of disinfectants and immersion time to simultaneously minimize contamination rate and maximize germination percentage. Generally, GRNN-NSGA-II as an up-to-date and reliable computational tool can be applied in future plant in vitro culture studies.

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Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

Cited by 8 publications

References 40 publications

Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis

Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis

Investigating the Neuroprotective and Cognitive-Enhancing Effects of Bacopa monnieri: A Systematic Review Focused on Inflammation, Oxidative Stress, Mitochondrial Dysfunction, and Apoptosis

Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia

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