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

Abstract

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.

Machine learning-mediated Passiflora caerulea callogenesis optimization

“…Using ML algorithms to predict and analyze tissue culture systems are promising to optimize in vitro culture procedures [ 18 , 22 , 86 ]. The application of different ANNs is an active area of research in tissue culture [ 18 ] which has been used in different systems of in vitro culture such as callogenesis [ 20 ], shoot proliferation [ 46 ], androgenesis [ 44 ], somatic embryogenesis [ 36 ], and direct shoot regeneration [ 87 ].…”

Optimizing PGRs for in vitro shoot proliferation of pomegranate with bayesian-tuned ensemble stacking regression and NSGA-II: a comparative evaluation of machine learning models

“…NSGA-II enables efficient solving and prediction of complex processes while providing a simplified interpretation of results, simultaneously [30]. In previous studies, the combining approach of ML with NSGA-II (ML-NSGA-II) has been acknowledged as a robust modeling technique for complex datasets, such as in optimizing the protocol of in vitro tissue culture on micropropagation phases [21,31,32] and in various plant science fields [30,33].…”

Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms

“…Analyzing tissue culture datasets and predicting optimized treatments using ML algorithms represents a favorable approach to in vitro research [ 32 , 36 , 50 ]. Specifically, regression versions of ML algorithms (e.g., generalized regression neural network (GRNN) and random forest (RF)) are currently being applied to several areas of plant tissue culture research [ 32 ], including callogenesis [ 51 ], shoot proliferation [ 52 ], androgenesis [ 53 ], somatic embryogenesis [ 54 ], and direct shoot regeneration [ 55 ].…”