Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms

Abstract

Common bean is considered a recalcitrant crop for in vitro regeneration and needs a repeatable and efficient in vitro regeneration protocol for its improvement through biotechnological approaches. In this study, the establishment of efficient and reproducible in vitro regeneration followed by predicting and optimizing through machine learning (ML) models, such as artificial neural network algorithms, was performed. Mature embryos of common bean were pretreated with 5, 10, and 20 mg/L benzylaminopurine (BAP) for 20 days followed by isolation of plumular apice for in vitro regeneration and cultured on a post-treatment medium containing 0.25, 0.50, 1.0, and 1.50 mg/L BAP for 8 weeks. Plumular apice explants pretreated with 20 mg/L BAP exerted a negative impact and resulted in minimum shoot regeneration frequency and shoot count, but produced longer shoots. All output variables (shoot regeneration frequency, shoot counts, and shoot length) increased significantly with the enhancement of BAP concentration in the post-treatment medium. Interaction of the pretreatment × post-treatment medium revealed the need for a specific combination for inducing a high shoot regeneration frequency. Higher shoot count and shoot length were achieved from the interaction of 5 mg/L BAP × 1.00 mg/L BAP followed by 10 mg/L BAP × 1.50 mg/L BAP and 20 mg/L BAP × 1.50 mg/L BAP. The evaluation of data through ML models revealed that R2 values ranged from 0.32 to 0.58 (regeneration), 0.01 to 0.22 (shoot counts), and 0.18 to 0.48 (shoot length). On the other hand, the mean squared error values ranged from 0.0596 to 0.0965 for shoot regeneration, 0.0327 to 0.0412 for shoot count, and 0.0258 to 0.0404 for shoot length from all ML models. Among the utilized models, the multilayer perceptron model provided a better prediction and optimization for all output variables, compared to other models. The achieved results can be employed for the prediction and optimization of plant tissue culture protocols used for biotechnological approaches in a breeding program of common beans.

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 ].…”

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

“…Data quality, the sample of data collected from the domain, and model fit are the three important sources of uncertainty in ML studies [ 44 ]. Several researchers have recommended the application of different machine learning to tackle uncertainty issues [ 29 , 43 , 60 , 63 ]. Therefore, three neural network approaches (MLP, RBF, and GRNN) were employed for modeling contamination rate and seed germination percentage on in vitro seed sterilization and germination of petunia.…”

“…Simplicity of network structure, very fast network training speed, strong non-linear mapping capability, ease of implementation, high fault tolerance, and high robustness in the solution of complex problems are excellent features of GRNN [ 50 ]. Recent studies have reported the good performance and superior predictive accuracy of ANNs tools over traditional statistics for predicting and optimizing in vitro culture systems of different plant species such as chrysanthemum [ 35 , 51 – 54 ], passion fruit [ 50 ], Prunus rootstock [ 55 – 57 ], tomato [ 58 ], chickpea [ 59 , 60 ], wheat [ 49 ], cannabis [ 28 , 29 , 61 – 64 ], and ajowan [ 65 ]. In addition, the combination of ANNs with an evolutionary optimization algorithm as a superior and reliable computational method confers advantages to predict critical factors that impact plant growth parameters in in vitro culture systems [ 39 ].…”

Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein and hairy roots: a perfect match for gene functional analysis and crop improvement

“…While recent advances in plant-culture regeneration, gene-editing, and machine learning technologies [44] are expected to tackle the bottlenecks associated with transformation and regeneration procedures for the recalcitrant crops. In the meantime, transitory or chimeric transformation meditated by R. rhizogenes combined with CRISPR/Cas9 system has been proven as an excellent technique to generate gene-edited hairy roots 41• , 45 .…”