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. 2022 Aug 24:13:897696.
doi: 10.3389/fgene.2022.897696. eCollection 2022.

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

Muhammad Aasim  1 , Ramazan Katirci  2 , Faheem Shehzad Baloch  1 , Zemran Mustafa  3 , Allah Bakhsh  4 , Muhammad Azhar Nadeem  1 , Seyid Amjad Ali  5 , Rüştü Hatipoğlu  6 , Vahdettin Çiftçi  7 , Ephrem Habyarimana  8 , Tolga Karaköy  1 , Yong Suk Chung  9
Affiliations

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

Muhammad Aasim et al. Front Genet. .

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 R 2 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.

Keywords: artificial neural network; coefficient of determination; in vitro regeneration; machine learning algorithms; mean squared error; plumular apices.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
In vitro regeneration and rooting of common bean Cv. Karacaşehir 90 (A) sterilized seed with the intact embryo, (B) isolated embryo ready for inoculating on the pretreatment medium, (C) pretreated mature embryo used for isolating the plumular apice explant, (D) multiple shoot induction from the plumular apice explant, and (E) acclimatized plant in a pot containing vermiculite.
FIGURE 2
FIGURE 2
3D response surface plots of in vitro regeneration of common bean (A) regeneration, (B) shoot count, and (C) shoot length.
FIGURE 3
FIGURE 3
Contour plots of in vitro regeneration of common bean (A) regeneration, (B) shoot count, and (C) shoot length.
FIGURE 4
FIGURE 4
Response prediction of individual output variables on in vitro regeneration of common bean (A) regeneration, (B) shoot count, and (C) shoot length.
FIGURE 5
FIGURE 5
Multiple response prediction of output variables on in vitro regeneration of common bean (A) regeneration × shoots × length, (B) regeneration × shoots, and (C) shoots × length.
FIGURE 6
FIGURE 6
The relationship between the prediction and actual values for (A) regeneration, (B) shoot length, and (C) shoot count.
FIGURE 7
FIGURE 7
Correlation matrix of inputs and outputs for common bean.
See this image and copyright information in PMC

References

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