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Plant Cell and Tissue Differentiation and Secondary Metabolites pp 1–34Cite as

Approaches for Modeling and Optimization of the Secondary Metabolite Production by Plant Biotechnology Methods

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Part of the book series: Reference Series in Phytochemistry ((RSP))

Abstract

The production of secondary metabolites on a large scale by plant biotechnology methods is achievable by optimizing cells, tissues, and plant organs culture techniques in bioreactors. In order to make this production economically viable, the researchers have developed several biotechnological strategies that maximize the yields of these metabolites, namely, the selection of high-performance cell or HR lines, the elicitation, the improvement of culture medium composition, the precursor feeding, the cells permeabilization, and the optimization of the environmental conditions. These strategies are often used simultaneously and depend on a large number of factors which renders the physiological response of plant cells very complex. Mathematical modeling simplifies the study and the optimization of the secondary metabolite production. Indeed, the mathematical model fitted to the experimental data describes the relationships between the factors involved in an experiment and their influence on biomass and secondary metabolite yield; this makes it easy to predict optimal conditions for production. This chapter will review the theory of some modeling approaches used in plant biotechnology, namely, Response Surface Method (RSM), Artificial Neural Network (ANN), Kriging and the ANN-RSM combined approach, as well as the main studies for these modeling approaches.

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Abbreviations

2,4-D:

2,4-Dichlorophenoxyacetic acid

AAD:

Absolute Average Deviation

ANN:

Artificial Neural Network

ANOVA:

Analysis of variance

BAP:

N6-benzylaminopurine

BBD:

Box-Behnken Design

CCD:

Central Composite Design

DW:

Dry Weight

FFD:

Fractional Factorial Design

FW:

Fresh Weight

HRs:

Hairy Roots

IAA:

Indole-3-acetic acid

Kin:

Kinetin

MS:

Murashige & Skoog

NAA:

1-Naphthaleneacetic acid

PBD:

Plackett-Burman Design

R2:

Coefficient of determination

RMSE:

Root Mean Square Error

RSM:

Response Surface Methodology

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Acknowledgments

The authors wish to thank Professor Toudert AHMED ZAÏD for the corrections made for this manuscript.

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Authors and Affiliations

  1. Institut National de Recherche Forestière (INRF), Baïnem, Alger, Algeria

    Ryad Amdoun, Fatiha Sahli & Nassim Bendifallah

  2. Laboratoire de Valorisation des Energies Fossiles, Ecole Nationale Polytechnique, Alger, Algeria

    El-Hadi Benyoussef

  3. ENSTP, Alger, Algeria

    Ahcene Benamghar

  4. Laboratoire des Ressources Génétiques et Biotechnologie, ENSA, Ecole Nationale Supérieure Agronomique – Kasdi merbah, Alger, Algeria

    Lakhdar Khelifi

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  1. Ryad Amdoun
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  5. Nassim Bendifallah
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  6. Lakhdar Khelifi
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Correspondence to Ryad Amdoun .

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Editors and Affiliations

  1. Department of Botany, M.L.Sukhadia university, Udaipur, Rajasthan, India

    Kishan Gopal Ramawat

  2. Department of Pharmaceutical Botany, Jagiellonian University, Medical College, Kraków, Poland

    Halina Maria Ekiert

  3. Amarillo, TX, USA

    Shaily Goyal

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Amdoun, R., Benyoussef, EH., Benamghar, A., Sahli, F., Bendifallah, N., Khelifi, L. (2020). Approaches for Modeling and Optimization of the Secondary Metabolite Production by Plant Biotechnology Methods. In: Ramawat, K.G., Ekiert, H.M., Goyal, S. (eds) Plant Cell and Tissue Differentiation and Secondary Metabolites. Reference Series in Phytochemistry. Springer, Cham. https://doi.org/10.1007/978-3-030-11253-0_37-1

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