PMID- 33424873
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210112
IS  - 1664-462X (Print)
IS  - 1664-462X (Electronic)
IS  - 1664-462X (Linking)
VI  - 11
DP  - 2020
TI  - Modeling and Optimizing Culture Medium Mineral Composition for in vitro 
      Propagation of Actinidia arguta.
PG  - 554905
LID - 10.3389/fpls.2020.554905 [doi]
LID - 554905
AB  - The design of plant tissue culture media remains a complicated task due to the 
      interactions of many factors. The use of computer-based tools is still very 
      scarce, although they have demonstrated great advantages when used in large 
      dataset analysis. In this study, design of experiments (DOE) and three machine 
      learning (ML) algorithms, artificial neural networks (ANNs), fuzzy logic, and 
      genetic algorithms (GA), were combined to decipher the key minerals and predict 
      the optimal combination of salts for hardy kiwi (Actinidia arguta) in vitro 
      micropropagation. A five-factor experimental design of 33 salt treatments was 
      defined using DOE. Later, the effect of the ionic variations generated by these 
      five factors on three morpho-physiological growth responses - shoot number (SN), 
      shoot length (SL), and leaves area (LA) - and on three quality responses - shoots 
      quality (SQ), basal callus (BC), and hyperhydricity (H) - were modeled and 
      analyzed simultaneously. Neurofuzzy logic models demonstrated that just 11 ions 
      (five macronutrients (N, K, P, Mg, and S) and six micronutrients (Cl, Fe, B, Mo, 
      Na, and I)) out of the 18 tested explained the results obtained. The rules "IF - 
      THEN" allow for easy deduction of the concentration range of each ion that causes 
      a positive effect on growth responses and guarantees healthy shoots. Secondly, 
      using a combination of ANNs-GA, a new optimized medium was designed and the 
      desired values for each response parameter were accurately predicted. Finally, 
      the experimental validation of the model showed that the optimized medium 
      significantly promotes SQ and reduces BC and H compared to standard media 
      generally used in plant tissue culture. This study demonstrated the suitability 
      of computer-based tools for improving plant in vitro micropropagation: (i) DOE to 
      design more efficient experiments, saving time and cost; (ii) ANNs combined with 
      fuzzy logic to understand the cause-effect of several factors on the response 
      parameters; and (iii) ANNs-GA to predict new mineral media formulation, which 
      improve growth response, avoiding morpho-physiological abnormalities. The lack of 
      predictability on some response parameters can be due to other key media 
      components, such as vitamins, PGRs, or organic compounds, particularly glycine, 
      which could modulate the effect of the ions and needs further research for 
      confirmation.
CI  - Copyright (c) 2020 Hameg, Arteta, Landin, Gallego and Barreal.
FAU - Hameg, Radhia
AU  - Hameg R
AD  - Applied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, 
      Faculty of Biology, University of Vigo, Vigo, Spain.
AD  - CITACA - Agri-Food Research and Transfer Cluster, University of Vigo, Ourense, 
      Spain.
FAU - Arteta, Tomas A
AU  - Arteta TA
AD  - Applied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, 
      Faculty of Biology, University of Vigo, Vigo, Spain.
AD  - CITACA - Agri-Food Research and Transfer Cluster, University of Vigo, Ourense, 
      Spain.
FAU - Landin, Mariana
AU  - Landin M
AD  - Departamento de Farmacologia, Farmacia y Tecnologia Farmaceutica, R+D Pharma 
      Group (GI-1645), Facultad de Farmacia y Agrupacion Estrategica en Materiales 
      (AeMat), Universidade de Santiago de Compostela, Santiago, Spain.
AD  - Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Santiago 
      de Compostela, Spain.
FAU - Gallego, Pedro P
AU  - Gallego PP
AD  - Applied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, 
      Faculty of Biology, University of Vigo, Vigo, Spain.
AD  - CITACA - Agri-Food Research and Transfer Cluster, University of Vigo, Ourense, 
      Spain.
FAU - Barreal, M Esther
AU  - Barreal ME
AD  - Applied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, 
      Faculty of Biology, University of Vigo, Vigo, Spain.
AD  - CITACA - Agri-Food Research and Transfer Cluster, University of Vigo, Ourense, 
      Spain.
LA  - eng
PT  - Journal Article
DEP - 20201223
PL  - Switzerland
TA  - Front Plant Sci
JT  - Frontiers in plant science
JID - 101568200
PMC - PMC7785940
OTO - NOTNLM
OT  - algorithms
OT  - artificial intelligence
OT  - kiwiberry
OT  - mineral nutrition
OT  - modeling
OT  - physiological disorders
OT  - plant tissue culture
COIS- 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.
EDAT- 2021/01/12 06:00
MHDA- 2021/01/12 06:01
PMCR- 2020/01/01
CRDT- 2021/01/11 05:37
PHST- 2020/04/23 00:00 [received]
PHST- 2020/12/01 00:00 [accepted]
PHST- 2021/01/11 05:37 [entrez]
PHST- 2021/01/12 06:00 [pubmed]
PHST- 2021/01/12 06:01 [medline]
PHST- 2020/01/01 00:00 [pmc-release]
AID - 10.3389/fpls.2020.554905 [doi]
PST - epublish
SO  - Front Plant Sci. 2020 Dec 23;11:554905. doi: 10.3389/fpls.2020.554905. 
      eCollection 2020.