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Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach
In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the eff...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828343/ https://www.ncbi.nlm.nih.gov/pubmed/29485995 http://dx.doi.org/10.1371/journal.pcbi.1005976 |
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author | Mridula, Meenu R. Nair, Ashalatha S. Kumar, K. Satheesh |
author_facet | Mridula, Meenu R. Nair, Ashalatha S. Kumar, K. Satheesh |
author_sort | Mridula, Meenu R. |
collection | PubMed |
description | In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background. |
format | Online Article Text |
id | pubmed-5828343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-58283432018-03-19 Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach Mridula, Meenu R. Nair, Ashalatha S. Kumar, K. Satheesh PLoS Comput Biol Research Article In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background. Public Library of Science 2018-02-27 /pmc/articles/PMC5828343/ /pubmed/29485995 http://dx.doi.org/10.1371/journal.pcbi.1005976 Text en © 2018 Mridula et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Mridula, Meenu R. Nair, Ashalatha S. Kumar, K. Satheesh Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach |
title | Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach |
title_full | Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach |
title_fullStr | Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach |
title_full_unstemmed | Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach |
title_short | Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach |
title_sort | genetic programming based models in plant tissue culture: an addendum to traditional statistical approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828343/ https://www.ncbi.nlm.nih.gov/pubmed/29485995 http://dx.doi.org/10.1371/journal.pcbi.1005976 |
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