Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach

Meenu R Mridula, Ashalatha S Nair and K Satheesh Kumar

PLOS Computational Biology, 2018, vol. 14, issue 2, 1-13

Abstract: 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.Author summary: Trials to find out the best combination of factors that contribute to the desired response takes up the chunk of time and resources in any plant tissue culture laboratory. The output of such experiments is analysed statistically to come to a conclusion. However, without prior statistical modifications, the results could be misleading. Recent reports from several labs point out the use of artificial neural networks to circumvent this. We have chosen to use a computational process that can predict the best combination of factors for the desired response after randomly testing the higher and lower limit of the factors with experiments. The magnitude of the desired response can be presumed at any concentration within this range using the models generated by symbolic regression. The procedure provides both optimum model function as well as the optimum variable values in the model. The variable sensitivity and percentage response add depth to the information thus obtained. The study indicated that these models would have significant potential for saving time and expenditure in plant tissue culture laboratories for the commercial establishment of in vitro protocols.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005976

DOI: 10.1371/journal.pcbi.1005976

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