Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity

Abstract

This study represents an ANN based computational scheming of physical, chemical and biological parameters at flask level for mass multiplication of plants through micropropagation using bioreactors of larger volumes. The optimal culture environment at small scale for Glycyrrhiza plant was predicted by using neural network approach in terms of pH and volume of growth medium per culture flask, incubation room temperature and month of inoculation along with inoculum properties in terms of inoculum size, fresh weight and number of explant per flask. This kind of study could be a model system in commercial propagation of various economically important plants in bioreactors using tissue culture technique. In present course of study the ANN was trained by implementing MATLAB neural network. A feed-forward back propagation type network was created for input vector (seven input elements), with single hidden layer (seven nodes) and one output unit in output layer. The 'tansig' and 'purelin' transfer functions were adapted for hidden and output layers respectively. The four training functions viz. traingda, trainrp, traincgf, traincgb were randomly selected to train four networks which further examined with available dataset. The efficiency of neural networks was concluded by the comparison of results obtained from this study with that of empirical data obtained from the detailed tissue culture experiments and designated as Target set (mean fresh weight biomass per culture flask after 40 days of in vitro culture duration). Efficiency of networks for better training initialization was judged on the basis of comparative analysis of 'Mean Square Error at zero epoch' for each network trained in which the least error at initial point was observed with trainrp followed by traincgb and traincgf. A comparative assessment between experimental target data range obtained from wet lab practice and all trained network output range for the efficiency of trained networks for least deviation from target range revealed the output range of network 'trainrp' was closest to the empirical target range while least comparison was worked out from network 'traincgb' which had output range more than the target decided and ultimately showed meaningless result.

Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

“…ANN technology has been found to be completely applicable for experiments with different numbers of data points, which makes it possible to use more casual experimental designs than is allowed with statistical approaches (Ahmadi and Golian, 2011). Recently, several studies have demonstrated the effectiveness of ANNs in the field of plant tissue culture for different purposes such as predicting the number of shoots per explant and average shoot length (Gago et al, 2010a, 2011; Nezami Alanagh et al, 2014), modeling the weight of root biomass (Mehrotra et al, 2008, 2013; Prakash et al, 2010), and predicting the number of roots per microshoots and survival percentage (Gago et al, 2010a,b). ANN-GA is a hybrid technology that combines the adaptive learning capabilities from ANN with a GA.…”

Machine Learning Techniques in Plant Biology

“…However, culture of these hairy roots for high secondary metabolite associated with better biomass production requires optimization of several physical and chemical parameters that affect the growth and productivity of these roots. A feed-forward back-propagation neural network model was developed (Mehrotra et al 2008) for prediction of in vitro culture conditions for hairy root growth. The model used inoculum size, fresh weight, density, culture temperature, pH, and time of inoculation as input parameters and final fresh weight of roots as final parameters.…”

Neural modeling of plant tissue cultures: a review

“…Successful attempts at modeling in vitro culture parameters have also been made in the hairy root cultures of Glycyrrhiza glabra (Mehrotra et al, 2008;Prakash et al, 2010). Initially, the prediction model of in vitro culture conditions was designed along with the inoculum properties for optimum root biomass production (Mehrotra et al, 2008). This model consisted of an MLP performed in Matlab software.…”